Metabonomics in Toxicity Assessment

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Metabonomicsin Toxicity

Assessment

DK3102_title 1/12/05 12:29 PM Page 1

Metabonomicsin Toxicity

Assessmentedited by

Donald G. RobertsonPfizer Global Research and Development

Ann Arbor, Michigan, U.S.A.

and John LindonImperial College London

London, United Kingdom

Boca Raton London New York Singapore

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Preface

The role of the toxicologist in the pharmaceutical industry haschanged significantly over the past 10 years. Historically, thetoxicologist’s responsibilities were often thought by theircolleagues in pharmaceutical companies as being akin to thatof the grim reaper whose appearance at project team meet-ings, presaged bad tidings for the future of the drug underconsideration. This news was seldom received before a signif-icant investment in time and resources had already beenplaced in the development of the drug and cancellation ofthe project due to drug toxicity, meant a significant setback.However, the paradigm for development of new chemical enti-ties has changed significantly in recent years. Toxicologistsare now required to help in prelead prioritization to help man-age the wealth of hits coming out of combinatorial synthesisand high throughput screening, with the goal of improvingpreclinical throughput by decreasing the number of failuresdue to untoward toxicity in safety studies. In silico and invitro approaches certainly have a place in early toxicityassessment, but there frequently comes a point at whichfurther distinction by these approaches is not possible or is

iii

self-defeating in terms of teasing out the real significance ofany data. In vivo evaluation still remains the gold standardfor safety assessment and will remain so for the foreseeablefuture, given the regulatory requirements for new drugs.Therefore techniques that enable rapid and=or more completeassessment of in vivo studies are of great interest to pharma-ceutical toxicologists.

A number of data–rich technologies have become avail-able for toxicity studies. Among these, metabonomics repre-sents a promising approach that enables relatively rapidthroughput in vivo toxicity assessment, frequently providingbasic biochemical information not typically available in stan-dard clinical pathology assessment. Therefore, a byproduct(or arguably the primary product) of the technique is identifi-cation of individual biomarkers or combinations of biomar-kers that can be associated with the toxicity, and whichcould act as surrogate endpoints. These biomarkers can beused for further prelead prioritization or may prove usefullater in development for clinical assessment of toxicity.Clearly, metabonomic technology represents a promisingmeans to achieve the goal of faster drug development withdecreased preclinical and clinical failure rates due to toxicity.

Metabonomics serves as one leg of the triad of ‘‘omic’’technologies that includes transcriptomics (also known astoxicogenomics in toxicology circles) and proteomics.Although these other omic technologies are not the subjectof this volume, it has become apparent to practitioners inthe field, that a ‘‘systems’’ approach to toxicity evaluation thatincorporates two or preferably all three omic approachesenables a synergy of data generation and more importantlydata interpretation that is not possible with any one ‘‘omic’’technology. While much has been written about transcrip-tomics and to a lesser extent proteomics, very little is avail-able to the toxicologist considering use of metabonomictechnology. This volume is meant to help fill that void.

Metabonomics can enable rapid generation of a moun-tain of data. However, generating mountains is not the roleof the pharmaceutical toxicologist, whose goal is to refine themountain down to the valuable jewels of mechanistic-based

iv Preface

safety screens and biomarkers. This is easier said than done,and this volume provides some essential wisdom for siftingthrough the tailings to find the jewels. Like other ‘‘omic’’ tech-nologies, experimental designs, protocol conduct, and propercontrol are absolutely imperative to metabonomics. Some ofthe essential tools for the toxicologist evaluating metabo-nomic technology are covered in some detail in the volumeincluding analytical, biological, and chemometric considera-tions. Additionally, varied applications are presented whichprovide a flavor for what metabonomics can do.

The aim of this book is to present a summary of the use ofmetabonomics in the safety assessment of new chemical enti-ties. Although a presentation of the instrumentation andmethods that are required to perform biofluid NMR will bemade for purposes of completeness, the main focus of the textwill deal with the use of the technique by toxicologists to aidin safety assessment of novel prelead candidates with furtherapplication to biomarker identification. Given the limitedexposure metabonomics has within the toxicology community,little background knowledge is assumed and each contributorhas attempted to identify methods and materials used to gen-erate data and to explain any assumptions made in their eva-luations. This balanced critique of the present state of the arthopefully encompasses both the strengths and weaknesses ofthe technology. We believe that this book will serve as a basicreference tool, since each chapter provides a comprehensivebibliography. The worldwide pharmaceutical toxicologistand other preclinical and clinical scientists involved in safetyassessment are the intended audience for this volume,although anyone involved in generating and=or interpretingsafety data on chemical entities or anyone who has an interestin metabonomics and systems biology as an academic pursuitwill find the reference thought provoking and useful.

Donald G. RobertsonJohn C. Lindon

Preface v

Contents

Preface . . . . iii

Contributors . . . . xi

1. An Overview of Metabonomics . . . . . . . . . . . . . . 1John C. Lindon, Elaine Holmes and Jeremy K. NicholsonIntroduction . . . . 1The Metabolic Continuum . . . . 5Biomarkers . . . . 12Brief Overview of Metabonomics Techniques . . . . 13Metabonomics Applications . . . . 18Summary . . . . 21

2. Overview of Biomarkers . . . . . . . . . . . . . . . . . . . 27John TimbrellIntroduction . . . . 27Biomarkers of Exposure . . . . 31Biomarkers of Response . . . . 44Biomarkers of Susceptibility . . . . 56

vii

3. NMR Spectroscopy: Principles andInstrumentation . . . . . . . . . . . . . . . . . . . . . . . . . 75Michael D. Reily and John C. LindonIntroduction . . . . 75Principles of NMR Spectroscopy . . . . 77Operational Methods . . . . 85Realization of NMR Spectroscopy in a Metabonomics

Laboratory . . . . 92Conclusions . . . . 102

4. NMR Spectroscopy of Biofluids . . . . . . . . . . . . . 105John C. Lindon, Jeremy K. Nicholson and Elaine HolmesIntroduction . . . . 105Practicalities of 1D 1H NMR Spectroscopy

of Biofluids . . . . 109Techniques for Resonance Assignment in NMR Spectra

of Biofluids . . . . 1091H NMR Spectroscopy of Cerebrospinal

Fluid (CSF) . . . . 1121H NMR Spectroscopy of Blood Plasma and Whole

Blood . . . . 1151H NMR Spectroscopy of Human and Animal

Urine . . . . 1291H NMR Spectroscopy of Seminal Fluids . . . . 1421H NMR Spectroscopy of Bile . . . . 146NMR Spectroscopy of Miscellaneous Body

Fluids . . . . 148NMR Studies of Dynamic Interactions . . . . 153Concluding Remarks . . . . 158

5. 1H Magic Angle Spinning NMR Spectroscopyof Tissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Julian L. Griffin, Jeremy K. Nicholson, Elaine Holmes andJohn C. LindonIntroduction . . . . 173Magic-angle-spinning (MAS) NMR Spectroscopy:

Principles and Practice . . . . 175Applications of 1H MAS NMR Spectroscopy

to Metabonomics . . . . 180

viii Contents

Future Directions and Challenges for 1H MAS NMRSpectroscopy . . . . 188

6. The Application of Metabonomics as an EarlyIn Vivo Toxicity Screen . . . . . . . . . . . . . . . . . . . 195Gregory J. Stevens, Alan J. Deese and Donald G. RobertsonIntroduction . . . . 195Experimental Considerations . . . . 197Examples . . . . 211Screening Models . . . . 218Conclusion . . . . 220

7. Strategies and Techniques for the Identificationof Endogenous and Xenobiotic Metabolites Detectedin Metabonomic Studies . . . . . . . . . . . . . . . . . . . 225John Shockcor and Ian D.WilsonIntroduction . . . . 225Xenobiotic and Endogenous Metabolite Identification

Directly from Biofluids . . . . 226Low Resolution, Off-Line, Techniques for the

Isolation of Unknowns . . . . 233Direct On-Line Methods of Identifying

Unknowns . . . . 239Miniaturization . . . . 252Conclusions . . . . 257

8. Multi- and Megavariate Data Analysis: Finding andUsing Regularities in Metabonomics Data . . . . 263Lennart Eriksson, Erik Johansson, Henrik Antti andElaine HolmesIntroduction . . . . 263Data-Analytical Methods . . . . 267Results for Example Data Set I—A MetabonomicInvestigation of Phospholipidosis . . . . 297

Results for Example Data Set II—Defining the DynamicSequence of Biochemical Events Following the Onsetof Toxicity . . . . 308

Discussion . . . . 319Concluding Remarks . . . . 330

Contents ix

9. Use of Metabonomics to Study Target OrganToxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337Craig E. Thomas, Elaine Holmes and Donald G. RobertsonIntroduction . . . . 337Hepatic Toxicity . . . . 338Renal Toxicity . . . . 359Vascular Toxicity . . . . 374

10. Physiological Variation in Laboratory Animalsand Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397M.E. Bollard, E.G. Stanley, Y. Wang, J.C. Lindon,J.K. Nicholson and E. HolmesIntroduction . . . . 397Physiological Variation in LaboratoryAnimals . . . . 401

Physiological Variation in Humans . . . . 432

11. Environmental Applications of MetabonomicProfiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453Jacob G. BundyDifferences to Clinical Studies . . . . 454Characterization of Baseline Data By NMRSpectroscopy . . . . 459

Toxicological and Related Studies . . . . 474Conclusions and Future Implications . . . . 484

12. Current Challenges and Future Developmentsin Metabonomics Technology . . . . . . . . . . . . . . 499Donald G. RobertsonPerspective . . . . 499The Power of the Metabonomic Approach inToxicology . . . . 500

Metabonomics as an ‘‘Omic’’ Technology . . . . 504Short Term Needs for Metabonomics as aScience . . . . 508

Cautionary Note . . . . 510Conclusion . . . . 512

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517

x Contents

Contributors

Henrik Antti Biological Chemistry, Biomedical SciencesDivision, Faculty of Medicine, Imperial College of Science,Technology and Medicine, South Kensington, London, U.K.

M.E. Bollard Biological Chemistry, Biomedical SciencesDivision, Imperial College, University of London, SouthKensington, London, U.K.

Jacob G. Bundy Biochemistry Department, University ofCambridge, Cambridge, U.K.

Alan J. Deese Analytical Research and Development, PfizerGlobal Research and Development, La Jolla, CA, U.S.A.

Lennart Eriksson Umetrics AB, Umea, Sweden

Julian L. Griffin Department of Biochemistry, University ofCambridge, Cambridge, U.K.

Elaine Holmes Biological Chemistry, Biomedical Sciences,Faculty of Medicine, Imperial College of Science, Technology andMedicine, London, U.K.

xi

Erik Johansson Umetrics AB, Umea, Sweden

John C. Lindon Biological Chemistry, Biomedical SciencesDivision, Faculty of Medicine, Imperial College London, SouthKensington, London, U.K.

Jeremy K. Nicholson Biological Chemistry, BiomedicalSciences, Faculty of Medicine, Imperial College of Science,Technology and Medicine, London, U.K.

Michael D. Reily Pfizer Global Research and Development,Michigan Laboratories, Ann Arbor, MI, U.S.A.

Donald G. Robertson Departments of Worldwide SafetySciences, Pfizer Global Research and Development, Ann Arbor,MI, U.S.A.

John Shockcor Metabometrix, South Kensington, London, U.K.

E.G. Stanley Biological Chemistry, Biomedical Sciences Division,Imperial College, University of London, South Kensington, London,U.K.

Gregory J. Stevens Drug Safety Evaluation Pfizer GlobalResearch and Development, La Jolla, CA, U.S.A.

Craig E. Thomas Investigative Toxicology, Lilly ResearchLaboratories, A Division of Eli Lilly and Company, Greenfield, IN,U.S.A.

John Timbrell Pharmacy Department, King’s College, London,U.K.

Y. Wang Biological Chemistry, Biomedical Sciences Division,Imperial College, University of London, South Kensington,London, U.K.

Ian D. Wilson Department of Drug Metabolism andPharmacokinetics, AstraZeneca, Mereside, Alderley Park,Macclesfield, Cheshire, U.K.

xii Contributors

1

An Overview of Metabonomics

JOHN C. LINDON, ELAINE HOLMES andJEREMY K. NICHOLSON

Biological ChemistryBiomedical Sciences Division,Imperial College London, U.K.

1. INTRODUCTION

The availability of the human genome sequence and thesequences of other species gave rise to hope that theywould leadto a new set of molecular markers of disease. Although a vastamount of information on human make up has been unlocked,in terms of the development of validated biochemical markersof disease and identification of new drug targets, the publishedresults have been so far disappointing. However, this processhas provided the impetus for wider searches for biomarkers ofdiseases and drug safety and theunderstanding that single sim-ple markers of complex processes are unlikely to be definitive.

Thus in terms of the better discovery and development ofnew medicines, although the human and many other species

1

genome sequences are now known, there has been surpris-ingly little impact on the numbers of new drug substancescoming to the clinic, even though there is now a greaterunderstanding of ‘‘druggable’’ targets (1). Over the last fewyears, billions of dollars have been pumped into a huge indus-try built up on measuring gene expression changes (transcrip-tomics), mostly involving the use of gene-chip technologies (2).This, in turn, has led to a corresponding expansion ofproteomics that comprises largely mass spectrometry-basedmethods for characterizing the consequent changes in proteinlevels (3). It is now accepted that despite much hype, a hugespend and the provision of much new knowledge, genomicsor proteomics is only just beginning to fulfill its promise. Thiscould be because neither genomics nor, to a lesser extent pro-teomics provides evidence of real world end points for diseasediagnosis, or evaluation of beneficial or adverse drug.

This book brings together information on an emergingtechnology for completing an understanding of biological pro-cesses, namely metabonomics and the focus in this volume isthe application of the technology to drug safety assessment.Metabonomics can be regarded as providing real biologicalendpoints and is defined as ‘‘the quantitative measurement ofthe time-related multiparametric metabolic response of livingsystems to pathophysiological stimuli or genetic modification’’(4). Application of metabonomics involves the generationof metabolic databases for control animals and humans,diseased patients, animals used in drug safety testing, etc.,allowing the simultaneous acquisition of multiple biochemicalparameters on biological samples. Metabonomics is usuallyconducted on biofluids, many of which can usually be obtainednon-invasively (e.g. urine) or relatively easily (e.g. blood), butother fluids such as cerebrospinal fluid, bile or seminal fluidcan be used. It is also possible to use cell culture supernatants,tissue extracts, and similar preparations.

The term metabonomics was derived from the Greekroots ‘‘meta’’ meaning change and ‘‘nomos’’ meaning rules orlaws (as used in economics), to describe the generation ofpattern recognition-based models that have the ability to clas-sify changes in metabolism. There has been a parallel set of

2 Lindon et al.

developments in a subject called metabolomics (5). This issimilar to metabonomics but is regarded as a subset of thetopics covered by metabonomics. Metabolomics has arisenfrom metabolic control theory (6) and was originally basedon the metabolome, the metabolic analogy of the genome orproteome, which was defined as being the metabolic composi-tion of a cell. In metabonomics, not only are static cellular andbiofluid concentrations of endogenous metabolites evaluated,but also full time courses of fluctuations in metabolites, exo-genous species, and molecules which arise from chemicalrather than enzymatic processing (metabonates). In addition,as originally defined, metabonomics, as well as providingmolecular concentrations, also covers the study of moleculardynamic information such as molecular reorientational corre-lation times and diffusion coefficients in intact tissues. Thus,metabonomics can be regarded as a full systems biologyapproach in that when studying a whole organism with sepa-rate organs and many cell types, effects which are displacednot only in time, but also in distance (e.g., the effects of oneorgan on another) can be integrated into a holistic view.

Metabonomics is a successful approach because disease,drugs or toxins cause perturbations of the concentrationsand fluxes of endogenous metabolites involved in key bio-chemical pathways. For example, the response of cells to toxicor other stressors generally results in an adjustment of theirintra- and=or extracellular environment in order to maintainconstancy of their internal environment (homeostasis). Thismetabolic adjustment is expressed as a fingerprint of bio-chemical perturbations which is characteristic of the natureor site of a toxic insult or disease process. Urine, in particular,often shows changes in metabolite profile in response to toxicor disease-induced stress because the attempt to maintainhomeostasis in the face of a toxic challenge results in changesto the composition of biofluids, particularly excreted fluidslike urine. Hence, even when cellular homeostasis is main-tained, subtle responses to toxicity or disease are oftenexpressed in altered biofluid composition (7).

A variety of analytical methods could in principle be usedto generate metabonomic data sets so long as the approach

An Overview of Metabonomics 3

provides information on the molecules that give rise to theexperimental data. Thus ultraviolet spectroscopy and otherforms of electronic spectroscopy are less than ideal since theyonly provide information on molecular fragments, such as dif-ferent types of aromatic molecules giving rise to the chromo-phores, and the spectral line widths are so broad thatsignals from all species overlap considerably. Infrared (IR)spectroscopy provides more molecular information in the formthat any differences in spectra due to a perturbation can beinterpreted crudely in terms of the functional groups of thesubstances involved. Again, resolution is limited, for examplecarbonyl stretch frequencies from all amides such as in differ-ent peptides appear overlapped and molecular identification isgenerally only possible by IR spectroscopy for pure compoundsby direct comparison with a database of authentic spectra.

However, the two most information-rich techniques thatgive atom-specific molecular structural information are massspectrometry (MS) and nuclear magnetic resonance (NMR)spectroscopy. Currently, for MS-based metabonomics, it isgenerally necessary to carry out a separation step, usuallyusing high performance liquid chromatography (HPLC) orchemical derivatization and gas chromatography (GC) beforethe MS stage (8). The use of Fourier transform MS with itsexceptional resolution may remove the need for the separa-tion step (9). Moreover, MS can be more sensitive thanNMR spectroscopy and can give lower detection limits.However, there are problems of non-uniform detection causedby variable ionization efficiency. Nevertheless, a few metabo-nomics studies of mammalian systems using MS detectionhave now been reported and these will be mentioned in subse-quent chapters.

1H NMR spectroscopy (see Chapter 3) is especially suita-ble for metabonomics as it requires little or no sample pre-paration, is rapid and non-destructive, and uses smallsample sizes (10,11). More recently, the technique of magic-angle-spinning NMR spectroscopy (see Chapter 5) has openedup the possibility of metabonomics applied to tissue samples.The NMR-detected metabolic response of an organism to aparticular disease, toxin or pharmaceutical compound can

4 Lindon et al.

then be extracted from the complex data sets, which are alsosubject to biological variation, by application of appropriatemultivariate statistical analyses.

Figure 1 summarizes the three main ‘‘omics’’ subjectsand the techniques used to generate the analytical data.The common philosophy that links all ‘‘omics’’ approaches liesin the need for in depth bioinformatics and chemometrics ana-lyses and the generation of databases of results.

2. THE METABOLIC CONTINUUM

As has been stated already, gene expression and proteomicdata may only indicate the potential for pathophysiologicalchanges because many pathway feedback mechanisms are

Figure 1 The relationship between the main ‘‘omics’’ technolo-gies. Transcriptomics, the study of changes in gene expression, usesmainly gene chips in which RNA binding is monitored using fluor-escent tags. Proteomics, the evaluation of protein expression, relieson a separation technique, usually 2D gel-electrophoresis followedby an analytical technique, usually MS. Metabonomics, the studyof low molecular weight metabolites, has mainly used 1H NMR spec-troscopy but other nuclides such as 13C can be used and increas-ingly LC–MS is finding a role. All approaches generatemegavariate data which need interrogation by appropriate chemo-metric or bioinformatic software.


simply not reflected in protein concentration or gene expres-sion changes. This realization has led to increased efforts bypharmaceutical companies to try to model transcriptomicand proteomic data in relation to metabolic pathway activity,and to map such data onto well-known pathway databasessuch as those provided by KEGG (12). However the human‘‘system’’ is very extensive and the functional integrity ofman is also dependent on many external factors and evenother genomes. The complexity of the situation is encapsu-lated in Fig. 2. Consideration of the interactions of the

Figure 2 The various levels of organization in molecular biology.As well as the genetic component, it is now clear that environmentaleffects such as diet and exposure to other substances in the realworld have major consequences. Additionally in humans and otherhigher species, the interaction between the host genome and thoseof colonizing species such as gut microbial populations needs to beincluded.

6 Lindon et al.

internal constituents and external factors in mammals indi-cates that simple pathway modeling will never capture theinformation richness needed to describe a human disease pro-cess or a drug interaction irrespective of the sophistication ofthe applied measurement technology. Disease or drug-induced modulations in transcriptomic, proteomic or metabo-nomic data probably do not relate to standardized metabolicpathways that have no compartmental constraints, sincemammalian metabolic control functions are dispersed acrossmany cell types in topographically distinct locations that arein different physiological states at the same time. Also, thereare many ‘‘extragenomic’’ sources of metabolites and otherinfluences not described in the individuals’ genome thatnonetheless have major effects on the integrated metabolismof the organism and on the disposition, fate and toxicity ofdrugs.

The meaning of the term ‘‘metabolism’’ as applied tomammals has been reevaluated and a more complete classifi-cation of the range of metabolic processes found in higheranimals and how they might interact probabilistically todetermine the outcome of a disease process or a drug interac-tion has been proposed (13).

Metabolic pathway diagrams have long been used asshorthand summaries of cellular biomolecular transforma-tions, and are accepted as being representative of underlyingcellular order and control. In single cell systems, metaboliccontrol analysis (MCA) (6) has long been used to describethe fluxes of metabolites through individual pathways orpathway units and these methods can accurately describekinetic properties of such systems. MCA cannot be appliedso easily to mammals because metabolic control is dispersedin many cell types through space and time. Complex interac-tions occur between endogenous pathway control and themetabolism of foreign compounds many of which also inducetheir own metabolism. Mammals also have well-developedgut microfloral communities [the ‘‘microbiolome’’ (14)], with>500 individual species in man (15), which exert a strongcontrolling influence on the host immune system and mayhave significant effects on the host metabolism as gutmicrobes


such as Bacteroides thetaiotaomicron even carry metabolicgenes that may assist other species including man (16).

The role of nutrition in the development of human dis-ease at the molecular level is widely appreciated and cancerand cardiovascular diseases have both dietary and geneticcomponents (17). It has also been shown that selective dietarysupplementation can markedly affect the metabolism, and thetoxicity of even commonly used drugs such as paracetamol(acetaminophen) (18). Dietary composition, in turn, influ-ences gut microfloral selection and probiotic food productsare now widely marketed ‘‘to improve gut health’’ (19). Thegeneral benefits and effects of probiotics are yet to be deter-mined, but in a wider context could still influence metabolismin subtle and important ways that could be relevant to drugmetabolism and toxicity.

Undoubtedly all these nutritional and microbial factorsalso influence the efficacy, metabolism, and toxicity of drugsin a variety of ways in a diverse population. Only by account-ing for all the major ‘‘metabolic axes’’, can it be possible to elu-cidate an individual’s overall metabolic status (part genome,part environment conferred) and relate this to the develop-ment of complex disease traits and the adverse idiosyncraticdrug toxicity reactions that can be fatal to both the patientand the drug.

It must be recognized that intracellular and extracellularmetabolites can come from diverse sources that cannot betreated equally with respect to process control exerted bythe mammalian genome. Hitherto, metabolites have beenclassified simply as being either ‘‘endogenous’’ or ‘‘xenobiotic’’,and have often been considered from different viewpointswith respect to pathway analysis. This whole concept hasbeen under review (13) and endogenous and xenobiotic meta-bolites represent ends of a continuum of integrated metabolicand non-enzymatic transformations of many kinds withnumerous intermediate categories that result from multisiteprocessing. Many so-called endogenous and xenobiotic com-pounds may be interconverted by a number of processesmediated by extragenomic elements or by facile chemicalreactions.

8 Lindon et al.

The range of metabolic types is encompassed by a fivelevel set of definitions. Endogenous species arise from pro-cesses under direct host cell genome=proteome control and=orsynthesis. Sym-endogenous metabolites are essential to hostbiological function and are metabolized or utilized by the host,but biosynthesis is not in the host genome. Sym-xenobioticsubstances are of extragenomic origin, are not necessarilyessential to the host, but may influence, and be incorporatedinto, endogenous and xenobiotic metabolism. The processinvolves cometabolism by two or more organisms. Transxeno-biotics are of extragenomic or chemical origin but are metabo-lically converted to endogenous species. Xenobiotics have nointrinsic biological function in mammals and are alien tothe host genome but may have major effects on endogenouspathway control and can be extensively metabolized.

Not all cellular transformations of small moleculesrequire enzymes. There are many examples of these so-calledmetabonates, and these are well known in drug degradationstudies, e.g., conversion of penicillins to penicilloic acids viab-lactam ring opening and these reactions may generatetoxicologically or allergenically active species (20). Anycompound of endogenous or exogenous origin that containsa carboxylate group can, in principle, undergo enzyme depen-dent ester glucuronidation (via UDP-glucuronosyl transfer-ase) and these glucuronides readily undergo facile internalrearrangement reactions (21) producing positional isomersand anomers. The subsequent reactivity of the transacylatedglucuronides toward macromolecules may be the basis of cer-tain immunological and toxicological interactions (22). Reac-tive ester glucuronides can also be formed from endogenousmetabolites with carboxylate groups including bile acids.Overall the widespread occurrence of these facile (but condi-tional) reactions can further detract from the determinacyof some metabolic processes in complex organisms, as suchmetabonates may also influence fluxes through enzyme-controlled pathways in unpredictable ways.

To capture the full power of ‘‘omics’’ tools for drug discov-ery and development, it is necessary to measure and modelthe whole system which includes the environmental factors.


Bioinformatic tools have a strong part to play in helping orga-nize massive data sets, but in themselves cannot be expectedto answer fundamental questions on ecological–biological–chemical interactions that influence the way a drug is meta-bolized or is toxic. There might be a number of usefulapproaches to global metabolic analysis. One is a mappingstrategy in which an individual occupying a position in ametabolic (or other omic) hyperspace is visualized as a partof a large cohort. The individual’s map position is a result ofthe interactions of a series of multivariate ‘‘influence vectors’’which exert a metabolic pressure on the individual. Thesepressures could be from intrinsic genetic sources or from exo-genous factors. A very simple representation of this is shownin Fig. 3 using a principal components (PC) map representingthe urinary composition of ca 5000 control Sprague Dawley(SD) rats. A series of hypothetical ‘‘influence vectors’’ areshown superimposed to indicate possible directions of meta-bolic pressure exerted by several interacting macroscopic fac-tors. Of course, this is a huge simplification as the influencevectors may be highly non-linear and also urine is only onecompartment that can be analyzed. The challenge is to findout the directions, magnitudes, and components of these ‘‘vec-tors’’ for each type of disease or physiological condition understudy. This will lead to combinations of biomarkers frommany pathways that are altered in each condition and prob-ably originating in many different perturbed sites in the body.These are useful as they can act both as diagnostic para-meters and also metrics of efficacy for treatments.

Another approach could involve using the minimumimposed rules or structure to account for the metabolic obser-vations, building a probabilistic system based on prior knowl-edge and outcome using the reverse procedure to the normaltypes of pathway analysis. In this type of model, one wouldtake the observed data andwork forward to find the bestmodelthat is consistent both with those data (given the analyticalerror) and prior knowledge. This would not necessarily resultin an ordered sequence of metabolic conversions (pathways)but would relate metabolic parameters and disease ortoxin-induced movements to each other in a probabilistic

10 Lindon et al.

way. The advantage of this is that it is not necessary toascribe every parameter or component to a pathway in a par-ticular cell type but to globally model the important changesfor a particular disease or toxic process. This would allow theinterventions of lifestyle change or drug treatment to be eval-uated in terms of bulk metabolic movement of the integratedsystem into ‘‘beneficial’’ or ‘‘adverse’’ hyperspaces and thusenable the creation of new metrics of efficacy and=or toxicitybased on probabilities. If idiosyncratic toxicity is really idio-syncratic, it will not be possible to predict. However, if itcan be assumed that it is due to an unfortunate combination

Figure 3 A principal component scores plot where each pointrepresents an NMR spectrum of a control rat urine. Superimposedon the plot are a number of arrows representing vectors which canbe regarded as indicating the various types of influence that canresult in alterations to a metabolic profile.


of measurable genetic and environmental factors, then by useof broader modeling concepts it may be possible to under-stand why idiosyncratic toxicity has occurred in particularcases and to find the relationships between gene and environ-ment and metabolism that has led to this outcome.

3. BIOMARKERS

The concept of a what constitutes a biomarker has been evol-ving to the stage where a comprehensive definition is neededand Chapter 2 provides a review. Biomarkers can be regardedof two main classes—those which provide a direct indicationof the molecular events occurring such as the presence of highplasma glucose in diabetes as a direct result of insulin defi-ciency and those which are really surrogates of the pathology,e.g., elevated plasma low density lipoprotein level in athero-sclerosis. Conventionally biomarkers have been regarded assingle molecular species or enzyme activities (e.g., elevationof aspartate aminotransferase in liver conditions) whichchange outside normal limits in some pathological situation,but the newer systems biology approaches whereby manyanalyzes are measured simultaneously lead to a new type ofbiomarker fingerprint. These arise typically from transcrip-tomics studies using gene microarray technology, proteomicsstudies whereby many proteins are detected and identifiedusing largely gel-electrophoresis MS approaches and finallymetabonomics using NMR spectroscopy or MS where diversesmall molecule metabolites are measured.

Thus biomarkers can comprise anything from a singlemolecular species, either based on small molecules or macro-molecules, through a complex fingerprint of molecularchanges indicative of the pathology. Other types of biomarkercan exist so long they fit with the general definition of a mea-surable change linked causatively with the pathology. As wellas biomarkers being potentially useful for drug target identi-fication, it is possible to generate biomarkers of adverseeffects such as drug toxicity or disease progression, biomar-kers of beneficial effects such as measures of therapeutic

12 Lindon et al.

effectiveness or reversal of toxicity, and finally, especially inthe environmental science area, biomarkers of exposure areimportant.

It is the latest concept of a metabolic fingerprint as a newtype of biomarker which is attracting much attention at pre-sent. One of the promises of this approach is the potentialability for such small molecule biomarkers to be less speciesdependent than gene or protein markers and hence the goalof using data obtained preclinically in the clinical situationis being addressed.

A number of aspects relating to biomarkers have to beaddressed and these include the analytical precision andreproducibility of the technology used to identify markers,and this is particularly true for gene expression arrays andproteomics platforms. In addition, the evaluation of statisticaldistributions for biomarker values for both normal popula-tions and for pathological groups has to be achieved becauseof the need to understand biological variation. These areaswill require further investigation before preclinical and clini-cal biomarkers can be used in submissions to regulatoryauthorities.

It is clear that, given the decreasing efficiency andincreasing expense of the drug discovery and developmentprocess and the associated need for therapies for chronic dis-eases especially in the neurological area where diagnosis isdifficult and differential diagnosis crucial, there will be agrowth in the use of surrogate measures and it is predictablethat molecular biomarkers will play a prominent role.

4. BRIEF OVERVIEW OF METABONOMICSTECHNIQUES

A variety of spectroscopic methods can be used to generatemetabonomic data sets on complex biological samples so longas the data sets are rich in molecular information. A numberof investigations, primarily in plant and microbiologicalcontexts (23), have used MS, mainly because of its overallgreater sensitivity compared with NMR spectroscopy. This


has usually been coupled either to HPLC, or to GC after che-mical derivatization. However, high resolution 1H NMR spec-troscopy has proved to be one of the most powerfultechnologies for biofluids and essentially the only one capableof studying intact tissues, producing a comprehensive profileof metabolite signals without the need for preselection of mea-surement parameters or selection of separation or derivatiza-tion procedures (10). Furthermore, variable detectionresponses, such as differential volatilization or ionizationeffects as in MS, are not an issue for NMR spectroscopy. How-ever, it is clear that the two approaches are complementary,giving information on different sets of biomarkers and inte-gration of both technologies to provide more comprehensiveclassification and biomarker information is now occurring. Asyet, there are few metabonomic studies on mammalian systemsin the literature that haveusedMSas an experimental approachand even fewer that have identified novel biomarkers. Wheresuch studies exist, they are mentioned, but at present sincenearly all metabonomics studies of drug safety are based on 1HNMR spectroscopy, this book concentrates on this methodology.

Typically, 1H NMR spectra of biofluids such as urine andplasma contain thousands of signals arising from hundreds ofendogenous molecules representing many biochemical path-ways. Conventional measurement of the major NMR signalscan be used to detect biochemical changes, but the complexityof the spectra and the presence of natural biological variationacross a set of samples often make it difficult to detect mean-ingful patterns of change by eye. Generally, it is necessary touse data reduction and pattern recognition (PR) techniques inorder to access the latent biochemical information present inthe spectra.

Water is present at such high concentration in biofluidsthat its NMR peak is so huge that it can obscure other mole-cular information. It can also cause dynamic range problemsin the NMR detector. For these reasons, 1H NMR spectra ofurine are measured using a water peak suppression techni-que (see Chapter 3). For serum or plasma, a suite of 1HNMR spectra is usually measured, selecting either mainlysmall molecule resonances or macromolecule profiles.

14 Lindon et al.

With developments in robotic sample preparation=-transfer systems and in NMR flow probes, the capacity forNMR analysis has increased enormously and now up to200–300 samples per day can be measured. Although,1H NMR spectra of urine and other biofluids are very com-plex, many resonances can be assigned directly based on theirchemical shifts, signal multiplicities, and by adding auth-entic material. However, further information can be obtainedby using spectral editing techniques, as described inChapter 3.With the advent of NMR detectors cooled to near cryogenictemperatures (cryoprobes), a sensitivity gain of about 500%is achievable making it possible to measure smaller samplesor use less time. In addition, natural abundance 13C NMRspectroscopy is now also feasible for metabonomics (24).

Although identification of molecules is not necessary toachieve classification of samples, working out the identifica-tion of the molecules that differentiate spectra from differentsamples classes (biomarker combinations) can lead to insightinto biochemical mechanisms of disease or drug effects.Usually, off-line chromatographic procedures such as solidphase extraction chromatography (SPEC) or HPLC can beused to simplify or clean up biofluid samples prior to NMRspectroscopy, as explained in more detail in Chapter 6. Inselected cases, directly coupled HPLC–NMR and HPLC–NMR–MS methods can be of value in determining endogen-ous metabolite structures (25).

If tissue samples are available, then complementaryinformation to that in biofluids can be obtained. Althoughin vivo NMR spectroscopy has been used to investigate abnor-mal tissue biochemistry, spectral quality is always severelycompromised by the low magnetic fields used, leading to poorsensitivity and peak dispersion. Heterogeneity in the sampleresults in magnetic susceptibility differences causing mag-netic field inhomogeneity and this combined with constrainedmolecular motions of molecules in some tissue compartmentsleads to poor resolution and lower sensitivity. Therefore, NMRspectral analysis of tissues has largely relied upon tissueextraction methods. However, extraction processes can resultin the loss of tissue components such as proteins and lipids.


The development of high resolution 1H magic-angle spin-ning (MAS) NMR spectroscopy has had a substantial impacton the ability to analyze intact tissues (26), and this approachis the subject of Chapter 5. Rapid spinning of the sample(�4–6kHz typically) at an angle of 54.7� relative to theapplied magnetic field serves to reduce line broadening effectsdue tomagnetic field inhomogeneity causedby sample heteroge-neity, dipolar couplings, and chemical shift anisotropy. Thus,it is possible to obtain very high quality NMR spectra of wholetissue samples with no sample pretreatment using only about20mg of material. Such experiments indicate that diseased ortoxin-affected tissues have substantially different metabolicprofiles to those taken from healthy organs (27,28). In addi-tion, MAS-NMR spectroscopy can be used to access informa-tion regarding the compartmentization of metabolites withincellular environments (29). 1H MAS-NMR spectroscopic ana-lysis of tissues has great potential within the pharmaceuticalindustry in toxicological screening of novel compounds. Usingthis technology, it is possible to ‘‘bridge the gap’’ between bio-fluid analysis and histopathology and to gain real insight intothe mechanisms of toxicity at a molecular level.

However, the problem of interpreting the data in metabo-nomics studies essentially reduces to how, in a large set ofNMR spectra of a biofluid from a cohort of animals or humansdemonstrating a variety of effects such as normal physiologi-cal variation, which might obscure a drug-induced effect, doesone determine the significant changes? This is achievedthrough the use of PR methods. In chemistry, the term che-mometrics is generally applied to describe the use of PR andrelated multivariate statistical approaches to chemicalnumerical data (30). Chapter 8 describes in detail the differ-ent types of chemometric approaches that can be used. Thegeneral aim of PR is to classify an object or to predict the ori-gin of an object based on identification of inherent patterns ina set of experimental measurements or descriptors. Patternrecognition can be used for reducing the dimensionality ofcomplex data sets, for example by two-dimensional (2D) orthree-dimensional (3D) mapping procedures, thereby facili-tating the visualization of inherent patterns in the data set.

16 Lindon et al.

Alternatively, multiparametric data can be modeled using PRtechniques so that the class of a separate sample can bepredicted based on a series of mathematical models derivedfrom the original data or ‘‘training set’’ (31).

Pattern recognition methods can be divided into two cate-gories, ‘‘unsupervised’’ and ‘‘supervised’’ methods. Unsuper-vised multivariate techniques are used to establish whetherany intrinsic clustering exists within a data set and consist ofmethods that map samples according to their properties with-out a priori knowledge of sample class. Examples of unsuper-vised methods include principal components analysis (PCA)and clustering methods such as hierarchical cluster analysis.Supervisedmethods of analysis use the class information givenfor a training set of sample data to optimize the separationbetween two or more sample classes. These techniquesinclude soft independent modeling of class analogy (SIMCA),K-nearest neighbor analysis, and neural networks. Supervisedmethods require a second independent data set to test or vali-date any class predictions made using the training set (31).

For situations where large numbers of samples need to beprocessed, there is a need for automatic data reduction andPR analysis. One example of a robust automatic data reduc-tion method, which has been widely used, is the division ofthe NMR spectrum into regions of equal chemical shift rangesfollowed by signal integration within those ranges (32). Auto-matic data reduction of 2D NMR spectra can be performedusing a procedure similar to that for one-dimensional (1D)spectra, in which the spectrum is divided by a grid containingsquares or rectangles of equal size, and the spectral integralin each volume element is calculated. This is not a universalsolution and other approaches are possible and have beenused, including shifting peak positions to take into accountsmall pH-dependent variations in chemical shift, in whichcase the full NMR spectrum can be used for PR. However, itshould be remembered that although the initial PR methodsmight have used segmented data, having identified regionsof interest which are changed in some pathological situation,it is always possible to return to the real NMR spectra forpeak assignment and metabolite identification.


5. METABONOMICS APPLICATIONS

Theoretically, healthy control animals or humans shouldoccupy a defined region of multidimensional metabolic hyper-space and hence a similar position in a PR map based on 1HNMR biofluid data. However, within a control population,natural variation related to factors such as gender, diet,age, physiological rhythms, and genotype occurs. In order toimprove the reliability of detecting specific pathologicalabnormalities in the biochemical profiles of biofluids, it isnecessary to establish the extent of normal physiological var-iance within the control population, and to analyze factorscontributing to alterations in normal physiological ‘‘mappingspace’’. Given the higher variation in biochemical profiles ofhumans, the use of supervised PR methods and large datasets is usually necessary to delineate and understand thecauses of the variation, and this is the subject of Chapter 10.

One of the factors that contributes to the overall varia-tion in control populations in laboratory studies is the avail-ability of different strains of animals. Recent studies havehighlighted the sensitivity of chemometric methods in differ-entiating between the 1H NMR urine profiles obtained fromtwo standard strains of laboratory rats, SD and Han Wistar(HW) with 90% (33). However, although strain-related differ-ences in the biochemical composition of urine could bedetected using NMR–PR methodology, perturbations in theurinary profile caused by administration of toxins were foundto be significantly larger than any strain-related urinaryvariation.

1H NMR spectroscopy has been used to study the compo-sition of biofluids before and after the administration of awide range of toxins. Linked with PR methods the toxin-induced deviation from normal metabolite profile can be mea-sured efficiently. Chapter 9 highlights applications in thisarea. Predictive statistical models have been constructed todeal with toxicological profiling on three levels. The firstand most basic level is distinguishing whether a sample isnormal, i.e., belongs to a control population. The second levelinvolves fitting abnormal samples to known classes of tissue

18 Lindon et al.

or mechanism-specific toxicity with a view to predicting thetoxicity of novel pharmacological compounds. The final stageis to identify the spectral regions that are responsible forthe deviation from the normal profile and to determine thebiomarkers of toxicity within those regions, which may helpelucidate mechanisms of toxicity.

Metabonomics has been used to evaluate many toxins,each toxin producing a distinctive series of metabolic pertur-bations that are characteristic of the type of tissue damageand=or the mechanism of toxicity. From fluids such as urine,plasma, and cerebrospinal fluid, the target organ of toxicityand, in some cases, the topographical region of injury withinthat organ have been identified. To date, toxicity of the liverand kidney has been most widely studied using metabonomictechniques but evaluation of testicular, cardiac, neuro- andmitochondrial toxicities has also proved successful.

The administration of a toxin does not generally cause asingle metabolic response but induces a series of metabolicevents in time, which may or may not return to their previoushomeostatic condition, depending on the severity of the lesion.Since the response to toxic insult is dynamic, biofluid profilesare in a constant state of flux and the time of metabolicresponses is also characteristic for specific toxins. Where bio-fluids are sampled over a series of time intervals, a biochem-ical trajectory of response can be calculated either forindividual animals or for groups. The extent and directionof deviation of the trajectory from the coordinate correspond-ing to the predose time period can yield information concern-ing the severity and type of biochemical lesion. Toxic effects intissues themselves can also be studied using 1H MAS-NMRspectroscopy with examples of renal and liver toxins beinginvestigated (34,35).

The interest in the use of metabonomics to evaluate drugsafety is highlighted by the Consortium on Metabonomic Tox-icology (COMET) (36). This is sponsored by five pharmaceuti-cal companies and is operated at Imperial College London.This project has derived and applied metabonomic data gen-erated using 1H NMR spectroscopy of urine, blood serum,and tissues for preclinical toxicological screening of candidate


drugs. It has generated databases of metabonomic results fora wide range of model compounds, toxins, and drugs linked tocomputer-based expert systems for toxicity prediction. Effortshave concentrated on liver and kidney toxicity in the rat andmouse. During the initial phase of the project, a successfuldetailed comparison was made of the ability of the companiesto provide consistent urine and serum samples using a com-mon study of the toxicity of hydrazine in the male rat. Adetailed statistical model has also been constructed based onthe NMR spectra of urine from control rats and this enablesidentification of outlier samples and the metabolic reasonsfor the deviation. Chemometric models have also been con-structed for the urine and serum data of dosed animals andexcellent consistency among all companies was demonstrated.Finally, a computer-based expert system for toxicity predictionhas been generated and delivered to the sponsoring companies.

Metabonomics has also been applied in fields outsidehuman and other mammalian systems. For example, studiesin the environmental pollution field have highlighted thepotential benefits of this approach in studies of caterpillarhemolymph and earthworm biochemical changes as a resultof soil pollution. In addition, a study of heavy metal toxicityin wild rodents living on polluted sites has been concludedsuccessfully. Chapter 11 highlights some of these studies.

The use of chemometrics in the interpretation of NMRspectra in the clinical area has an established history.NMR spectroscopy is a powerful tool in the investigation ofmany diseases such as inherited metabolic disorders, organfailure, and cancers. For example, PCA has been used to dif-ferentiate between tissue extract spectra obtained from nor-mal tissues (extracts) and to classify further tumors intotype including differentiation among pituitary tumor, fibro-sarcoma, hepatoma, and Walker sarcoma (37). NMR–PR hasbeen used to establish normal physiological variance in apopulation of human urine samples (38), to classify severalinborn errors of metabolism from PCA of urine spectra (38),and monitor the growth of tumors from analysis of NMR spec-tra of serum samples (39). Recently the use of metabonomicsto identify patients suffering from coronary artery occlusion

20 Lindon et al.

based on 1H NMR spectra of blood serum has been high-lighted (40). In addition to monitoring the onset or progres-sion of a disease, NMR–PR can be used to assess thetherapeutic efficacy of treatment.

6. SUMMARY

Metabonomics is a powerful approach for generating a sub-stantial body of information on an intact biological sample.Because NMR spectra and mass spectra contain informationon a wide range of biochemical pathways, they are both usefulpathological fingerprinting tools. This metabolic fingerprint isperturbed in a characteristic fashion in disease or toxic pro-cesses and this shift in position can be readily visualizedand modeled using a range of chemometric techniques.Understanding the biochemical reason for such a shift inmetabolic space leads to the identification of biomarkers ofdisease or toxicity. Taken together, these methods constitutea metabonomic approach to studying the quantitative meta-bolic consequences of pathophysiological insult. NMR-basedmetabonomics is a relatively new tool in the armory of ‘‘omics’’techniques, but shows considerable promise in its efficiency ofacute lesion detection and, perhaps more importantly, in itsability to give mechanistic insight into toxic and disease pro-cesses. However, all of the ‘‘omics’’ approaches, genomics, pro-teomics, and metabonomics offer complementary informationon physiological function and pathological dysfunction intheir respective systems of analysis. Furthermore, theseapproaches also share the use of the same basic bioinformaticand chemometric tools that are needed for enhanced infor-mation recovery. It is, therefore, possible to integrate thedatabases and search for relationships among genomic,proteomic, and metabolic perturbations using appropriatestatistical methods, leading to ‘‘bionomics’’.

A particular strength of spectroscopic-based metabo-nomic methods is that they are rapid and not labor intensive.Furthermore, the recurrent expenditure is very low whenflow-injection high-throughput studies are considered. In


biological terms, the most important advantage of metabo-nomics lies in its ability (especially with urinalysis) to followindividual animals or subjects non-invasively through a com-plete toxin-related metabolic trajectory giving a holistic pic-ture of integrated biological function over time. This isparticularly important where multiorgan effects are a possibi-lity. In such cases, genomic or proteomic methods are weakerbecause of the analytical necessity of choosing very limitedtime points for a study and selected tissue or cell samples.MAS-NMR spectroscopy of tissues also represents a signifi-cant advance in clinical chemistry and experimental toxicol-ogy, but is not yet suited to high-throughput screeningbecause of the technology limitations. Nevertheless, MAS-NMR spectroscopy is complementary to biofluid NMR spectro-scopic studies as it allows biochemical evaluation of the targetorgans for disease and toxicity and the identification of noveltissue-specific biomarkers of damage. It also enables animportant new bridge to be constructed between tissue bio-chemistry studies and conventional histopathology in a waythat has not been possible previously. As spectroscopic tech-nologies and chemometric methodologies continue to advance,they are likely to make increasingly important contributionsto experimental toxicology studies and disease diagnosis inthe future.

REFERENCES

1. Stumm G, Russ A, Nehls M. Deductive genomics—a functionalapproach to identify innovative drug targets in the post-genomic era. Amer Pharmacogenom 2002; 2:263–271.

2. Kahl G. The Dictionary of Gene Technology. Weinheim:Wiley-VCH, 2001.

3. Aebersold R. Constellations in a cellular universe. Nature2003; 422:115–116.

4. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: under-standing the metabolic responses of living systems to patho-physiological stimuli via multivariate statistical analysis ofbiological NMR data. Xenobiotica 1999; 29:1181–1189.

22 Lindon et al.

5. Raamsdonk LM, Teusink B, Broadhurst D, Zhang N, Hayes A,Walsh M, Berden JA, Brindle KM, Kell DB, Rowland JJ,Westerhoff HV, van Dam K, Oliver SG. A functional genomicsstrategy that uses metabolome data to reveal the phenotype ofsilent mutations. Nat Biotechnol 2001; 19:45–50.

6. Derr RF. Modern metabolic control theory. 1. Fundamentaltheorems. Biochem Archives 1985; 1:239–247.

7. Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabo-nomics: a platform for studying drug toxicity and gene func-tion. Nat Rev Drug Discovery 2002; 1:153–161.

8. Niessen WMA. Liquid Chromatography–Mass Spectrometry.New York: Marcel Dekker, 1999.

9. Asamoto B, ed. FT-ICR=MS: Analytical Fourier Transform IonCyclotron Resonance. Weinheim: Wiley-VCH, 1991.

10. Nicholson JK, Wilson ID. High resolution proton magneticresonance spectroscopy of biological fluids. Prog NMRSpectrosc 1989; 21:444–501.

11. Lindon JC, Nicholson JK, Holmes E, Everett JR. Metabo-nomics: metabolic processes studied by NMR spectroscopy ofbiofluids. Concepts Magn Reson 2000; 12:289–320.

12. KEGG: Kyoto Encyclopaedia of Genes and Genomes. Released26 April 2003.

13. Nicholson JK, Wilson ID. Understanding global systems biol-ogy: Metabonomics and the continuum of metabolism. NatureReviews Drug Discovery 2003; 2:668–676.

14. Gilmore MS, Ferretti JJ. The thin line between gut commensaland pathogen. Science 2003; 299:1999–2002.

15. Tannock GW. Normal Microflora. London: Chapman and Hall,1995.

16. Xu J, Bjursell MK, Himrod J, Deng S, Carmichael LK, ChaingHC, Hooper LV, Gordon JI. A genomic view of the human–Bacteroides thetaiotaomicron symbiosis. Science 2003; 299:2074–2076.

17. Willet WC. Balancing life-style and genomics research fordisease prevention. Science 2002; 296:695–698.


18. Ghauri F, McLean A, Beales D, Wilson ID, Nicholson JK.Induction of 5-oxoprolinuria in the rat following chronic feed-ing with N-acetyl 4-aminophenol (paracetamol). BiochemPharmacol 1993; 46:953–957.

19. Beale B. Probiotics: their tiny worlds are under scrutiny. TheScientist 2002; 16:20–22.

20. Schwartz MA. Chemical aspects of penicillin allergy. PharmSci 1969; 58:643–661.

21. Caldwell J, Hutt AJ, Marsh MV, Sinclair K. In: Reid E,Leppard JP, eds. Drug Metabolite Isolation and Determina-tion. New York: Plenum Press, 1983:161–179.

22. Spahn-Langguth H, Benet LZ. Acyl glucuronides revisited: isthe glucuronidation process a toxification as well as a detoxifi-cation mechanism? Drug Metabol Rev 1992; 24:5–48.

23. Fiehn O. Metabolomics—the link between genotypes andphenotypes. Plant Mol Biol 2002; 48:155–171.

24. Keun HC, Beckonert O, Griffin JL, Richter C, Moskau D,Lindon JC, Nicholson JK. Cryogenic probe 13C NMR spectro-scopy of urine for metabonomic studies. Anal Chem 2002;74:4588–4593.

25. Lindon JC, Nicholson JK, Wilson ID. Directly-coupled HPLC–NMR and HPLC–NMR–MS in pharmaceutical research anddevelopment. J Chromatogr B 2000; 748:233–258.

26. Garrod SL, Humpfer E, Spraul M, Connor SC, Polley S,Connelly J, Lindon JC, Nicholson JK, Holmes E. Highresolution magic angle spinning 1H NMR spectroscopic studieson intact rat renal cortex and medulla. Magn Reson Med 1999;41:1108–1118.

27. Cheng LL, Lean CL, Bogdanova A, Wright SC Jr, AckermanJL, Brady TJ, Garrido L. Enhanced resolution of protonNMR spectra of malignant lymph nodes using magic-anglespinning. Magn Reson Med 1996; 36:653–658.

28. Tomlins A, Foxall PJD, Lindon JC, Lynch MJ, Spraul M,Everett J, Nicholson JK. High resolution magic angle spinning1H NMR analysis of intact prostatic hyperplastic and tumourtissues. Anal Commun 1998; 35:113–115.

24 Lindon et al.

29. Griffin JL, Troke J, Walker LA, Shore RF, Lindon JC,Nicholson JK. The biochemical profile of rat testicular tissueas measured by magic angle spinning 1H NMR spectroscopy.FEBS Lett 2000; 486:225–229.

30. Lindon JC, Holmes E, Nicholson JK. Pattern recognitionmethods and applications in biomedical magnetic resonance.Prog NMR Spectrosc 2001; 39:1–40.

31. SharafMA, IllmanDL,KowalskiBR. Chemometrics.Chichester:J. Wiley and Sons, 1986.

32. Farrant RD, Lindon JC, Rahr E, Sweatman BC. An automaticdata reduction and transfer method to aid pattern-recognitionanalysis and classification of NMR spectra. J Pharm BiomedAnal 1992; 10:141–144.

33. Holmes E, Nicholls AW, Lindon JC, Connor SC, Connelly J,Haselden JN, Damment SJP, Spraul M, Neidig P, NicholsonJK. Chemometric models for toxicity classification based onNMR spectra of biofluids. Chem Res Toxicol 2000; 13:471–478.

34. Garrod S, Humpher E, Connor SC, Connelly JC, Spraul M,Nicholson JK, Holmes E. High-resolution 1H NMR and magicangle spinning NMR spectroscopic investigation of thebiochemical effects of 2-bromoethanamine in intact renaland hepatic tissue. Magn Reson Med 2001; 45:781–790.

35. Waters NJ, Holmes E, Williams A, Waterfield CJ, Farrant RD,Nicholson JK. NMR and pattern recognition studies on thetime-related metabolic effects of alpha-naphthylisothiocyanateon liver, urine, and plasma in the rat: an integrative metabo-nomic approach. Chem Res Toxicol 2001; 14:1401–1412.

36. Lindon JC, Nicholson JK, Holmes E, Antti H, Bollard ME,Keun H, Beckonert O, Ebbels TM, Reily MD, Robertson D,Stevens GJ, Luke P, Breau AP, Cantor GH, Bible RH, Nieder-hauser U, Senn H, Schlotterbeck G, Sidelmann UG, LaursenSM, Tymiak A, Car BD, Lehman-McKeeman L, Colet J-M,Thomas C. Contemporary issues in toxicology—the role ofmetabonomics in toxicology and its evaluation by the COMETproject. Toxicol Appl Pharmacol 2003; 187:137–146.

37. Howells SL, Maxwell RJ, Peet AC, Griffiths JR. An investiga-tion of tumor 1H nuclear magnetic resonance spectra by the


application of chemometric techniques. Magn Reson Med1992; 28:214–236.

38. Holmes E, Foxall PJD, Nicholson JK, Neild GH, Brown SM,Beddell CR, Sweatman BC, Rahr E, Lindon JC, Spraul M,Neidig P. Automatic data reduction and pattern recognitionmethods for analysis of 1H NMR spectra of human urinefrom normal and pathological states. Anal Biochem 1994; 220:284–296.

39. Kruse S, Kvalheim OM, Gadeholt G, Halsteinslid L, Sletten E.Multivariate-analysis of proton NMR spectra of serum fromrabbits—monitoring progressive growth of implanted VX-2carcinoma. Chemometrics Intell Lab Syst 1991; 11:191–196.

40. Brindle JT, Antti H, Holmes E, Tranter G, Nicholson JK,Bethel HWL, Clarke PM, Scofield E, McKilligan DE, MosedaleD, Grainger D. Rapid and non-invasive diagnosis of the pre-sence of coronary heart disease using 1H NMR-based metabo-nomics. Nat Med 2002; 8:1439–1444.

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2

Overview of Biomarkers

JOHN TIMBRELL

Pharmacy DepartmentKing’s College,London, U.K.

1. INTRODUCTION

When studying the toxicity of drugs and other chemicals inhumans and other animals, it is necessary to use biologicalmarkers or biomarkers. There are three reasons for this;firstly, the study of toxicity requires a knowledge of the doseor level of the substance to which the animals or patient isexposed. Secondly, the study of toxicity also requires a meansof detecting and quantifying the pathological effect. Finally,the study of the toxicity may require an understanding ofthe factors which affect the occurrence of the pathologicalresponse.

Thus, we need to be able to measure exposure tothe drug or other chemical and a knowledge of the external

27

concentration or administered dose is often not enough. Weneed to be able to quantify the toxic response caused by thatdrug or chemical. Finally, we need if possible to be able to pre-dict the response or effect in sensitive individuals. Biomarkersare tools that enable us to do these three things. The use ofbiomarkers in toxicology is, therefore, becoming of increasingimportance, especially in relation to risk assessment.

There are three types of biomarker in relation to expo-sure to chemicals as originally defined by the U.S. NationalAcademy of Sciences Committee on Biological Markers

Biomarkers of exposure;Biomarkers of response;Biomarkers of susceptibility.

Each of these aspects of the toxicity of a drug or chemicalrequires a different type of biomarker which include a largevariety of biological end points (Table 1). These are allinter-related and are part of the general scheme shown inFig. 1. The categories may overlap sometimes and somebiomarkers may fall into more than one category.

Thus, biomarkers of exposure are required to determinewhat level of chemical is present in the body of the patient oranimal. There are three different types of such biomarker,depending on the stage at which they are measured. Some bio-markers of exposure are closely associatedwith themechanism.

Table 1 Examples of Types of Biomarker

Type ofBiomarker Biomarker

SpecificExample Toxicant Medium

Exposure Metabolite S-phenylmercapturic acid

Benzene Urine

Exposure DNAadduct

O6 Methyldeoxyguanine

N-methyl-N-nitroso urea

Lymphocytes

Response Plasmaenzyme

ALT Paracetamol Plasmaserum

Response Protein Metallothionein Cadmium Liver tissueSuscepti-bility

phenotype Acetylatorphenotype

Hydrazinedrugs

Urine

28 Timbrell

Provided exposure has occurred, biomarkers of responseare measured in order to both detect and quantify any toxicand pathological effects the chemical may have caused.Thereare many different types of biomarkers of response and someare directly related to mechanism of toxicity.

Biomarkers that indicate which factors may be impor-tant in determining which species or individual within thespecies is affected by the chemical, such as genetic factors,are known as biomarkers of susceptibility. These are oftenintimately connected with the mechanism of the toxicity.

Therefore, for some, but not all of these biomarkers, aknowledge of the mechanism underlying the toxicity may beimportant. Also the use of biomarkers must be part of a hol-istic approach to the study of and evaluation of the toxicity ofthe chemical in which a number of markers are usedtogether. Furthermore, new biomarkers need to be validatedin relation to specificity and critically compared with othermarkers.

Figure 1 Inter-relationships between the different types ofbiomarkers. From Ref. 2.

Overview of Biomarkers 29

Some biomarkers may be measured both in vivo and invitro, some may be specific to a particular organism, whereasothers apply to most species.

Biomarkers may be simple or very sophisticated andinvasive or noninvasive (Table 2). Ideally, for use in vivo theyshould be noninvasive, specific, diagnostic, early warning,sensitive, easily measured, and related to the mechanism oftoxicity.

It also should be borne in mind that single biomarkersare rarely enough, as chemicals often damage more thanone organ and biomarkers are rarely totally specific. There-fore, several biomarkers will usually be needed and inter-preted together in order to give a complete picture.

The term biomarker is very broad and covers many mea-surements and parameters some of which have been in use formany years in medicine and toxicology. Some, however, arevery new and utilize the latest technologies such as genomics,proteomics and, as described in this text, NMR spectroscopy.This latter technology may be used for the detection orevaluation of all three types of biomarker.

Consequently, in this chapter biomarkers will berestricted to those relevant to exposure to drugs and chemi-cals and having application to mammals as used in drugsafety evaluation. Although biomarkers of disease may bedual purpose neither these nor biomarkers of efficacy willbe specifically discussed.

Table 2 Types of Biomarker Divided into Invasive andNoninvasive and of Differing Complexity and Specificity

Noninvasive Invasive

Body weight Organ weightUrine volume HistopathologyUrinary=serum enzymes Tissue enzymeUrinary=serum metabolites levels=activityWBC DNA adducts Gene changesDNA fragments AntibodiesSerum=urinary proteins Genotyping

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Before discussion of the individual types of biomarker, itis necessary to put them in context relative to the toxicity ofdrugs and chemicals. Thus, the overall process of exposure toa chemical through to development of a disease resultingfrom that exposure can be represented as shown in Fig. 1.This is not a series of isolated steps but a continuum andthe various types of biomarkers and their inter-relationshipsare shown in this diagram also. Thus, the process starts withan exposure phase in which a proportion of the chemical isabsorbed into the blood stream, possibly followed by metabo-lism and metabolic activation. The metabolite will then inter-act in some way with a target molecule, possibly a specificreceptor or simply available enzymes or other proteins,DNA, lipids, or carbohydrates. One or more of these interac-tions may lead to a biochemical response which could presagea pathological process which eventually leads to a grosspathological lesion or a physiological change. Thus, biomar-kers are involved at each step. Biomarkers of exposure arerequired to determine the extent of exposure and the natureand quantity of the metabolites produced. Biomarkers ofeffective dose will reveal any interactions with macromole-cules which may be relevant targets. Biomarkers of responseindicate that biochemical changes have taken place and pos-sibly that a pathological lesion has been produced. Biomar-kers of susceptibility will be markers such as geneticparameters relating to the particular exposure, such as varia-bility in metabolism or a particular type of response.

Thus, the details of the overall process, what was oncesimply described as a ‘‘black box’’, are now known to includethe stages of toxicokinetics, toxicodynamics, and pathogenesis(Fig. 1). The development of biomarkers relating to these hasallowed risk assessment and prediction to progress.

2. BIOMARKERS OF EXPOSURE

Biomarkers of exposure can be conveniently divided into:Biomarkers of internal dose, for example, the compound

or perhaps a metabolite of a drug in a body fluid;


Biomarkers of effective dose, for example, a conjugate=adduct formed at the target site.

The first type gives an indication of the occurrence andextent of exposure of the whole organism to the drug or otherchemical. The exposure of the organism may be very differentfrom that expected from the dose because of the interveningprocesses of absorption, distributionmetabolism, and excretion.The absorbed dose will often be less than the exposure dose.

Thus, although an animal may be exposed to the chemi-cal, these intervening processes may reduce the amountreaching the target to zero effectively (Fig. 2).

The second type of marker includes the process of meta-bolic activation if relevant, and so is a composite or aggregatebiomarker. It reflects the true exposure of the target site butwillusually be a fraction of the absorbed dose and reflects the toxi-cokinetics and physicochemical characteristics of the chemical.

Both types of marker take into account the processes ofabsorption, distribution, excretion, andmetabolism. Therefore,individual variation in the animal is included in the measure-ment and can give rise to biomarkers of susceptibility.

Figure 2 Effect of biological barriers on exposure of target to atoxic chemical.

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Examples of each of these types of biomarkers will be dis-cussed particularly in relation to the mechanism of toxicity.

2.1. Biomarkers of Internal Dose

These markers indicate that exposure to a particular com-pound has taken place by measuring the compound or itsmetabolite(s) in body fluids.

Although human exposure to a particular chemical maybe estimated from biomonitoring studies using workplacemonitoring, for example, or preferably personal monitors,there is individual variability in absorption, and the distribu-tion and excretion of a chemical may influence exposure of thetarget site. Therefore, it is preferable to measure the actualamount of compound or better its metabolite in a tissue orfluid from an individual in order to estimate the actual expo-sure rather than the expected exposure. This is particularlyimportant for environmental and industrial chemicals wherethe dose is only often approximately known. Where there areseveral possible routes of absorption, actual measurement ofthe internal dose is essential. Even with medicines where adefined dose is administered, determination of the actualinternal dose is still essential in animal studies and in initialstudies in human volunteers.

Sophisticated techniques are now available for measur-ing chemicals and their metabolites at very low levels in orderto assess exposure.

Particular attention has been paid to metabolites derivedfrom glutathione conjugation as potential markers of expo-sure (3). This is because glutathione (GSH) detoxifies reactivechemicals to which biological systems are exposed. The resultof this conjugation is the excretion of a variety of sulfur con-taining metabolites. These may give some indication thatmetabolic activation has taken place (4).

Measurement of specific metabolites such as particularmercapturic acids, the final product of GSH conjugation,is a better biomarker of internal dose but requires priorinformation about the structure of the compound and oftensophisticated analytical techniques.


For example, for determination of benzene exposure inindustry or from gasoline, there are a number of candidatemetabolites. However, although benzene is metabolized byhydroxylation to several metabolites, none of these could beused as a specific biomarker of exposure (internal dose). Themetabolite used for confirming exposure and measuring thisexposure, which is specific, is the phenyl N-acetylcysteineconjugate which results from glutathione conjugation (Fig. 3).This is a minor metabolite which can be quantitated byGC=MS or now immunoassay and HPLC (5,6). This particularmetabolite is probably not be involved with the toxicity of ben-zene, therefore, would not be classified as a biomarker ofeffective dose. However, some glutathione conjugates maybe used as biomarkers of effective dose because they areclosely related to the mechanism of toxicity. For example,the glutathione derived metabolites of the drug paracetamolindicate the level of metabolic activation in humans and it

Figure 3 Metabolism of benzene to S-phenylmercapturic acid,measured in urine as a biomarker of exposure.

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has been shown that this varies in individuals by as much astenfold between the limits of the frequency distribution (7).Another is the chemical and experimental anticancer drugN-methylformamide. The N-acetylcysteine conjugate derivedfrom this compound was first detected by proton NMR ofurine from dosed animals and is believed to directly resultfrom conjugation of glutathione with the reactive metaboliteresponsible for the hepatotoxicity (8).

It should be mentioned at this point that the term bio-markers of exposure (internal dose) can also be applied todrugs in relation to metabolites which are pharmacologicallyactive. Thus, therapeutic drug monitoring requires a biomar-ker which can quantitate the true dose received by thepatient. In some cases, the biomarker may be the parent drugbut could be a metabolite if this is responsible for the pharma-cological effect of the drug.

2.2. Biomarkers of Effective Dose

Biomarkers of effective dose indicate that exposure hasresulted in a biologically active compound reaching a toxicolo-gically significant target. This is often a reactive metaboliterather than the compound to which the organism was exposed(Fig. 1). This is crucial to the toxicity, and a knowledge of thiswill dramatically improve risk assessment and the interpreta-tion of toxicological data.

Because of the many possible interindividual differencesin the rate and route of metabolism of compounds, the effec-tive dose at the target site is a preferred measurement overthe internal dose. This is often determined by measuring spe-cific adducts in tissues or body fluids.Chemicals that are reac-tive or are metabolized to reactive intermediates which reactwith DNA are of particular interest and concern in relation togenotoxicity and therefore possible carcinogenicity. Thus, pro-tein and DNA adducts in blood are used as biomarkers ofexposure to reactive alkylating agents such as methylatingand hydroxyethylating agents in tobacco smoke and also frommany other sources (8a). DNA adducts, such as 7-methylgua-nine andN-7-(2-hydroxyethyl) guanine, have been detected in


lymphocytes (9) and a adducts such as N-(2-hydroxyethyl)va-line have also been detected in hemoglobin from smokers (10).Hemoglobin adducts are surrogate markers which have alonger half-life than DNA adducts in lymphocytes. Thus,hemoglobin adducts reflect longer term exposure. DNAadducts have also been detected in the white blood cells andurine of patients treated with anticancer drugs such asN-methyl-N-nitrosourea (11).

It is not the remit of this chapter to discuss the details ofbiomarker methodology. However, it can be mentioned thatthe measurement of macromolecular adducts either DNA orprotein can be approached in several ways. For example,the whole adduct macromolecule can be measured or it canbe degraded into nucleotides or individual amino acids.

A recent study on polycyclic aromatic hydrocarbonadducts to DNA in white blood cells from smokers has shownthat the dosimetry from these adducts will significantly pre-dict risk of cancer (12).

Detection and quantitation of specific DNA adducts orthe fragments from them can be used very effectively to deter-mine true exposure to environmental chemicals and thereforeimprove risk assessment. For example, in countries such asChina many people may have dietary exposure to aflatoxinB1, a potent carcinogen produced by the mold Aspergillusflavus, which grows on nuts and grain. Reactive metabolitesof aflatoxin interact with DNA bases and one of the productsof this interaction, 2,3-dihydro-2-(N-7-guanyl)-3-hydroxyafla-toxin B1, can be detected in the urine of people exposed to afla-toxin in the diet. Measurement of DNA adducts in blood orsuch fragments in urine has allowed an association betweenthe incidence of liver cancer and intake of the toxin to bemade and has been invaluable in epidemiological studiesand risk assessment (12a). However, it should be noted thatthe particular metabolite or adduct measured is very impor-tant. For example, with aflatoxin exposure in both experimen-tal animals and humans environmentally exposed, the amountof total metabolites excreted in urine is not related to risk ofaflatoxin-inducd liver disease. However, this minor urinarymetabolite (2,3-dihydro-2-(N-7-guanyl)-3-hydroxyaflatoxin B1)

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does reflect relevant exposure and is a good short-termnoninvasive biomarker for aflatoxin exposure and risk ofgenetic damage. This biomarker reflects effective dose butonly relatively recent exposure. However aflatoxion-albuminadducts in blood reflect exposure over 2–3 months (12b).

It must also be recognized that both carcinogenic andnoncarcinogenic chemicals can interact with a variety of tar-get molecules and in a variety of ways. Therefore, measuringan adduct needs to be based on a knowledge of the mechanismof toxicity in order to be able to detect and quantitate the bio-marker relevant to the subsequent pathological processes.This, therefore, must be borne in mind especially when mea-suring adducts by 32P postlabeling or other nonspecific means(see below). Even measuring specific biomarkers in tissuesmay be problematic if a surrogate tissue or target moleculeis used. Although in experimental animals all tissues can betaken at postmortem, and subsequently analyzed, in humansit is often not possible to obtain the target tissue except from abiopsy which is inherently hazardous or when a postmortemis carried out. Therefore, surrogate tissues or molecules haveto be used such as white blood cells, hemoglobin, or serumalbumen. However, the different types of white cells have dif-ferent lifetimes and also different amounts of adduct maybe formed in the different cell types. Thus, such surrogatebiomarkers must always be validated in relation to the truetarget exposure if possible or alternatively in relation to thepathological change of interest.

One human tissue which may be used effectively andis available is placenta, and a study has shown using 32Ppostlabeling that DNA adducts occur and that these werehigher in urban as opposed to rural areas (13).

Other factors such as DNA repair (see below) may alsolead to variation in the amount of adduct in any particulartissue, and when this is not the true target tissue may resultin an underestimate or indeed overestimate of the exposure.

A recently devised method for detecting DNA adducts,accelerator mass spectrometry, utilizes radiolabeled com-pound. The technique is exquisitely sensitive, being able todetect 1 adduct per 1014 bases, which is probably equivalent


to less than 1 adduct per cell. Immunochemical techniquesfor the detection of adducts may also be very specific andsensitive.

Although the amount of DNA available for detectingadducts may be limited, with adducts to protein, such ashemoglobin, much greater amounts are readily available.Another advantage of hemoglobin adducts is that they cangive an indication of chronic exposure, as the turnover timeof hemoglobin is up to 4 months in humans. Thus, suchadducts have been explored as biomarkers of exposure to her-bicides such as propanil in experimental animals (14) and alsoin human industrial workers exposed to hexahydrophthalicanhydride (15). However, it should be reiterated that adductswith blood proteins such as hemoglobin or DNA adducts inwhite blood cells are strictly speaking ‘‘surrogate’’ markersbecause the actual target molecule or tissue is different. Thus,they may not be true markers of effective dose, especially pro-tein adducts when used as surrogates for DNA adducts of car-cinogens. One potential problem of measuring hemoglobinand white blood cell DNA adducts is that these surrogatemarkers require the reactive metabolite to leave its site of for-mation in a metabolically active tissue and travel through thered or white blood cell membrane. For some reactive metabo-lites, this will not occur. Adducts with serum albumen avoidthe problem of crossing cell membranes and are thereforean alternative but the half-life of hemoglobin is much shorter(20 days in man).

For DNA up to 18 sites for the formation of adducts existalthough some such as the N7 of guanine and N3 of adenineand O6 of adenine are more commonly found. The thiol,amino, carboxyl and side chain hydroxyl groups of proteinstend to be those commonly targeted. Many DNA adducts havenow been described and some used as biomarkers of exposurefor carcinogens. For example, DNA adducts of polycyclic aro-matic hydrocarbons such as those found in cigarette smokewere detected in both lung tissue and white blood cells fromlung cancer patients and compared with those in controlpatients. Higher levels of adducts were found in the lungcancer patients (16) (Fig. 4). The reactivity of the nucleophilic

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site on the macromolecule and the electrophilic metabolite ofthe particular chemical will vary and these will affect theextent and rate of adduction. As well as environmental andindustrial chemicals such as polycyclic aromatic hydrocar-bons, nitrosamines, and aflatoxins, drugs such as the antican-cer drugs have also been shown to form adducts with DNAand protein.

Most DNA, hemoglobin, and albumen adducts are ‘‘selec-tive’’ biomarkers of effective dose because the identity of theadduct is known.

Figure 4 Correlation between DNA adducts in lymphocytes andthose in lung tissue from patients with lung cancer. Data fromRef. 16.


2.2.1. Aselective Biomarkers—32P-postlabeling

There are also ‘‘aselective’’ biomarkers which indicate that areaction has taken place but give no information about thestructure of the adduct. These include the 32P-postlabelingassay which is a widely used, aselective biomarker for DNAadducts (17,18). It is an extremely sensitive technique fordetecting DNA adducts (it will detect 1 adduct per 109–1010

bases) but gives no structural information. This assay hasbeen used in both mammalian studies (including human)and in other animals such as fish (18a). The techniqueinvolves preparation of DNA from a tissue such as white bloodcells for example (Fig. 5)

Other aselective biomarkers are those for oxidative DNAdamage and lipid peroxidation such as urinary 8-hydroxy-20-deoxyguanosine which has been proposed as a biomarker ofoxidative damage to DNA (19) and various aldehydes (20),respectively (Fig. 6). Recently urinary 5-hydroxymethyluracilhas been proposed as a general biomarker of oxidative stressin humans (21). Although malondialdehyde is a widely usedbiomarker of lipid peroxidation, other aldehydes such as pen-tanal and hexanal can also be used. These, however, shouldperhaps be considered as biomarkers intermediate betweenexposure and initial response.

The advantages of aselective markers include: (i) a specificassay for a known adduct need not have to be devised; (ii) theycan be used as markers in human populations where exposureis lower than experimental studies; (iii) they can be used todetect exposure to a variety of potential toxicants, several ofwhich might produce DNA adducts or oxidative damage.

Despite the fact that many nongenotoxic toxic chemi-cals also produce reactive metabolites, few studies havebeen carried out to establish biomarkers of effective doseof such compounds, apart from experimental studies utiliz-ing the covalent binding of radiolabeled metabolites to pro-teins. This reflects the fact that the target molecules, ifthey exist, are generally unknown although surrogatessuch as hemoglobin or serum albumen can be used. Recentstudies with paracetamol, however, identified that some

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Figure 5 The 32P-postlabeling technique for detecting DNAadducts.


cellular protein targets are now known and fragments of pro-tein adducts have been detected in serum during paraceta-mol-induced liver damage (Fig. 7). There is a good correlationbetween the degree of liver damage and the level of this adductin the serum (21a). Again this may be considered an intermedi-ate biomarker between exposure and effect.

However, there are relatively few studies on proteinadducts of chemicals and their metabolites in relation to nonge-notoxic endpoints such as might be relevant to drug toxicity.

The use of biomarkers of effective dose such as adductsto protein or DNA is particularly important in toxicologyand risk assessment because it can reduce the uncertaintyassociated with environmental and industrial exposures and

Figure 6 Products of oxidative processes which may be used asbiomarkers. (From Ref. 20.)

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where administered doses of drugs are variably and poorlyabsorbed. Thus, for exposure to a carcinogen in relation tolow dose risk assessment (Fig. 2), a knowledge of exactlyhow much actually gets to the target molecule reduces theerror in estimating the risk from exposure. This is wellillustrated by studies with aflatoxin and tobacco smokeconstituents in humans.

Figure 7 Mechanism underlying paracetamol-induced liverdamage and potential biomarkers of exposure in urine and plasma.


Other techniques can also be used such as collectionof expired air or other tissues. For the detection of volatilechemicals to which individuals are exposed, collection andanalysis of the exhaled breath may be carried out usingspecialized techniques (22).

A novel source of tissue for evaluating exposure ishuman hair. Thus, dietary derived heterocyclic aromaticamines and drugs have been detected in human hair (23,24).

3. BIOMARKERS OF RESPONSE

Biomarkers of response are parameters measured in order toboth detect and quantify any toxic and pathological effects achemical may have caused. There are many different typesof biomarkers of response and some are directly related tothe underlying mechanism of toxicity.

Biomarkers of effect=response range from the simplesuch as monitoring body weight and population changes, tothe sophisticated such as determination of specific isoenzymesby immunochemical techniques. They can be broadly dividedinto invasive and noninvasive and those that indicate patho-logical damage and those that detect biochemical changes orresponses. Some biomarkers of response may measure ordetect the progress of pathological damage caused by a drugas well as its initial occurrence.

There are a very large number of potential markers fordetermining the biological effect of chemicals and it is notwithin the scope of this chapter to attempt to review themall; some of the types are illustrated in Table 3.

Invasive markers in tissues cover an array of pathologi-cal techniques including gross pathology and histopathologyusing either light or electron microscopy through to measure-ment of biochemical changes. These are usually carried out atpostmortem. Biomarkers of pathological change in responseto exposure to chemicals applicable to continuous exposuresuch as markers sampled in blood may be more useful.This type of biomarker includes a wide range of enzymes.The sophistication and usefulness of this technique may be

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further enhanced by the separation of the activity intoisoenzymes (e.g., lactate deydrogenase (LDH) or creatinekinase (CK) which may indicate more precisely which organ,tissue, or organelle is damaged. The disadvantage of serumenzymes is that the changes are usually transient, dependingon the stability of the enzyme, rate of leakage, and excretion.Such biomarkers usually only indicate that significant patholo-gical damage has occurred but are useful during subchronicand chronic toxicity tests. These markers are often used todetect damage tomajor organs such as liver, heart, and kidney,for example.

Bai et al. (25) found that the levels of certain bile acids inserum, notably cholic, glycocholic, and taurocholic acids, weremore sensitive markers of liver dysfunction than serumenzymes. Exposure of humans to low levels of organicsolvents has been shown to lead to significant increases inserum bile acids (26).

Blood cells of various types can yield different types ofinformation which can be used as biomarkers of response tochemicals. Thus, both the number cells and type of damageto blood cells can be evaluated. For example, the presence ofsister chromatid exchanges in white blood cells indicatespotential damage to the chromosomes and has been detectedin workers exposed to ethylene oxide. Similarly, a reductionin numbers of particular lymphocytes may be caused by a

Table 3 Types of Biomarker of Response

Biomarker Type Effect=Response

Body weight Integrative, noninvasive DysfunctionOrgan weight Integrative, invasive Organ damageSerum enzymes Specific, noninvasive Organ=tissue damageEnzyme activity Invasive=noninvasive

depending ontissue=method

Adaptive response,mechanism of toxicity

Urinarymetabolites

Noninvasive Biochemical perturbation=tissue damage

Temperature Noninvasive Biochemical perturbation


chemical and is indicative of immunosuppression which maybe caused by compounds such as dioxin (TCDD). The presenceof particular antibodies (such as antinuclear antibodies) mayrepresent a biological response to exposure to a drug or otherchemical (e.g., the drug hydralazine). Specific antibodies pro-duced by the animal against a particular chemical may bedetected and measured and used as biomarkers of responseto that particular chemical. For example, in some individualsdosing with the drug penicillin can lead to specific antibodiesbeing raised and circulating in the blood. For example, theanesthetic halothane, which may cause a serious immuno-toxic effect, produces metabolites which bind to proteins andantibodies are generated against the conjugates which aredetectable in blood. A recent study evaluating such a specificbiomarker, IgG specific for hexahydrophthalic anhydride inexposed workers, however, found no significant correlationwith exposure (15). Such antibodies have not been widelyused as biomarkers but could hold great potential in termsof sensitivity and specificity possibly indicating potentialimmunotoxicity.

For detecting chemical-induced damage to most organsor tissues, the available biomarkers usually require either tis-sue samples or blood. Although blood sampling is technicallyinvasive, it is not normally a problem in either humans orexperimental animals. However, the sampling of tissuesrequires either a biopsy or tissue to be taken at postmortemwhich may not be possible or appropriate.

Apart from metabolic dysfunction which can be detectedwith a variety of biochemical markers, detection of tissuedamage is generally limited by the availability of specific bio-markers. For liver damage, a number of enzymes=isozymesare available. By using ratios of isozymes, it is possible todetect damage in some other organs but for some organsfew if any specific biomarkers are known. Recently, a plasmaprotein specific for lung damage has been described, the so-called CCl6 protein (26a) used in humans (26b). Furthermore,it has been found relatively recently that exhaled substancesmay indicate damage to the lungs and also other organs suchas the liver (see below).

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For the detection of damage to the kidney, there are avariety of markers in serum but also noninvasive urinary bio-markers. These include specific enzymes released fromdamaged tissue as well as metabolites, proteins, amino acids,and glucose, all of which can be used as biomarkers of kidneydysfunction. However, there are urinary biomarkers forpathological responses and damage to tissues other than thekidney which are noninvasive. For example, urinary metabo-lite profiles can indicate liver dysfunction and the sulfuramino acid taurine has been shown to be elevated in the urineof animals in which there is liver dysfunction including stea-tosis, caused by a variety of chemicals (27). Recent researchhas indicated that changes in certain endogenous urinarymetabolites (28) and serum (29) are associated with abnormalphospholipid accumulation which have potential as usefulbiomarkers for this pathological effect commonly associatedwith drug exposure in experimental animals.

3.1. Enzyme Activity

Measurement of enzyme activity can be an important and sen-sitive biomarker of response which in some cases may be mea-sured in the blood. However, in other cases the activity of theenzyme can only bemeasured in tissues which requires a biopsyor postmortem. Alternative ways to determine enzyme activityare to measure metabolites of endogenous substrates or to givethe patient or experimental animal a specific substrate andthen determine amounts of known metabolites. The measure-ment of enzyme activity can reveal either increases (induction)or decreases (inhibition) due to exposure to chemicals.

Inhibition and induction of enzyme activity may be sus-tained and so can be an important and useful biochemicalmarker of effect. However, changes in enzyme activity perse may not necessarily be indicators of a toxic response; thisdepends on the relationship to the toxicity.

For example, there are several markers for the biochem-ical and toxic effects of lead on the red blood cell resultingfrom the inhibition of several of the enzymes of hemo-globin synthesis (Fig. 8). The pathological effect detectable


by microscopy, for example, is a reduced red blood cell countwhich indicates that an exposed individual is suffering fromanemia, a pathological effect. However, more subtle biochem-ical measurements may be made such as measurement ofserum aminolaevulinic acid dehydrase activity which isreduced by lead exposure. However, this is an overly sensitivemarker and many normal healthy individuals in an urbanpopulation will have decreased enzyme activity. A less sensi-tive and more useful marker is zinc erythrocyte protopor-phyrin. Lead blocks the final stage of haem synthesis byinhibition of ferrochelatase which prevents iron incorporationinto the protoporphyrin. Consequently excess protoporphyrinbecomes available and zinc is incorporated instead. Thiszinc erythrocyte protoporphyrin can be detected and quanti-tated using fluorescence spectroscopy. A less sensitive but

Figure 8 The effect of lead on hem synthesis in the red cell andthe application to biomarkers. Ref. 29a

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noninvasive marker of lead toxicity is the level of urinaryaminolevulinic acid. The level of this precursor rises whenaminolevulinic acid dehydrase is inhibited by lead (Fig. 8).

There are also effective biomarkers of exposure for lead,both acute (blood lead levels) and chronic (x-ray analysis ofbone). Therefore, coupled with the biomarkers of effectdescribed, lead toxicity can be effectively detected, controlled,and treated. Induction of cytochrome P450 isozymes, an adap-tive response, may be used as a biomarker of the effect ofexposure of many species to a variety of chemicals such asorganochlorine compounds and polycyclic hydrocarbons.Detection and measurement of this response can be carriedout in a variety of ways: enzyme levels can be determinedin tissue homogenates or microsomal fractions. Alternatively,the activity of cytochrome P450 may be determined in vivo bystudying the metabolism of selected xenobiotics in exposedorganisms. Also there are urinary markers for cytochromeP450 induction, such as increased D-glucaric acid excretion(30) and the excretion of 6-b-hydroxycortisol for which thereis a readily available method (31). These are especially usefulfor the determination of enzyme activity in humans (32) butcan also be used in other species (33). Recently, analysis ofbreath constituents has been shown to reflect cytochromeP450 activity. Thus, in rats exposed to a cytochrome P450inhibitor large rises in volatile organic compounds occurredwhich diminished when enzyme activity returned (34).

Such biochemical effects as enzyme induction may beadaptive or protective responses to exposure to toxic chemi-cals. Another important example is the induction of metal-lothionein in response to exposure to metals such ascadmium (35). However, a number of other insults will causethis response such as oxidative stress.

Similarly, another potential biomarker for toxic effects isthe induction of heat shock or stress proteins (36). An increasein the synthesis of these proteins results from alterations ingene expression in response to a variety of environmentalstressors such as temperature, salinity changes in aquaticorganisms, teratogens, oxidative stress, chemical exposure,and anoxia. The response is relatively rapid (occuring in


hours) and leads to the accumulation of proteins such as hsp90 and hsp 70, hsp 60 (chaperonin) and ubiquitin. Severalmethods can be used to measure these proteins such as (i)metabolic labeling followed by autoradiography; (ii) cDNAprobes to measure the mRNA coding for the protein; and(iii) immunocytochemistry. Hsp 72 is one of the better mar-kers, as normally very little is present in biological systemsand so any increase is obvious and easily measured. It isfound in mammals and other animals, in plants and microor-ganisms, making it a widely applicable biomarker. However,studies have revealed that although levels of hsp 72 areincreased by cadmium another toxicant, hydrazine, did notraise levels (37).

For any given toxicant there may be several biomar-kers of effect which can be measured in different bodyfluids and tissues and which may have different levels ofsensitivity and specificity. The questions of sensitivity andspecificity are important because if a biomarker is too sen-sitive or is nonspecific it may detect effects which are nottoxicologically relevant. There are unfortunately relativelyfew biomarkers measureable in urine which indicate signif-icant biochemical or pathological changes, but here NMRhas made and will continue to make an enormous contribu-tion by facilitating the detection and measurement of novelbiomarkers as discussed in this volume. For example, NMRanalysis of urine (38) lead us to investigate changes in theurinary levels of the amino acid taurine as a biomarker forvarious types of liver dysfunction (27). NMR also revealed apotential urinary biomarker for testicular damage, urinarycreatine (39). Reliable biomarkers of testicular damageare few and generally require blood or tissue samples. Arise in urinary creatine in rats has been shown to reflecttesticular damage of different types caused by a variety oftoxicants (40), and the effect has also recently been shownin mice (41).

Levels of mutation and mutation frequency are a bio-marker of response which may be directly related to DNAdamaging chemicals such as aflatoxin. For example, in indi-viduals exposed to dietary aflatoxin in China, the HPRT

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mutation frequency was measured in T cells as a biomarkerof response and correlated with aflatoxin–albumen adductsfrom serum (42). This study showed that for those with highexposure to aflatoxin, the odds ratio for high HPRT mutationfrequency was 19.3. This suggests that the DNA damage inlymphocytes caused by aflatoxin leads to increased muta-tions. This is an example of two types of biomarkers beingused together to improve diagnosis and risk assessment .

3.2. Breath Analysis

A relatively new technique which is applicable to the field ofbiomarkers is the analysis of breath for volatile componentswhich may be used as biomarkers of response reflectingunderlying pathological change caused by disease or chemicalexposure. The concept of breath as a diagnostic aid is not new,however, and distinctive breath odors have been recoznizedand used for diagnosis by physicians for centuries.

This technique has become more viable due to recenttechnological advances for the collection, concentration, andsensitive analysis of the components of breath which are gen-erally present at low concentrations. The advantages of thetechnique are that it is noninvasive and breath can be repeat-edly sampled.

The details of the techniques, which will be dependent onthe substances beingmeasured, can be found elsewhere (43,44).

Breath analysis has been used therefore to detect lungdisease (44), liver and other diseases (43), and for the detec-tion of the effects of chemicals on metabolic processes (34,45).

Thus, for lung disease a range of substances can bedetected in breath, especially NO, which occur in response toinflammation or oxidative stress. Thus, exhaled NO is raisedin atopic asthma but reduced in cystic fibrosis. CO is alsoincreased in asthma. As well as analyzing the volatile=gaseousgaseous components, it is also possible to analyze breath con-densate. For example, in inflammatory lung diseases there areincreased amounts of isoprostane, hydrogen peroxide, nitrite,and 3-nitrotyrosine in the breath condensate. Furthermore, itis possible to monitor the efficacy of treatment with drugs


administered to patients on these parameters. With NO theeffects, for example, of corticosteroid treatment can bedetected relatively rapidly—i.e., within a few hours. Occupa-tional asthma induced by dusts can also be detected and mon-itored using these techniques.

Similarly, liver diseases give particular but different pro-files of exhaled markers. The most abundant hydrocarbon inhuman breath is isoprene which is a possible marker for cho-lesterol metabolism. Thus, when the drug lovastatin, whichblocks HMGCoA reductase, is given to patients, isopreneexhalation is decreased (45). It has long been known fromanimal experiments that ethane and ethylene exhalationincrease as a result of lipid peroxidation caused by substancessuch as carbon tetrachloride and this correlates with theproduction of malondialdehyde which is often used as abiomarker for lipid peroxidation. Ethane and 1-pentane areincreased when there is reactive oxygen-mediated damageto tissues.

Liver disease has been especially studied in relation toexhaled biomarkers and sulfur containing compounds featureespecially prominently. Thus, exhaled carbonyl sulfide levelsare increased in various types of liver disease and possiblylung necrosis. Other sulfur compounds can be detected inbreath in liver cirrhosis, such as methyl and ethyl mercaptan,dimethyl sulfide and dimethyl disulfide (43).

Renal disease can also give rise to changes in exhaledbreath, for example, dimethylamine and trimethylaminelevels are increased.

3.3. Genomics

The recent development of -omics, viz. genomics, proteomics,and metabonomics has already had an impact and undoubt-edly will have an increasing impact on the development ofnew biomarkers of response=disease and of susceptibility.More importantly, the integration of these three technologiesand the application of bioinformatics will potentially revolu-tionize the detection of the effects of drugs and diseaseprocesses at earlier stages. The three approaches reflect a

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biochemical continuum, as the information encoded in thegenome is transcribed and then translated into proteinswhich function in various ways leading to metabolic changes.Although any of the techniques can be used in isolation,integrating them is more powerful and gives a mechanisticbasis to the biomarker which makes it potentially morerobust, reliable, and more specific.

Genomic tools include transcript profiling, which mea-sures the steady state levels of mRNA, can give rise to poten-tial biomarkers such as the human metallothionein genewhich is influenced in humans by exposure to cadmium(46). However there may be many hundreds of gene changesoccurring. The use of software packages will allow analysisof the data to discern clustering of genes with similar expres-sion patterns, for example. It is not the purpose of thisoverview to give the details of these technologies which canbe found elsewhere (47).

If a database of gene profiles can be assembled forparticular toxic endpoints, this may be useful for predictingtoxicity and for identifying potentially new biomarkers. Someprofiles are available commercially. For instance, a gene pro-file for pancreatic cancer has been described (48). However,gene changes must really be confirmed using other methodssuch as RT PCR. Validation in relation to a particular toxicendpoint is essential. Thus, an increase in the expression ofa gene and an increase in mRNA does not necessarily meanan increase in the corresponding protein or the activity ofan enzyme. Therefore, a change in gene transcription maynot have relevance as a toxic endpoint. Furthermore, changesin protein levels can occur as a result of post-translationalmodifications without there being any change in geneexpression and transcription. Thus, there is a need to showthat gene changes in a gene, in mRNA protein level, orenzyme or other activity are consistently associated witha toxic endpoint either with a specific toxicant or a group oftoxicants.

A combination of genomic techniques may be used, suchas RT PCR and SSH. For example, one study examined thegene changes in hepatocytes from dogs exposed to a novel


drug which caused lipid accumulation. It was found using thetechniques of RT PCR and SSH that genes associated withhepatic steatosis were changed. Thus, the gene for the proteinAPOAII was downregulated, whereas the genes for theenzyme CYP2E1 and protein APOB100 were upregulated. Itwas found that in general PCR data correlated with that fromSSH except in the case of the gene for the enzyme stearoylCoA desaturase where the two techniques gave contradictoryeffects (47). Data such as this indicate that potential biomar-kers have been identified.

Other factors that need to be taken into account arethe time course of changes in relation to pathological changes,the time when the gene changes are measured and therelationship between changes in genes and the dose of thechemical.

It should be said, however, that genomics has yet tomake its mark in relation to biomarkers useful in the drugsafety evaluation process (47).

3.4. Proteomics

Proteomics involves the separation, identification, and quan-titation of proteins typically using 2D gel electrophoresis toseparate on the basis of charge and then molecular weight(49). An important recent improvement which has made thetechnique more amenable is the enrichment of samples andthe removal of abundant proteins such as albumen. Thisallows the separation and visualization of many otherwisehidden proteins. The pattern can be digitally imaged and ana-lyzed and the spots of interest can be excised and analyzed forstructure using mass spectrometry. Other techniques havebecome available such as the use of tandem mass mass spec-trometry for successive fragmentation and anlysis. Systemsexist for the separation of proteins by the use of ‘‘protein-chips’’ with attached antibodies or anionic or cationic surfaceswhich bind particular types of proteins and then the chips canbe analyzed by SELDI TOF mass spectrometry. The use ofstable isotope coded affinity tags (ICAT) for isolating peptides

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is a useful technique. Capillary electrophoresis coupled tomass spectrometry is another very promising technique forthe separation and analysis of proteins.

However, improvements to the technique will still benecessary before widespread use of the technique, butimprovements can be done (50).

After the large or small proteins are analyzed by massspectrometry, this can be coupled to peptide sequence search-ing. Bioinformatic techniques can then help to identify theprotein. Indeed for genomics, proteomics, and metabonomics,bioinformatics is an essential additional tool for the completeanalysis of the data generated.

Using these techniques, changes in the protein comple-ment of a cell or organism in response to exposure to a chemi-cal can be evaluated. A particular protein can then be used asa biomarker. The changes can be most easily detected in vivoas changes in serum or urinary proteins. This is a distinctadvantage over genomics where cells or tissue are requiredto generate samples of DNA. Furthermore, transcriptomicswill only indicate the potential for increase in a protein, byvirtue of increased mRNA, for example, but this may not betranslated into a functional protein. Post-translational modi-fication and assembly of protein complexes are also importantfactors.

The disadvantage of proteomicsis is that when 2D elec-trophoresis is used, the throughput is slow and requiresconsiderable automation. It is important to relate the functionof the protein to the mechanism of toxicity if possible.

Although it is early, the data published to date indicatethat proteomics can identify potentially important biomar-kers. For example, study of the drug lovastatin, a lipid lower-ing agent which inhibits HMGCoA reductase, revealed thattreatment of rats with the drug changed 36 different liver pro-teins. As well as those expected from the action of the drug,other proteins were changed which indicated potential toxi-city such as changes in stress proteins, calcium homeostasis,and cytoskeletal structure. Such subtle effects would not belikely to be detected by conventional methods used in safetyevaluation.


If the proteins changed by exposure to a chemical can beeasily detected and measured in serum or urine, then thesechanges may become sensitive biomarkers.

With all of these techniques, however, changes observedmay not be necessarily related to the toxicity mechanistically.Also some changes may not be useful biomarkers because oflack of sensitivity or specificity.

4. BIOMARKERS OF SUSCEPTIBILITY

Different species of animals and individual animals within aspecies and individual humans often differ in their handlingof drugs and other chemicals and in their responses to theexposure to these chemicals. This variation often has agenetic basis and gives rise to a third type of biomarker whichmay indicate susceptibility to damage and dysfunction causedby the chemical. These are unlike the two previous types ofbiomarkers as they do not form part of the continuous processfrom exposure to pathological change shown in Fig. 1. Rather,biomarkers of susceptibility are measurable factors inherentin the organism irrespective of the chemical exposure, butwhich influence the mechanism of toxicity and so the pro-cesses generating biomarkers of exposure (both internal doseand effective dose) and response. For example, biomarkers ofexposure (internal dose) can be part of the toxicokinetic phaseas metabolites of the particular chemical of interest. The pro-duction of these metabolites and their detoxication may beinfluenced by genetic factors peculiar to the individual suchas variations in the enzyme(s) catalyzing a metabolic route.Measurement of the variability of these enzymes either bymeasuring the genotype or the phenotype can be used there-fore as biomarkers of susceptibility. However, not all enzymesinvolved in the metabolism of chemicals show significantgenetic or other variation.

Alternatively, the response to a chemical may be modu-lated in the toxicodynamic phase by a process such as DNArepair. Variations in this process can be measured and usedas biomarkers of susceptibility. If receptors are involved

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with the pathological response, or oncogenes, tumor sup-pressor genes, or immune system components, any of thesemay be biomarkers of susceptibility if there is significantvariability between individuals. In some cases, as discussedbelow there may be several factors having an impact on theoverall response, each of which is a different biomarker ofsusceptibility.

The variation in disposition and response is of crucialimportance in risk assessment and, therefore, there is nowincreasing interest in biomarkers which can detect increasedsusceptibility to drugs and other chemicals. Of the fourphases of disposition of a xenobiotic, the most importantsource of variability is metabolism and this has been the areaof most research. As biotransformation and metabolic activa-tion are so often intimately involved in the toxicity of chemi-cals, individual genetic variation in the enzymes controllingthese processes is often the basis of variations in susceptibil-ity to toxicity (51). These differences may be reflected indifferences in biomarkers such as DNA or protein adductsin individuals who have the same exposure.

There are a number of enzymes which show genetic poly-morphisms and which have been associated with diseasessuch as cancer (52).

Differences in susceptibility due to variation in otherprocesses such as DNA repair could lead to increased muta-tion rates and hence greater incidence of tumors followingexposure to carcinogens.

For determination of genetic polymorphisms, severaltechniques are available. Thus, either the genotype or pheno-type can be measured. Genotyping will give information aboutthe genes and alleles for specific enzymes. However, althoughthere may be variations at this level, these may not be mani-fested in the response of the whole organism. Phenotypingwhich looks at the difference between individuals in termsof the response of the whole organism, takes into account bothgenetic variation and other factors such as influencing andother processes and environmental influences.

Genotyping can be carried out by analysis of restrictionfragment length polymorphisms (RFLPs) by Southern blot


analysis or by amplification and analysis of cDNA or mRNAsequences using the polymerase chain reaction (PCR).

Phenotype can be determined by quantitating the rele-vant metabolites of a probe substrate such as caffeine, inhuman subjects or animals. Alternatively, determination ofthe enzyme activity could be carried out in vitro in tissue orcells. Measurement of the level of the specific enzyme proteinor the amount of specific mRNA can also reveal the pheno-type. Data from the two techniques are often, but not always,in agreement.

4.1. Enzymes of Biotransformation asBiomarkers of Susceptibility

Because metabolism is such a crucial part of the dispositionand toxicity of drugs and other chemicals, variations in theenzymes which catalyze the process are important biomar-kers of susceptibility. A number of different enzymes andenzymes systems have been studied in this regard.

Cytochrome P450, the CYP enzyme system, is the mostimportant Phase 1 metabolizing system, particularly localizedin the liver. There are known to be polymorphisms of anumber of the isozymes in humans, namely CYPs 1A1, 1A2,2C19, 2D6, and 2E1 (53–55).

The first polymorphism in this system to be describedwas that affecting CYP2D6 which metabolizes debrisoquineand sparteine. The ability to hydroxylate debrisoquine showsa clear bimodal distribution in a human population (Fig. 9).Consequently, there are two phenotypes for this enzyme,labeled extensive metabolizers and poor metabolizers. Theycan be typed on the basis of a ratio of metabolites of debriso-quine in urine. Dextromethorphan, however, may be the pre-ferred probe substrate because of its safety margin (56). Thepoor metabolizer phenotype has been associated withincreased susceptibility to toxicity of a number of drugs suchas penicillamine and perhexiline. In the former case therewas found to be an increased incidence of skin rashes, in thelatter case an increased incidence of liver damage. Conver-sely, the extensive metabolizer phenotype has been associated

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with increased cancers in smokers. Polymorphisms ofCYP1A1 (MspI and Val=Val) are associated with increasedrates of lung cancer especially in the Japanese population.This may be due to increased activity of the enzyme whichmetabolizes polycyclic hydrocarbons or possibly increasedinducibility. Using adducts to DNA from white blood cells asa surrogate marker of exposure, a relationship betweenCYP1A1 activity and adduct levels was found (57).

Caffeine is in many respects an ideal probe substratefor determining the in vivo activity and phenotype for

Figure 9 Frequency distribution for the debrisoquine hydroxyla-tor status or the variation in activity of the CYP2D6 isozyme in ahuman population. Data from Ref. 57a


cytochrome P450 isozymes and indeed other enzymes suchas xanthine oxidase and N-acetyltransferase (NAT2, seebelow). Thus, N-3 demethylation to paraxanthine, the majorpathway of metabolism for caffeine, is catalyzed by CYP1A2and the level of this metabolite and plasma caffeine clear-ance may reflect CYP1A2 activity in vivo. However, it mustbe recognized that other factors may influence the urinarymetabolic ratios and other isozymes may also be involved(e.g., caffeine is also a nonspecific substrate for CYP1A1)(58,59).

As well as enzymes catalyzing Phase I reactions, thosecatalyzing Phase II metabolic pathways may also be used asbiomarkers of susceptibility.

One well-studied example is the enzyme N-acetyltrans-ferase (NAT) and both enzymes (NAT1 and NAT2) showgenetic variation and each allele shows several genotypes(60). However, for aromatic amines and hydrazine deriva-tives, NAT2 is much more active (10�). The acetylator pheno-type is a biomarker of susceptibility for a number of toxicresponses including drug-induced liver damage, drug-inducedlupus erythematosus, and aromatic amine-induced cancer(see Table 4). However, resolution of the particular genotypewill be important, for example, the slowest NAT2 acetylatorphenotype, genotype NAT2(�)5, is the most susceptible toaromatic amine-induced bladder cancer (60).

Thus, in humans (and some other species) the ability toacetylate amines, hydrazines, and sulfonamides varies andshows a bimodal frequency distribution. This gives rise to

Table 4 Acetylator Phenotype and Susceptibility to Toxic Effects

Drug=Toxicant Adverse Effect Susceptible Group

Isoniazid Liver damage and peripheralneuropathy

Slow acetylators

Procainamide Lupus erythematosus Slow acetylatorsHydralazine Lupus erythematosus Slow acetylatorsSulfasalazine Hemolytic anemia Slow acetylatorsAromatic amines Bladder cancer Slow acetylators

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two distinct groups, known as slow and fast acetylators, theproportions of which vary with ethnic origin. Slow acetyla-tors are homozygous for the dominant allele, fast acetylatorsmay be either homozygous or heterozygous. Slow acetylatorshave mutations which result in the production of less func-tional N-acetyltransferase enzyme. The acetylator status orphenotype can be readily measured by giving animals orhuman subjects drugs such as sulfamethazine and analyzingurine for free and acetylated drug. A more acceptable alter-native for human subjects is to use caffeine metabolism, asone of the metabolites is further metabolized by acetylation(NAT2) to yield 5-acetylamino-6-formylamino-3-methyluracil(AFMU).

A number of adverse drug reactions are associated withthe slow acetylator phenotype and bladder cancer is associatedwith occupational exposure to aromatic amines. However,other types of cancer (colorectal cancer) have been linked tothe fast acetylator phenotype (52).

Another Phase II enzyme system which shows geneticpolymorphisms which affect susceptibility to chemical expo-sure is the glutathione transferase system (GST). This is per-haps the most important detoxication system as it catalyzesthe removal of toxic metabolites by conjugation withglutathione, a protective agent.

As with cytochrome P450, there are several isozymes andpolymorphisms with each isozyme. Thus, genes for GST-M1,M3, P1, and T1 have been shown in humans with polymorph-isms for each. As with NAT 2, the distribution of the poly-morphism varies with ethnic origin, and with GST-M1, forexample, some individuals have a decreased capability forconjugating with glutathione due to inheriting a gene deletionwhich is homozygous. This null genotype has been correlatedwith increased susceptibility to cancer of the colon, bladder,and lung, enhanced sister chromatid exchange and increasedlevels of DNA adducts (61).

Individuals with both the GST-M1 deficiency (GST M1null, �=�) and CYP1A1 Val=Val genotypes may be moreat risk from cancer than those with only one of the nullgenotypes.


Because of the rapid advance of molecular biology, it isnow possible to determine the genotype instead of the pheno-type in individuals for these biomarkers of susceptibility.However, a particular genotype may not necessarily resultin a significant effect on susceptibility or metabolism.

4.2. Other Biomarkers of Susceptibility

As well as enzymes involved in the metabolic activationand detoxication of drugs, other factors that influencethe processes of exposure and response may be used as bio-markers of susceptibility. For example, repair of damage toDNA may involve several enzymes which show genetic varia-bility and this could be a major factor in determining theoccurrence of damage and cancer after exposure to a DNAdamaging agent (62). For example, specific repair enzymessuch as O6-alkyldeoxyguanine-DNA alkyltransferase anduracil DNA glycosylase show very large levels of variation(200–300 fold) in the human population. Humans with lungcancer have been found to have decreased levels of the alkyl-transferase activity. The capability for DNA repair can bemeasured in white blood cells and used as a biomarker of sus-ceptibility (63). Human subjects with reduced levels of DNArepair capacity were found to have a fivefold increased riskof skin cancer. As multiple pathways may be required forDNA repair, several defects could increase susceptibility;simultaneous evaluation may be needed therefore (62).

Levels of receptor proteins, oncogenes and gene products,and tumor suppressor genes also show variability which mayinfluence the outcome of the response to exposure and hencecan be used as biomarkers of susceptibility. Components ofthe immune system, both humoral and cellular, also showvariability and some may be used as biomarkers of suscept-ibility.

Receptor proteins may be possible biomarkers of suscept-ibility such as the aryl hydrocarbon receptor (AhR) whichbinds various hydrocarbons. This results in various responsesincluding induction of cytochrome P4501A1. It has been foundthat human individuals show variation with some (10%)

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having a high affinity form of the receptor (64). This leads togreater expression of cytochrome P4501A1 and increased sus-ceptibility to cancer and other toxic effects.

Tumor suppressor genes, such as p53, are important fac-tors that can affect susceptibility to cancer and hence arepotential biomarkers of susceptibility. If one of the alleles isinactive in humans, as in the heterozygous genotype, this isassociated with an increased rate of cancer. This increasedsusceptibility is also found in mice heterozygous forthe p53 when they are exposed to chemical carcinogens.Polymorphisms in the ras oncogene family are also linkedto high cancer rates.

It must be noted, however, that although some mutationsmay be biomarkers of susceptibility, they may also be conse-quences of the exposure to a chemical and, therefore, a mar-ker of response rather than susceptibility.

Levels of antioxidants in tissues may also be factorswhich can increase susceptibility. For example, studies in ratshave suggested that urinary taurine levels may be a potentialmarker of susceptibility. As urinary taurine correlates withand therefore reflects the liver taurine concentration, lowurinary taurine levels in an animal can be an indication oflow levels of liver taurine .There was a significant correlationbetween the levels of taurine in the urine of rats and theirsusceptibility to a variety of hepatotoxic agents (64a). Thisimplies that taurine present in the liver is protective andwhen levels are low, the liver is more susceptible to damage.This has important implications as humans are relativelypoor synthesizers of taurine and rely partly on diet as asource.

The interaction of several genetic and possibly environ-mental factors will often be important in the development ofa particular toxic response, some of which will be biomarkersof susceptibility. An example of this is the development ofdrug-induced lupus syndrome in patients taking the drughydralazine, which depends on several factors. Apart fromdose and duration of dosing, the acetylator phenotype, theHLA (tissue) type, and gender are all biomarkers of suscept-ibility. Thus, this syndrome only occurs in those patients with


the slow acetylator phenotype and although the drug is acety-lated, it is not clear which metabolite is responsible for thetoxic effect. The syndrome is four times more common infemales than males but again the reason is not known.Finally, the occurrence of the HLA type DR4 is a factor andan indicator of susceptibility as it is more prevalent in thosewith the disease, occurring in 33% of controls and in 73% ofpatients who present with the syndrome. All of these factorsare biomarkers of susceptibility and could be used to reducethe occurrence of the syndrome.

4.2.1. Validation and Interpretation of Biomarkers

The validation of biomarkers before serious use is essentialand critical to their effective use in relation to safety evalua-tion and risk assessment. Some aspects of validation will varydepending on the type of biomarker although some featureswill apply to all three types.

Thus, some considerations such as the sensitivity, speci-ficity, variability, availability, and robustness of the analyti-cal technique(s) apply to all three types of markers. Alsoanother important feature of all three types of biomarker iswhether it requires an invasive procedure: it is clearly prefer-able if it does not. Sampling urine or blood is generally accep-table but tissue is more difficult as this requires a biopsy inhumans and either this or a postmortem in animals.

For determination of exposure, the biomarker should bespecific for the compound of interest, which may be a metabo-lite of the compound to which the individual is exposed or anadduct with a macromolecule such as DNA or hemoglobin.Thus, the same metabolite may be formed from a number ofsimilar chemicals and the same adducts may also result fromactive metabolites from different chemicals. The factors whichcan affect the level of the biomarker must also be consideredand if possible evaluated, such as the toxicokinetics and therelationship with dose or ambient concentration. For surro-gate biomarkers such as hemoglobin adducts, the relationshipto and relevance to the real target must be established. Insome cases it may be possible to correlate the biomarker of

64 Timbrell

exposure to a toxic effect, as for example with the DNAadducts of aflatoxin, but not all measurable metabolites oradducts relate to the toxic effects of aflatoxin (see above).

Another important consideration is the temporal rela-tionship between the biomarker and the exposure. After acuteexposure most biomarkers will only be useful for a few hoursor at the most days after the exposure for most readily meta-bolized and excreted chemicals. The exception to this areDNA adducts which may persist in white blood cells for longeror hemoglobin adducts in red blood cells. With continuousexposure, however, many biomarkers may reflect the currentexposure if the steady state situation is achieved. Therefore, itmay be necessary to use several biomarkers for retrospectiveexposure. For example, urinary metabolites of a chemical willbe excreted and measurable early after exposure, typically afew hours, whereas DNA and albumen adducts may be detect-able for a number of days in the blood. Breakdown products ofthese may be detectable in urine possibly a month after expo-sure and adducts with hemoglobin can survive for 120 days.

With biomarkers of response, as well as the general con-siderations already mentioned, validation in relation to theexposure and a particular pathological effect(s) is essential.Thus, biochemical changes in particular which may occurbefore any observable pathological changes must be validatedin relation the toxic and or pathological effect and must beshown to be dose related. Although the ultimate aim is to findbiomarkers of response which are very sensitive and ‘‘earlywarning,’’ occurring prior to pathological or other adversechanges, the problem is to interpret changes which may sim-ply be reversible, adaptive, biochemical perturbations of noconsequence. This dilemma is revealed in the following state-ment ‘‘it is becoming more difficult to distinguish betweenmeasured alterations that are ‘‘adaptive and reversible’’ andthose that are ‘‘pathological and irreversible’’ ’’ (65).

As with biomarkers of exposure, the relationship to time isimportant. Does the alteration in the biomarker of responseoccur before or at the same time as the pathological change. Isit transient or sustained. These factors will determine whethera biochemical change is a useful biomarker of response.


Similarly, biomarkers of susceptibility must show a sig-nificant correlation with a pathological or toxic outcome suchas cancer. This may require considerable statistical power forvalidation. This will have significant implications in terms ofthe number of individuals evaluated and the number and spe-cificity of the polymorphisms or variants.

The multifactorial nature of toxic responses, therefore,necessitates the use of early biomarkers of effect as well asbiomarkers of exposure and susceptibility.

Although as indicated in this brief review there are alarge number of biomarkers available to indicate exposure,response, and susceptibility, the difficulty lies in the interpre-tation in relation to risk. The detection of potentially toxiccompounds in biological samples is now possible at the levelof 1 DNA adduct per cell. Biological responses may be mea-sured in terms of increases or decreases in enzyme activityor levels of proteins. Genetic factors may be defined precisely.Yet our ability to translate this knowledge into reasonablerisk factors is relatively poor. Just because we can detectthe presence of a chemical or measure a biochemical effectdoes not mean that this represents a hazard and thereforethat the individual is at risk. Some biomarkers may be irrele-vant to toxicity, whereas others may be too sensitive.

Therefore, different types of biomarker need to be used incombination where possible and appropriate with a holisticapproach being adopted.

REFERENCES

1. National Research Council, US National Academy of SciencesCommittee on Biological Markers, 1989.

2. Waterfield CJ, Timbrell JA. Biomarkers. In: Ballantyne B,Marrs T, Syversen S, eds. Textbook of Applied and GeneralToxicology. 2 Macmillan Ltd, 1999.

3. Van Welie RTH, van Dijck RGJM, Vermeulen NPE, van Sit-tert NJ. Mercapturic acids, protein adducts, and DNA adductsas biomarkers of electrophilic chemicals. Crit Rev Toxicol1992; 22(5=6):271–306.

66 Timbrell

4. De Rooij BM, Commandeur JNM, Vermeulen NPE. Mercaptu-ric acids as biomarkers of exposure to electrophilic chemicals:applications to environmental and industrial chemicals.Biomarkers 1998; 3:239–303.

5. Aston JP, Ball RL, Pople JE, Jones K, Cocker J. Developmentand validation of a competitive immunoassay for urinaryS-phenylmercapturic acid and its application in benzenebiological monitoring. Biomarkers 2002; 7:103–112.

6. Inoue O, Kanno E, Yusa T, Kakizaki M, Watanabe T,Higashikawa K, Ikeda M. A simple hplc method to determineurinary phenylmercapturic acid and its application to gasolinestation attendants to biomonitor occupational exposure tobenzene at less than 1 ppm. Biomarkers 2001; 6:190–203.

7. Davies DS, Kobayashi S. Metabolism and pharmacokinetics.In: Roberts, ed. Risk assessment—The Common Ground. Pro-ceedings of the Life Science Research Symposium. Interna-tional Safety Evaluation Symposium,Tokyo,Japan, 1987.

8. Tulip K, Timbrell JA, Nicholson JK, Wilson I, Troke J. A pro-ton magnetic resonance study of the metabolism of n-methyl-formamide in the rat. Drug Metab Dispos 1986; 14:746–749.

8a. Hemminki K, Vodicka P. Styrene: from characterisation ofDNA adducts to application in styrene-exposed laminationworkers. International Symposium on Human Health andEnvironment: Mechanisms of Toxicity and Biomarkers toassess Adverse Effects of Chemicals. Toxicol Lett 1995;77:153–161.

9. Zhao C, Kumar R, Hemminki K. Measurement of 7-methyl- and7-(2-hydroxyethyl)-guanine DNA adducts in white blood cells ofsmokers and non-smokers. Biomarkers 1998; 3:327–334.

10. Osterman-Golkar S, Bond JA. Biomonitoring of 1,3-butadieneand related compounds. Environ Health Perspect 1996;104(suppl 5):907–915.

11. Prevost V, Likhachev AJ, Loktionova NA, Bartsch H, Wild CP,Kazanova OI, Arkhipov AI, Gershanovich ML, Shuker DEG.DNA base adducts in urine and white blood cells of cancerpatients receiving combination chemotherapies which includeN-methyl-N-nitrosourea. Biomarkers 1996; 1:244–251.


12. Tang D, Phillips DH, Stampfer M, Mooney LA, Hsu Y, Cho S,Tsai WY, Ma J, Cole KJ, She MN, Perera FP. Associationbetween carcinogen–DNA adducts in white blood cells andlung cancer risk in the physicians health study. Cancer Res2001; 61:6708–6712.

12a. Groopman JD, Wind CP, Hasler J, Junshi C, Wogan GN,Kensler TW. Molecular epidemiology of aflatoxin exposures:Validation of aflatoxin-N7-guanine levels in urine as a biomar-ker in experimental rat models and humans. Environ HealthPerspect 1993; 99: 107–113.

12b. Wild CP, Ziang Y-Z, Allen SJ, Jansen LAM, Hall AJ. Monte-sano R Charcinogenesis 1990;11:2271–2274.

13. Carlberg CE, Moller L, Paakki P, Kantola M, Stockmann H,Purkunen R, Wagner P, Lauper U, Kaha M, Elovaara E,Kirkinen P, Pasanen M. DNA adducts in human placenta asbiomarkers for environmental pollution analysed by the32P-HPLC method. Biomarkers 2000; 5:182–191.

14. McClure GYH, Freeman JP, Lay JO, Hinson JA. Haemoglobinadducts as biomarkers of exposure to the herbicides propaniland fluometuron. Biomarkers 1996; 1:136–140.

15. Jonsson Bo AG, Lindh CH, Welinder H. Haemoglobin adductsand specific immunoglobulin G in humans as biomarkers ofexposure to hexahydrophthalic anhydride. Biomarkers 1997;2:239–246.

16. Christiani DC. Utilization of biomarker data for clinical andenvironmental intervention. Environ Health Perspect 1996;104:921–925.

17. Randerath K, Reddy MV, Gupta RC. 32P-labelling test forDNA damage. Proc Natl Acad Sci USA 1981; 78:6126–6129.

18. Keith G, Dirheimer G. Postlabelling: a sensitive method forstudying DNA adducts and their role in carcinogenesis. CurrOpin Biotechnol 1995; 6:3–11.

18a. Koskinen M, Vodicka P, Vodickova L, Hemminki K.32P-postlabelling=hplc analysis of various styrene-inducedDNA adducts in mice. Biomarkers 2001; 6:175–189.

19. Toraason M. 8-Hydroxydeoxyguanosine as a biomarker ofworkplace exposures. Biomarkers 1999; 4:3–26.

68 Timbrell

20. De Zwart LL, Hermanns RCA, Meerman JHN, CommandeurJNM, Salemink PJM, Vermeulen NPE. Evaluation of urinarybiomarkers for radical induced liver damage in rats treatedwith carbon tetrachloride. Toxicol Appl Pharmacol 1998;148:71–82.

21. Bianchini F, Hall J, Donato F, Cadet J. Monitoring urinaryexcretion of 5-hydroxymethyluracil for assessment of oxidativeDNA damage and repair. Biomarkers 1996; 1(3):178–184.

21a. Roberts DW, Bucci TJ, Benson RW, Warbritton AR, McRaeTA, Pumford NR, Hinson JA. Immunohistochemical localiza-tion and quantification of the 3-(cystein-S-yl)-acetaminophenprotein adduct in acetaminophen hepatotoxicity. Am J Pathol1991; 138(2):359–371.

22. Lindstrom AB, Pleil JD. A review of the USEPA’s single breathcanister(SBC) method for exhaled volatile organic biomarkers.Biomarkers 2002; 7(3):189–208.

23. Reistad R, Nyholm SH, Haug LS, Becher G, Alexander J.2-Amino-1-methyl-6-phenylimidazo [4,5-6] pyridine (PhIp inhuman hair) as biomaker for dietary exposure. Biomarkers1999; 4:263–271.

24. Nakahara Y, Takahashi K, Kikura R. Hair analysis for drugsof abuse. X. Effect of physicochemical properties of drugs onthe incorporation rates into hair. Biol Pharmaceut Bull 1995;18:1223–1227.

25. Bai C-l, Canfield PJ, Stacey NH. Individual serum bile acids asearly indicators of carbon tetrachloride and chloroforminduced liver injury. Toxicology 1992; 75:221–234.

26. Neghab M, Stacey NH. Serum bile acids as a sensitive biologi-cal marker for evaluating hepatic effects of organic solvents.Biomarkers 2000; 5(2):81–107.

26a. Hermans C, Bernard A. Clara cell protein (CC16): characteris-tics and potential applications as biomarker of lung toxicity.Biomarkers 1996; 1:3–8.

26b. Carbonelle S, Francaux M, Doyle I, Dumont X, de Burbure C,Morel G, Michel O, Bernard A. Changes in serum pneumopro-teins caused by short-term exposures to nitrogen trichloride inindoor chlorinated swimmingpools. Biomarkers 2002; 7:464–478.


27. Waterfield CJ, Turton JA, Scales DC, Timbrell JA. Investiga-tions into the effects of various hepatotoxic compounds onurinary and liver taurine levels in rats. Arch Toxicol 1993;67:244–254.

28. Nicholls A, Nicholson JK, Haselden JN, Waterfield CJ. Ametabonomic approach to the investigation of drug-inducedphospholipidosis. Biomarkers 2000; 5(6):410–423.

29. Mortuza GB, Neville WA, Delaney J, Waterfield CJ, CamilleriP. Characterisation of potential biomarkers of phospholipido-sis from amiodarone treated rats. Biochem Biophys Acta2003; 1631:136–146.

29a. Timbrell JA. Principals of Biochemical Toxicology. Taylor andFrancis, 2000.

30. Lecamwasam OS, Franklin C, Turner P. Effect of phenobarbi-tal on hepatic drug metabolising enzymes and urinary d gluca-ric acid excretion in man. Br J Clin Pharmacol 1975; 2:257–262.

31. Yeung JHK, Wong JKL, Park BK. Development of a monoclo-nal antibody to 6b-hydroxycortisol and its application in anenzyme-linked immunosorbent assay (ELISA) for 6b-hydroxy-cortisol in urine. J Pharmacol Toxicol Methods 1997; 38:71–79.

32. Ohnhaus EE, Park BK. Measurement of urinary 6b-hydroxy-cortisol excretion as an in vivo parameter in the clinical assess-ment of the microsomal enzyme-inducing capacity ofantipyrine, phenobarbitone and rifampicin. Eur J Clin Phar-macol 1979; 15:139–145.

33. Totsuka S, Watanabe T, Koyanagi F, Tanaka K, Yasuda M,Manabe S. Increase in urinary excretion of 6b-hydroxycortisolin common marmosets as a marker of hepatic CYP3A induc-tion. Arch Toxicol 1999; 73:203–207.

34. Mathews JM, Raymer JH, Velez GR, Garner CE, Bucher JR.The influence of cytochrome P450 enzyme activity on the com-position and quantity of volatile organics in expired breath.Biomarkers 1996; 1:196–301.

35. Harley CB, Menon CR, Rachubinski RA, Nieboer E. Metal-lothionein mRNA induction by cadmium in peripheral bloodleukocytes. Biochem J 1989; 262:873–879.

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36. Nover L. Heat Shock Response. Boca Raton, FL: CRC Press.

37. Dilworth C, Timbrell JA. An investigation into the sensitivityof heat shock proteins as markers of cellular damage: a com-parative study of hydrazine and cadmium chloride in primaryrat hepatocyte cultures. Biomarkers 1998; 3:177–190.

38. Sanins SM,Nicholson JK, Elcombe C, Timbrell JA. Hepatotxin-induced hypertaurinria: a proton NMR study. Arch Toxicol1990;64 401–411.

39. Nicholson JK, Higham DP, Timbrell JA, Sadler PJ. Quantita-tive high resolution 1H-NMR urinalysis studies on thebiochemical effects of cadmium in the rat. Mol Pharmacol1989; 36:398–404.

40. Timbrell JA. Urinary creatine as a biochemical marker of che-mical induced testicular damage. Arch Ind Hyg Toxicol 2000;3:295–302.

41. Traina ME, Fazzi P, Urbani E, Mantovani A. Testicular crea-tine and urinary creatine–creatinine profiles in mice after theadministration of the reproductive toxicant methoxyaceticacid. Biomarkers 1997; 2:103–110.

42. Wang SS, O’Neill JP, Qian G-S, Zhu Y-R, Wang J-B, ArmenianH, Zarba A, Wang J-S, Kensler TW, Groopman JD, SwenbergJA. Elevated HPRT mutation frequencies in aflatoxin exposedresidents of Daxin, Qidong County, Peoples Republic of China.Carcinogenesis 1999; 20(11):2181–2184.

43. Risby TH. Breath markers in normal and diseased humans.In: Marczin N, Yacoub MH, eds. Disease Markers in ExhaledBreath: Basic Mechanisms and Clinical Applications. NATOASI Series. Amsterdam: IOS Press, 2002:113–122.

44. Kharitonov SA, Barnes PJ. Biomarkers of some pulmonarydiseases in exhaled breath. Biomarkers 2002; 7(1):1–32.

45. Stone BG, Besse TJ, Duane WC, Evans CD, DeMaster EG.Effects of regulating cholesterol biosynthesis on breath iso-prene excretion in men. Lipids 1993; 28:705–708.

46. Ganguly S, Taioli E, Baranski B, Cohen B, Toniolo P, Garte S.Human metallothionein gene expression determined by quan-titative reverse transcription polymerase chain reaction as a


biomarker of cadmium exposure. Cancer Epidemiol Biomar-kers Prevent 1996; 5:297–301.

47. Tugwood J. Genomics and the search for novel biomarkers intoxicology. Biomarkers 2003; 8: 79–92.

48. Iacobuzio-Donahue CA, Maitra A, Shen-Ong GL, van Heek T,Ashfaq R, Meyer R, Walter K, Berg K, Hollingsworth MA,Cameron JL, Yeo CJ, Kern SE, Goggins M, Hruban RH.Discovery of novel tumor markers of pancreatic cancer usingglobal gene expression technology. Am J Pathol 2002; 160:1239–1249.

49. Kennedy S. The role of proteomics in toxicology: identificationof biomarkers of toxicity by protein expression analysis.Biomarkers 2002; 7(4):269–290.

50. Celis JE, Gromov P. 2D protein electrophoresis: can it beperfected? Curr Opin Biotechnol 1999; 10:16–21.

51. Albertini RJ, Nicklas JA, O’Neill JP. Future researchdirections for evaluating human genetic and cancer risk fromenvironmental exposures. Environ Health Perspect 1996; 104:503–510.

52. D’Errico A, Taioli E, Chen X, Vineis P. Genetic metabolic poly-morphisms and the risk of cancer: a review of the literature.Biomarkers 1996; 1:149–173.

53. Vineis, P. Use of biomarkers in epidemiology. The exampleof metabolic susceptibility to cancer. Toxicol Lett 1995; 77:163–168.

54. Nebert DW, McKinnon RA, Puga A. Human drug metabolisingenzyme polymorphisms: effects on risk of toxicity and cancer.DNA Cell Biol 1996; 15:273–280.

55. Miller MS, McCarver DG, Bell DA, Eaton DL, Goldstein JA.Genetic polymorphisms in human drug metabolising enzymes.Fund Appl Toxicol 1997; 40:1–14.

56. Streetman DS, Bertino JS, Nafziger AN. Phenotyping of drugmetabolising enzymes in adults: a review of in vivo cytochromeP450 phenotyping probes. Pharmacogenetics 2000; 10(3):187–216.

72 Timbrell

57. Kaderlik KR, Kadlubar FF. Metabolic polymorphisms andcarcinogen DNA adduct formation in human populations.Pharmacogenetics 1995; 5:S108–117.

57a. Magoub A, Idle JR, Dring LG, Smith RL. Polymorphic hydro-xylation of debrisoquine in man. Lancet ii 1977; 584–586.

58. Tassaneeyakul W, Mohamed Z, Birkett DJ, McManus ME,Veronese ME, Tukey RH, Quattrochi LC, Gonzalez FJ, MinersJO. Caffeine as a probe for human cytochromes P450: valida-tion using cDNA expression immunoinhibition and microso-mal kinetic and inhibitor techniques. Pharmacogenetics1992; 2(4):173–183.

59. Miners JO, Birkett DJ. The use of caffeine as a metabolicprobe for human drug metabolising enzymes. Gen Pharmacol1996; 27(2):245–249.

60. Hein DW. Molecular genetics and function of NAT1 and NAT2:role in aromatic amine metabolism and carcinogenesis. MutatRes 2002; 506–507:65–77.

61. Barrett JC, Vainio H, Peakall D, Goldstein BD. 12th Meetingof the Scientific Group on Methodologies for the Safety Evalua-tion of Chemicals—Susceptibility to Environmental Hazards.Environ Health Perspect 1997; 105(4): 699–737.

62. Hu JJ, Mohrenweiser HW, Bell DA, Leadon SA, Miller MS.Symposium overview: genetic polymorphisms in DNA repairand cancer risk. Toxicol Appl Pharmacol 2002; 185:64–73.

63. Athas WF, Hedayati MA, Matanoski GM, Farmer ER,Grossman L. Development and field test validation of an assayfor DNA repair in circulating human lymphocytes. Cancer Res1991; 51:5786–5793.

64. Feigelsen HS, Ross RK, Yu MC, Coetzee GA, Reichardt JKV,Henderson BE. Genetic susceptibility to cancer from exogen-ous and endogenous exposures. J Cell Biochem 1996;25S:15–22.

64a. Waterfield CJ, Turton JA, Scales DC, Timbrell JA. Correlationbetween urinary and liver taurine levels and between predoseurinary taurine and liver damage. Toxicology 1993; 77:1–5.

65. Kelce WR, Ewing LL. Male reproductive toxicology. BiomedEnviron Sci 1991; 4:35–47.


66. Cosma GN, Currie D, Squibb KS, Snyder CA, Garte SJ. Detec-tion of cadmium exposure in rats by induction of lymphocyte.J Toxicol Environ Health 1991; 34:39–49.

67. Groopman JD, Wogan GN, Roebuck BD, Kensler TW. Molecu-lar biomarkers for aflatoxins and their application to humancancer prevention. Cancer Res 1994; 54:1907s–1911s.

68. Hugget RJ, Kimerle RA, Mehrle PM, Bergman HL. Biomar-kers Biochemical, Physiological, and Histological Markers ofAnthropogenic Stress. Chelsea, MI, USA: Lewis Publishers,1992.

69. Schulte P. Contribution of biological markers to occupationalhealth. Am J Industr Med 1991; 20:435–436.

70. Biomarkers in Human Cancer. Part I, Predisposition and usein risk assessment. Environmental Health Perspectives. Vol.98, 1992. Part II, Exposure Monitoring and Molecular Dosime-try. Environmental Health Perspectives. Vol. 99, 1993.

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3

NMR Spectroscopy: Principles andInstrumentation

MICHAEL D. REILY

Pfizer Global Research andDevelopment, Michigan Laboratories,

Ann Arbor, MI, U.S.A.

JOHN C. LINDON

Biological Chemistry,Biomedical Sciences Division,

Faculty of Medicine,Imperial College London,

South Kensington, London, U.K.

1. INTRODUCTION

The objective of this chapter is to familiarize the reader withnuclear magnetic resonance (NMR) spectroscopy, its basicprinciples, its utility as an analytical tool for investigatingbiofluids, and to describe the instrumentation and relatedhardware necessary to operate a functional NMR-based meta-bonomics laboratory. Nuclear magnetic resonance spectro-scopy is a powerful approach because it combinesthe provision of detailed molecular information with the

75

possibility of understanding whole molecule dynamic proper-ties such as diffusion, plus the ability to carry out quantita-tion. Although powerful in its own right, NMR spectroscopycan be regarded as complementary to other analytical chemi-cal techniques. For example, it can provide information onsubstances with no UV chromophores such as carbohydrates.It is a universal detector in that if the molecule under studycontains NMR-active nuclei these should be detectable, unlikein mass spectrometry where analyte observation can be influ-enced by selective ionization. Most NMR spectroscopic experi-ments are carried out in solution for the purpose ofidentifying the structures of small chemical molecules, includ-ing natural products, but there is a wealth of high resolutionapplications in other areas, such as determining the three-dimensional (3D) structures of proteins as well as analyzingcomplex biological mixtures such as biofluids for metabo-nomics applications. In addition, there is much effort devotedto solid state NMR spectroscopy where special techniqueshave to be used to overcome very broad NMR peaks and henceto recover useful chemical information. Finally, NMR spectracan be obtained from living humans and animals and in vivoNMR or magnetic resonance spectroscopy (MRS), as it isknown, has found use in disease diagnosis. The same technol-ogy and principles lie behind magnetic resonance imaging(MRI), now widely available in hospitals for clinical diagnosis.

Since it was first commercialized in the 1950s, NMRspectroscopy has been an invaluable analytical tool for struc-ture elucidation of solubilized analytes. In its humble begin-nings, NMR spectrometers were only sufficient for analyzingrelatively simple organic molecules that could be dissolved athigh concentrations (0.1M). The limitations of early systemswere primarily due to low sensitivity and poor resolvingpower. As more powerful systems became available in the1970s, researchers began looking at more complicatedanalytes and mixtures. Soon, it was realized that NMR couldalso be also be a valuable bioanalytical tool and manufac-turers of NMR equipment responded. Gradual advances inNMR spectroscopy beginning in the 1980s through to the pre-sent time have led to its routine use in analyzing increasingly

76 Reily and Lindon

complex samples, including medium-sized biomolecules (up to40KDa) and biological matrices comprising hundreds of spec-troscopically distinct molecules with thousands of resonances.Indeed, as a direct consequence of these advances, entirelynew applications of NMR have emerged, including biomolecu-lar NMR and metabolic profiling, which can be consideredsub-fields in their own right. The latter, the theme of thisbook, relies on the ability of NMR to provide detailedanalysis on the biomolecular composition (primarily ofmolecules less than 500 molecular weight) very quickly withrelatively little sample preparation.

There are several excellent textbooks on NMR spectro-scopy that describe the theoretical basis of the subject,provide information on operational methods and give gooddescriptions of applications (1–3). For these reasons, only anoverview of the technique is given here.

2. PRINCIPLES OF NMR SPECTROSCOPY

2.1. Basic Theory

The phenomenon of NMR arises because the positivelycharged nuclei of certain atoms possess a quantized propertycalled spin. This spin is associated with a nuclear magneticmoment, also quantized, such that in a magnetic field, it ispossible for the nuclear magnetic moment to take up variousorientations with respect to the field. Each orientation is asso-ciated with a discrete energy state and in the presence of themagnetic field these states have different energies. The quan-tized state is characterized by specific values that can have aninteger or half-integer value including zero. For nuclei withspin¼ 0, there is no magnetic moment and this is the casewhere both the atom number and the atomic weight are even,such as 12C and 16O. For the simplest magnetic nuclei, withspin¼ 1

2, there are just two levels. As a consequence of thediffering energies of the states, the populations of spins inthe states are not equal and there will be an excess of nuclearspins in the lower level. It is possible to induce transitionsof nuclear spins between these levels by applying an

NMR Spectroscopy: Principles and Instrumentation 77

oscillating frequency field and for commercially availableNMR magnets, these transitions are in the radio-frequencyregion of the electromagnetic spectrum. There is a linear rela-tionship between the magnitude of the nuclear magneticmoment and the observation frequency of the NMR phenom-enon for a given magnetic field strength. There is also a linearrelationship for a given nucleus between observation fre-quency and magnetic field strength. As will be seen below,not all nuclei of a given atomic isotope in a molecule will havethe same resonance frequency and hence the NMR phenom-enon gives rise to a range of resonance frequencies corre-sponding to peaks in an NMR spectrum.

Most NMR spectra are based on just a few nuclear types.There are several reasons for this. One is that nuclei withspin > 1

2 have a property called a nuclear quadrupole momentwhich, in general, results in short lifetimes in the excited spinstates and a rapid return to the low energy state, resulting invery broad NMR lines. Second, many nuclei exist at low nat-ural abundances and so are difficult to detect without isotopicenrichment. Third, the strength of the NMR response isrelated to the size of the nuclear magnetic moment and manynuclei have rather small values of this and so have low detect-ability. Finally, some nuclei, once excited to the upper level,are slow to relax back to the ground state and this must occurbefore another scan can be added. This then incurs a timepenalty for acquiring summed scans necessary to improvedetection limits. Sometimes, these difficulties of low sensitiv-ity, low natural abundance, and long relaxation times cometogether.

The ubiquitous 1H nucleus, or proton, has one of thehighest relative sensitivities, surpassed only by its radioac-tive isotope tritium, 3H. The 13C isotope is useful for charac-terizing the carbon skeleton of organic molecules and with anatural abundance of about 1.1%, the chance of finding two13C nuclei in a given molecule is thus only about 0.01% andthis simplifies the spectra considerably. Many spectroscopictricks have been developed to allow routine observation of13C NMR spectra of organic molecules. The 19F nucleus isalmost as sensitive as the 1H nucleus (�83%) in NMR terms

78 Reily and Lindon

and 19F NMR spectroscopy has been used extensively in stu-dies of the metabolism of fluorine-containing drugs. More lim-ited use is made of other nuclei in pharmaceutical andbiochemical research and nuclei such as 15N have been usedextensively for protein structure determination followingisotope enrichment. The use of 31P NMR spectroscopy hasbeen widespread in biochemistry and medicine as a means ofinvestigating the various phosphorylated molecules importantin biology, including many studies in vivo.

In addition to being an unparalleled structural tool,NMR can also provide dynamical information on moleculardiffusion, orientational correlation times, and intermolecularinteractions in solution. The molecular self-diffusion coeffi-cient is a valuable measure of molecular mobility and in freesolution is directly related to molecular size. Using NMR tech-niques, it is possible to separate individual components in asample such as urine based on their molecular self-diffusioncoefficients (4,5). Also as molecular correlation times increase,they lead to broadening of the resonance lines, an effect thatis roughly proportional to the molecular weight. Thus,although albumin is highly abundant in plasma, its line widthmakes it a component that is difficult to characterize in a nor-mal NMR spectrum. Conversely, very rapid internal motionin the lipid groups of very large lipoprotein particles producelines narrow enough to interpret. When small molecules bindto large ones, the NMR properties depend on the associationand dissociation rates and the molecular size of the macromo-lecule. Such protein–ligand interactions are especially promi-nent in protein-rich biofluids, such as plasma and in intacttissues and are manifested in broadening of NMR peaks forsome small molecules that would ordinarily have very sharplines.

High-resolution NMR studies of small tissue samples invery high magnetic field have been reported using the techni-que of magic angle spinning (MAS) as detailed in Chapter 5.This technique, also developed originally for chemical analy-sis, overcomes some of the unfavorable aspects of intacttissues for NMR analysis and holds great promise, butcurrently is rather low throughput. In the case of intact


tissues, differential relaxation effects of a small molecule canbe used to distinguish compartmentalization.

In NMR spectroscopy, only a very small excess of thespins are in the low energy state. The net result of this is thatNMR is a rather insensitive technique relative to many otheranalytical methods. Typically, even todays spectrometersrequire a minimum of several nanomoles of material for ana-lysis in reasonable times. This can be compared with femto-moles or less for mass spectrometry under favorableconditions. Thus, poor sensitivity has been the bane of bioana-lytical uses of NMR and increasing NMR sensitivity has beenthe focus of most of the technical developments that haveoccurred over the past four decades.

However, in contrast to the low intrinsic sensitivity inthe applications of NMR to biofluids, the non-selectivity ofNMR makes it a very powerful tool for surveying the molecu-lar content of a sample without prejudging which analytes tosearch for. What was mentioned as an advantage above canalso be a nuisance. Scarce analytes often need to be measuredand although above the limit of detection, these lower levelspecies may be fully or partially obscured by analytes at muchhigher concentrations. Similarly, exogenous components thatcontain interfering nuclei (water, buffers, xenobiotics, etc.)can obstruct regions of the NMR spectrum.

2.2. Parameters from an NMR Spectrum

Not all nuclei of a given isotope resonate at exactly the samefrequency. This is because a given nucleus is surrounded byelectrons, which are also magnetic and in the presence ofthe magnetic field, these provide a fluctuating magnetic fieldopposing the main field of the NMR magnet. As a conse-quence, the nuclei are shielded from the main magnetic fieldand require a higher field to bring them to resonance and thusthey can be considered to have higher resonance frequencies.The degree of shielding depends on the electron distributionaround the nucleus and hence on the chemical environment.Thus, the exact nature of the chemical bonds and non-bondedinteractions that are experienced by the nucleus influence its

80 Reily and Lindon

resonant frequency. This is termed the chemical shift and ispredominantly determined by close-range effects within themolecule itself. Thus, while solvent and salt can affect thechemical shift to a limited extent, the NMR chemical shift islargely insensitive to the analytical matrix. Chemical shiftsare measured relative to that of a reference substance placedinto the sample. For 1H and 13C shifts in organic solvents, thisis tetramethylsilane (TMS). The chemical shift is then definedas d(H)¼ (difference in the resonance frequency in Hzbetween the analyte and TMS)� 106=(operating frequency ofthe spectrometer). Chemical shifts are thus quoted in ppmand are independent of the operating frequency of the spectro-meter, allowing comparisons irrespective of magnetic fieldstrength. For aqueous samples, an alternative reference com-pound is used and trimethylsilyl [2,2,3,3-2H4] propionic acidsodium salt (TSP) is the most commonly used example. Thechemical shifts for TMS and TSP are set arbitrarily to 0. Ifthe intrinsic resonant frequency of a proton in a magneticfield of 14 Tesla is 600MHz, 1 ppm is 600Hz. If nitrotyrosineis taken as an example, a proton contained in the aromaticring will have a different chemical shift, or characteristic fre-quency, than one in the aliphatic portion, as seen in Fig. 1.This shows separate peaks for each chemically distinct hydro-gen. It should be noted that the two hydrogens of the CH2

group have different chemical shifts because they are chemi-cally distinct due to the proximity of the asymmetric carbonatom. Indeed, the resolving power of modern spectrometersallows distinguishing even between different aromatic andaliphatic protons. This results in a unique fingerprint forvirtually every molecule, including geometric isomers of thesame molecule.

The resonance lines of individual nuclei can show furthersplitting due to indirect spin–spin coupling. This, given thesymbol, J, is measured in Hz and is independent of the obser-vation frequency. Such spin coupling arises from a magneticinteraction between NMR-active nuclei and is transmittedvia the intervening electrons, hence the term ‘‘indirect.’’ Cou-pling is only observed within a molecule. Thus, for two spin-12nuclei such as protons, the resonance line for each proton will


be split into a doublet, the two lines corresponding to the twopossible orientations of the adjacent proton relative to themagnetic field. For extended coupling chains, each componentof a doublet can be split further into doublets of doublets andso on. An example of this is shown in Fig. 2 for 3-nitrotyrosinewhere the J-coupling between the CH and CH2 groups resultsin such splittings. If a given proton is adjacent to two equiva-lent other protons, as in a CH2 group, then, of the four possi-ble orientations of the two protons, two of them are identical(up=down is the same as down=up) and a 1:2:1 triplet results.For such ‘‘first-order’’ systems, the multiplicity can bededuced on the basis of Pascal’s triangle according to thenumber of equivalent coupled nuclei. In situations wherethe chemical shift difference between the protons is largecompared to the J-coupling, then this simple rule applies.

Figure 1 500MHz NMR spectrum of 10mM 3-nitrotyrosinedissolved in 66mM sodium phosphate buffer, pH 7.4. The buffercontains 0.33mM trimethylsilyl [2,2,3,3-2H4] propionic acid sodiumsalt (TSP) as an internal chemical shift standard. Labels assignspecific protons in the molecule to specific NMR signals in thespectrum. In aqueous solvents, the amino and carboxylate protonsare generally exchange broadened and not observed.

82 Reily and Lindon

For situations where the chemical shift between coupled part-ners is not large compared to the magnitude of the couplingconstant (d=J < 10) or in symmetrical molecules, more com-plex rules have to be applied and sometimes the only solutionto interpreting a spectrum is via a computer simulation. For1H–1H interactions, the coupling does not normally extendbeyond three bonds, with four-bond couplings being quitesmall, if resolvable. Three bond 1H–1H couplings providevaluable information on the dihedral angles between C–Hvectors through an empirical equation known as the Karplusequation. Hence the J-coupling is a valuable parameter fordistinguishing between isomers and for measuring molecularconformations. Compilations of coupling constants have beenmade and empirical models for calculating them in variousconformations have been proposed (6).

If the NMR data are acquired under conditions whereeach scan is acquired on a spin system at equilibrium, thenthe areas under the NMR peaks are directly proportional tothe number of nuclei contributing to that peak and to the

Figure 2 Expansion of the 500MHz 1H NMR spectrum of 3-nitrotyrosine showing J-coupling splitting patterns in the aliphaticregion. The superscript denotes the number of bonds between thecoupled nuclei. It should be noted that the different magnitudesfor the triple bond couplings are a direct result of differential orbitaloverlap (and hence geometry) between the methine proton (a) andthe two magnetically non-equivalent prochiral methylene protons(b, b0).


concentration of the molecule in the sample. If an internalstandard of known concentration is added to the sample, thenabsolute concentrations can be determined. Because the NMRphenomenon is a quantum mechanical one, to a firstapproximation the signal intensity is linear, directly propor-tional to concentration over many orders of magnitude andindependent of molecular characteristics. Thus, there is norequirement for extinction coefficients as in ultraviolet spec-troscopy, and unlike mass spectrometry, NMR is unaffectedby the ability of the analyte to ionize or to other matrix-related suppression issues. Since NMR is non-selective, onecan quantitatively assess the concentration of many compo-nents in a biofluid spectrum without a prior knowledge ofwhat one might want to measure.

There are two times which define how fast a nuclearspin interacts with the rest of the sample as a whole (knownas the lattice) and how nuclear spins interact with eachother in a pairwise fashion. These are designated T1 andT2. T1 is known as the spin-lattice or longitudinal relaxationtime, and is the characteristic time for the process of nuclearspins reaching equilibrium populations in the spin states.For small molecules in non-viscous solutions, 1H T1 valuesare usually in the range of 1–10 sec. The other relaxationtime is known as T2, the spin–spin or transverse relaxationtime, and is related to the rate of spin-dephasing caused byspin–spin flips. For small molecules in free solution T1¼T2.However, macromolecules and exchanging species haveshort T2 times, typically in the range 10–100msec, eventhough T1 may be much longer. The difference in values ofT2 between small molecules and macromolecules can be usedto edit NMR spectra.

As mentioned earlier, the molecular self-diffusioncoefficient is a whole molecule property which does notnormally appear in NMR spectra. However, it is a valuablemeasure of molecular mobility and in free solution is directlyrelated to molecular size. It is possible to measure diffusioncoefficients using a specially designed NMR experiment,which includes the application of magnetic field gradients(4,5).

84 Reily and Lindon

There is another interaction which is important in NMRspectroscopy called the dipolar coupling. This is a direct mag-netic interaction between nuclei through space, not throughbonds as for J-coupling. This dipolar coupling can be severalorders of magnitude larger than J-couplings. However it isaveraged to 0 in isotropic liquids, but in solids is largelyresponsible for the observed very broad resonance bands. Insemisolids such as tissues, the dipolar couplings betweennuclei are partially averaged out by the considerable molecu-lar freedom and the residual couplings and hence the linebroadening can be removed by the technique of MAS asdescribed in more detail in Chapter 5. However, for moleculestumbling in solution, the fluctuating dipolar interaction is animportant relaxation mechanism and because of the distancedependence involved in its definition, it can be used to providemolecular structural information (7).

3. OPERATIONAL METHODS

Amajor gain in efficiency is obtained by simultaneously detect-ing all signals. This is achieved by the application of a shortintense pulse of r.f. radiation to excite the nuclei followed bythe detection of the induced magnetization in an r.f. detectorcoil as the nuclei relax. The decaying, time-dependent signal,known as a free induction decay (FID) is then converted tothe usual NMR spectrum by the process known as Fouriertransformation (FT). The efficient calculation of the digitalFourier transform requires the number of data values to be apower of 2, typically perhaps 16K points for modest spectralwidths, up to 128K or even 256K points for wide spectralwidths on high field spectrometers (1K is 1024 or 210 points).Acquisition of a 1H FID requires typically a few seconds andopens up the possibility of adding together multiple FID scansto improve the spectrum signal-to-noise ratio (S=N) since forperfectly registered spectra, the signals will coadd but thenoise will only increase in proportion to the square root ofthe number of scans (8). The S=N gain therefore is proportionalto the square root of the number of scans. This for the first time


made routine the efficient and feasible acquisition of NMRspectra of less sensitive or less abundant nuclei such as 13C.

In many molecular systems such as proteins and othermacromolecules, or in multicomponent mixtures such as bio-fluids, the spectral complexity and signal overlap in 1DNMR spectra can be too great for simple assignment ofNMR resonances. Under these circumstances, it would bedesirable to improve the dispersion of the signals. Oneapproach is to simplify the complex 1D NMR spectrum toleave only resonances, which are amenable to interpretationby the use of special pulse sequences. These can edit the ori-ginal spectrum into subsets of data, or excite or detect onlyspecific types of resonances, for example, only those whichare spin–spin coupled to a given resonance. An alternativeapproach to achieve this dispersion is through the use oftwo-dimensional (2D) or higher dimensional (n-D) methods.The pulse sequences which form the basis of such multidi-mensional NMR approaches not only enable increased spec-tral dispersion, but can also have the added advantage ofgiving information on relationships between the various reso-nances in the spectrum, for example showing which ones arecoupled to each other by spin–spin coupling (9).

The general approach adopted in 2D NMR is to apply aseries of r.f. pulses and delay periods to the sample such thatthere are two independent variable time intervals in the pulsesequence. One of these is the acquisition time, denoted by t2,and the other is some incremental delay denoted t1. If anNMR FID is acquired for a period t2 for each of a set of t1values, the digital NMR signal intensity (S) will be a functionof both t1 and t2 giving a matrix of data S(tl,t2). If FT iscarried out with respect to both t1 and t2 a matrix of NMRintensity as a function of two frequencies will result. This isnow a 2D NMR spectrum as it represents signal intensityas a function of two frequency axes. The delay t1 and theactual nature of the pulse sequence will define exactly whatthe two frequency axes will represent. The 2D NMR spectraare usually plotted as contour maps as though the 2D spectralpeaks are a series of mountains viewed from above relative tothe orthogonal o1 and o2 axes.

86 Reily and Lindon

There are basically two types of 2D NMR experiments.These are termed (a) resolved and (b) correlated, accordingto the type of dispersion in the second frequency dimensiono1. Resolved experiments are a way of improving spectral dis-persion by rotating the appearance of one NMR parameter atright-angles to another so that the o1 and o2 axes correspondto different parameters. The most common version of this is tocause the rotation of all spin–spin coupled multiplets by 90�,thus producing only chemical shifts along the o2 axis and ateach chemical shift the spin–spin coupled multiplet is spreadalong the o1 axis. Generally, no information is available froma resolved experiment on the spin–spin coupling connectivitybetween resonances. The other class of proton 2D NMRexperiment, the correlated type, provides information onconnectivity between resonances such as those which have acommon spin–spin coupling or which arise from nuclei whichare close together in space. In these cases, for identical nuclei,the o1 and o2 axes are identical.

There are further classes of experiment that result inpseudo-2D NMR spectra. These do not have a second fre-quency axis resulting from FT of a variable time, but the sec-ond axis is some other parameter. One example is provided bycontinuous-flow directly coupled HPLC–NMR spectra wherethe second axis in the pseudo-2D plot is the chromatographicretention time (10) (see Chapter 7). Another example is diffu-sion-ordered NMR spectroscopy (DOSY) where the secondaxis plots the molecular diffusion coefficient associated witheach NMR peak, this parameter being derived from thedependence of peak intensities on the square of an appliedmagnetic field gradient (4).

The principal pulse sequence in the resolved experimentcategory for high-resolution solution state NMR is theJ-resolved experiment (JRES), which produces a 2D NMRspectrum with both chemical shifts and spin–spin couplingson the o2 axis at their normal frequency positions, but onlythe couplings appear on the o1 axis. In fact, the pulsesequence results in multiplets which appear at an angle of45� relative to the axes and a representation is usually shownin which these multiplets have been further tilted by 45� to


make them orthogonal to the o2 axis. Under these circum-stances, the projection of the spectrum on to the o2 axis pro-duces a 1H NMR spectrum consisting of singlets at each 1Hchemical shift, i.e., a fully proton-decoupled proton NMRspectrum. The JRES experiment can be a very useful aid tothe assignment of resonances in the complex spectra whicharise from mixtures of small molecules such as biofluidswhere many of the individual components give first-orderspectra. Also, because the JRES experiment is based on thespin echo sequence, any nuclei with short T2 values will relaxwithin the t1 evolution period and not contribute to the finalspectrum. This has proved useful for the analysis of bloodplasma by proton NMR as the broad, short T2 resonancesfrom proteins and lipoproteins are greatly suppressed. Thereis a corresponding heteronuclear experiment, usually for 13CNMR, where the 13C–1H coupling patterns for each resonanceare rotated into the o1 dimension.

The proton homonuclear experiment, termed COSY(COrrelation SpectroscopY), is used to show which resonancesin a proton spectrum have mutual spin–spin couplings. Theresulting spectrum has the conventional 1D NMR spectrumalong the diagonal and off-diagonal cross-peaks at chemicalshifts corresponding to pairs of coupled nuclei. This is exem-plified by the spectrum of 3-nitrotyrosine given in Fig. 3.A modification to the COSY sequence called COSYLR ispossible which is better suited to elucidating connectivitiesthrough small- or long-range spin couplings. A double-quan-tum filtered COSY (DQF-COSY) approach causes the suppres-sion of diagonal peaks arising from singlets (i.e., resonanceswithout any spin couplings). This simplifies complex spectraand improves resolution. Refinements of the technique can beused which eliminate all resonances from one- and two-spinsystems, i.e., singlets and doublets (triple-quantum filteredCOSY), and so on for higher spin systems. The DQF-COSYexperiment is not as sensitive as the normal COSY approach.

A very powerful experiment, called TOCSY, providesinformation on unbroken chains of coupled protons in one2D NMR spectrum. The experiment is sometimes also calledthe homonuclear Hartmann–Hahn experiment (HOHAHA).

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Again, the results are not very dependent on the magnitude ofthe spin–spin couplings involved in the spectrum. The methodrelies on the application of a pulse to produce what is called aspin-lock field, which causes the nuclear magnetizations toprecess about this r.f. field, i.e., the spins are said to be lockedto this field. During this period, transfer of magnetizationoccurs between coupled spins. The longer the spin-lock periodis applied, the further the magnetization will be transferreddown a chain of coupled nuclei. The most widely used spin-lock sequence is based on a train of pulses called MLEV-17.

Figure 3 Contour plot of the 1H–1H 2-dimensional 500MHzCOSY-NMR spectrum of 3-nitrotyrosine. Peaks labeled on the diag-onal correspond to the 1D 1H NMR spectrum and off-diagonal peakscorrelate nuclei that are spin-coupled to each other.


There is a very useful 2D NMR approach, which providesconnectivities based on the nuclear Overhauser effect (NOE).The NOE is an alteration in signal intensity based on a directthrough-space mechanism which is distance dependent, andthus the experiment can be used to probe internucleardistances (7). The 2D NMR NOE experiment is called NOESYand the spectrum consists of a diagonal, plus cross-peaks atthe chemical shifts which demonstrate an NOE.

One of the problems with this experiment is that bothchemical exchange and NOEs cause transfer of magnetiza-tion, and so any peaks involved in chemical exchange whichis slow on the NMR timescale (i.e., those components whichgive rise to separate peaks) will also show cross-peaks inthe 2D NOESY spectrum. On the other hand, this can beuseful for studying complex exchanging systems. The 2DNOESY experiment is the major tool in the determination ofthe detailed 3D structure of proteins.

The magnitude and sign of a proton–proton NOE isdependent on both the molecular tumbling rate and theNMR observation frequency. When using high-field NMRspectrometers, for molecules in the molecular mass rangearound 1000Da, NOEs can be close to 0, even if the nucleiare close together in space. One way of overcoming this lim-itation is to carry out a so-called ROESY experiment. Thepulse sequence is identical to the TOCSY sequence given ear-lier for connectivity via spin–spin coupling. The distinction ofNOE cross-peaks from other effects in ROESY spectra is notalways easy. Cross-peaks are observed at the chemical shiftsof protons, which experience a direct through-space dipolarinteraction which is usually strongly distance dependent,thus indicating which hydrogens are close in space.

Correlation between 1H chemical shifts and heteronu-cleus chemical shifts such as 13C or 15N can be achievedthrough a number of 2D NMR experiments in which the het-eronucleus is detected directly. However, it is also possible toobtain the same type of correlation but with the advantage ofthe detection (i.e., the t2 dimension) at the much more sensi-tive 1H nucleus. This type of approach is called inverse detec-tion. One problem is that 1H NMR signals from protons

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attached to the �99% of naturally abundant 12C nuclei haveto be suppressed, leaving only the 1H signals from protonsattached to the 1.1% of carbon nuclei which are 13C at naturalabundance. This can be readily achieved, and if the pulsesequence also includes broadband heteronucleus decoupling(e.g., covering the 13C chemical shift range) then a 2D NMRpresentation is possible in which each 1H–13C correlationpeak appears as a singlet, the connectivity being based on1H–13C spin coupling. This presentation, unlike the homo-nuclear correlation experiments, has no diagonal peak andthe axes correspond to the appropriate chemical shift rangesof the 1H and 13C nuclei.

One main experimental approach is termed heteronuc-lear multiple quantum coherence, or HMQC, and the resultis achieved by making use of the fact that coupled 1H and13C nuclei can experience magnetization effects involvingboth spins, i.e., multiple quantum effects, which evolve dur-ing the evolution period t1. The

1H–13C HMQC spectrum of3-nitrotyrosine is shown in Fig. 4. Another related pulsesequence is termed heteronuclear single quantum coherence(HSQC) and this gives analogous information, but with bettersensitivity. Analogous pulse sequences exist based on long-range 1H–13C couplings, which can then give connectivityinformation for quaternary carbons. A cross-peak is observedwhere the chemical shifts of directly bonded C–H nuclei inter-sect. It should be noted that where a CH2 group has twoprotons with different chemical shifts, then two peaks appearon the 1H axis at the same position on the 13C axis.

Many methods have been introduced for removing spec-tral artifacts. These often use pulsed magnetic field gradientsalong the main magnetic field axis. At least two such gradi-ents are inserted into a pulse sequence, the first to causedephasing of transverse magnetization and the second, sometime later, to refocus only the components of magnetizationthat are desired. This can all be done in one scan, so removingthe need for multiple scans for each t1 increment. This resultsin much more efficient data acquisition. Field gradients arenow used routinely for selecting exactly which part of themagnetization is detected.


4. REALIZATION OF NMR SPECTROSCOPY INA METABONOMICS LABORATORY

All NMR spectrometers are comprised of several basic compo-nents that are illustrated in Fig. 5. The components thatmake up the core of an NMR spectrometer include the mag-net, r.f. console, and probes. Additional ancillary equipment

Figure 4 Contour plot of the 1H–13C 2D HMQC-NMR spectrum of3-nitrotyrosine. Each peak identifies a single C–H pair at the inter-section of the 13C (vertical axis) and 1H (horizontal axis) chemicalshifts for all CH and CH2 and groups in the molecule.

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that makes application of metabonomics simpler includesautomation for sample preparation, and methods for automa-tically introducing samples into the spectrometer. The lattercomprise tube-changing robots, fluid handling devices, andvarious chromatographic approaches. Each of these, alongwith typical specifications, is discussed further below.

At the heart of any NMR system is the magnet and theseare based on solenoids of super-conducting wire cooled to4.2K or lower, using liquid helium. The higher the magneticfield, the greater the dispersion and sensitivity, but it is alsoimportant to consider the homogeneity and stability of themagnet. It is also the single most costly part of a spectro-meter, often representing at about half of the purchase priceand this cost goes up non-linearly with field strength. Com-mercial instruments are available which allow 1H observationat 400, 500, 600, 700, 750, and 800, and 900MHz. For instru-ments which operate for 1H NMR at 700MHz or greater, theliquid helium bath is kept at about 2K by a pumped refrigera-tion system so that the higher currents need for the higherfields can be achieved. For reasons of sensitivity and spectral

Figure 5 A block diagram of an NMR spectrometer.


dispersion, it is important to utilize the highest field strengththat the budget will allow. Nearly all systems that can be pur-chased today, up to 800MHz, are available with activelyshielded magnets, which are usually specified at an extracost. However, this extra expense is more than offset by dra-matically smaller space requirements. This also greatlyreduces the distance from which necessary accessories (e.g.,liquid handling robotics, cryogenic probe accessories, andchromatography systems) can be located. The footprint of anNMR magnet is usually given by the area defined by theradial 5G line, which for an actively shielded 600MHz spec-trometer can be a factor or 10 or more smaller than with aconventional magnet.

Typically, the magnetic field drift should be less than10Hz=hr and relatively unaffected by barometric changes.Ultra high magnetic field homogeneity (in the parts per bil-lion) is required to do high resolution NMR. The overall homo-geneity of an NMRmagnet is achieved by a combination of thestable field of the superconducting solenoid, the supercon-ducting shims (usually an orthogonal set of three electromag-netic windings that can add to or subtract from the solenoidsintrinsic field) and the room temperature shims (anywherefrom 28 to 40 non-superconducting coils). The superconduct-ing shims are used to rough in the homogeneity and are notreadily adjustable after the system is installed. The room tem-perature shims are then routinely adjusted to fine-tune homo-geneity for each sample. Homogeneity is typically assessed asthe line shape on a standard sample such as chloroform orwater with certain minimum widths at half-height and nearthe baseline. One if the hallmarks of a good magnet are excel-lent ‘‘cryo line shape,’’ that is, the line width of an H2O protonresponse with no current in the room temperature shims. Inpart, this parameter is also dependent on the probe designand sample configuration.

An important aspect of conducting NMR spectroscopy onbiological fluids and tissues is suppression of large interferingresonances, in particular from water, buffers, and cosolvents(in the case of extracts). It is also important to be able toapply accurately shaped (non-rectangular) r.f. pulses and=or

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magnetic field gradients across samples to enable diffusionmeasurements, multidimensional NMR experiments, andthe latest solvent suppression approaches. It is also usefulto be able to observe non-hydrogen nuclei. To achieve theseneeds, the spectrometer should be equipped with a minimumof two r.f. excitation channels (one capable of observing hydro-gen only and one tunable for other nuclei), both of whichshould have the capability of producing shaped pulses. Thespectrometer should also have a gradient amplifier capableof producing a gradient across the sample of 30G=cm in thedirection of the applied static magnetic field (the z axis) witha rapid gradient recovery time.

The NMR probe consists of one or more saddle-type r.f.coils that enclose a cylindrical space (the coil volume) filledby sample contained in a cylindrical tube. Solution stateNMR spectra are usually measured in glass tubes of standardexternal diameters, 5mm being the most common, but arange of narrow tubes are available for limited sample studies(4, 3mm, and even smaller specially shaped cavities such ascapillaries or spherical bulbs, etc.). Very small sample sizescan be accommodated in specialized microprobes with samplevolumes in the microliter range. As described below, it is nowpossible to measure NMR spectra using special probes in aflow-injection mode avoiding the use of sample tubes comple-tely. Surrounding the sample and r.f. coils are one or moregradient coils. This whole assembly is located in the centerof the static magnetic field and the room temperature shims.The r.f. coils deliver excitation energy and detect the subse-quent response for amplification and digitization. For a givenfield strength, the probe design can have a large impact onsensitivity, homogeneity, stability, and sample throughput.

In any kind of NMR probe, there are two sample volumesto consider. First is the total volume of sample required (the‘‘sample volume’’) and second is the ‘‘active volume,’’ or thevolume of sample that is exposed to the r.f. coils as depictedin Fig. 6. For probes with the commonly used saddle coil,the ratio of active volume=sample volume is �0.5. This isnecessary due to the need for homogenous magnetic suscept-ibility above and below the tops of the saddle coils. Typical


sample volumes for metabonomics applications range from120 to 500 mL, a range that is normally adequate for com-monly available biofluids such as urine or plasma from any-thing larger than a mouse. There are also numerousexamples of small volume probes (1–30mL) that could havepotential uses in certain applications on rare or hard-to-obtain biofluids such as CSF or synovial fluids from smalllaboratory animals. Increasing the active volume will providesubstantial sensitivity gains but can be mediated by severalfactors. First, it is crucial that the receiver coil be as closeto the sample as possible to maximize the filling factor,approximated as the (active volume)=(coil volume). For manybiological fluid applications, the actual amount of sample is

Figure 6 Illustration of a typical NMR probe showing the saddlecoil arrangement.

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limited, and so any gains from a larger coil volume are lost ifdilution is required to achieve a necessary sample volume.Also, the ability to achieve a highly homogeneous magneticfield across the entire sample becomes a challenge for largevolumes.

If cost and commercial availability were not an issue, ide-ally one would have a probe whose coil volume matched theanticipated sample volume within the constraints outlinedabove. For example, a probe with a large active volume(�250mL) might be chosen for dog, primate, or human urineanalysis, whereas one with an active volume of 60–120mLwould be best for small rodent urine or blood analysis. Ifmouse CSF was the primary analytical matrix, a microcoilprobe with 5 mL active volume might be the most appropriate.

Probes with cryogenically cooled r.f. coils represent amilestone achievement in NMR systems development, withup to a factor of four sensitivity improvements over probeswith room temperature coils. The sensitivity increase isdiminished by salty samples such as urine, but initial resultssuggest that cryogenically cooled probes will perform at leastas well on such samples but markedly better on samples withlow salt and particularly with samples analyzed with hyphe-nated systems such as HPLC–NMR.

Overall magnetic field homogeneity should ultimately bespecified and measured for each probe that is to be used. Thisparameter is typically assessed as the line shape on a stan-dard sample such as chloroform with certain minimum linewidths at half-height and at 0.55% of the peak height. For bio-logical fluids, good water suppression often requires eventighter tolerances than most manufacturers publish, sincecontributions from the water (present at 110M proton concen-tration) at 0.001% of its full height can be comparable inintensity to analyte peaks of interest. Hence, it is advisableto specify water suppression performance on each probe. Anexcellent and accepted standard is 2mM sucrose in 90%H2O=10% D2O. The signal-to-noise ratio of the anomeric pro-ton signal is indicative of sensitivity and homogeneity, andoverall water suppression capabilities of the system arerevealed by observation of the residual water. The importance


of good water suppression is illustrated in Fig. 7. Finally, theprobe should be coupled to a sample temperature control unitcapable of maintaining a uniform temperature across thesample of �0.1�C or better.

The performance of an NMR spectrometer depends astable environment. The room should be isolated from unne-cessary traffic, vibrations, and r.f. noise and be outfitted tomaintain a consistent temperature �1�C. It should also befree of large amount of ferrous metals, particularly those thathave any chance of moving during the course of an experi-ment. Periodic cryogen filling (weekly for liquid nitrogenand bimonthly to quarterly for liquid helium) is facilitatedby plumbed-in liquid nitrogen and gaseous helium withnearby access doors large enough to allow ready access to por-table liquid helium tanks. A raised aluminum floor surround-ing the NMR magnets facilitates cryogen fills and loadingsamples into the system sample changers. Alternatively, onemust have a high ceiling and non-ferrous ladders to accessthe top of the magnet.

One of the advantages that NMR analysis provides is theability to measure samples with little or no sample prepara-tion. However, due to the large number of samples that aretypically analyzed in a metabonomics study, automation ofboth sample preparation and introduction into the NMR spec-trometer is an important aspect of setting up a laboratory.

Sample preparation requirements are typically simple formetabonomics studies, with normally only addition of a bufferrequired. However, a single metabonomics study can containseveral hundred samples so automated sample preparationcanmaximize precision andminimize the tedium of mass sam-ple production. The basic work flow for sample preparationinvolves transfer of tens to hundreds of microliters of biofluidfrom the collection vessel into a final analysis container con-taining dilution buffer. Most flow injection devices that areinterfaced with NMR spectrometers are based on a commer-cially available liquid handling devices and an ideal samplecontainer for use with this is a polyethylene 96 well deep wellplate. Samples can be made up in 96-well plates using a spe-cialized robotic system; the plate is then transferred to a

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Figure 7 Demonstration of the importance of good water peaksuppression for 1H NMR spectra of aqueous solutions. All spectrawere recorded at 500MHz on a sample of dilute rat urine in20mM phosphate buffer, pH 7.4. Top trace: Simple single-pulse1H NMR spectrum with the vertical scale expanded 4000-foldhigher than the height of the water peak. Middle trace: Sameexperiment as shown in the top trace, but with the addition of aselective 100mW irradiation of the water resonance for 1 sec priorto data acquisition. This serves to equalize the spin population ofthe water protons so that they do not contribute to the NMRspectrum and is referred to as presaturation. Bottom trace: One-dimensional version of the NOESY experiment with 100msecmixing time and presaturation of the water peak. The 100msecdelay and phase cycling utilized in the pulse sequence cancel outbroad components from the intense water signal arising frominhomogeneous parts of the sample and results in better overallwater suppression than presaturation alone.


second robotic system in which the contents of a well can beextracted and flowed into the NMR probe where the sampleis stopped and any NMR experiments carried out. After mea-surement, the sample is then sent back to the same well, to awell in a different plate, or to waste as desired. As such, thereare many off-the-shelf devices available that can manage sam-ple preparation for flow injection NMR. If the final destinationis a conventional NMR tube, then customization of third partyequipment or a solution from one of the NMR manufacturerswill generally be required.

Under many circumstances, flow NMR can providehigher throughput than conventional tube automatic sam-plers. Firstly, unlike tubes that vary slightly in concentricity,wall thickness, and straightness, the sample geometry isidentical from sample to sample because of the fixed flow celldesign. This minimizes the time required to perform homoge-neity optimization after delivery of each samples. Also pump-ing the sample into the probe can be quicker than the robotarms that pick up and deliver individual NMR tubes.

The system should be coupled to a liquid handling devicecapable of holding deep well 96-well polyethylene plates orother sample containers of choice. Sample handling equip-ment and the lines attaching it to the flow probe should becapable of delivering and withdrawing 60–500mL of aqueoussamples to and from the probe at a minimum rate of1–2ml=min without cavitation. The flow probe designrequires that the samples be free of solids to avoid clogging.Even though samples may be clear when first prepared, pre-cipitation of various amounts of calcium phosphates and otherparticulates after dilution with buffer is generally observed.As a result, care should be taken to leave a few millimetersclearance between the bottom of the sample tube or welland the tip of the probe to avoid aspirating any settledparticulate. A needle with a side orifice is also advisable foravoiding this. Obtaining the liquid handler from the NMRspectrometer supplier is advisable, since this provides directsoftware control during automated data acquisition. Thirdparty accessories that enhance the liquid handling hardwareinclude an electronic valve that allows selection of various

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solvents for probe cleaning, storage, and inter-sample rinsingand electronic cooling racks that can maintain the samplecontainers at a temperature of 5–10�C while they are in thequeue for analysis.

Since it was first commercialized, NMR spectroscopy hasbeen carried out on samples contained in glass tubes, themost common of which is 5mm in diameter and requiresapproximately 0.5ml of solution. Nuclear magnetic resonancevendors have met the demand for higher throughput applica-tions by developing robotic delivery systems for conventionalNMR tubes with a capacity of up to 120 samples. While thedevelopment of higher capacity flow based systems that canhandle many hundreds of samples in the late 1990s has founda new niche market, there are still advantages associatedwith using tubes even in metabonomics studies. The benefitsof using tubes are twofold: recovery and containment. It iscertainly possible to reclaim samples from a flow system,but it is very difficult to recover 100% of the sample or to avoiddilution with buffer used to wash the flow cell. When usingsamples such as human blood materials that may pose ahealth threat, or when analyzing very precious samples thatneed to be used for subsequent analysis, tubes provide a con-venient way to ensure that the sample is sealed from outsideexposure. At present, commercially available systems thatcan either make samples just-in-time for analysis or chilledautomatic tube samplers are just coming onto the marketand should provide a resurgence of tube-based approachesfor metabonomics which has been dominated by flow NMR.

NMR-based metabonomics studies can be broken downinto two broad components that are partially but not entirelyseparable: (1) pattern recognition of processed NMR spectrafor sample classification, and (2) biomarker identification.For the former, the NMR spectrum serves as the raw inputfor multivariate analysis and little additional work need bedone with the sample. For biomarker analysis, follow-up stu-dies are sometimes required to identify unknown endogenouscomponents that change in response to external stimulation.For example, one peak of unknown origin may appear in aspectrum of urine from an animal treated with a toxicant


and other peaks from that molecule may be obscured fromidentification. The challenge is then to isolate the componentfrom which that one peak arises and identify the componentas a potential biomarker. This is the subject of Chapter 7.

One powerful method for such a follow-up analysis onindividual samples is HPLC–NMR (10). With the peak loca-tions of unknown components from the initial spectralchanges in a given sample, it is a straightforward matter ofusing HPLC to fractionate the sample while continuouslymonitoring the NMR spectrum of the eluent. Once the peaksof interest elute, the spectrum obtained is free from interfer-ing peaks and is more analytically useful for identifying theunknown. The analytes need not have UV chromophores tobe detected (because of the non-selective nature of the NMRspectrometer) nor does separation need to be complete, sincethe inherent frequency resolution of the NMR spectrummakes it possible to analyze more than one component in asingle spectrum. The biofluid complexity problem is thusreduced chromatographically. Of course, the eluent can becollected in fractions on the outlet side of the NMR flow probefor further work up and analysis. By incorporating an inlinemass spectrometer analyzing a small fraction of the HPLCeluent, valuable supplementary information on the elutedanalytes can also be obtained. The addition of HPLC andMS capabilities to an NMR system used for metabonomicshas clear advantages. Currently, HPLC systems and massspectrometers with integrated software to control both theNMR spectrometer all of the other components are availablefrom major NMR manufacturers.

5. CONCLUSIONS

All of the tremendous advantages outlined above for NMRspectroscopy do not come without a price. A modern 600MHzNMR spectrometer with all of the accessories necessary tocarry out metabonomics studies and to identify metaboliteswill, as of this writing, cost in excess of $1.5M. Upgrading thisto an 800MHz system the price goes up to $3.7M. If one


wants the largest and most sensitive commercially availablemagnet (900MHz) the price tag goes up to about $5.3M.These estimates do not include space considerations that alsogo up disproportionately with field strength.

In summary, the real utility of NMR spectroscopy inmetabonomics-type analyses derives from the fact that it isnon-selective and provides both a qualitative profiling tooland a quantitative analytical tool simultaneously on a singlesample. This gives NMR an advantage over many other ana-lytical techniques that can be applied to metabonomics andrelated technologies. For example, other analytical techni-ques, such as direct infusion mass spectrometry (MS), andinfrared (IR) and Raman spectroscopies are very good at pro-viding reproducible biofluid profiles, but are either very selec-tive (e.g., not everything ionizes in a mass spectrometer) orhopelessly unable to directly provide the identity of individualcomponents that contribute to the pattern. Often these alsorequire elaborate sample preparation procedures. Technolo-gies that are powerful at measuring individual componentsoften involve coupling to chromatographic methods and aretherefore not as amenable to higher throughputs (e.g., LC–MS, GC–MS, etc.). These adjunctive methods are importantfor follow-up identifications of unknown metabolites.

As mentioned earlier, NMR spectrometers are expensiveand are likely to represent the bulk of equipment investmentin a metabonomics facility. Metabonomics technology isadvancing rapidly and NMR equipment continues to evolvethat is especially suited to biofluid analysis, although it isquite possible to perform metabonomics studies with NMRequipment that is not specifically designed for it. Collaborat-ing with an existing NMR group is an excellent way for alaboratory to initiate proof-of-concept studies before makinga large investment in capital and expertise. Virtually anyNMR spectrometer with an operating frequency of 400MHzor greater with probes that provide good water suppressioncan be used to obtain useful data. Designing a dedicatedNMR laboratory requires careful consideration of manyfactors, including cost and space, and the exact equipmentneeded will depend on what types of analyses are desired.


REFERENCES

1. Ernst RR, Bodenhausen G, Wokaun A. Principles of NuclearMagnetic Resonance in One and Two Dimensions. Oxford:Clarendon Press, 1987.

2. Sanders JKM, Hunter BK. Modern NMR Spectroscopy: AGuide for Chemists. 2nd ed. Oxford: Oxford University Press,1993.

3. Claridge TD. High-resolution NMR Techniques in Chemistry.Oxford: Elsevier Science & Technology Books, 1999.

4. Johnson CS Jr. Diffusion ordered NMR spectroscopy: princi-ples and applications. Prog Nucl Magn Reson Spectrosc 1999;34:203–256.

5. Lindon JC, Liu M, Nicholson JK. Diffusion coefficient mea-surement by high resolution NMR spectroscopy: biochemicaland pharmaceutical applications. Rev Anal Chem 1999;18:23–66.

6. Pretsch E, Siebl J, Simon W, Clerc T. Tables of Spectral Datafor Structure Determination of Organic Compounds. Berlin:Springer-Verlag, 1989.

7. Neuhaus D, Williamson MP. The Nuclear Overhauser Effect inStructural and Conformational Analysis. New York: VCH Pub-lishers Inc., 1989.

8. Lindon JC, Ferrige AG. Digitisation and data processing inFourier transform NMR. Prog Nucl Magn Reson Spectrosc1980; 14:1–27.

9. Martin GE, Zektzer AS. Two-dimensional NMR Methods forEstablishing Connectivity. A Chemist’s Guide to ExperimentSelection. Performance and Interpretation. New York: VCHPublishers Inc., 1988.

10. Lindon JC, Nicholson JK, Wilson ID. Direct coupling of chro-matographic separations to NMR spectroscopy. Prog NuclMagn Reson Spectrosc 1996; 29:1–49.


4

NMR Spectroscopy of Biofluids

JOHN C. LINDON, JEREMY K. NICHOLSON,and ELAINE HOLMES

Biological Chemistry, Biomedical SciencesDivision, Imperial College of Science,

Technology and Medicine, University of London,South Kensington, London, U.K.

1. INTRODUCTION

Analysis of biofluids can provide a window into the biochem-ical status of a living organism. The composition of a givenbiofluid is changed according to the level of function of thecells that are intimately concerned with its manufactureand secretion. For this reason, as described elsewhere in thisvolume, the composition of a particular fluid carries biochem-ical information on many of the modes and severity of organdysfunction whether due to beneficial or adverse drug effectsor disease processes. Dietary and diurnal variations may alsoinfluence biofluid compositions. One of the most successful

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approaches to biofluid analysis has been the application ofNMR spectroscopy (1,2) with the technique described in detailin Chapter 3. In addition to analytical applications, it is pos-sible to obtain a detailed understanding of the interactions ofthe various components in the whole biological matrix, suchas enzymatic biotransformations, metal complexation reac-tions, binding of small molecules to macromolecules, andcellular and micellar compartmentation which occur indifferent biofluids to varying degrees.

The information content of biofluid spectra is potentiallyvery high and the complete assignment of the 1H NMR spec-trum of most biofluids is not possible (even by using 900MHzNMR spectroscopy) due to the complexity of the matrix.However, the assignment problems vary considerablybetween biofluid types. For instance, blood plasma and semi-nal fluids have highly regulated metabolite compositions andthe majority of the NMR signals have been assigned for nor-mal human individuals. Urine composition is much more vari-able because its composition is normally adjusted by the bodyin order to maintain homeostasis and hence complete analysisis much more difficult. There is also an enormous variation inthe concentration range of NMR-detectable metabolites inurine samples. Those metabolites present in concentrationsclose to the limits of detection for one-dimensional (1D)NMR spectroscopy at around 100nM for many metabolites,have a 109 concentration range compared to water on a protonbasis. Even if the water resonance is suppressed, as is usual,by a factor of 104, there is still a concentration range ofaround 105 to be accommodated and this can lead to problemsin spectral detection and assignment. With every newincrease in available spectrometer frequency, the number ofresonances that can be resolved in a biofluid increases andalthough this has the effect of solving some assignment pro-blems, it also poses new ones. Furthermore, there are stillimportant problems of spectral interpretation that arise dueto compartmentation and binding of small molecules in theorganized macromolecular domains that exist in some bio-fluids such as blood plasma and bile. Although lower fieldstrength measurements can be useful for the detection of

106 Lindon et al.

the most abundant metabolites, and in certain circumstances,give quantitatively accurate results, comparable higher field1H NMR measurements are generally more accurate. Eventhe dispersion gain on going from 600 to 800MHz is signifi-cant in 1H NMR spectroscopy of biofluids and allows moresignals to be assigned, considerably easing the analysis ofcomplex biofluid spectra.

In addition to the usual limitations caused by instru-ment noise in the spectra, for complex mixtures such as bio-fluids, there is an additional type of noise, known aschemical noise. Unlike instrument noise, chemical noise isrelated to the sample itself and is the result of the extensiveoverlap of signals from compounds that are low in abundancein the matrix and, individually, close to the detection limits ofthe spectrometer. Nonetheless, they give rise to detectable 1HNMR responses due to their frequency superimposition. It isgenerally true that for 1H NMR work on biofluids, it is thechemical noise rather than instrument noise that usuallylimits the amount of recoverable spectral information.Normally only increasing the NMR frequency can allowrecovery of the information that was in the chemical noiseat lower frequencies. Furthermore, the problem of chemicalnoise interference varies in severity according to the biofluidtype and chemical shift ranges that are under considerationfor each fluid.

Two types of information, which are potentially availablein an NMR spectrum of a biological fluid, have been termed aslatent and patent (3). Patent biochemical information hasbeen defined as that which can be measured quantitativelyin a single pulse experiment. Latent information in an NMRspectrum measured at a particular field is not available inthe single pulse spectrum and the biochemical data containedtherein can only be obtained by selection of appropriate multi-pulse sequences to achieve either spectral editing or fre-quency dispersion in a second or higher dimension. Latentinformation can also be transformed into patent data byincreasing the measurement field strength. Biochemicalinformation can be latent in two ways, namely, through mul-tiple peak overlap and by undergoing some type of dynamic

NMR Spectroscopy of Biofluids 107

molecular interaction resulting in the lack of resolved signalsfor a compound present at levels in the NMR detection rangefor the spectrometer involved. On increasing the frequency atwhich an NMR experiment is performed, there is a conse-quent increase in sensitivity and so signals from moleculesin solution that were too dilute to be measured may becomemeasurable at a higher frequency and become latent or patentinformation. Thus, there is an effective increase in theamount of information of all levels on increasing fieldstrength, thereby increasing the amount of useful biochemicaldata in the NMR spectrum. The complexity of the NMR spec-tra of biofluids can be judged from the 1H NMR spectrum ofcontrol human urine measured at an observation frequencyof 900MHz as shown in Fig. 1.

Figure 1 900 MHz 1H NMR spectrum of control human urine.

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It is clear that even at the present level of technology inNMR, it is not yet possible to detect many important biochem-ical substances, e.g., hormones, in body fluids because of pro-blems with sensitivity, dispersion, and dynamic range andthis area of research will continue to be technology-limited.Alternative analytical approaches such as LC–MS thenbecome necessary and applications of this technique for meta-bonomic studies are now appearing in the literature.

2. PRACTICALITIES OF 1D 1H NMRSPECTROSCOPY OF BIOFLUIDS

One major advantage of using NMR spectroscopy to studycomplex biomixtures is that measurements are often madewith minimal sample preparation, usually with only the addi-tion of 5–10% D2O, and a detailed analytical profile can beobtained on the whole biological sample (1). The main NMRspectroscopic techniques used for biological fluid studies arecovered in Chapter 3 of this volume. However, much efforthas been expended in discovering efficient new NMR pulsesequence techniques for spectral simplification and watersuppression especially for biofluids and more recently, thecommercial availability of microprobes (4) and cryoprobes(5) has led to improved sensitivity or shorter data collectiontimes.

3. TECHNIQUES FOR RESONANCEASSIGNMENT IN NMR SPECTRA OFBIOFLUIDS

Usually in order to assign 1H NMR spectra of biofluids,comparison is made with spectra of authentic materials andby standard addition to the biofluid sample. This has servedto assign the major peaks in biofluids.

Chapter 3 of this volume covers the theoretical and prac-tical aspects of NMR spectroscopy and NMR instrumentationand describes the major types of NMR spectroscopic experi-ments, which can be used to provide information on biofluids.


Thus complex 1H NMR spectra can be simplified by attenuat-ing resonances due to macromolecules and other species withshort spin–spin relaxation times by employing a spin-echoediting approach, nowadays usually comprising a Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence. Alternatively,the NMR resonances of the small metabolites which diffuserapidly can be attenuated by using a diffusion-edited spec-trum where the intensities of the resonances are relatedto the diffusion times of the molecules giving rise to them.Thus molecules which diffuse rapidly have resonancesattenuated when a longitudinal-eddy-current-delay (LED)pulse sequence is used with a relatively long diffusion periodbuilt into it.

Additional confirmation of assignments can be achievedwith the application of two-dimensional (2D) NMR methods,particularly COSY and the total correlation spectrum(TOCSY) and, increasingly, inverse-detected heteronuclearcorrelation methods such as HMQC and HSQC. Additionally,the application of the 2D J-resolved (JRES) pulse sequence isimportant for spreading out the coupling patterns of the mul-titude of small molecules in a biofluid. Even 2D correlationNMR spectra of complex biofluids show much overlap ofcross-peaks and further editing is often desirable. Thus sim-plification of NMR spectra of biofluids can also be achievedusing (i) spin-echo methods particularly for fluids containinghigh levels of macromolecules, (ii) relaxation editing in gen-eral based on T1 and=or T2, (iii) diffusion editing, and (iv) mul-tiple quantum filtering. To this end, a method based on theseparation of 1H NMR resonances into subspectra accordingto whether the protons arise from CH, CH2 or CH3 groupsvia use of maximum quantum coherence spectroscopy(MAXY) (6) has been demonstrated. This has been extendedto produce 2D NMR spectra such as MAXY–TOCSY,MAXY–NOESY, and MAXY–JRES (7). The use of 800MHzNMR spectroscopy combined with 1H–13C HSQC studiesincluding band-selective pulses has aided assignment of lipidsignals (8,9). The development of metabonomics has requiredthe automatic reduction of large numbers of complex NMRspectra into easily manipulated descriptors and one way to

110 Lindon et al.

do this has been using spectral segmentation (10). A methodfor determining the actual temperature inside a biofluidsample inside an NMR magnet has been presented (11).

Molecular diffusion coefficients are parameters that arenot related directly to NMR spectral intensities under normalconditions. However, molecular diffusion can cause NMR sig-nal intensity changes when pulsed field gradients are appliedduring the FT NMR experiment. A number of pulse sequencedevelopments, particularly the LED sequence, have meantthat measurement of diffusion coefficients is relatively rou-tine (12). The editing of 1H NMR spectra of biofluids basedon diffusion alone or with a combination of spin relaxationand diffusion has been demonstrated (13). This has beentermed the Diffusion and Relaxation Editing (DIRE) pulsesequence. This approach is complementary to the editing of1H NMR spectra based on differences in T1 and T2.

New methods for editing TOCSY NMR spectra of bio-fluids have been proposed based on differences in moleculardiffusion coefficients and this has been termed diffusion edi-ted TOCSY (DETOCSY). This approach complements theediting of TOCSY NMR spectra based on coherence selectionand promises to provide an efficient alternative strategy forassignment of resonances in complex mixtures such as bio-fluids and cell extracts (13,14). When a very low gradientstrength is used, the spectrum is similar to that in theabsence of gradients. Increasing the gradient strength causesthe resonances from the small molecules to be reducedsubstantially due to their relatively fast diffusion comparedto those of the larger molecules that give rise to the broadpeaks in the spectrum.

Finally identity of metabolites which give rise to NMRpeaks in biofluid spectra can be obtained using conventionalmeans by separation and off-line spectroscopic methods.Alternatively as explained in Chapter 7, the employment ofdirectly coupled HPLC–NMR–MS can prove to be an efficientapproach.

Detailed 1H NMR spectroscopic data for a wide range ofmetabolites and biomolecules found in biofluids have beengiven in several literature compilations of data (15,16).


4. 1H NMR SPECTROSCOPY OFCEREBROSPINAL FLUID (CSF)

4.1. Properties and Biochemical Compositionof CSF

The CSF surrounds the brain and spinal cord, where it acts asa barrier against mechanical shock, as a lubricant betweenthe brain surface and the meninges, and helps support theweight of the brain. By means of the CSF, substances areremoved from the brain and spinal tissue and returned tothe blood stream. Drugs may also be distributed within thebrain with the aid of the CSF circulation. CSF is normally acrystal clear, low viscosity liquid of pH 7.3–7.4. The cere-brospinal fluid has a much lower protein content than plasma,although this may be elevated significantly in various diseasestates. Altered composition and physical characteristics ofCSF can reflect damage to the central nervous system ormeninges, and CSF is often sampled from patients with sus-pected cerebral disease. In general, the biochemical composi-tion of CSF reflects the composition of the ultrafiltered bloodplasma, but it also contains a number of metabolites that aresecreted by the CNS tissue. It may also be depleted of certainconstituents (e.g., glucose) relative to plasma, because of theirutilization by the cerebral cells. It is, therefore, a good prac-tice to compare concentrations of metabolites in CSF withthose in the plasma, because alterations in the latter maybe reflected in the CSF even if cerebral metabolism is normal.

CSF is normally collected for diagnostic purposes by lum-bar puncture from the subarachnoid space. Given the inva-sive nature of this intervention, it is only performed withgood reason and it is extremely rare to obtain CSF from atruly healthy donor. The cerebrospinal fluid may also be col-lected at post mortem, but the composition may be very differ-ent to that found ante mortem. After collection of CSF,samples can be measured directly by NMR or freeze-driedand reconstituted in D2O. In the latter case, the preconcentra-tion step allows many more minor metabolites to be detected.Unlike plasma, most CSF samples can be concentrated by upto 10 times after freeze-drying because of the low protein

112 Lindon et al.

content, and this allows collection of good quality NMR datain reasonable times. Fluoride can be added to the CSF soonafter collection where glucose measurements are required asthis strongly inhibits glycolysis.

4.2. Assignment of 1H NMR Spectra of CSF

Due to the low protein content of normal human CSF, it ispossible to use standard 1H NMR experiments to obtain bio-chemical information without recourse to the spin-echo tech-niques often required for studies on blood and plasma.However, where serious cerebral damage has occurred, or inthe presence of an acute infection, spectra may become domi-nated by protein resonances. Problems may also arise fromprotein-binding and metal binding of some metabolites, witha consequential broadening of their proton resonances.

There are a number of 1H NMR studies that assign peaksin CSF (17,18), with Petroff et al. (17) showing that highquality 2D 1H NMR spectra can be obtained from humanCSF, and that changes in NMR patterns can be related todisease states in the donor. In addition, many resonanceshave been assigned through the use of 2D JRES spectra andCOSY-45 spectra (19). Examination of a number of ex vivocontrol samples showed high consistency in the aliphaticregion of the 1D 1H NMR spectra of CSF and many assign-ments could be made by inspection but glutamate and gluta-mine had distinctly broadened resonances. Wevers et al. (20)have proposed a standardized method for acquiring NMRspectra of CSF and identified 50 compounds in CSF.

4.3. NMR Spectroscopy of CSF in DiseaseStudies

Although this volume is primarily concerned with applicationof metabonomics to toxicity, and toxicological studies have notused this fluid in the main, CSF remains a potentially usefulfluid for such work. However, NMR spectroscopy of CSF hasfound use in human disease studies with a number of metabo-lites detected and quantified (21,22). For patients with


lumbar disk herniations, no significant differences from con-trols were found. However, differences were observedbetween controls and one patient with a medulloblastomathat showed a decreased glucose level plus new signals, whichwere assigned to valine and alanine. Population overlap pro-hibited diagnosis based on any one ratio, and so discriminantanalysis using 16 metabolite concentration ratios was per-formed to investigate the diagnostic potential of NMR. Theresults demonstrated good predictive capability, except fortumor diagnosis.

A number of other studies have been carried out includ-ing studies from patients suffering from drug overdose (17),bacterial meningitis (23), Huntington’s disease (24), and dia-betes (24). Changes in the 1H NMR spectra of CSF frompatients included the detection of methylmalonate in apatient with vitamin B12 deficiency (25). One study of CSFfrom rats in a model of stroke has been reported using500MHz 1H NMR spectroscopy including 2D COSY methodsand a number of spectral changes could be observed as aresult of the experimentally induced lesion (26).

NMR spectroscopy of CSF has been used in a number ofstudies of multiple sclerosis (MS). One study showedincreased lactate and fructose levels in MS patients but nocorrelations between NMR spectra and the differentiation ofrelapsing, remitting, and primary, progressive MS (27).Another study examined cases of MS plus others with a vari-ety of neurological disorders and showed that the observedlevel of lactate correlated with the number of CSF mononuc-lear cells in patients with clinical activity. Also a decreasein formate was detected during active and inactive clinicalphases of MS (28). A third study examined CSF from patientswith actively progressing MS and found that acetate levelswere higher in patients whilst formate levels were lower inpatients compared to controls. There were no significant dif-ferences between spectra from early and longstanding MSpatients. An unidentified peak, probably from an N-methylcompound, was seen in spectra from patients with activelyprogressing disease. However, this was not found inspectra of CSF from patients with AIDS dementia complex

114 Lindon et al.

or Parkinson’s disease but it did appear in one out of threeCreutzfeld–Jakob disease patients and one out of sevenpatients with Guillan–Barre syndrome (29).

One study (30) has reported 500 and 600MHz 1H NMRdata on the post mortem CSF from Alzheimer’s disease (AD)patients and controls. The main differences between the spec-tra of the two groups were found to be in the region d2.4–2.9,where the resonances of aspartate, N-acetylaspartate, citrate,glutamate, and methionine occur. Principal components ana-lysis showed that separation of the two groups was possibleand that citrate was the principal marker with citrate levelsin the AD patients much reduced when compared with thecontrols. Patient age and the time interval between deathand autopsy were examined to see whether these factorsmight account for the differences between the AD and controlgroups. Allowing for these factors, the inter-group differenceswere reduced but still significant (p< 0.05). It was hypothe-sized that the reduction in CSF citrate found in the ADpatients may be due to the reductions in pyruvate dehydro-genase (PDH) reported in the parietal cortex and temporalcortex of AD patients.

5. 1H NMR SPECTROSCOPY OF BLOODPLASMA AND WHOLE BLOOD

5.1. Properties of Blood and Blood Plasma

Vertebrate blood consists of cellular elements suspended in acomplex fluid matrix of proteins, principally of albumin,immunoglobulins, glycoproteins and lipoproteins, togetherwith a large number of inorganic and low molecular weightorganic solutes. The functions of blood include the transportof oxygen, carbon dioxide, products of metabolism, and hor-mones. It is through the medium of the circulating blood thatthe constancy of the internal environment is maintained; dis-ease processes or abnormalities anywhere in the body arereflected to various extents in altered blood composition.The dominant cell type is the erythrocyte that normallymakes up about 45–50% of the blood volume. Other cell types


normally make up only about 1% of the packed cell volume.The remaining volume of the blood is the plasma. Plasma isthe term given to the fluid separated from whole, untreatedblood by centrifugation, whereas serum is separated fromwhole blood after the addition of an anticoagulant (normallytrisodium EDTA or lithium heparin). Serum is depleted infibrinogen (the major clotting protein precursor) and is conse-quently less viscous than plasma.

The physico-chemical complexity of plasma is expressedin its 1H NMR spectra by the range of linewidths of the sig-nals. This means that a number of different NMR experi-ments and=or physico-chemical interventions must beapplied to extract useful biochemical information. Numeroushigh resolution 1H NMR studies have been performed onthe biochemistry of blood and its various cellular componentsand plasma. The physical properties of whole blood pose ser-ious limitations on direct NMR investigations, but packederythrocytes yield more useful information on cell biochemis-try. The best resolved spectra are given by plasma and serumand such 1H NMR measurements can provide a plethora ofuseful biochemical information on both low molecular weightmetabolites and macromolecular structure and organization(3). The difficulties of obtaining quantitative determinationsof substances in blood plasma using 1H NMR spectroscopyhave been noted (31). The relative benefits of using formateover the more widely used TSP as an internal standard hasbeen evaluated (32).

5.2. Comparative Biochemistry of Blood Plasmausing 1H NMR Spectroscopy

Standard 1H NMR spectra of human blood plasma are verycomplex and resonances of metabolites, proteins, lipids andlipoproteins are heavily overlapped even at 800MHz 1Hobservation frequency. Most blood plasma samples are quiteviscous and this gives rise to relatively short T1 relaxationtimes for small molecules compared to simple aqueous solu-tions allowing relatively short pulse repetition cycles withoutsignal saturation. The complex spectral profile given in the

116 Lindon et al.

750MHz 1H NMR spectrum of blood plasma (Fig. 2(a)) can besimplified by use of spin-echo experiments with an appropri-ate T2 relaxation delay to allow signals from broad macromo-lecular components and compounds bound to proteins to beattenuated. The effect of applying the CPMG spin-echo pulsesequence to blood plasma results in a substantial reductionin the contributions from the albumin, lipoprotein and lipid(Fig. 2(b)). A number of metabolites have been detected innormal blood plasma using 400MHz spectrometers, butassignments were, in general, based on the observation ofonly one or two resonances for each metabolite. In addition,peaks from mobile N-acetyl neuraminic acid and related sialicacid fragments of certain macromolecules such as a1-acidglycoprotein have been assigned and used diagnostically (33).

The signals from some lipid and lipoprotein components,e.g., very low density lipoprotein (VLDL), low density lipopro-tein (LDL), high density lipoprotein (HDL) and chylomicrons,have also been partially characterized. In the single pulseNMR spectrum of blood plasma in the chemical shift regiond0.7–1.5, there are also many overlapping signals from smallorganic species such as lactate, 3D-hydroxybutyrate, alanineand branched chain amino acids together with those fromterminal CH3 and long chain (CH2)n groups of fatty acidsand triglycerides integral to the various lipoprotein particles,especially VLDL, LDL, and HDL (34).

Two approaches can be adopted for dealing with theoverlap problem. Firstly, a simple sample preparation suchas centrifugal ultrafiltration can be used to remove the macro-molecules. This results in a spectrum of all the non protein-boundmetabolites contributing to the spectrum. Alternatively,to avoid sample manipulation, a spectral editing technique canbe applied. Although the Hahn spin-echo (HSE) method wasused originally, nowadays the Carr–Purcell–Meiboom–Gill(CPMG) spin-echo method is the principal method. Both arehighly effective means of editing plasma 1H NMR spectraaccording to solute T2 relaxation times. Many signals fromlow molecular weight species are readily using the spin-echoapproach that cannot normally be resolved. TheHSE spectrumgives phase-modulation of signals that is dependent on the


Figure 2 750 MHz 1H NMR spectra of control human bloodplasma. (a) Conventional spectrum acquired with the NOESYPRE-SAT pulse sequence; (b) CPMG spin-echo spectrum; (c) diffusion-edited spectrum.

118 Lindon et al.

spin–spin coupling multiplicity so that singlets are alwaysphased upright as are triplets when the spin-echo delay isselected to be 1=2JHH. With a delay of about 68msec, doubletsand quartets with coupling constants of approximately 7.4Hzappear phase-inverted. The CPMG spin-echo pulse sequencedoes not J-modulate signal phases and losses of signal intensityduring the T2 relaxation delays by diffusion through fieldgradients are minimized by virtue of the short delaysemployed. For normal plasma at pH 7, the largest peak inthe spectral region to high frequency of water is that of thea-anomeric H1 resonance of glucose at d5.223 (which providesa useful internal chemical shift reference). Alternatively, ifit is only the macromolecules which are of interest, then asshown in Fig 2(c), spectral editing according to moleculardiffusion coefficients is possible, attenuating peaks from fast-diffusing small molecule metabolites (14).

The use of 2D NMR methods is important for spectralassignment. The application of the JRES experiment resultsin a dramatic simplification of the blood plasma spectrum,and hence enables the complex overlapped resonances inthe chemical shift range from d3–4 to be more completelyresolved (35). Furthermore, the protein resonances are atte-nuated as effectively as was seen in the application of the sim-ple spin-echo experiment. The skyline projection through theJRES map results in a greatly simplified spectral profile of theeffectively 1H-decoupled 1H NMR spectrum of the motionallyunconstrained metabolites in plasma. The skyline projectionmight, therefore, offer an attractive method for quantitatingminor metabolites in plasma where attenuation due to T2

relaxation can be accounted for or calibrated. It should benoted that signals from any small molecules that are exten-sively protein-bound are also severely attenuated due toconstrained molecular tumbling and a shortening of theT2 relaxation time.

1H–1H COSY spectra provide an additional assignmentaid enabling the confirmation of the presence of a number ofamino acid resonances and those from the acids 3D-hydroxy-butyrate, citrate, taurine and lactate as well as polyolssuch as myo-inositol and all resonances of a- and b-glucose.


In addition, the resolving power of the COSY experiment isdemonstrated by the connectivities observed for many of thelipidic resonances, which are not observable in the 2D-JRESexperiment because of their short T2 relaxation times. Thetotal correlation spectrum (TOCSY) of human blood plasmaallows the assignment of extra metabolites because of thenarrower lineshape of the TOCSY spectrum and becausethe coupling connectivity along complete chains of protonsprovide a more certain indication of the molecular identity.Figure 3 shows a 1H–1H TOCSY NMR spectrum of humanblood plasma which has also been edited on the basis of T2

relaxation times (36). The 1H–13C 2D HMQC and HSQCexperiments applied to blood plasma are also useful formetabolite identification giving information on 13C chemicalshifts using 1H detection.

Figure 3 A 600 MHz T2-edited1H–1H TOCSY NMR spectrum of

human blood plasma.

120 Lindon et al.

5.3. Lipoprotein Analysis in Plasma from NMRSpectra

Much study has been devoted to the problem of lipoproteinanalysis in blood plasma using 1H NMR spectroscopyand this has been comprehensively reviewed recently byAla-Korpela (37). Lipoproteins are complex particles thattransport molecules normally insoluble in water. They arespherical with a core region of triglyceride and cholesterolester lipids surrounded by phospholipids in which areembedded various proteins known as apolipoproteins. In addi-tion, free cholesterol is found in both the core and surfaceregions. The lipoproteins are in a dynamic equilibrium withmetabolic changes going on in vivo. Lipoproteins are usuallyclassified into five main groups, chylomicrons, VLDL, LDL,intermediate density lipoprotein (IDL), and HDL based onphysical separation using centrifugation. Based on the mea-surement of 1H NMR spectra of the individual fractions andusing lineshape fitting programs, it has been possible to iden-tify the chemical shifts of the CH2 and CH3 groups of the fattyacyl side chains. Quantification can be carried out using time-domain NMR data. Alternatively, a frequency domainapproach has used the program FITPLAwhich uses resonancepositions and linewidths frommodel solutions to fit the overalllineshape of the CH2 and CH3 signal region (37). The useful-ness of 1H NMR spectra for lipoprotein analysis (38) and 31PNMR spectroscopy for phospholipid analysis in blood plasmahas been explored (39). The fatty acyl peaks have also beendeconvolved based on diffusion-edited NMR spectra, providingconfirmation of the assignment of the bands to particular lipo-protein fractions (40) and a neural network software approachhas been used to provide rapid lipoprotein analyses (41).

5.4. Molecular Dynamics and Interactions from1H NMR Spectra of Blood Plasma

Bell et al. (42) have shown that a significant proportion ofplasma lactate is present in an ‘‘NMR-invisible’’ pool due tobinding to transferrin, a-1-antitrypsin and possibly otherplasma proteins and it has been suggested that this may have


an important role in lactate transfer in plasma. The spin-echoNMR assay of serum lactate is reported to underestimate byabout 30% when compared with conventional biochemical pro-cedures. Lactate can be liberated from its protein binding siteby the addition of 0.5M ammonium chloride or the anionicdetergent SDS and then becomes NMR detectable in spin-echospectra together with non-protein-bound lactate; a similareffect was reported for 3D-hydroxybutyrate and acetoacetate.

In conventional and spin-echo spectra of normal humanand animal plasma, there are only a few low intensity reso-nances in the chemical shift range to high frequency of d5.3when measured in the pH range 3–8.5. However, on acidifica-tion of the plasma to pH< 2.5, resonances from histidine andphenylalanine become easily detectable. In the plasma frompatients with Wilson’s disease (liver degeneration secondaryto an inborn error of caeruloplasin=copper metabolism), weaksignals from histidine and tyrosine are seen in spin-echo spec-tra at pH 7.6, but increase in strength on acidification and sig-nals from phenylalanine appear at pH 1.8 or below.Experiments with model solutions suggested that serum albu-min has a high capacity for binding aromatic amino acids andhistidine at neutral pH and this is responsible for their NMR-invisibility in normal human blood plasma. Serum albuminalso binds a large number of other species of both endogenousand xeniobiotic origin (3). Blood plasma also has intrinsicenzymatic activities although many of these are not stable(particularly if the sample is not frozen immediately on collec-tion). It has been noted that under certain pathological condi-tions, such as those following liver or kidney damage,enzymes that are present at elevated levels in the plasmabecause of leakage from the damaged tissue, can causeNMR-detectable alterations to spin-echo spectra of plasma.The levels of these enzymes, e.g., alanine aminotransferase(ALT), are often used as primary evidence for organ damage.In order to observe the effect of elevated ALT it is neccessaryto freeze-dry the plasma sample soon after collection andthen to reconstitute the sample in D2O. The metabolitesinvolved are at equilibrium in the plasma when collected.However, reconstitution in D2O is then associated with the

122 Lindon et al.

establishment of an isotopic equilibrium that results in theprogressive incorporation of deuterons at the a-CH positionof alanine (43). Consequently, the alanine methyl protons nolonger experience the coupling to the CH and the signalchanges from a doublet to a singlet with a small deuteriumisotope shift. This is clearly observed in Hahn spin-echo spec-tra because the phase of the signal is shifted by 180� and thiscan also be monitored by adding ALT to normal blood plasmaredissolved in D2O. More complex signal modulations alsooccur on enzymatic incorporation of deuterons into glutamine.

5.5. NMR Spectra of Blood Plasma inPathological States

In 1986, a paper was published which reported that 1H NMRspectroscopy of human blood plasma could be used to discri-minate between patients with malignant tumors and othergroups, namely normals, patients with non-tumor diseaseand a group of patients with certain benign tumors (44). Thispaper stimulated many other research groups but they wereunable to repeat the original observations, either wholly orin part. The test as originally published involved the mea-surement of the averaged width at half signal height of thetwo composite signals at d1.2 and d0.8 in the single pulse1H NMR spectrum of human blood plasma. On comparingthe averaged signal width, W, for normal subjects with thosefor patients with a variety of diseases and with those for preg-nant women, it was reported that several statistically signifi-cant differences existed among the groups. It was proposedthat the lowering of W observed in the malignant group wasdue to an increase in the T2

� of the lipoprotein signals atd0.8 and d1.2, due in turn to ‘‘lack of supramolecular orderingof lipoprotein lipids’’ in the plasma of the patients with malig-nant tumors. In addition to the presence of cancer, a numberof other factors have been found to cause changes in the line-width index, W. These include diet, age and sex, pregnancy,and trauma as well as hyperlipidemia (45–48). In fact, theobserved changes inW are caused by alterations in the plasmalipoprotein composition, especially the VLDL=HDL ratio.


Much useful information on the variation of blood plasmalipoprotein content as a function of malignancy was obtainedduring studies on the cancer test. After the realization thatthis test was unlikely to be of any clinical utility, attentionhas again focused on the changes that occur in the profiles ofplasma metabolites. A series of experiments have focused onchanges in the ratios of signals from lipoproteins and glycopro-teins in the 1H NMR spectra of the plasma from patients witha variety of different cancers. A ‘‘star plot’’ pattern recognition(PR) method was used to distinguish three types of metabolicalterations induced by the cancer metabolism: (i) an ‘‘inflam-matory’’ pattern, (ii) a ‘‘lipid modified’’ pattern, and (iii) a‘‘sarcoma’’ pattern (49). Other studies of 1H NMR spectra ofblood plasma in cancer have been published (50–52).

Mountford et al. (53) reported the isolation by ultracen-trifugation of two lipoprotein bands, lying between the HDLand LDL bands of the plasma of a patient with a borderlineovarian tumor. These bands were later termed ‘‘malignancyassociated lipoprotein’’ (MAL).1H NMR spectroscopy studieson these bands showed that they contained a methyl signalat d1.3 which correlated in 2D 1H–1H COSY NMR spectrawith a methine signal at d4.2. The methyl signal had anabnormally long T2

� value and was consistent with that of afucose moiety, a carbohydrate that is often found in theantigenic compounds on the surface of cancer cells, but thisidentification was not proved. It was suggested that thisMAL could provide a non-invasive and specific method ofassaying for cancer. In a larger study, less than 50% of thecancer patients had the MAL band, but so did the same num-ber of normal controls. No correlation was found between thelinewidth index W and the appearance of the MAL. However,problems were reported with the isolation of the MAL, andthe fractions of interest are dialyzed for up to two hours toremove KBr and any free lactate prior to analysis. The signalof interest was lost if the dialysis was too extensive. Itwas suggested that the MAL particle may be disassociatingduring the dialysis, but other explanations are possible (53).Lipoprotein-bound lactate, which would slowly dissociate dur-ing dialysis, would be one possibility. Unless the nature and

124 Lindon et al.

origin of the MAL band are conclusively defined, its signifi-cance for cancer detection and diagnosis remain unclear.

Reports of the measurement of the linewidth index W forthe assessment of heart graft rejection after transplantationhave been published using a total of 410 measurements ona total of 46 patients (54). However, the overlap betweenthe values observed for each group meant that the W valuealone could not be used to classify the patients into the fourrejection grades.

It has also been reported that the areas of two glycopro-tein signals in the spin-echo 1H NMR spectra of blood plasmafrom heart transplant patients correlated with a standardechocardiography parameter used to monitor rejection (55).The sum of the areas of the N-acetyl signals of N-acetyl gluco-samine and N-acetyl neuraminic acid moieties of plasma gly-coproteins (NAGþNANA) was measured and then this areawas divided by that of the methyl signal of alanine. When thisarea ratio was plotted against isovolumetric left ventricularrelaxation times (measured by Doppler echocardiography), agood correlation was found in five patients and an acceptablecorrelation in three, but only a poor correlation in five furtherpatients. It was noted that infections and inflammatory statesunrelated to rejection interfered with the correlation.

Nishina et al. (56) have reported W and lactate concen-tration measurements on the blood sera from 20 Nigeriansseropositive to Plasmodium, 13 seronegative Nigerians andsix healthy Japanese controls. Significantly lower W valuesand higher lactate concentrations were reported for the seraof the malaria positive group than for the other two groups.

In human beings, diabetes is a relatively common condi-tion which can have serious, complex and far-reaching effectsif not treated. It is characterized by polyuria, weight loss inspite of increased appetite, high plasma and urinary levels ofglucose, metabolic acidosis, ketosis and coma, but in manycases, diabetes can be controlled by the administration of insu-lin. Based on NMR spectra, there are marked elevations in theplasma levels of the ketone bodies and glucose, postinsulinwithdrawal (57). The levels of these metabolites, as well aslactate, valine and alanine, were also measured by standard


clinical chemistry methods, in order to test the accuracy of theNMRmethod. In general, the NMR results were in good agree-ment with the conventional assay results. The CH3 and CH2

resonances of the lipoproteins VLDL and chylomicronsdecreased significantly in intensity relative to the CH3 signalof HDL andLDL, indicating the rapidmetabolism of themobilepool of triglycerides in VLDL and chylomicrons. By fluori-metric measurements, the concentration of the so-called ‘‘freefatty acids’’ rose from 0.33 to 1.92mM in the period from 0 to12hr postwithdrawal. However, these free fatty acids areimmobile (due to binding to albumin) and NMR invisible.

The 1H NMR spectra of the blood plasma from patientswith chronic renal failure during dialysis, patients in theearly stages of renal failure and normal subjects have beenanalyzed. For patients on acetate dialysis, the method clearlyshowed how the acetate was accumulated and metabolizedduring the course of the dialysis, as well as allowing changesin the relative concentrations of endogenous plasma compo-nents to be monitored. 1H, 13C and 14N NMR spectra of theplasma and urine from chronic renal failure patients showedthat the plasma levels of TMAO correlated with those of ureaand creatinine, suggesting that the presence of TMAO is clo-sely related to the degree of renal failure. Differences in theinteraction of lactate with the plasma proteins were alsoobserved in the uremic patients. One study of renal failurepatients also showed increased levels of lactate in the plasma,ascribed to metabolic disturbances (mainly acidoses) asso-ciated with decreased renal function. Elevated creatininelevels were found in the plasma of renal failure patients.The patients undergoing hemodialysis were differentiatedby the presence of elevated dimethylamine, whilst glycinewas predominantly raised in the plasma of the peritonealdialysis patients (58–61).

5.6. 1H NMR Spectroscopy of Whole Blood andRed Blood Cells

Conventional 1H NMR measurements on whole blood givevery little biochemical information due to the presence of a

126 Lindon et al.

broad envelope of resonances from hemoglobin and plasmaproteins but spin-echo spectra can give rise to moderately wellresolved signals from plasma metabolites and those presentinside erythrocytes, notably glutathione. However, wholeblood spectra are not easily reproducible because of erythro-cyte sedimentation which progressively (within a few min-utes) degrades the sample field homogeneity during thecourse of collecting a series of FIDs. Furthermore, there aresubstantial intracellular=extracellular field gradients whichgive a major contribution to the T2 relaxation processes forthe nuclei of molecules diffusing through those gradients.Spin-echo NMR measurements on packed erythrocyte sam-ples do give rise to well-resolved signals from intracellularmetabolites and a variety of transport and cellular biochem-ical functions can be followed by this method.

The first paper which showed that NMR spectra ofendogenous small molecules could be obtained from ery-throcytes appeared in 1977 (62) and this also showedhow using a spin-echo approach the large broad hemoglo-bin resonances could be eliminated. In cells which werewashed with H2O and D2O, time courses for the conversionof glucose to lactate were determined. In another study,the cells were washed to remove all glucose and lactateand the intracellular glutathione (GSH) was oxidized toGSSG and the conversion monitored by 1H NMR of theGSH resonances. After conversion to GSSG, glucose wasadded to the suspension and the reformation of GSH wasdetermined as a function of time. Isotope exchange meth-ods have been developed and used to study the kineticeffects of intra-erythrocyte enzymes by adding a labeledcompound to a suspension of red blood cells and then mon-itoring the subsequent distribution of label with time (63).Reactions of GSH have been also studied. In erythrocytes,GSH and its constituent amino acids (cysteine, glycine, andglutamate) are in dynamic exchange and this has been fol-lowed using 2H-labeled glycine (64).

One major parameter which can be obtained from NMRis the intracellular pH, and the pH inside erythrocytes canbe measured using 1H NMR spectroscopy, using a suitable


1H NMR pH indicator which has an NMR chemical shiftwhich varies with pH in the range desired. Candidatesinclude the C2-H protons from histidines in hemoglobin(65) but also an exogenous compound could be added asan indicator.

The transport of substances between the inside and out-side of the red cell can be monitored using NMR if the reso-nances from the two environments have different chemicalshifts or intensities. In spin-echo NMR spectra of erythro-cytes, the intensity of resonances from metabolites insidethe cells is less than outside because of magnetic susceptibil-ity differences inside and outside the cells. Also, resonancesoutside the cell can be selectively broadened by the additionof paramagnetic species that do not cross the red cellmembrane. Those used include the ferric complex of desfer-rioxamine, dysprosium-DTPA and the copper–cyclohexane-diaminetetraacetic acid complex (66). To measure the rate ofinflux of a compound into red cells, the compound is addedwith the paramagnetic agent to a red cell suspension andthe intensity of the resonance from the intracellular compo-nent is monitored as a function of time. This approachhas been used to study the transport of a number of smallmolecules including glycerol, alanine, lactate, choline, andglycylglycine (67).

Kuchel et al. (68) have used NMR spectroscopy of ery-throcytes to study the effect of lithium treatment in manicdepressive patients showing an increase in choline. In heavymetal poisoning, a considerable proportion of the metal isfound in the blood and the binding of heavy metals inside ery-throcytes has received much attention. For example, methyl-mercury(II) was shown to cross the red cell membrane rapidlyand be complexed principally to the thiol groups of GSH andhemoglobin. The antiarthritic gold drug aurothiomalate hasalso been studied in intact red cells using NMR spectroscopy.Rabenstein (69) has reviewed much of this work in the area ofred blood cell NMR spectroscopy. Diffusion coefficient mea-surement has also been used to study the binding betweendiphosphoglycerate and hemoglobin inside intact red bloodcells (70).

128 Lindon et al.

The Kuchel group has applied a new form of analysiscalled ‘‘diffusion–diffraction’’ to suspensions of red blood cells.This approach is based on the pulsed-field gradient diffusionNMR experiment and demonstrated that the cells are alignedby the static magnetic field of the NMR spectrometer. In addi-tion, conversion of the intracellular hemoglobin caused a pre-dictable change in the diffraction pattern and it was alsoshown that water transport inhibition affected the results.Finally the cell diameter and inter-cell spacing could bemeasured from the diffraction plots and these have beencompared with electron micrographs (71,72).

The technique of magic angle spinning (MAS) has alsobeen applied to 1H NMR spectra from red blood cell suspen-sions and under the MAS conditions two water peaks can beobserved, corresponding to intra- and extracellular water(73). Under relatively rapid spinning conditions, the cellscan centrifuge to the perimeter of the sample space and theextracellular water arises from both bulk water and intersti-tial water (74).

6. 1H NMR SPECTROSCOPY OF HUMAN ANDANIMAL URINE

6.1. Sample Details for NMR Spectroscopy

The composition and physical chemistry of urine is complexand highly variable both between species and within speciesaccording to lifestyle. A wide range of organic acids and bases,simple sugars and polysaccharides, heterocycles, polyols, lowmolecular weight proteins and polypeptides are presenttogether with inorganic species such as Naþ, Kþ, Ca2þ,Mg2þ, HCO3

2�, SO42� and phosphates. It is possible to detect

large numbers of the organic species with modern NMR spec-trometers. Many of these moieties also interact extensively,forming complexes that may undergo chemical exchange reac-tions on a variety of different time-scales, some of which areamenable to NMR study. The ionic strength of urine variesconsiderably and may be high enough to adversely affectthe tuning and matching of the RF circuits of a spectrometer


probe, particularly at high field strengths. Because of its highionic strength, it is sometimes counterproductive to concen-trate urine samples by freeze-drying and subsequent reconsti-tution in smaller volumes of solvent, unless this is performedconservatively. Urinary osmolalities vary from about 150 to1300mOsmoles in normal human urine, but animal urinescan have much higher osmolarities (up to 2000mOsmoles inrodents and >3000mOsmoles in desert species). The viscosityof human urine samples is normally low, but may be higher inlaboratory rodents that are physiologically proteinuric; thiswill generally shorten relaxation times in comparison withpure aqueous solutions of metabolites. The presence of highconcentrations of protein in the urine, e.g., due to renal glo-merular or tubular damage, can result in the broadening ofresonances from low molecular weight compounds whichmay bind to these urinary proteins.

Urinary pHs may vary from pH 5 to pH 8, according tothe physiological condition in the individual, but usually liebetween 6.5 and 7.5. Urine samples should be frozen as soonas possible after collection if NMR measurements cannot bemade immediately. When experiments involve collectionsfrom laboratory animals housed in metabolic cages, urinesamples should be collected into receptacles that are eithercooled with dry ice or have a small amount of sodium azidepresent as a bacteriocide. However, both these proceduresmay inhibit or destroy urinary enzymes that may frequentlybe assayed by conventional biochemical methods for assess-ment of kidney tubular integrity in toxicological experiments.Correction of urinary pH to a standard value of 7.4 can beattempted for normalization of chemical shifts. However, pHcorrection can be time consuming and it can be shown thaturinary pHs are unstable (even with added strong buffer)because of the progressive and highly variable precipitationof calcium phosphates which may be present in urine closeto their solubility limits. One possible solution to this problemis the addition of 100–200mM phosphate buffer in the D2Oadded for the lock signal followed by centrifugation to removeprecipitated salts. This has the effect, in most cases, of nor-malizing the pH to a relatively narrow range near neutrality

130 Lindon et al.

which is stable for many hours during which NMR measure-ments can be made. Relatively few metabolites show majorchemical shift variations over this pH range (with the excep-tion of histidine and citrate).

The vast majority of urinary metabolites have 1H T1

relaxation times of 1–4 sec, the relaxation process beingslightly more efficient than in pure aqueous solutions due tothe presence of small amounts of paramagnetic metal ionsin the urine. Given this range of T1s, the general rule ofapplying a 5�T1 relaxation delay between successive 90�

transients to obtain >99% relaxation and hence quantitativeaccuracy cannot be applied routinely. Applying this delay tothe longest T1 signal in the sample unnecessarily compro-mises the best signal-to-noise ratios for most metabolites(unless the measurement of a selected group of metabolitesis particularly important in which the delays are optimizedfor those). Therefore, by using smaller tip angle pulses andleaving a total pulse recycle period of 5 sec between successivepulses, spectra can usually be obtained for most metabolitesin 5–10min at high field with a generally low level of quanti-tative signal distortion. For a particular quantitative problemon a defined set of metabolites, the instrumental conditionsand relaxation delays would then be optimized for those mea-surements. But it is the overall speed (and lack of necessity ofrigorous optimization) with which biofluids can be screenedand fingerprinted that makes 1H NMR particularly usefulin studies on comparative biochemistry and physiology. Ithas become a standard practice to replace simple solvent reso-nance presaturation with pulse methods which either inducesuch saturation or which leave the solvent water resonanceunexcited. One popular method involves using simply the firstincrement of a two-dimensional NOESY sequence and, as aconsequence, this requires the use of only 90� pulses.

A vast number of metabolites may appear in urine sam-ples and problems due to signal overlap can occur in singlepulse experiments. The magnitude of the signal assignmentproblem in urine is also apparent at ultra high field. Thereare probably >5000 resolved lines in ultra high field 1HNMR spectra of normal human urine (Fig. 1), but there is still


an extensive peak overlap in certain chemical shift ranges.Thus chemical noise is still a significant feature of NMR spec-tra of urine even at 800MHz and many signals remain to beassigned. The dispersion gain at 800MHz even over600MHz spectra is particularly apparent in two (or three)dimensional experiments that can be used to aid signalassignment and simplify overlapped spectra.

6.2. Chemical Exchange and Solvent Effects onNMR Spectra of Urine

In urine samples, T2 relaxation times for some metabolitesare dominated by chemical exchange contributions and canbe variable according to the pH and endogenous metal ionconcentrations. For example, the T2 of citrate CH2 protonsis dependent on pH, and the relative concentrations of theacid itself and the Ca2þ and Mg2þ ions which are present inconcentrations ranging from 1 to 10mM. The T2 relaxationtime of water in urine is highly dependent both on pH andthe presence of the more abundant endogenous species whichhave exchangeable protons. In normal human subjects, themost important of these are urea, uric acid (and allantoin inmost non-primates), phosphate and ammonium ions. Urea isthe most abundant proton-exchanging solute and is often pre-sent at concentrations of up to 0.7M in human urine, and pos-sibly twice this in rodents. The urinary water NMR linewidthis typically about 4Hz at 400MHz, broadening to >10Hz at600MHz. Supplementation of the natural amounts of thesecompounds in urine and adjustment of pH can give waterlinewidths of >30Hz and allow efficient water suppressionvia the selective augmented T2 relaxation (WATR method)(75). At low pH (i.e., <1) proton-exchange reactions areslower and T2 relaxation times become longer for water,ammonium, and urea protons. Ammonium ions (present asan endogenous metabolite in all animal urines) can bedetected directly in urine at low pHs, as the proton exchangerate is slow and the lines sharpen to give a 1:1:1 triplet (due tothe 14N–1H coupling) at d7.2. If deuterium oxide is presentin the sample (e.g., at 20% for a field frequency lock), the

132 Lindon et al.

ammonium signals may show additional splittings due to thepresence of various deuterium=proton isotopomers of ammo-nium (i.e., NH4

þ, NH3Dþ, NH2D2

þ, and NHD3þ).

In addition, paramagnetic species can be added to urinesamples. The paramagnetic ions bind to polar species such aswater, causing a considerable shortening of the water T2

relaxation time and a consequent line broadening. The waterpeak can then be eliminated simply by measuring a spin-echospectrum using a suitable delay between the 90� and 180�

pulses. This process will of course also attenuate theresonances of any other species which interacts with theparamagnetic ion.

An effective method of changing the solvation conditionsof a biofluid sample is to freeze-dry samples and reconstitutewith the desired solvent. There are several reasons for under-taking such procedures including replacement of water withdeuterium oxide as a method of avoiding the dynamic rangecaused by the water resonance. This is a facile procedurebut may result in selective deuteration of exchangeable meta-bolite protons, mainly NH, SH and OH, but also certain CH orCH2 protons can exchange, e.g., CH2 of acetoacetate, whichundergoes keto-enol tautomerism, and the CH2 of creatininewhich is mildly acidic. Changing to a deuterated aprotic sol-vent, e.g., dimethyl sulphoxide, prior to NMR study confersthe benefit of being able to observe protons which are nor-mally exchange-broadened in aqueous solutions or to observeprotons which are coincident with the water resonance fre-quency. It is possible to freeze-dry urine samples followedby their reconstitution in deuterated dimethylsulphoxide(DMSO) as a method for eliminating the water signal in orderto detect signals that would be otherwise masked by the sol-vent resonance. One of the problems of such reconstitutionsis the differential solubility of metabolites in non-aqueous sol-vents, i.e., the metabolites of interest may not dissolve at all.This can be shown by reconstituting freeze-dried rat urine inDMSO in which citrate resonances are noticeably absent. Thebenefit of this type of reconstitution lies in the extra informa-tion forthcoming in the form of, for example, the resolvedresonances of NH groups and their proton couplings.


6.3. Physiological Effects on Urine Compositionby NMR Spectroscopy

This topic is discussed in detail in Chapter 10 of this book andonly a brief overview is given here. The biochemical composi-tion of urine varies considerably from species to species andalso with the age of the animal as almost all species haveage-related changes in renal function. Rats and otherrodents have much higher levels of taurine, citrate, succinate,2-oxoglutarate and allantoin than humans and this is clearlyapparent in the 1H NMR spectra. Rat urine (and that of otherrodents) is generally much more concentrated than humanurine, and so NMR signal-to-noise ratios may be better formany metabolites. All animals have physiological processeswhich are modulated by biological rhythms. This includesexcretory processes and the urinary composition of an animalmay vary considerably according to the time it is collected.Given these types of variation, it is obviously of paramountimportance to have closely matched (and in many casestimed) control samples where toxicological or diseaseprocesses are being studied.

Dietary composition also affects the urinary metaboliteprofiles of man and animals, and it is important to distinguishthese from disease-related processes in clinical or toxicologicalstudies. For example, persons consuming large quantities ofmeat=poultry before urine collectionmay haveNMRdetectablelevels of carnosine and anserine in their urine, consumption ofcherries is associated with elevated urinary fructose and con-sumption of shellfish and fish are associated with high levelsof betaine and trimethylamine in the urine (1). The age of alaboratory animal can also influence the excretion profiles ofurinarymetabolites, for instance increasing age of a rat is asso-ciated with increased urinary taurine but a decrease in urinarycitrate (1). These variations due to age or diet must beaccounted for in biochemical or toxicological studies, in whichit is very important to match controls as closely as possible tothe experimental subjects with respect to age and weight.

The 1H NMR spectra of neonate urines are charact-erized by strong signals from a number of metabolites and

134 Lindon et al.

in particular, high concentrations of myo-inositol can bedetected in both pre- and full-term urines (76). This is animportant intracellular organic osmolyte in renal cells, pre-sent in high concentrations in the renal inner medulla. Theturnover of myo-inositol in the human, however, is substan-tially greater than can be accounted for by dietary intakealone. Hyperglycemic-induced alterations in polyol metabo-lism (e.g., depletion of myo-inositol) may be contributoryfactors to the development of diabetic renal complications.The levels of betaine in human neonatal urine and indeveloping rat urine have also been studied (77).

The effects of fasting on the 1H NMR spectra of theurines from five normal human volunteers have beenreported (78). These volunteers underwent a 48hr period offasting, and 1H NMR analysis was able to simultaneouslydetect the rise in levels of all three ketone bodies as well asthose of acetylcarnitine, dimethylamine, and creatinine. Theexcretion rates of dimethylamine and creatinine remainedrelatively constant throughout the course of the entireexperiment whilst the excretion rate of 3D-hydroxybutyratemeasured by 1H NMR was in reasonable agreement withconventional enzyme assay methods. However, the levels ofacetylcarnitine excretion determined by 1H NMR were muchhigher than those measured by conventional assay.

Finally, the differential urinary composition of severalspecies of wild rodent has been examined using 1H NMR-based metabonomics (79).

6.4. NMR Spectra of Urine in Disease

Nuclear magnetic resonance spectroscopy of urine has foundwidespread application in disease areas, particularly in thefield of in-born errors of metabolism. Given the serious out-come of many inborn errors of metabolism, if not diagnosedand treated at an early stage, there has been a search for arapid, sensitive, and general method for the detection anddiagnosis of inborn errors of metabolism in neonates. Conven-tional methods including specific enzyme assays and gas


chromatography=mass spectrometry are sensitive but timeconsuming, involve considerable sample preparation and arenot general. However, NMR spectroscopy of biofluids has beenshown to be a very powerful and general method for the detec-tion of inborn errors of metabolism. Many metabolic disordershave been studied using biofluid NMR spectroscopy (15). Mostof the work has involved 1H NMR studies on human urine,although some studies are based on blood plasma. In manycases, the disease diagnosis had been made by gas chromato-graphy=mass spectrometry and in some cases furtherconfirmed by enzymology on cultured skin fibroblasts.

The effects of therapy have been studied using 1H NMRspectroscopy (80), including the effects of carnitine therapy onpatients with methylmalonic aciduria and propionic acidemia,disorders characterized by the accumulation of acyl CoAintermediates. Acylcarnitine excretion is much increased inthese disorders and it is likely that this results in intracellu-lar carnitine insufficiency. The administration of carnitine inlarge quantities to these patients is designed to reduce theharmful excess of acyl CoA intermediates by increased excre-tion of acylcarnitine conjugates. In these NMR studies, theadult patients and controls were given oral or intravenousl-carnitine at a variety of doses. Spectra from a methylmalo-nic aciduria patient not only showed a fall in methylmalonateexcretion as a result of therapy but also established that alarge fraction of the ‘‘propionyl pool’’ had combined with thecarnitine to produce propionylcarnitine, which was excretedin the urine. The patient showed a marked clinical improve-ment as the level of propionylcarnitine excretion rose. Bycontrast, a patient with propionic acidemia exhibited littleclinical improvement with carnitine therapy.

Videen and Ross (81) have emphasized the usefulness of1H NMR spectroscopy of urine for disease diagnosis and anumber of disparate studies have been reported. Knubovetsand coworkers (82) reported a statistical analysis of the 1HNMR spectral data from the urines of 52 patients with glo-merularnephritis (GN) and eight healthy volunteers on apseudonephrological diet. Progression of GN was shown tobe accompanied by a change in the amino aciduria from

136 Lindon et al.

involving non-essential amino acids to essential amino acids.The 1H NMR urinalysis was found to be more sensitive thanstandard biopsy techniques in certain cases and was capableof establishing tubular and papillary damage in GN patientswith clinically preserved kidney function, especially thosewith the nephrotic variant of the disease.

Foxall et al. (83) used 500 and 600MHz 1H NMR spectro-scopy to study the urine samples from 33 kidney transplantpatients over a period of 14 days postoperation. For each ofthe patients the NMR data were correlated with clinical obser-vations, graft biopsy pathology, and conventional renal func-tion tests. All of the patients received immunosuppressivetreatment, although none of the patients showed clinical or his-topathological signs of cyclosporin A nephrotoxicity. Theassessment of renal graft dysfunction following transplantationrelies on plasma creatinine concentration measurements (toassess creatinine clearance) and graft biopsy results, whichare often inconclusive. A statistically significant (p< 0.025)increase in urinary trimethylamine-N-oxide (TMAO)was foundfor patients with graft dysfunction when compared with eithernormal controls or patients with good graft function. However,there was an overlap between the TMAO concentrations of eachof the groups, indicating that TMAO measurements alonewould not be a reliable marker of graft dysfunction. Urinarylevels of dimethylamine were also elevated in the patients withgraft dysfunction, but this was not statistically significant. Inpatients with clinical evidence of renal ischemia and acute tub-ular necrosis, the 1H NMR of the patient’s urines showed highlevels of lactate, acetoacetate, hippurate and acetoacetate butminimal aminoaciduria. In a similar study, Le Moyec et al.(84) reported an analysis of the 1H NMR spectra of urine andplasma from 39 patients following renal transplantation. Peakheights of several urinary metabolite signals were comparedwith the peak height of the creatinine signal for two groupsof patients, divided according to their renal function (group1, creatinine clearance greater than or equal to 17mL=min;group 2, less than 17mL=min). It was suggested that the useof three parameters could provide a means of discriminatingbetween patients with rejection, cyclosporin A nephrotoxicity


or cyclosporin A overdose. The three parameters were: (i) theratio TMAO=creatine in the urine, (ii) the presence of TMAOin plasma, and (iii) the ratio of a peak at d3.7 to that of creatinein the urine 1H NMR spectrum. From the above two studies, itis apparent that urinary TMAO concentrations are an impor-tant marker of renal transplant dysfunction, although the sen-sitivity and specificity of the measurement are insufficient forclinical diagnosis.

1H NMR spectroscopy has also been used, in conjunctionwith standard clinical biochemical analyses to monitor renalfunction in an unusual case of phenol poisoning (85). A manfell into a shallow vat containing 40% phenol in dichloro-methane, at his work place. He did not ingest any solventand was partially immersed for only a few seconds. However,he was found collapsed and badly burned in the nearbyshower unit. Subsequently, his plasma creatinine levelsbegan to rise, and he did not pass urine. This acute renal fail-ure was treated by hemodialysis and the i.v. administration offurosemide. Not only were metabolites of phenol clearly visi-ble in the spectra taken within a few days of the incident,but the abnormally high levels of lactate, amino acids suchas alanine and valine and glucose were all indicative of severeproximal tubular damage. Recovery from these cortical renallesions was relatively rapid (about four weeks). However,levels of dimethylamine and dimethylglycine began to riseafter two to three weeks. These metabolites are not normallymeasured in clinical chemical studies, but have been shownto be markers for experimentally induced renal papillarylesions. The patient was discharged from hospital aftersix weeks, when renal function was judged to be normal bystandard clinical chemistry criteria (blood plasma levels ofurea, potassium, sodium, creatinine, calcium, phosphate,and urine levels of glucose and protein), but 1H NMR revealedresidual renal papillary damage. One year after the incident,the patient was still polyuric and passing 3L of urine per day.

Vermeersch et al. (86) have reported the case of a 4-monthold girl who presented with agitation, hyperexcitation, fever,dehydration, polypnea, and metabolic acidosis.1H NMRspectroscopy of the lyophilized urine from the patient showed

138 Lindon et al.

the presence of 2-hydroxybenzoic acid (salicylic acid),o-hydroxyhippuric acid and 2,5-dihydroxyhippuric acid,which indicated that she had been poisoned with salicylate(aspirin). Malhotra et al. (87) reported on the 1H NMR analy-sis of urine (and plasma) from five patients presenting withmetabolic acidoses and elevated anion-gaps at an hospitalrenal consultation service. Analysis of the biofluids byNMR was found to be extremely beneficial in the rapid deter-mination of the causes of the metabolic disturbances. Inthree of the patients, alcohol ingestion (paint thinner, anti-freeze) was identified in only 10–15min allowing promptdecisions on the treatment to be taken with increasedchances of patient recovery. A typical case involved a youngmale in a comatose state who was brought into the hospitalby his wife, who had observed him drinking a can of paintthinner (containing methanol, toluene and methylenedichloride). Ethanol infusion was begun in order to protectagainst methanol intoxification and a sample of the patient’surine was analyzed by NMR. Characteristic resonances fromthe methanol, toluene and methylene dichloride in the paintthinner were readily observed confirming the cause of thesymptoms. Signals were also observed from the ethanol inthe infusion. After hemodialysis, the patient recovered andwas discharged. A similar 1H NMR application has beenreported (88), identifying and quantitating ethanol, isopro-panol, acetone and methanol in biofluids from 15 patients.Hence, it is likely that 1H NMR spectroscopy with its attri-butes of speed, lack of sample preparation and detection ofa very wide range of metabolites will enjoy much increasedusage in this area in the future.

6.5. Evaluation of Toxic Effects of XenobioticsUsing NMR Spectroscopy of Urine

These applications are covered in Chapter 9 of this volumeand so only a brief outline is given here. The successful appli-cation of 1H NMR spectroscopy of biofluids to study a varietyof metabolic diseases and toxic processes has now beenwell established and many novel metabolic markers of


organ-specific toxicity have been discovered. The method isbased on the fact that the biochemical composition of a bio-fluid is altered when organ damage occurs. This is particu-larly true for NMR spectra of urine in situations wheredamage has occurred to the kidney or liver. It has been shownthat specific and identifiable changes can be observed whichdistinguish the organ which is the site of a toxic lesion. Alsoit is possible to focus on particular parts of an organ such asthe cortex of the kidney and even in favorable cases to verylocalized parts of the cortex. Finally, it is possible to deducethe biochemical mechanism of the xenobiotic toxicity, basedon a biochemical interpretation of the changes in the urine.

A wide range of toxins have now been investigated (seeChapter 9) including the kidney cortical toxins mercury chlor-ide, p-aminophenol, ifosfamide, the kidney medullary toxinspropylene imine and 2-bromoethanamine hydrochloride, andthe liver toxins hydrazine, allyl alcohol, thioacetamide andcarbon tetrachloride. The testicular toxin cadmium chloridehas also been investigated in detail, including the effects ofchronic exposure at environmentally realistic levels. Otherstudies include the toxicity of the aldose reductase inhibitorHOE-843 and lanthanum nitrate. Toxic stress in earthwormshas also been investigated using metabonomics (89).

The first studies of using pattern recognition (PR) toclassify biofluid sampleswere those ofGartland et al. (90) usinga simple scoring system used to describe the levels of 18 endo-genous metabolites in urine from rats which were either in acontrol group or had received a specific organ toxin whichaffected the liver, the testes, the renal cortex or the renalmedulla. The data were used to construct non-linear mapsand various types of principal components (PC) scores plotsin two or three dimensions. The samples were divided intotwo subsets, a training set and a test set. This study showedthat samples corresponding to different organ toxins mappedinto distinctly different regions. Various refinements in thedata analysis were investigated, including taking scored dataat three time points after the toxin exposure for the nephrotox-ins only (this used only 16 metabolites as taurine and creatinewere not altered in this data subset) as well as using a simple

140 Lindon et al.

dual scoring system (the time and magnitude of the greatestchange from control). The maps derived from the full time-course information provided the best discrimination betweentoxin classes. The study described above was further extendedto incorporate actual metabolite NMR resonance intensitiesrather than simple scores (91). This was carried out for thenephrotoxins in the earlier group plus additional nephrotoxiccompounds. A good separation of renal medullary from renalcortical toxins was achieved. In addition, it was possible to dif-ferentiate cortical toxins according to the region of the proxi-mal tubule, that was affected, and also by the biochemicalmechanism of the toxic effect.

The time course of metabolic urinary changes inducedby two renal toxins has been investigated in detail usingPR of NMR spectra (92). In this case, toxic lesions wereinduced in Fisher 344 rats by a single acute dose of the renalcortical toxin mercuric chloride and the medullary toxin2-bromoethanamine. The onset, progression, and recovery ofthe lesions were also followed using histopathology to providea definitive classification of the toxic state relating to eachurine sample. The concentrations of 20 endogenous urinarymetabolites were measured at eight time points after dosingand mapping methods were used to reduce the data dimen-sionality. A number of ways of presenting the data wereinvestigated and by taking the animal group mean value forthe metabolite concentrations, it was possible to constructPC scores plots which showed metabolic trajectories whichwere quite distinct for the different toxins. These show thatthe points on the plot can be related to the development of,and recovery from, the lesions.

The neural network approach to sample classificationhas also been used and it was in general predictive of the sam-ple class (93). It appears to be reasonably robust and once thenetwork is trained, the prediction of new samples is rapid andautomatic. It is possible to tune the network with respect toimproving its predictive power and optimizing its architecture(i.e., the number of nodes in the hidden layer). However, theprincipal disadvantage is common to all neural network stu-dies in that it is difficult to ascertain from the network which


of the original sample descriptors are responsible for theclassification. However, probabilistic neural networks appearto be a useful and effective method for sample classification(94). Recently, more comprehensive studies have beenpublished using PR to predict and classify drug toxicity effectsincluding lesions in the liver and kidney and using supervisedmethods as an approach to an expert system (95,96).

7. 1H NMR SPECTROSCOPY OF SEMINALFLUIDS

7.1. Composition of Seminal Fluids

Seminal plasma is biochemically distinct from other mamma-lian body fluids in that it contains unusually high levels ofsmall peptides, organic and amino acids as well as a rangeof polyols and compounds with positively charged quaternarynitrogen atoms such as choline and glycerophosphorylcholine.These substances are secreted by the testis and the otheraccessory glands of the genital tract, particularly the prostateand seminal vesicles and the reasons for the high concentra-tions of the components are poorly understood. Seminalplasma contains many organic compounds and inorganic ions,some of which, e.g., Zn2þ, some amino acids, spermine andcitrate, are present in concentrations up to two orders of mag-nitude higher than any other mammalian body fluid. The pro-tein composition of seminal fluid is also varied and complex.The seminal fluid is predominantly formed by the secretionsof the prostate (45–50%) and the seminal vesicles (45–50%),and to a lesser extent the testis (<5%). The composition ofeach secretion being biochemically distinct and the exact pro-portion of each varies considerably from subject to subject.Seminal vesicle fluid (SVF) forms the later part of the ejacu-late. Obtaining pure samples of this can only be achieved byinvasive means (surgery or aspiration with a wide boreneedle). Each of the contributory fluids to seminal fluid isbiochemically active and a series of enzymatic reactions areinitiated on mixing (prior to ejaculation) which result in majoralterations to the biochemical composition.

142 Lindon et al.

7.2. NMR Spectroscopy of Seminal Fluids

The 750MHz 1H NMR spectra of seminal fluid obtained froma healthy individual are complex but many of the signalshave been assigned. Samples are usually left standing for30min for enzymatic liquefaction to occur prior to freezingand then diluted by 50% with D2O before NMR measure-ment. Fresh undiluted seminal fluid gives rise to NMR spec-tra with very broad and poorly resolved signals due to thepresence of high concentrations of peptides (which arecleaved to amino acids by endogenous peptidase activity)and by the high viscosity of the matrix. The complexity ofthe biochemical composition of seminal fluids together withtheir reactivity poses a number of assignment and quantita-tion problems.

The application of the JRES experiment on human SFeither at 600 or 750MHz results in a dramatic simplificationof the spectrum due to the dispersion of the chemical shift andcoupling constant data in two orthogonal frequency domains(97). This experiment enables the complex overlapped reso-nances in some of the chemical shift ranges (especially fromd3–4) to be more completely resolved. The 600MHz 1H singlepulse NMR spectrum of normal human seminal fluid is shownin Fig. 4, illustrating the levels of assignment achieved for theendogenous species.

The inverse-detected 1H–13C HMQC spectrum providesdispersion in the 13C frequency domain with its much greaterchemical shift range. Many of the major amino and organicacid signals can be assigned based on their 1H chemical shiftsin the 2D experiments but some are much more easily distin-guished and separated in the heteronuclear 2D 1H–13CHMQC experiment, e.g., the spermine CH2N groups, thatfrom the CH2N group of arginine and the choline Nþ(CH3)3group. The combination of the various homonuclear and het-eronuclear techniques has resulted in the assignment of mostof the major and many of the minor resonances in the spec-trum. Seminal fluid contains moderate concentrations ofamino acids and these, because their a-CH proton resonancesfall into a narrow range of chemical shifts, contribute to


Figure 4 600 MHz 1H NMR spectra of human seminal fluid withassignments as marked. The sample was incubated for 4 hr at 37�Cprior to analysis. Key to uncommon abbreviations used:uri¼uridine; a-glu¼ a-d-glucose; cho¼ choline; sp¼ spermine;succ¼ succinate; N-ac¼N-acetylated compounds.

144 Lindon et al.

the severe overlap in the NMR spectral region betweend3.5–4.2 and it is possible to investigate this region usingtwo-dimensional experiments such as COSY and TOCSY.

An investigation of dynamic molecular processes thatoccur in seminal fluid has been undertaken using 1H NMRspectroscopy. Reactions that could be followed includedhydrolysis of phosphorylcholine and nucleotides and zinccomplexation (98).

In view of the importance of artificial insemination infarming, it is somewhat surprising that very few studies ofanimal seminal fluid have been reported. However, thereare studies on boar seminal plasma giving details of reso-nance assignments (99).

The comparison of 1H NMR spectra of seminal fluidfrom normal controls with those from patients with vasalaplasia (obstruction of the vas deferens leading to blockageof the seminal vesicles) and those with non-obstructiveinfertility has been reported (97). The 1H NMR spectra ofthe seminal fluid from patients with non-obstructive inferti-lity were similar to those of normal subjects. However, the1H NMR spectra of the seminal fluid from patients withvasal aplasia were grossly different from those of normalsubjects and corresponded closely to those of prostatic secre-tions from normals, due to the lack of seminal vesicle secre-tion into the fluid. In the 1H NMR spectra of the vasalaplasia patients, signals from amino acids were eitherabsent or present at very low levels. Similarly, choline isat a low level or absent in the seminal fluid from vasal apla-sia patients, as it derives (indirectly) from the seminal vesi-cle component. Very significant differences were observedbetween the normal and vasal aplasia patient groups forthe molar ratio of citrate:choline and spermine:choline.Other studies of infertility include a procedure providingautomatic diagnosis based on NMR spectroscopy and workon azoospermic subjects (100,101). The effect of an injectablemale contraceptive on seminal plasma metabolite composi-tion has been evaluated (102). In addition 31P NMR spectro-scopy has also been used to distinguish semen from healthyand infertile men (103).


8. 1H NMR SPECTROSCOPY OF BILE

8.1. Composition of Bile

One important function of the liver is bile formation. Bile isboth a secretory and an excretory fluid and, as such, its com-position is complex and varies according to the nutritionalstate of the individual. The secretory functions most promi-nently include the delivery to the intestinal tract of bile saltsand their associated lipids to aid fat digestion and absorption,while the excretory functions include the excretion of liver-derived metabolites of potentially toxic endogenous (e.g.,steroid hormones, bilirubin) or exogenous (e.g., drugs, envir-onmental chemicals) materials. The analysis of bile by con-ventional biochemical techniques is a difficult proceduresince it has complex physico-chemical properties including amicellar substructure with a lipid-rich matrix, together withdetergent properties. The chief emulsifying action is providedby the bile salts that consist of different types of free bile acidsand each of these acids may in turn conjugate with glycine ortaurine to form more complex acids and salts. Bile is one ofthe most complex and least understood of all the body fluidsin terms of physico-chemical properties. The bile acids arepresent in a matrix which contains cholesterol, cholesterolesters, phosophatidyl choline, neutral fats, fatty acid (allpresent in micelles) and glycoproteins together with inorganicsalts including large amounts of bicarbonate.

8.2. NMR Spectroscopy of Bile and DynamicInteractions of Metabolites

The 1H NMR spectra of bile are dominated by broad reso-nances that arise from bile acids that are present in mixedmicelles with phospholipids and cholesterol (Fig. 5). Theyare broad as a result of short spin–spin (T2) relaxation timesreflecting constrained molecular motions within micellar par-ticles. On lyophilization and reconstitution with water, themolecular mobility of a number of biliary metabolites changessignificantly due to disruption of the micellar compartments.

146 Lindon et al.

In particular, the T2 relaxation times of the aliphaticside-chains of lipid moieties are increased in lyophilized bilesuggesting greater mobility of these molecules. Increases insignal intensities that occur on lyophilization reflect changesin compartmentation of molecules that are related to the dis-ruption=reorganization of the biliary micellar compartments.Interestingly, signals from b-hydroxybutyrate, valine andother branched chain amino acids do not contribute signifi-cantly to the 1H NMR spectra of non-lyophilized bile, but reso-nances from these components are clearly resolved afterlyophilization indicating a dramatic lengthening of their T2sand consequently their molecular mobility in untreated bile.The sharper signals in bile give rise to well-resolved 2D COSYspectra that allow a comprehensive assignment of the bile saltsignals.

Variable temperature 1H NMR studies on human bileshow that considerable dynamic structural information isavailable particularly at very high fields, e.g., 600MHz. Themicellar cholesteryl esters that are abundant in bile appearto show liquid crystal behavior, and it is possible to useNMR measurements to map the phase diagram for thecomplex biliary matrix.

Figure 5 600 MHz 1H NMR spectrum of human gall bladder bile.


A number of studies have used both 1H and 13C NMRspectroscopy of bile to aid characterization of its compositionand structure. Thus, 13C spectra of bile from fish exposed topetroleum have been studied (104). 1H NMR spectroscopy ofbile has been used to investigate the micellar cholesteroland lipid content, and both 1H and 31P NMR have been usedto study the distribution of lecithin and cholesterol (105,106).

9. NMR SPECTROSCOPY OF MISCELLANEOUSBODY FLUIDS

9.1. Amniotic and Follicular Fluids

The first study of human amniotic fluid using 1H NMR spec-troscopy detected 18 small molecule metabolites in samplesof human amniotic fluid including glucose, leucine, isoleu-cine, lactate, and creatinine (107). Following this, other stu-dies have used a combination of 1D and 2D COSYspectroscopy to assign resonances, assess NMR methods ofquantitation and to investigate the effects of freezing andthawing. In addition, NMR results have been correlated withother clinical chemical analyses. A total of 70 samples weremeasured at different stages of gestation and with differentclinical complications and significant correlations betweenthe NMR spectral changes and maternal preeclampsia andfetal open spina bifida were observed (108). The effects ofvarious pathological conditions in pregnancy were investi-gated using 1H NMR spectroscopy of human amniotic fluid(109). Studies of amniotic fluid using 31P NMR spectroscopyhave also been carried out, principally to analyze the phos-pholipid content (110). One study of the metabolic profiling ofovarian follicular fluids from sheep, pigs, and cows has beenreported (111).

9.2. Milk

Little has been published on the NMR spectroscopy of milkgiven that it is both a biofluid and a food substance. The stu-dies generally focus on milk as a food and the first spectra

148 Lindon et al.

were available in 1986 (112). Since then Belton (113) has alsoreviewed the information content of NMR spectra of milk.

9.3. Synovial Fluid

1H NMR spectroscopy has been used to measure the levels ofa variety of endogenous components in the synovial fluid (SF)aspirated from the knees of patients with osteoarthritis (OA),rheumatoid arthritis (RA) and traumatic effusions (TE)(114). The spin-echo 400MHz NMR spectrum of synovialfluid shows the signals of a large number of endogenous com-ponents. Many potential markers of inflammation could notbe monitored because of their low concentrations (e.g., pros-taglandins) or because of their slow tumbling (hyaluronicacid, a linear polysaccharide that imparts a high viscosityto synovial fluid). The low molecular weight endogenous com-ponents showed a wide patient-to-patient variability andshowed no statistically significant correlation with diseasestate. However, correlations were reported between the dis-ease states and the synovial fluid levels of the N-acetyl sig-nals from acute phase glycoproteins. These molecules havea high molecular weight and their signals are not observedin spin-echo 1H NMR spectra. However, most glycoproteinscontain N-acetylneuraminic acid or N-acetylglucosamineunits and the N-acetyl signals from these units are NMRdetectable, if the carbohydrate sidechains are sufficientlymobile. Correlations between the disease state and the levelsand type of triglyceride in the synovial fluid were alsoreported. Triglyceride CH3, CH2 and vinylic CH group sig-nals are observed in the 1H NMR of synovial fluid. In OA,the CH3 and CH2 levels were very low compared with RA orTE, and in addition, the ratio of intensities CH2:CH3 waslower in OA than in RA or TE, thus implying a shorter chainlength for the fatty acid chains. The levels of triglycerides inRA were slightly lower than in TE and thus, on the basis ofliterature data for triglyceride levels in TE, the RA levelsare greater than those expected in controls.

The 1H NMR spectra of the SF of a female patient withseronegative erosive RA and of another female patient with


sarcoidosis and independent inflammatory OA were followedover the course of several months and standard clinical testswere performed on paired blood serum samples taken at thesame time. It was found that the SF levels of triglycerideCH3, CH2 and CH, glycoprotein N-acetyl signals and creati-nine all correlated well with one another, and with standardclinical measures of inflammation. The correlation of diseasestate with creatinine level is of particular interest, and thealtered triglyceride composition and concentration in OAwas suggested as a potential marker for the disease in SF(115).

500 MHz spin-echo 1H NMR spectroscopy has been usedto detect the production of formate and a low molecularweight, N-acetyl-containing oligosaccharide, derived fromthe oxygen radical-mediated depolymerization of hyaluro-nate, in the SF of patients with RA, during exercise of theinflamed joint. Gamma radiolysis of rheumatoid SF and ofaqueous hyaluronate solutions was also shown to produce for-mate and the oligosaccharide species. It was proposed thatthe hyaluronate-derived oligosaccharide and formate couldbe novel markers of reactive oxygen radical injury duringhypoxic reperfusion injury in the inflamed rheumatoid joint.Secondly, the role of N-acetylcysteine in the protection of SFfrom radiolytic damage has been investigated (116) and itwas shown that metabolite analysis using NMR spectroscopywas a useful approach in such studies. More recently, thehydrogen peroxide and hydroxyl radical scavenging capacityof SF has been studied, particularly the roles of pyruvateand lactate (117).

Albert et al. (118) showed that 13C NMR could be usefullyused to monitor the synovial fluids from patients with arthri-tis. In contrast to the 1H NMR studies discussed above, sig-nals are seen from hyaluronic acid, the main determinant ofthe viscoelasticity of the synovial fluid, even though the mole-cular weight is in the region 500–1600kD. 13C NMR spectraof synovial fluids from patients with RA, OA, TE and cadavercontrols were compared with one another and with spectra ofauthentic hyaluronic acid, both before and after the incuba-tion of the latter with hyaluronidase, an enzyme which

150 Lindon et al.

depolymerizes hyaluronic acid. Depolymerization of thehyaluronic acid was accompanied by a decrease in the half-band widths of its 13C resonances. The SF NMR spectra fromthe patients with RA had sharper signals for the C-1 and C-10

carbons of hyaluronic acid than those from the osteoarthriticpatients, which in turn exhibited sharper signals than thosefrom the cadavers or the joint trauma patients. Thus thedegree of polymerization of hyaluronic acid was deduced todecrease in the order controls=joint trauma patients>osteoarthritic patients>RA patients. Since it is known thatthe consequence of hyaluronate depolymerization may bearticular cartilage damage, it was concluded that 13C NMRspectroscopy might be a valuable method for studying theseclinically relevant biophysical changes in SF.

9.4. Miscellaneous Fluids

The first study by NMR spectroscopy on aqueous humor wason nine samples taken during surgery for other conditionsand NMR spectra were measured at 400MHz (119). A num-ber of metabolites were detected, including acetate, acetoace-tate, alanine, ascorbate, citrate, creatine, formate, glucose,glutamine or glutamate, b-hydroxybutyrate, lactate, threo-nine and valine. Following this, there have been a numberof other studies. These include 1H NMR spectra from aqueoushumor of rabbits and 31P NMR spectra of aqueous andvitreous humor from pigs (120,121).

Limited studies using NMR spectroscopy of saliva havebeen reported. Initially Harada et al. (122) used 1H NMRspectroscopy of human saliva in a forensic study. No age- orsex-related differences were observed for saliva from healthysubjects but marked differences were observed in cases of sia-lodentitis (123). The studies have been broadened by theapplication of 1H and 13C NMR spectroscopy, which was usedto distinguish endogenous substances from those related tooral health care and pharmaceuticals (124). Finally, thebiochemical effects of an oral mouthwash preparation anda tooth-whitening substance have been studied using 1HNMR spectroscopy (125).


The analysis of pancreatic juice and small bowel secre-tions using 1H NMR spectroscopy has also been reported(126).

Foxall et al. (127) have reported a 1H NMR study of thefluid from the cysts of six patients with autosomal dominantpolycystic kidney disease (ADPKD). Autosomal dominantpolycystic kidney disease in adults is characterized by theslow progressive growth of cysts in the kidney which are linedby a single layer of renal tubular epithelium. When thesecysts reach a large size they can significantly distort the kid-ney, and disrupt both the blood supply and renal function.Autosomal dominant polycystic kidney disease is one of thecommonest causes for renal transplantation in adults. Littlewas known about the exact biochemical composition of cystfluids prior to this study, or about the relationship betweencyst fluid composition and the pathogenesis of the disease.The 1H NMR spectra of the cyst fluids were assigned by stan-dard methods developed earlier for other biofluids and by theuse of 600MHz 1H spin-echo and 2D J-resolved experiments.The spectra revealed a number of unusual features andshowed the cyst fluids to be distinct from both blood plasmaand urine. Isoleucine, lysine, threonine, and valine were pre-sent at millimolar concentrations. High concentrations ofacetate, lactate, succinate, creatinine, and dimethylaminewere also found in the cyst fluids, and in ratios different fromthose of blood plasma or urine. Glucose concentrations variedfrom 3.4 to 9.6mM, and the majority of the fluids containedsignals from the N-acetyl groups of mobile glycoprotein sugarsidechains. Unusually, the fluids from all the six patients con-tained high levels of ethanol, which was not related to con-sumption of alcoholic beverages or drug preparations. Ingeneral, there was little variation in the composition of thecyst fluids as revealed by 1H NMR, although the protein sig-nal intensity did vary somewhat. It was hypothesized thatthis constancy of composition reflected the chronic nature ofthe accumulation of the cyst fluid and a long turnover timeof the cyst components, which thus has the effect of averagingthe compositions. The unique biochemical composition of thecyst fluids was ascribed to abnormal transport processes

152 Lindon et al.

occurring across the cyst epithelial wall, reflecting polarityreversal of the cystic epithelium.

It should be remembered that it is also possible to studyartificial fluids that have been administered. These includerenal dialysis fluids, rectal dialysates, and bronchial alveolarlung fluid (BALF). An example of a 1H NMR spectrum ofBALF is given in Fig. 6, showing the quality of the data thatcan be obtained.

Finally, it is possible to examine biofluidswhich have beenderived by extraction of tissue or cell samples. Figure 7 showsan example of a 1H–31P HMQC-TOCSY two-dimensionalNMR spectrum of a lipid extract of mouse liver tissue. Thisshows which 1H NMR peaks are spin-coupled to 31P, the 31Pchemical shifts being easily assigned to various phospholipidsbased on the literature. The experiment also allows assignmentof unbroken chains of 1H–1H coupling to the hydrogen that iscoupled to the phosphorus, thus giving useful information onthe chemical nature of the phospholipid head groups (128).

10. NMR STUDIES OF DYNAMICINTERACTIONS

Although NMR spectroscopy of biofluids is now a well-established technique for probing a wide range of biochemicalproblems, there are still many poorly understood physico-chemical phenomena occurring in biofluids, particularly thesubtle interactions occurring between small molecules andmacromolecules or between organized multiphasic compart-ments. The understanding of these dynamic processes is ofconsiderable importance if the full diagnostic potential ofbiofluid NMR spectroscopy is to be realized.

Many biological fluids contain significant amounts ofactive enzymes. This may be because they fulfill a biologicalfunction in the fluid, e.g., the esterase and peptidases presentin prostatic fluid. Additionally, they may have leaked into thefluid due to disease or toxin-induced organ damage such asraised plasma alanine aminotransferase levels in liver andkidney disease, or raised urinary N-acetylglucosaminidase


Figure6

600MHz1H–31PHMQC–TOCSYNMRsp

ectrum

ofach

loroform

–methanol

extract

ofmou

seliver

tissue.

154 Lindon et al.

in kidney disease. When provided with the appropriatesubstrates, these enzymes will manufacture new productswhich can be NMR-detectable. Collection of sequential NMRdata may then allow the time course of this enzymatic conver-sion to be followed. This may yield kinetic data on the activityof the enzyme in a ‘‘real’’ biological medium and may alsoprovide indirect NMR evidence of organ damage.

Figure 7 600 MHz 1H NMR spectrum of bronchial alveolar lavagefluid (BALF). (Top) one-dimensional NMR spectrum; (center) two-dimensional COSY spectrum; (bottom) two-dimensional J-resolvedspectrum.


Many biofluids are not chemically stable and for this rea-son care should be taken in their collection and storage. Forexample, cell lysis in erythrocytes can easily occur. In addi-tion, if the biofluid has been reconstituted into D2O afterfreeze-drying or if a substantial amount of D2O has beenadded to provide an NMR field lock, then it is possible thatcertain 1H NMR resonances will be lost. These include notonly NH and OH protons as expected but also CH groupswhere the C–H bond is labile such as H2 of imidazole moieties(as in histidine or histidinyl-containing proteins such ashemoglobin) or the CH2 group of acetoacetate which partici-pates in keto-enol tautomerism. It should be noted thatfreeze-drying of biofluid samples also causes the loss of vola-tile components such as acetone.

Biofluids are very prone to microbiological contamina-tion, especially fluids, such as urine, which are difficult to col-lect under sterile conditions. Samples should be stored deepfrozen to minimize the effects of such contamination but evi-dence of bacterial growth will be seen in a time-dependentpattern of metabolites if NMR spectra are measured over aperiod of time or if the sample is kept at room temperaturefor extended periods. It has been noticed that bacteria canincorporate a 2H atom from D2O into metabolites and the pre-sence of isotopically labeled acetate (CDH2�COOHand CD2H�COOH observable in the 1H NMR spectrum) forexample is a good indication of bacterial contamination(129). Prevention of microbial growth can be achieved bythe addition of sodium azide at the point of sample collectionand during preparation of samples for NMR spectroscopy.

Some biofluids such as blood plasma contain high levelsof proteins and many endogenous metabolites bind to suchmacromolecules. There are many examples of this such asaromatic amino acids binding to serum albumin in bloodplasma and this has a direct consequence of making the detec-tion and quantitation of such species less easy (130). In NMRterms, the molecule can appear to be in fast, intermediate orslow exchange with the macromolecule. Thus interpretationof metabolite levels by NMR spectroscopy must always beundertaken after consideration of whether the result is

156 Lindon et al.

perturbed by macromolecule binding with resulting relaxa-tion and line width changes. Many widely used therapeuticcompounds also bind to biofluid macromolecules and thisprocess can be studied using NMR spectroscopy based onchanges in chemical shifts, line widths, relaxation times anddiffusion coefficients.

Some biofluids, particularly blood, contain cells withintact cell membranes. Other fluids such as bile or bloodplasma have high levels of lipids organized into supramolecu-lar particles such as micelles in bile and lipoproteins in bloodplasma. Small molecules can therefore be inside or outside ofthe lipid membrane or even inside the lipid membrane itself.In the extreme case of micellar aggregations, e.g., in bile, thiscan be regarded as compartmentation as the small moleculecan be outside the micelles, in the micelle wall or within themicelle itself. Many small molecules are more freely solublein biological fluids than they are in water alone (e.g., choles-terol and its esters in blood plasma). In all cases, compart-mentation of small molecules results in changes in therotational correlation times and hence relaxation propertiesof their nuclei with respect to free solution conditions, andin some cases chemical shift and coupling constant changesas well. Observation of such species may be further compli-cated by chemical exchange. Visualization of the signals fromcompartmentalized molecules usually requires some physicalperturbation of the sample to make the NMR lines sharpenough to be detected, e.g., methanol extraction to observecholesterol in blood or seminal plasma.

All biological fluids contain a variety of potential metal-chelating agents, sometimes at very high concentrations.The most ubiquitous metal chelators in biofluids are freeamino acids, especially, glutamine, glutamate, cysteine, histi-dine and aspartate, and organic acids such as citrate and suc-cinate. Ca2þ, Mg2þ and Zn2þ are the main endogenous metalions involved in complexation reactions with organic biofluidcomponents and many of these reactions can be studiedusing NMR spectroscopy. Chelation reactions and thephysico-chemical effects of other metals such as Fe3þ=Fe2þ

metallodrugs and toxic metals such as Cd2þ in red blood


cells and whole blood can also be studied under certaincircumstances. Paramagnetic ions such as Gd2þ and Mn2þ

can also be used to effect chemical editing of NMR spectra(and spin-echo based solvent suppression) by selectively bind-ing to endogenous metal chelating agents such as citrate andhence broadening their signals (131). The metal-chelatingagent ethylenediaminetetraacetic acid (EDTA) is very effec-tive for many di- and trivalent metal ions and can be addedto biological fluids to remove metal from the endogenouschelating species with consequent changes in the NMR signalpattern and the appearance of signals from metal EDTAcomplexes (130). EDTA addition also results in a generalsharpening of NMR signals from biofluids because it alsocomplexes trace levels of paramagnetic ions.

Biofluids contain many endogenous species that can par-ticipate in chemical exchange processes covering a variety ofexchange time-scales. These processes may be connected withmacromolecular binding or with metal complexation reactionsor, more simply, involve exchange of protons with each otherand=or solvent water. Some molecules such as citrate may beinvolved with all the three types of chemical exchange phe-nomena, and hence signal positions vary considerably accord-ing to solution conditions. Depending on the exchange rate,spectral lines may be broadened or shifted from their freesolution condition. Citrate signals are generally broadenedin the presence of the metal ions present in biofluids (usuallyCa2þ, Mg2þ and Zn2þ), and this broadening is reversed by theaddition of EDTA which outcompetes citrate for binding ofdivalent metal ions.

11. CONCLUDING REMARKS

Nuclear magnetic resonance spectroscopy, particularly usingthe 1H nucleus, of biofluids has been able to characterizethe normal metabolic profile in animal and human species,to evaluate the type and degree of natural physiological meta-bolic variation and to demonstrate altered metabolic profilesdue to human disease processes and xenobiotic adverse effects

158 Lindon et al.

in man and in animal models. With the advent of highersensitivity and greater spectral dispersion from ultra highmagnetic fields and the application of cryoprobes, it isexpected that the application areas of 1H NMR sepctroscopyof biofluids will continue to expand.

REFERENCES

1. Nicholson JK, Wilson ID. High resolution proton magneticresonance spectroscopy of biological fluids. Prog NMR Spec-trosc 1989; 21:444–501.


3. Nicholson JK, Foxall PJD, Spraul M, Farrant RD, Lindon JC.750MHz 1H and 1H–13C NMR spectroscopy of human bloodplasma. Anal Chem 1995; 67:793–811.

4. Wolters AM, Jayawickrama DA, Sweedler JV. MicroscaleNMR. Curr Opin Chem Biol 2002; 6:711–716.

5. Keun HC, Beckonert O, Griffin JL, Richter C, Moskau D,Lindon JC, Nicholson JK. Cryogenic probe 13C NMR spectro-scopy of urine for metabonomic studies. Anal Chem 2002;74:4588–4593.

6. Liu M, Farrant RD, Nicholson JK, Lindon JC. Selective detec-tion of 1H NMR resonances of CHn groups using a heteronuc-lear multiple quantum filter and pulsed field gradients. JMagn Reson 1995; 106B:270–278.

7. Liu M, Farrant RD, Nicholson JK, Lindon JC. Selectivedetection of 1H NMR resonances of 13CHn groups usingtwo-dimensionalmaximumquantum correlation spectroscopy.J Magn Reson 1995; A113:208–219.

8. Willker W, Leibfritz D. Assignment of mono- and polyunsatu-rated fatty acids in lipids of tissues and body fluids. MagnReson Chem 1998; 36:S79–S84.

9. Willker W, Flogel U, Leibfritz D. Ultra-high-resolved HSQCspectra of multiple-C-13-labeled biofluids. J Magn Reson1997; 125:216–219.


10. Spraul M, Niedig P, Klauck U, Kessler P, Holmes E, NicholsonJK, Sweatman BC, Salman SR, Farrant RD, Rahr E, BeddellCR, Lindon JC. Automatic reduction of NMR spectroscopicdata for statistical and pattern recognition classification ofsamples. J Pharm Biomed Anal 1994; 12:1215–1225.

11. Farrant RD, Lindon JC, Nicholson JK. Internal temperaturecalibration for 1H NMR spectroscopy studies of blood plasmaand other biofluids. NMR Biomed 1994; 7:243–247.

12. Gibbs SJ, Johnson CS Jr. A PFG NMR experiment foraccurate diffusion and flow studies in the presence of eddycurrents. J Magn Reson 1991; 93:395–402.

13. Liu M, Nicholson JK, Parkinson JA, Lindon JC. Measure-ment of biomolecular diffusion coefficients in blood plasmausing 2-dimensional 1H–1H diffusion-edited total correlationNMR spectroscopy (DETOCSY). Anal Chem 1997; 69:1504–1509.

14. Liu M, Nicholson JK, Lindon JC. High resolution diffusion-and relaxation-edited one- and two-dimensional 1H NMRspectroscopy of biological fluids. Anal Chem 1996; 68:3370–3376.

15. Lindon JC, Nicholson JK, Everett JR. NMR spectroscopy ofbiofluids. In: Webb G.A.,ed. Ann Rep NMR Spectrosc 1999;38:1–88.

16. Fan TW-M. Metabolite profiling by one- and two-dimensionalNMR analysis of complex mixtures. Prog NMR Spectrosc1996; 28:161–219.

17. Petroff OAC, Yu RK, Ogino T. High-resolution proton mag-netic resonance analysis of human cerebrospinal fluid. J Neu-rochem 1986; 47:1270–1276.

18. Bell JD, Brown JCC, Sadler PJ, Macleod AF, Sonksen PH,Hughes RD, Williams R. High resolution proton nuclear mag-netic resonance studies of human cerebrospinal fluid. Clin Sci1987; 72:563–570.

19. Sweatman BC, Farrant RD, Holmes E, Ghauri FY, NicholsonJK, Lindon JC. 600MHz 1H NMR spectroscopy of human cer-ebrospinal fluid: effects of sample manipulation and assign-ment of resonances. J Pharm Biomed Anal 1993; 11:651–664.

160 Lindon et al.

20. Wevers RA, Engelke U, Wendel U, de Jong JGN, GabreelsFJM, Heerschap A. Standardized method for high-resolution1H NMR of cerebrospinal fluid. Clin Chem 1995; 41:744–751.

21. Koschorek F, Gremmel H, Stelten J, Offermann W, LiebfritzD. Cerebrospinal fluid—detection of tumors and diskherniations with MR spectroscopy. Radiology 1988; 167:813–816.

22. Koschorek F, Offermann W, Stelten J, Braunsdorf WE,Steller U, Gremmel H, Leibfritz D. High resolution H-1NMR spectroscopy of cerebrospinal fluid in spinal diseases.Neurosurg Rev 1993; 16:307–315.

23. Nicoli F, Vion-Dury J, Maloteaux JM, Delwaide C, Confort-Gouny S, Sciaky M, Cozzone PJ. CSF and serum metabolicprofile of patients with Huntingtons-chorea—a study by highresolution proton NMR spectroscopy and HPLC. NeurosciLett 1993; 154:47–51.

24. Bell JD, Brown JCC, Kubal G, Sadler PJ. NMR-invisible lac-tate in blood plasma. FEBS Lett 1988; 235:81–86.

25. Commodari F, Arnold DL, Sanctuary BC, Shoubridge E. H-1NMR characterization of normal human cerebrospinal fluidand the detection of methylmalonic acid in a vitamin-B12deficient patient. NMR Biomed 1991; 4:192–200.

26. Yates MA, James MF, Woods NI, Middleton DA, Cottrell LA,Reid DG. 1H NMR study of rat cerebrospinal fluid composi-tion: effects of photothrombotically induced experimentalstroke. J Magn Reson Anal 1995; 1:13–19.

27. Nicoli F, Vion-Dury J, Confort-Gouny S, Maillet S, GastautJL, Cozzone PJ. Cerebrospinal fluid metabolic profiles in mul-tiple sclerosis and degenerative dementias obtained by highresolution proton magnetic resonance spectroscopy. ComptesRendus Serie III—Science de la Vie 1996; 319:623–631.

28. Simone IL, Federico F, Trojano M, Tortorella C, Liguori M,Giannini P, Picciola E, Natile G, Livrea P. High resolutionproton MR spectroscopy of cerebrospinal fluid in MS patients.Comparison with biochemical changes in demyelinating pla-ques. J Neurol Sci 1996; 144:182–190.


29. Lynch J, Peeling J, Auty A, Sutherland GR. Nuclear mag-netic resonance study of cerebrospinal fluid from patientswith multiple sclerosis. Can J Neurol Sci 1993; 20:194–198.

30. Ghauri FYK, Nicholson JK, Sweatman BC, Beddell CR,Lindon JC. NMR spectroscopy of human post mortem CSF:distinction of Alzheimer’s disease from controls using patternrecognition and statistics. NMR Biomed 1993; 6:163–167.

31. Lindon JC, Sweatman BC. Multicentre assessment of smallmolecule quantitation in human blood plasma using 1HNMR spectroscopy. J Magn Reson Anal 1996; 2:66–74.

32. Kriat M, Confort-Gouny S, Vion-Dury J, Sciaky M, Viout P,Cozzone PJ. Quantitation of metabolites in human bloodserum by proton magnetic resonance spectroscopy—a com-parative study of the use of formate and TSP as concentrationstandards. NMR Biomed 1992; 5:179–184.

33. Grootveld M, Claxson AWD, Chander CL, Haycock P, BlakeDR, Hawkes GE. High resolution proton NMR investigationsof rat blood plasma—assignment of resonances for the mole-cularly mobile carbohydrate side chains of acute-phase glyco-proteins. FEBS Lett 1993; 322:266–276.

34. Bell JD, Sadler PJ, Macleod AF, Turner PR, La Ville A. H-1NMR studies of human blood plasma assignment of reso-nances for lipoproteins. FEBS Lett 1987; 219:239–243.

35. Foxall PJD, Parkinson JA, Sadler IH, Lindon JC, NicholsonJK. Analysis of biological fluids using 600MHz proton NMRspectroscopy: application of homonuclear two-dimensionalJ-resolved spectroscopy to urine and blood plasma for spec-tral simplification and assignment. J Pharm Biomed Anal1993; 11:21–31.

36. Tang H, Wang Y, Nicholson JK, Lindon JC. Use of relaxation-edited 1D and 2D 1H NMR spectroscopy to improve detectionof small metabolites in blood plasma. Anal Biochem 2004;325: 260–272.

37. Ala-Korpela M. 1H NMR spectroscopy of human bloodplasma. Prog NMR Spectrosc 1995; 27:475–554.

38. Otvos JD, Jeyarajah EJ, Bennett DW, Krauss RM. Develop-ment of a proton nuclear magnetic resonance spectroscopic

162 Lindon et al.

method for determining plasma lipoprotein concentrationsand subspecies distributions from a single, rapid measure-ment. Clin Chem 1992; 38:1632–1638.

39. Bradamante S, Barchiesi E, Barenghi L, Zoppi F. Nn alterna-tive expeditious analysis of phospholipid composition inhuman blood plasma by P-31 NMR spectroscopy. Anal Bio-chem 1990; 185:299–303.

40. Liu M, Tang H, Nicholson JK, Lindon JC. Use of 1H NMR-determined diffusion coefficients to characterize lipoproteinfractions in human blood plasma. Magn Reson Chem 2002;40:S83–S88.

41. Hiltunen Y, Heiniemi E, Ala-Korpela M. Lipoprotein lipidquantification by neural network analysis of H-1 NMR datafrom human blood plasma. J Magn Reson 1995; B106:191–194.

42. Bell JD, Brown JCC, Kubal G, Sadler PJ. NMR-invisible lac-tate in blood plasma. FEBS Lett 1988; 235:81–86.

43. Anthony ML, Beddell CR, Lindon JC, Nicholson JK. Raisedtransaminase activity of blood plasma from rats withexperimentally-induced kidney damage detected by 1HNMR spectroscopy. J Pharm Biomed Anal 1993; 11:897–902.

44. Fossel ET, Carr JM, McDonagh J. Detection of malignant—water-suppressed proton nuclear magnetic resonance spec-troscopy of plasma. N Engl J Med 1986; 315:1369–1376.

45. Engan T, Krane J, Kvinnsland S. Proton nuclear magneticresonance spectroscopy measurements of methylene andmethyl line widths in plasma—significant variations withextent of breast cancer, duration of pregnancy and aging.NMR Biomed 1991; 4:142–149.

46. Engan T, Krane J, Klepp O, Kvinnsland S. Proton nuclear mag-netic resonance spectroscopy of plasma from healthy subjectsand patients with cancer. N Engl J Med 1990; 322:949–958.

47. Hiltunen Y, Ala-Korpela M, Jokisaari J, Eskelinen S,Kiviniitty K. Proton nuclear magnetic resonance lineshapestudies on human blood plasma lipids from newborn infants,healthy adults and adults with tumors. Magn Reson Med1992; 26:89–99.


48. Herring FG, Phillips PS, Pritchard H, Silver H, Whittal KP.The proton NMR of blood plasma and the test for cancer.Magn Reson Med 1990; 16:35–48.

49. Vion-Dury J, Favre R, Sciaky M, Kriat M, Confort-Gouny S,Harle JR, Gazziani N, Viout P, Grisoli F, Cozzone PJ. Gra-phic-aided study of metabolic modifications of plasma in can-cer using proton magnetic resonance spectroscopy. NMRBiomed 1993; 6:58–65.

50. Hofeler H, Scheulen ME. Monitoring of patients with nonse-minomatous testicular cancer by nuclear magnetic resonancespectroscopy of plasma. Eur J Cancer Clin On 1989; 25:1141–1143.

51. Kriat M, Vion-Dury J, Rafre R, Maraninchi D, Harle JR,Confort-Gouny S, Sciaky M, Fontanarava E, Viout P, CozzonePJ. Variations of plasma sialic acid and N-acetylglucosaminelevels in cancer, inflammatory diseases and bone-marrowtransplantation—a proton NMR spectroscopy study. Biochi-mie 1991; 73:99–104.

52. Schuhmacher JH, Conrad D, Manke HG, Clorius JH, MatysER, Hauser H, Zuna I, Maier-Borst W, Hull WE. Investiga-tions concerning the potential for using 1H NMR relaxometryor high-resolution spectroscopy of plasma as a screening testfor malignant lung disease. Magn Reson Med 1990; 13:103–132.

53. Mountford CE, Lean CL, Mackinnon WB. The use of protonMR in cancer pathology. Ann Rep NMR Spectrosc 1993;27:173–215.

54. Eugene M, Le Moyec L, de Certaines JD, Desruennes M, LeRumeur E, Fraysse JB, Cabrol C. Lipoproteins in heart trans-plantation: proton magnetic resonance spectroscopy ofplasma. Magn Reson Med 1991; 18:93–101.

55. Pont H, Vion-Dury J, Kriat M, Mouly-Bandini A, Sciaky M,Viout P, Confort-Gouny S, Messana T, Goudart M, MontiesJR, Cozzone PJ. NMR spectroscopy of plasma during acuterejection of transplanted hearts. Lancet 1991; 337:792–793.

56. Nishina M, Hori E, Matsushita K, Takahashi M, Ohsaka A.H-1 NMR spectroscopic study of serums from patients withmalaria. Physiol Chem Phys Med NMR 1988; 20:269–271.

164 Lindon et al.

57. Bell JD, Brown JCC, Sadler PJ. NMR studies of body fluids.NMR Biomed 1989; 2:246–255.

58. Grasdalen H, Belton PS, Pryor JS, Rich GT. Quantitativeproton magnetic resonance of plasma from uremic patientsduring dialysis. Magn Reson Chem 1987; 25:811–816.

59. Holmes E, Foxall PJD, Nicholson JK. Proton NMR analysis ofplasma from renal failure patients—evaluation of samplepreparation and spectral editing methods. J Pharm BiomedAnal 1990; 8:955–958.

60. Bell JD, Lee JA, Sadler PJ, Wilkie DR, Woodham RH.Nuclear magnetic resonance studies of blood plasma andurine from subjects with chronic renal failure—identificationof trimethylamine-N-oxide. Biochim Biophys Acta 1991;1096:101–107.

61. Foxall PJD, Spraul M, Farrant RD, Lindon JC, Neild GH,Nicholson JK. 750MHz 1H NMR spectroscopy of human bloodplasma. J Pharm Biomed Anal 1993; 11:267–276.

62. Brown FF, Campbell ID, Kuchel PW, Rabenstein DL. Humanerythrocyte metabolism studies by 1H spin echo NMR. FEBSLett 1977; 82:12–16.

63. Simpson RJ, Brindle KM, Brown FF, Campbell ID, FoxallDL. Studies of pyruvate-water isotope exchange catalyzedby erythrocytes and proteins. Biochem J 1981; 193:401–406.

64. York MJ, Kuchel PW, Chapman BE, Jones AJ. Incorporationof labeled glycine into reduced glutathione of intact humanerythrocytes by enzyme-catalyzed exchange—a NMR study.Biochem J 1982; 207:65–72.

65. Rabenstein DL, Isab AA. Determination of the intracellularpH of intact erythrocytes by H-1 NMR spectroscopy. AnalBiochem 1982; 121:423–432.

66. Brindle KM, Brown FF, Campbell ID, Grathwol C, KuchelPW. Application of spin-echo nuclear magnetic resonance towhole-cell systems. Membrane transport. Biochem J 1979;180:37–44.

67. King GF, York MJ, Chapman BE, Kuchel PW. Proton NMRspectroscopic studies of dipeptidase in human erythrocytes.Biochem Biophys Res Commun 1983; 110:305–312.


68. Kuchel PW, Hunt GE, Johnson GFS, Beilharz GR, ChapmanBE, Jones AJ, Singh BS. Renal function and lithium treat-ment—initial and follow-up tests in manic-depressivepatients. J Affect Disord 1984; 6:249–263.

69. Rabenstein DL. 1H NMR methods for the noninvasive studyof metabolism and other processes involving small moleculesin intact erythrocytes. J Biochem Biophys Methods 1984;9:277–306.

70. Lennon AJ, Scott NR, Chapman BE, Kuchel PW. Hemoglobinaffinity for 2,3-bisphosphoglycerate in solutions and intacterythrocytes—studies using pulsed-field gradient nuclearmagnetic resonance and monte-carlo simulations. Biophys J1994; 67:2096–2109.

71. Kuchel PW, Coy A, Stilbs P. NMR ‘‘diffusion-diffraction’’ ofwater revealing alignment of erythrocytes in a magnetic fieldand their dimensions and membrane transport characteris-tics. Magn Reson Med 1997; 37:637–643.

72. Torres AM, Michniewicz RJ, Chapman BE, Young GAR,Kuchel PW. Characterisation of erythrocyte shapes andsizes by NMR diffusion–diffraction of water: correlations withelectron micrographs. Magn Reson Imaging 1998; 16:423–434.

73. Humpfer E, Spraul M, Nicholls AW, Nicholson JK, LindonJC. Direct observation of resolved intra- and extracellularwater signals in intact human red blood cells using 1H MASNMR spectroscopy. Magn Reson Med 1997; 38:334–336.

74. Chen J-H, Enloe BM, Xiao Y, Cory DG, Singer S. Isotropicsusceptibility shift under MAS: the origin of the split waterresonances in 1H MAS NMR spectra of cell suspensions.Magn Reson Med 2003; 50:515–521.

75. Connor S, Everett J, Nicholson J. Spin-echo proton NMRspectroscopy of urine samples—water suppression via aurea-dependent T2 relaxation process. Magn Reson Med1987; 4:461–470.

76. Foxall PJD, Kingdom JCP, Rodeck CH, Neild GH, NicholsonJK. The effect of gestational age on the urinary excretion oforganic osmolytes. J Am Soc Nephrol 1995; 6:361.

166 Lindon et al.

77. Davies SEC, Chalmers RA, Randall EW, Iles RA. Betainemetabolism in human neonates and developing rats. ClinChim Acta 1988; 178:241–250.

78. Bales JR, Bell JD, Nicholson JK, Sadler PJ. 1H NMR studiesof urine during fasting: excretion of ketone bodies acetylcar-nitine. Magn Reson Med 1986; 3:849–856.

79. Griffin JL, Walker LA, Garrod S, Holmes E, Shore RF,Nicholson JK. NMR spectroscopy based metabonomic studieson the comparative biochemistry of the kidney and urine ofthe bank vole (Clethrionomys glareolus), wood mouse (Apode-mus sylvaticus), white toothed shrew (Crocidura suaveolens)and the laboratory rat. Comp Biochem Physiol B—BiochemMol Biol 2000; 127:357–367.

80. Iles RA, Jago JR, Williams SR, Stacey TE, de Sousa C,Chalmers RA. Human carnitine metabolism studied by H-1nuclear magnetic resonance spectroscopy. Biochem Soc Trans1986; 14:702–703.

81. Videen JS, Ross BD. Proton nuclear magnetic resonance uri-nalysis: coming of age. Kidney Int 1994; 46:S122–S128.

82. Lundina TA, Knubovets TL, Sedov KR, Markova SA, SibeldinLA. Variability of kidney tubular interstitial distortions inglomerulonephritis as measured by 1H NMR urinalysis. ClinChim Acta 1993; 214:165–173.

83. Foxall PJD, Mellotte GJ, Bending MR, Lindon JC, NicholsonJK. NMR spectroscopy as a novel approach to the monitoringof renal transplant function. Kidney Int 1993; 43:234–245.

84. Le Moyec L, Pruna A, Eugene M, Bedrossian J, Idatte JM,Huneau JF, Tome D. Proton nuclear magnetic resonancespectroscopy of urine and plasma in renal transplantation fol-low-up. Nephron 1993; 65:433–439.

85. Foxall PJD, Bending MR, Gartland KPR, Nicholson JK.Acute renal failure following accidental cutaneous absorptionof phenol: application of NMR urinalysis to monitor the dis-ease process. Human Toxicol 1989; 9:491–496.

86. Vermeersch G, Marko J, Cartigny B, Leclerc F, Roussel P,Hermitte ML. Salicylate poisoning detected by H-1 NMRspectroscopy. Clin Chem 1988; 34:1003–1004.


87. Malhotra D, Shapiro JI, Chan L. Nuclear magnetic resonancespectroscopy in patients with anion-gap acidosis. J Am SocNephrol 1991; 2:1046–1050.

88. Pappas AA, Thompson JR, Fuller GL, Porter WH, GadsenRH. High resolution proton nuclear magnetic resonance spec-troscopy in the detection of low molecular weight volatiles. JAnal Toxicol 1993; 17:273–277.

89. Bundy JG, Osborn D, Weeks JM, Lindon JC, Nicholson JK.An NMR-based metabonomic approach to the investigationof coelomic fluid biochemistry in earthworms under toxicstress. FEBS Lett 2001; 500:31–35.

90. Gartland KPR, Beddell CR, Lindon JC, Nicholson JK. Theapplication of pattern recognition methods to the analysisand classification of toxicological data derived from protonNMR spectroscopy of urine. Mol Pharmacol 1991; 39:629–642.

91. Anthony ML, Sweatman BC, Beddell CR, Lindon JC,Nicholson JK. Pattern recognition classification of the siteof nephrotoxicity based on metabolic data derived from pro-ton nuclear magnetic resonance spectra of urine. Mol Phar-maco 1994; 46:199–211.

92. Holmes E, Bonner FW, Sweatman BC, Lindon JC, BeddellCR, Rahr E, Nicholson JK. NMR spectroscopy and patternrecognition analysis of the biochemical processes associatedwith the progression and recovery from nephrotoxiclesions in the rat induced by mercury(II)chloride and2-bromo-ethanamine. Mol Pharmacol 1992; 42:922–930.

93. Anthony ML, Rose VS, Nicholson JK, Lindon JC. Classifica-tion of toxin-induced changes in 1H NMR spectra of urineusing an artificial neural network. J Pharm Biomed Anal1995; 13:205–211.

94. Holmes E, Nicholson JK, Tranter G. Metabonomic character-ization of genetic variations in toxicological and metabolicresponses using probabilistic neural networks. Chem ResToxicol 2001; 14:182–191.

95. Holmes E, Nicholls AW, Lindon JC, Ramos S, Spraul M,Neidig P, Connor SC, Connelly J, Damment SJP, HaseldenJ, Nicholson JK. Development of a model for classification

168 Lindon et al.

of toxin-induced lesions using 1H NMR spectroscopy of urinecombined with pattern recognition. NMR Biomed 1998;11:235–244.

96. Holmes E, Nicholson JK, Nicholls AW, Lindon JC, ConnorSC, Polley S, Connelly J. The identification of novel biomar-kers of renal toxicity using automatic data reduction techni-ques and PCA of proton NMR spectra of urine.Chemometrics Intell Lab Sys 1998; 44:245–255.

97. Lynch MJ, Masters J, Pryor JP, Lindon JC, Spraul M, FoxallPJD, Nicholson JK. Ultra high field NMR spectroscopic stu-dies on human seminal fluid, seminal vesicle and prostaticsecretions. J Pharm Biomed Anal 1994; 12:5–19.

98. Tomlins AM, Foxall PJD, Lynch MJ, Parkinson J, Everett JR,Nicholson JK. High resolution 1H NMR spectroscopic studieson dynamic biochemical processes in incubated human semi-nal fluid samples. Biochim Biophys Acta 1998; 1379:367–380.

99. Kalic M, Kamp G, Lauterwein J. Nuclear magnetic resonancestudies of boar seminal plasma. Problems encountered in theidentification of small molecules: hypotaurine and carnitine.NMR Biomed 1997; 10:341–347.

100. Segalen J, de Certaines JD, le Calve M, Colleu D, BansardJY, Rio M. H-1 nuclear magnetic resonance of human semi-nal plasma in in-vitro fertilization attempts—use of auto-matic spectrum analysis. J Reprod Fertil 1995; 103:181–187.

101. Hamamah S, Seguin F, Barthelemy C, Akoka S, le Pape A,Lansac J, Royere D. 1H nuclear magnetic resonance studiesof seminal plasma from fertile infertile men. J Reprod Fertil1993; 97:51–55.

102. Sharma U, Chaudhury K, Jagannathan NR, Guha SK. A pro-ton NMR study of the effect of a new intravasal injectablemale contraceptive RISUG on seminal plasma metabolites.Reproduction 2001; 122:431–436.

103. Levine AS, Foster N, Bean B. A comparison of human semenfrom healthy, sub-fertile and post-vasectomy donors byP-31 NMR spectroscopy. Ann NY Acad Sci 1987; 508:466–468.


104. Hellou J, Banoub JH, Payne JF. C-13 NMR spectroscopy inthe analysis of conjugate metabolites in the bile of fishexposed to petroleum. Chemosphere 1986; 15:787–793.

105. Sequeira SS, Parkes HG, Ellul JPM, Murphy GM. In vitrodetermination by 1H NMR studies that bile with shorternucleation times contain cholesterol-enriched vesicles. Bio-chim Biophys Acta 1995; 1256:360–366.

106. Degraaf MP, Groen AK, Bovee WMMJ. Determination of thedistribution of lecithin (pl) and cholesterol (ch) between themicellar and vesicular phases in bile by H-1 and P-31 NMR.Gastroenterology 1993; 104:A894.

107. Nelson TR, Gillies RJ, Powell DA, Schrader MC, ManchesterDK, Pretorius DH. High resolution proton NMR spectroscopyof human amniotic fluid. Prenatal Diagn 1987; 7:363.

108. Bock JL. Metabolic profiling of amniotic fluid by protonnuclear magnetic resonance spectroscopy: correlation withfetal maturation and other clinical variables. Clin Chem1994; 40:56–61.

109. Lemoyec L, Muller F, Eugene M, Spraul M. Proton magneticresonance spectroscopy of human amniotic fluids sampled at17–18 weeks of pregnancy in cases of decreased digestiveenzyme activities detected cystic fibrosis. Clin Biochem1994; 27:475–483.

110. Pearce JM, Komoroski RA. Resolution of phospholipid molecu-lar species by 31P NMR. Magn Reson Med 1993; 29:724–731.

111. Gosden RG, Sadler IH, Reed D, Hunter RHF. Characteriza-tion of ovarian follicular fluids of sheep, pigs and cows usingproton nuclear magnetic resonance spectroscopy. Experientia1990; 46:1012–1015.

112. Eads TM, Bryant RG. High resolution proton NMR spectro-scopy of milk, orange juice, and apple juice with efficientsuppression of the water peak. J Agric Food Chem 1986;34:834–837.

113. Belton PS. Spectroscopic approaches to the measurement offood quality. Pure Appl Chem 1997; 69:47–50.

114. Williamson MP, Humm G, Crisp AJ. 1H nuclear magneticresonance investigation of synovial fluid components in

170 Lindon et al.

osteoarthritis, and rheumatoid arthritis traumatic effusions.Br J Rheumatol 1989; 28:23–27.

115. Grootveld MC, Herz H, Haywood R, Hawkes GE, NaughtonD, Perera A, Knappitt J, Blake DR, Claxson AWD. Multicom-ponent analysis of radiolytic products in human body fluidsusing high field proton nuclear magnetic resonance (NMR)spectroscopy. Radiat Phys Chem 1994; 43:445–453.

116. Herz H, Blake DR, Grootveld M. Multicomponent investiga-tions of the hydrogen peroxide- and hydroxyl radical-scaven-ging antioxidant capacities of biofluids: the roles ofendogenous pyruvate and lactate—relevance to inflamma-tory joint diseases. Free Radic Res 1997; 26:19–35.

117. Grootveld M, Silwood CJL, Lynch EJ, Patel IY, Blake DR.The role of N-acetylcysteine in protecting synovial fluid bio-molecules against radiolytically-mediated oxidative damage:A high field proton NMR study. Free Radic Res 1999;30:351–369.

118. Albert K, Michele S, Gunther U, Fial M, Gall H, Saal J. 13CNMR investigation of synovial fluids. Magn Reson Med1993; 30:236–240.

119. Brown JCC, Sadler PJ, Spalton DJ, Juul SM, MacLeod AF,Sonksen PH. Analysis of human aqueous humour by highresolution 1H NMR spectroscopy. Exp Eye Res 1986;42:357–362.

120. Srivatsa GS, Chan MF, Chien DS, Tobias B. Detection andidentification of endogenous small molecules in ocular tissuesby proton nuclear magnetic resonance spectroscopy. CurrEye Res 1991; 10:127–132.

121. Greiner JV, Chanes LA, Glonek T. Comparison of phosphatemetabolites of the ocular humors. Opthalmol Res 1991;23:92–97.

122. Harada H, Shimizu H, Maeiwa M. H-1 NMR of human sal-iva—an application of NMR spectroscopy in forensic science.Forensic Sci Int 1987; 34:189–195.

123. Yamadanosaka A, Fukutomi S, Uemura S, Hashida T,Fujishita M, Kobayashi Y, Kyogoku Y. Preliminary nuclear


magnetic resonance studies on human saliva. Arch Oral Biol1991; 36:697–701.

124. Silwood CJL, Lynch E, Claxson AWD, Grootveld MC. H-1and C-13 NMR spectroscopic analysis of human saliva. JDent Res 2002; 81:422–427.

125. Lynch E, Sheerin A, Claxson AWD, Atherton MD, Rhodes CJ,Silwood CJL, Naughton DP, Grootveld M. Multicomponentspectroscopic investigations of salivary antioxidant consump-tion by an oral rinse preparation containing the stable freeradical species chlorine dioxide (ClO2�). Free Radic Res1997; 26:209.

126. Powell JJ, Gartland KPR, Nicholson JK, Ainley CC, ThompsonRPH. Bile, pancreatic-juice, and small-bowel secretions con-tain endogenous metal-binding ligands. Gut 1990; 31:A1197.

127. Foxall PJD, Price RG, Jones JK, Neild GH, Thompson FD,Nicholson JK. High resolution proton magnetic resonancespectroscopy of cyst fluids from patients with polycystic kid-ney-disease. Biochim Biophys Acta 1992; 1138:305–314.

128. Coen M, Nicholson JK, Lindon JC, Lenz EM, Wilson ID,Ruepp SU, Pognan F. Integrated application of transcrip-tomics and metabonomics yields new insight into the toxicitydue to paracetamol in the mouse. J Pharm Biomed Anal 2004;35:93–105.

129. Sweatman BC, Farrant RD, Lindon JC. NMR of biofluids:detection of 2H-acetate and 2H-formate in urine as an indica-tor of bacterial contamination. J Pharm Biomed Anal 1993;11:169–172.

130. Nicholson JK, Buckingham MJ, Sadler PJ. High resolutionH-1 NMR studies of vertebrate blood and plasma. BiochemJ 1983; 211:605–615.

131. Connor S, Nicholson JK, Everett JR. Chemical exchange andparamagnetic T2 relaxation agents for water suppression inspin-echo proton nuclear magnetic resonance spectroscopyof biological fluids. Anal Chem 1987; 59:2885–2891.

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51H Magic Angle Spinning NMR

Spectroscopy of Tissues

JULIAN L. GRIFFIN

Department of Biochemistry,University of Cambridge,

Cambridge, U.K.

JEREMY K NICHOLSON, ELAINEHOLMES, and JOHN C. LINDON

Biological Chemistry, BiomedicalSciences, Faculty of Medicine,Imperial College of Science,Technology and Medicine,

London, U.K.

1. INTRODUCTION

If the ultimate aim of metabonomics is to detect every smallmolecule metabolite and xenobiotic in a biofluid, tissue, ororganism then it would be supposed that the most sensitiveanalytical techniques should be used. One of the most sensi-tive atom-specific analytical approaches remains mass spec-trometry (MS) but this is destructive. However, oneadvantage that is intrinsic to NMR spectroscopy is that the

173

technique is nondestructive and in many cases noninvasive.Indeed, this has led to many medical applications of theNMR effect to detect molecules in vivo, particularly in termsof imaging (magnetic resonance imaging, MRI). However,the initial successes of in vivo magnetic resonance spectro-scopy (MRS) have since been impeded by the relatively smallnumber of metabolites that can be observed routinely. WhileMRS has found extensive applications in following cerebraldisorders, the biochemical changes recorded have been con-fined to a small number of metabolites that are readily obser-vable using this technique as depicted in Fig. 1. For example,in cerebral tissue the major observable metabolites are cho-line, N-acetyl aspartate (NAA), creatine, and lactate. How-ever, the exact role of NAA, often the largest resonancedetected in vivo and the level of which has since been corre-lated with the progression of diseases such as Parkinson’s dis-ease, Huntingdon’s disease, Duchenne muscular dystrophy,and stroke (1–3), is still strongly debated.

Figure 1 A 1H MRS in vivo spectrum from the human brain. Suchspectra are dominated by the large singlets arising from N-acetylaspartate (NAA), creatine, and choline, and thus, most of MRSstudies have to date focused on biochemical changes in thesemetabolites. Spectrum supplied By Dr. C. Rae, University ofSydney.

174 Griffin et al.

NMR spectroscopy in vivo is impaired by a number ofphysical processes which serve to broaden spectral reso-nances. Typically, for in vivo studies complex editingsequences and spatial localization approaches have to beused. In addition, relaxation times are often short giving riseto broader lines, and finally, in heterogeneous samples suchas tissue biopsies, anisotropic NMR parameters are not aver-aged completely to zero, also causing line broadening. A num-ber of second rank tensor interactions which might includedipolar couplings, chemical shift anisotropy, bulk magneticsusceptibility differences, both across the whole sample andmicroscopically, all give rise to broadened lines in spectra.(For an excellent discussion see Ref. 4.) To overcome theseproblems, it is possible to spin the sample at the so-calledmagic angle (the theory is shown later in Sec. 2.1)

An alternative approach to gain information on low-con-centration metabolites in tissues is to make tissue extracts.However, the concentration of a metabolite in an extractionmedium depends both on the metabolite’s relative solubilityas well as its tissue concentration. If aqueous and lipophillicmetabolites are investigated simultaneously, this requiresmultiphase extractions which can be time consuming, andmetabolites may even be trapped in the remaining tissuepellet. An example is shown in Fig. 2. For example, synapticglutamate in cerebral tissue is contained within a lipidvesicle, and thus may be resistant to aqueous extractionprocesses.

2. MAGIC-ANGLE-SPINNING (MAS) NMRSPECTROSCOPY: PRINCIPLES ANDPRACTICE

2.1. Theory

High resolution magic angle spinning (MAS) 1H NMR spec-troscopy circumvents both sets of problems associated within vivo spectroscopy and tissue extracts. The tissue is exam-ined directly avoiding tissue extraction. The process averagesall second rank tensor interactions to zero, hence removing

MAS NMR Spectroscopy 175

line broadening mechanisms. For 1H NMR spectroscopy,chemical shift anisotropies are small, quadrupolar couplingsare not present and the J-coupling anisotropy is negligible.However, both dipolar coupling and diamagnetic suscep-tibility anisotropy are both significant. Hence as an example,

Figure 2 Six hundred megahertz high resolution 1H NMR spec-troscopy of bank vole renal tissue. Renal tissue was examined usingeither a perchloric acid extraction procedure (A), 1H MAS NMRspectroscopy (B) or using a chloroform=methanol lipid extractionprocedure (C). The 1H MAS NMR spectrum demonstrated featurespresent in spectra A and C showing its ability to detect both lipophi-lic and hydrophilic metabolities. In D, a 1H MAS NMR spectrum ofthe extraction pellet following aqueous and lipid extraction proce-dures is shown. As well as broad lipid resonances, a number ofsharp resonances are also clearly visible, demonstrating that anumber of low molecular weight metabolites were not extracted.

176 Griffin et al.

the dipolar Hamiltonian (in Hz) for two spin one-half nuclei ina rigid solid is

HD=h ¼ Sðh=8p2Þgigjr�3ij ð3 cos2 yij � 1ÞðIi � Ij � 3IziIzjÞ

The value of the dipolar coupling depends on the angle(y) which the internuclear vector makes with the field direc-tion and the internuclear distance. For two protons close inspace in a rigid solid, the value can be of the order of20 kHz. The isotropic average of this is zero and is the reasonwhy dipolar couplings do not appear in spectra measured onfree solutions. If there is some molecular motion, the angularterm is partially averaged and for tissues this can be consider-able leaving line widths of the order of 1 kHz. If the sample isspun at some angle (b) to the field then this also causes anaveraging process according to

HD=h ¼1=2ð3 cos2 b� 1ÞSðh=8p2Þgigjr�3ij

� ð3 cos2 yij � 1ÞðIi � Ij � 3IziIzjÞHence if b is set to cos�1(1=

ffiffiffi

3p

) and the spinning rate islarge compared to the partially averaged dipolar coupling,then the angular term also goes to zero irrespective of theaverage value due to theta.

As an example of the effectiveness of MAS for tissueNMR spectra, Fig. 3 shows a typical result for a liver sample.

2.2. Sample Preparation

One of the practical advantages of 1H MAS NMR spectroscopyis that there is relatively little sample preparation necessary.Despite this, the technique can simultaneously detectchanges in both aqueous and lipophilic environments (6),making the technique ideal for use as a diagnostic tool. Sam-ples are loaded into ZrO2 rotors as depicted in Fig. 4.

The tissue may be soaked in a small quantity of D2Oprior to loading if spectra are acquired ‘‘locked.’’ When usedin conjunction with 4mm diameter rotors and Teflon spacers,tissue size may be limited to �5mg wet weight, with thespacer also improving sample packing and homogeneity byforcing out trapped air ((7) Fig. 4). To ensure that spinning


Figure 4 A schematic diagram showing sample position in aHRMAS NMR spectroscopy zirconium oxide rotor with (right) andwithout (left) teflon spacer (Kel-F). Figure provided by Dr.N. Waters (Ref. 8).

Figure 3 A comparison of 1H NMR spectra from an intact piece ofliver tissue either (a) static or (b) spun at 6000Hz to demonstratethe spectral improvements produced by magic angle spinning(Ref. 5).

178 Griffin et al.

side bands associated with the spectra are outside, a 10p.p.m.region was used to observe metabolites in the tissue. Samplesare spun at a rotation rate to ensure that spinning sidebandsof the peaks lie outside the typical 10p.p.m. spectral window,i.e., at a 600MHz observation frequency, a 6000Hz rotation ratewould be used.

With increasing spin rate, the samples may suffer degrada-tion, especially for softer tissues or cells, as centripetal forceincreaseswith the square of spin speed. This has led to a numberof research groups developing pulse sequences, such as TOSSand PASS, for use with 1HMAS NMR spectroscopy to minimizetissue degradation Wind, Hu and Rommerein (2001). However,even at speeds of 5000–6000Hz Griffin et al. (9,10) found no evi-dence of increased metabolic changes in cultured neuronal cellsat these speeds, and only minor damage to cell membranesaccording to the Trypan Blue exclusion assay. Furthermore, tominimize enzymatic degradation, samples can be chilled towithin a fewdegrees of freezingduring the acquisition of spectra.

However, MAS NMR spectroscopy does not detect all themetabolites found within a tissue. Fats within restricted envir-onments such as the cell membrane are subjected to dipolarcouplings greater than those removed by the typically modestspin speeds used in tissue-based 1H NMR NMR spectroscopy(11), and thus, the lipids observed using 1H MAS NMR spec-troscopy are relatively mobile (rotationally, if not translation-ally). These lipids still consist of a large number of differentchemical moieties, as 1H NMR spectroscopy detects the differ-ent chemical groups present within the lipid compoundsrather than returning a single resonance for each lipid as awhole. Thus, broad resonances associated with lipids obscurelarge proportions of spectra from lipid rich tissues, such asadipose tissue, skeletal muscle, and cardiac tissue (12–14).For metabolites that are coresonant with intense lipid signals,spectral editing is needed either using the molecular environ-ment as a contrast agent (14) or using spin coupling along themolecular backbone in two dimensional pulse sequences suchas during COSY and TOCSY pulse sequences (15).

Using the cellular environment as a contrast mech-anism, small and large molecules can be readily separated.


Metabolites within restricted environments have short longi-tudinal (T2) relaxation times, resulting in broad resonances inthe spectra. However, if a delay is placed in the pulsesequence so that the signals from these restricted metaboliteshave decayed to an insignificant level, the signals from theunrestricted metabolites can be more readily detected. Onepulse sequence that utilizes this procedure is the Carr–Purcell–Meiboom and Gill (CPMG) sequence, where magneti-zation is trapped in the transverse plane by a train of 180�

pulses, while the signal from the broad, short T2 resonancesdecays at a relatively faster rate than those from more mobilemetabolites. Alternatively, a pulse sequence can be usedwhich edits spectra based on differences in molecular diffu-sion coefficients. This involves the application of magneticfield gradients which code the spatial position of the mole-cules. At some later time, a decoding gradient is then appliedand if no molecular translation motion has occurred, the fullNMR signal intensity is retained. However, if there is diffu-sion, then the signals will be reduced in intensity such thatthey can be effectively completely removed for fast diffusingmolecules (16). For field gradients rectangular in time, theattenuation of a resonance intensity is given by

Ag ¼ A0 exp½�g2d2g2DðD� d=3� t=2Þ�

where A is the signal intensity at gradient strength g, d is thetime the gradient is applied for, g is the gyromagnetic ratio ofthe nucleus, and D is the total diffusion time. Thus, data setscan be produced that are dependent on the cellular environ-ment of the metabolites. In this respect, NMR spectroscopyhas a huge intrinsic advantage over mass spectrometry forderiving metabolic profiles.

3. APPLICATIONS OF 1H MAS NMRSPECTROSCOPY TO METABONOMICS

3.1. Toxicology in Animal Systems

While the use of metabonomic-based techniques to biofluidsoffers a minimally invasive procedure for investigating drug

180 Griffin et al.

toxicity, it is often necessary to confirm specific organ toxicitythat has been suggested by biofluid NMR spectroscopy.1H MAS NMR spectroscopy provides a rapid mechanism forinvestigating the biochemical changes that occur in intacttissue. It also has a large practical advantage over other‘‘omic’’ technologies such as transcriptomics and proteomicsin that metabolism is readily transferable from one speciesto the next.1H MAS NMR spectroscopy provides a quick andconvenient technique to investigate potential toxicity in tis-sues from any species.

However, diet affects tissue composition, particularly forrenal and liver tissue. While this has been previously exam-ined in many laboratory species, little is known about wildsmall mammals, with these animals being most at risk ofexposure to agrochemicals and pesticides. Furthermore, thehigh lipid content of renal and liver tissue of many wildrodents suggests that these animals may be particularly vul-nerable to lipophilic xenobiotics when compared with thelaboratory rat (17). Indeed, comparing the herbivorous bankvole (Clethrionomys glareolus), the granivorous wood mouse(Apodemus sylvaticus), and the insectivorous white-toothedshrew (Crocidura suaveolens) with a widely used strain oflaboratory rat (Sprague Dawley), 1H MAS NMR spectra fromall the wild species were found to contain large lipid reso-nances, particularly wood mice as shown in Fig. 5 (18).

Extending the hypothesis that wild mammals may beparticularly prone to toxic insults when compared withlaboratory species, metabonomic-based 1H MAS NMR spec-troscopy was used to examine cadmium and arsenic toxicityin the bank vole (18,19). Following acute exposure of bankvoles to cadmium chloride, biochemical changes in lipid andglutamate metabolism that preceded classical nephrotoxicitywere detected. Furthermore, these changes occurred afterchronic dosing, at a low level of exposure and at a renal cad-mium concentration (8.4 mg=g drywt) that was nearly twoorders of magnitude below the WHO critical organ concentra-tion (200 mg=g wetwt) (20). These early stage effects of cad-mium on the biochemistry of renal tissue may reflectadaptation mechanisms to the toxic insult or the preliminary


stages of the toxicological cascade, and demonstrated thepotential sensitivity of the technique. Intriguingly, bank volesalso appear susceptible to arsenic toxicity, with diffusion-weighted 1H MAS NMR spectroscopy being used to follow

Figure 5 1H MAS NMR spectra from the outer renal cortex oftissue taken from (A) rat, (B) bank vole, (C) shrew, and (D) woodmouse kidneys. Increased lipid triglyceride content (correspondingto the broad resonances in the spectra such as at 1.3 and1.0 p.p.m. chemical shift) was found in the wild mammals comparedwith the laboratory rat. As well as lipids, smaller metabolites suchas amino acids and sugars could be detected in all animals.

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the effects of arsenic-induced hemorrhage via changes in thediffusion properties of water (18). To follow such investiga-tions using transcriptomics and proteomics would be vastlycomplicated by the lack of a sequenced genome for the animaland species-related differences in protein structures.

Despite a recurring problem of coresonant lipid peaksobscuring low molecular weight metabolites in many tissues,including the liver, kidneys, and cardiac tissue, the techniquehas proved sensitive not only to biochemical changes acrosstissue types but also to drug toxicity, and is particularly use-ful at confirming organ specific toxicity following the identifi-cation of biomarkers in biofluids. Garrod et al. (21) exploredthe techniques potential of linking histopathology and urin-ary biomarkers by investigating 2-bromoethanamine toxicity,a known renal papillary toxin, in two regions of the kidneyand the liver. The drug induces mitochondrial dysfunctionand inhibits fatty acyl-CoA dehydrogenases, with 1H MASNMR spectroscopy detecting a transient rise in glutaric acidin all three tissue types. While both the renal cortex andpapilla demonstrated evidence of changed osmolarity, withdecreases in known osmolytes detected in both tissues, theosmolytes were different for the two tissue types; decreasesin glycerophosphocholine, betaine, and myo-inositol in therenal papillar and TMAO in the renal cortex. Furthermore,toxicity was also detected in the liver where BEA causedincreases in lipid triglycerides, lysine, and leucine.

Waters et al. (6,7) took this analysis one stage furtherwhen investigating alpha-napthylisothiocyanate toxicity inrats livers, by comparing urinary and blood plasma biomar-kers of toxicity directly with metabolite changes in liver tis-sue. 1H MAS NMR spectra of intact liver clearly showedincreases in hepatic liver triglycerides, accompanied bydecreases in glucose and glycogen. This perturbation in lipidand carbohydrate handling was correlated with increasedplasma ketone bodies, and a decrease in TCA cycle intermedi-ates in urine. Such a holistic approach clearly demonstratesthe correlation between the biofluid markers detected andhepatic tissue toxicity, and hence could be used to confirmspecific organ toxicity during the drug development process.


Furthermore, 1H MAS NMR spectroscopy can demonstratewhen a biofluid biomarker does not originate in a given organ.Nicholson et al. (22) demonstrated that acute exposure ofmale rats to cadmium chloride resulted in creatinuria follow-ing organ-specific toxicity in the testes. Thus, it seemedreasonable that similar creatinuria detected in a chronicexposure study of male rats to cadmium chloride may alsoresult from testicular damage (23). However, on closer inspec-tion using 1H MAS NMR spectroscopy, no biochemicalchanges were detected in testicular tissue, and in particularthere was no decrease in tissue creatine content or a changein redox potential in the tissue, known to precede cadmium-induced testicular toxicity. Instead, the most likely explana-tion for the creatinuria was breakdown of muscle tissue inorder to supply glutamine to renal tissue and prevent renaltubular acidosis in the tissue.

The small sample size of tissue required for 1H MASNMR spectroscopy is also a highly attractive feature of thetechnique. With high-quality spectra still being obtainablefrom as little as �5mg of tissue, it is possible to sample organsin a region-specific manner. This has allowed the monitoringof cerebral toxins such as kainic acid and 3-nitropropionicacid across different regions of the rat brain, with toxicityand histology being directly related by the co-preparation of1H MAS NMR spectroscopy and histology samples.

3.2. Toxicology Using Cell Culture Systems

One of the first applications of 1H MAS NMR spectroscopywas the investigation of cultured adipocytes and how such cellculture systems could be used to follow cell proliferation inadipocarcinomas (24). However, cells obtained from cultureshave no connective structures to protect them from themechanical degradation induced by spinning and there wasa possibility that improved spectral resolution arose from celllysis rather than the physical averaging of dipole–dipoleinteractions and chemical shift anisotropy. To examine this,Weybright et al. (24) stained cells with Trypan Blue to inves-tigate whether cell membrane damage was significantly

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greater after spinning at speeds up to 3000Hz. Reassuringlythere was only a modest increase in compromised cells, evenfor particularly large adipocytes, which might be expected tobe particularly vulnerable to such damage. To demonstratethat this was not confined to one cell type and was true forhigher speeds up to 6000Hz, Griffin et al. (9) demonstratedsimilar cell membrane stability and metabolite content inneuronal cells, suggesting this technique would be valuablefor following cell culture investigations into drug toxicity.

This speculation has since been confirmed by two studiesinvestigating cell culture-based methodologies. Ishikawa cellsare a human cell line derived from endometrial adenocarci-noma, and being hormone responsive are potentially very use-ful for investigating drugs which modulate the estrogenreceptor and hence may be agents that will cause epigeneticcancer (25). 1H MAS NMR spectroscopy of intact cells pro-duced spectra with line widths typically less than 5Hz, andlow molecular weight metabolites could further be investi-gated by applying a spin-echo to edit out the more intenselipid signals (Fig. 6). Examining the action of tamoxifen, aprediction to latent structures through partial least squaresmodel (PLS), a regression extension of principal componentanalysis was built to model metabolic changes caused bytamoxifen against dose (10). Amongst the metabolites thatcontributed to this model were ethanolamine, myo-inositol,uridine, and adenosine, suggesting both alterations in cellmembrane turnover and RNA transcription. Furthermore,the metabolic effects of other estrogen modulators could bemonitored using this PLS model, effectively scoring thesedrugs in terms of tamoxifen doses (26).

Bollard et al. (27) have also examined d-galactosamine inliver spheroids, a cell culture preparation of hepatocytes thatmaintain cell–cell interactions. Using a combined approach ofPCA and Orthogonal Signal Correction (OSC) to examinechanges in NMR spectral profiles, treated spheroids hadincreased lipid triglycerides and cholesterol content, parallel-ing the changes known to occur in hepatic tissue. As pre-viously mentioned these lipids are most likely to arise fromcytosolic lipid pools, rather than cell membranes, with 1H


MAS NMR spectroscopy providing a unique mechanism forinvestigating these metabolites. While chloroform=methanolextraction followed by either NMR spectroscopy, HPLC, ormass spectrometry would be alternatives, the extraction pro-cedure would also extract membrane bound lipids, dilutingany changes detected in the cytosol.

Potentially, such techniques could be used to monitordrug interactions and subsequent toxicity in any cell culturesystem. However, the technique is also sensitive to certainchemical and physical changes that are not often considered,for example, during molecular biology manipulations. Choline

Figure 6 Aliphatic region of two 1H MAS NMR spectra acquiredusing the CPMG pulse sequence with 10ms (B) and 320ms (A) totalspin echo time. The expanded region shows the effects of T2

attenuation on the resonances of choline containing metabolitesbetween 3.2 and 3.3 ppm. Key: 1. CH3CH2 lipid groups, 2. leucine,valine, isoleucine, 3. lactate, 4. CH2CH2 lipid groups, 5. alanine,6. COCH2CH2 lipid groups, 7. C¼CHCH2CH2 lipid groups, 8. acetylgroups, 9. glutamate, 10. creatine, 11. choline, 12. phosphocholine,13. glycerophosphocholine=phosphatidylcholine, 14. phosphoetha-nolamine, 15. taurine, 16. myo-inositol.

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containing metabolites have been associated with a number ofdisorders, including malignant cell growth, Duchenne muscu-lar dystrophy and multiple sclerosis (2,28,29). During transi-ent transfection of an unknown cloned gene thought to beinvolved in lipid metabolism using electroportation into hepa-tocytes, large changes in the relative proportion of choline tophosphocholine and phosphatidylcholine were detected asshown in Fig. 6. Under such circumstances it would be tempt-ing to suggest that the gene’s function was related to cholinemetabolism. However, on closer inspection, such changeswere found to accompany transient transfection with theLacZ reporter gene and even naked plasmids, demonstratingthat the effect arose from the action of electroportation ratherthan anything related to the gene function (30).

3.3. Correlation of Metabonomics Data Withother ‘‘-omic’’ Technologies

One potential application of 1H MAS NMR spectroscopy isapplying the technique to provide a metabolic phenotype tocorrelate with other so-called ‘‘omic’’ technologies, such astranscriptional analysis and proteomics during drug toxicity.By allowing the dual observation of aqueous and lipid solublemetabolites, transcriptional changes can be correlated directlywith all the metabolites present within a tissue rather thanlimiting the analysis to a particular subset. Griffin andco-workers (unpublished work) have examined oroticacid-induced fatty liver disease in rats using metabonomics,transcriptomics, and proteomics. The metabolic changesdetected in liver tissue consisted of both increases in lipid tri-glycerides and cholesterol esters, as well as choline containingmetabolites and their degradation products as seen in Fig. 7.Only by observing metabolites directly in the tissue could thisrange of compounds be followed. Furthermore, by providing ametabolic phenotype, or metabotype (31), different timepoints and strains of rats could be compared directly in thesubsequent analyses. Effectively, transcriptional changescould be modeled in terms of a natural ‘‘metabolic time’’rather than the artificial sampling time used in the study.


4. FUTURE DIRECTIONS AND CHALLENGESFOR 1H MAS NMR SPECTROSCOPY

Although spinning speeds used at present do not appear toproduce significant damage in tissue samples, as supercon-ducting magnets move to increased field strength and spectro-meters attain higher observation frequencies samples willpotentially be spun at higher speeds to remove spinning sidebands from the region of interest. At some point, tissue degra-dation and the heating effect caused by spinning the samplewill become significant. Thus, there is a need to develop pulsesequences that can operate at slower speeds but still providehigh resolution MAS 1H NMR spectra. Wind et al. (32) have

Figure 7 1H MAS NMR spectroscopy of liver tissue from ratsexposed to orotic acid over a 3 day time period. Clear increases in-CH2CH2- and -CH3 lipid moieties could be detected across the timeperiod. These changes were also accompanied by alteration in therelative proportions of choline containing metabolites and adecrease in both glucose and glycogen content within the tissues.

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recently described a pulse sequence capable of operating witha sample spinning speed of only 1–4Hz. While their systemwas somewhat artificial, examining finely chopped up liverpieces, it does suggest that slower spinning speeds are possi-ble while still maintaining high resolution. Meanwhile, thedevelopment of in vivo MRS continues, and with improved

Figure 8 A 1H MAS NMR spectrum of an intact glioma removedfrom a rat brain obtained at 600MHz compared with two in vivopulse MRS sequences (LASER and STEAM) examining the sameglioma in vivo at 400MHz. Spectra were provided by Dr. Griffin,University of Cambridge and Prof. Kauppinen, University ofManchester.


coil design and better localization pulse sequences spectra areobtainable that are comparable in resonance line width tothose obtainable using 1H MAS NMR spectroscopy, particu-larly for fatty tissue as demonstrated in Fig. 8. Thus, therewill be wider scope for metabonomic studies in vivo.

Even prior to these advances, the scope of being able todetect metabolites in tissues on a comparable scale to thoseused by histologists makes 1H MAS NMR spectroscopy adesirable technique for toxicologists. If the technique can befully automated a routine step in histopathology will be send-ing sections to be analyzed by both histology and 1H MASNMR spectroscopy, providing another tier in the systemsapproach to drug toxicity.

REFERENCES

1. Matthews PM, Francis G, Antel J, Arnold DL. Proton magneticresonance spectroscopy for metabolic characterization of pla-ques in multiple sclerosis. Neurology 1991; 41(8):1251–1256.

2. Cadoux-Hudson TAD, Blackledge MJ, Rajagopalan B,Taylor DJ, Radda GK. Human primary brain tumour metabo-lism in vivo. Br J Cancer 1989; 60:430–436.

3. Rae C, Scott RB, Thompson CH, Dixon RM, Dumughn I,Kemp GJ, Male A, Pike M, Styles P, Radda GK. Brainbiochemistry in Duchenne muscular dystrophy: a IH magneticresonance and neuropsychological study. J Neurol Sci 1998;160(2):148–157.

4. Andrew ER. The narrowing of NMR spectra of solids by high-speed specimen rotation and the resolution of chemical shiftand spin multiplet structure for solids. Prog Nucl Magn ResonSpectrosc 1972; 8:1.

5. Bollard ME, Garrod S, Holmes E, Lindon JC, Humpfer E,Spraul M, Nicholson, JK. High resolution 1H and 1H-13Cmagic angle spinning NMR spectroscopy of rat liver. MagnRes Med 2000; 44:201–207.

6. Waters NJ, Holmes E, Waterfield CJ, Farrant RD, NicholsonJK. NMR and pattern recognition studies on liver extracts

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and intact livers from rats treated with alpha-naphthylisothio-cynate. Biochem Pharmacol 2002; 64(1):67–77.

7. Waters NJ, Holmes E, Williams A, Waterfield CJ, Farrant RD,Nicholson JK. NMR and pattern recognition studies on thetime-related metabolic effects of alpha-naphthylisothiocyanateon liver, urine, and plasma in the rat: an integrative metabo-nomic approach. Chem Res Toxicol 2001; 14(10):1401–1412.

8. Waters N. Ph.D. thesis, Imperial College of Science Tech-nology and Medicine, 2001.

9. Griffin JL, Bollard ME, Nicholson JK, Bhakoo K. Metabolicprofiles of intact cultured neuronal and glial cells derived fromHRMAS 1H NMR spectroscopy. NMR Biomed 2002; 15:375–384.

10. Griffin JL, Pole JCM, Nicholson JK, Carmichael PL. Cellularenvironment of metabolites and a metabonomic study oftamoxifen in endometrial cells using gradient high resolutionmagic angle spinning 1H NMR spectroscopy. BBA GeneralSubjects 2003; 278(46):45915–45923.

11. Siminovitch DJ, Ruocco MJ, Olejiniczak ET, Das Gupta SK,Griffin RG. Anisotropic 2H nuclear magnetic resonance spin-lattice relaxation in cerebroside- and phospholipids cholesterolbilayer membranes. J Biophys 1988; 54:373–381.

12. Millis K, Maas E, Cory DG, Singer S. Gradient high resolutionmagic angle spinning nuclear magnetic resonance spectro-scopy of human adipocyte tissue. Magn Reson Med 1997;38:399–403.

13. Millis K, Weybright P, Cambell N, Fletcher JA, Fletcher CD,Cory DG, Singer S. Classification of human liposarcoma andlipoma using ex vivo proton NMR spectroscopy. Magn ResonMed 1999; 41:257–267.

14. Griffin JL, Williams HJ, Sang E, Nicholson JK. Abnormal lipidprofile of dystrophic cardiac tissue as demonstrated by one-and two-dimensional magic-angle spinning (1)H NMR spectro-scopy. Magn Reson Med 2001; 46:249–255.

15. Chen J-H, Enloe BM, Fletcher CD, Cory DG, Singer S.Biochemical analysis using high-resolution magic anglespinning NMR spectroscopy distinguishes lipoma-like


well-differentiated liposarcoma from normal fat. J Am ChemSoc 2001; 123(37):9200–9201.

16. Wu D, Chen A, Johnson CS. An improved diffusion-orderedspectroscopy experiment incorporating bipolar-gradientpulses. J Magn Reson (Series A) 1995; 115:260–264.

17. Griffin JL, Walker L, Garrod S, Holmes E, Shore RF, Nichol-son JK. Metabolic differences exemplified by urinary profilesand renal tissue in the bank vole (Cleithrionomys glareolus),and the wood mouse (Apodemus sylvaticus), the white toothedshrew (Crocudura suaveolens) and a strain of laboratory ratusing 1H NMR. J Compar Biochem Physiol Part B 2000;127:357–367.

18. Griffin JL, Walker L, Shore RF, Nicholson JK. High-resolutionmagic angle spinning 1H-NMR spectroscopy studies on therenal biochemistry in the bank vole (Clethrionomys glareolusand ) the effects of arsenic (As3þ) toxicity. Xenobiotica 2001;31:377–385.

19. Griffin JL, Walker LA, Troke J, Osborn D, Shore RF,Nicholson JK. The initial pathogenesis of cadmium inducedrenal toxicity. FEBS Lett 2000; 478:147–150.

20. World Health Organisation. Environmental health Criteria134:cadmium. WHO, Geneva, 1992.

21. Garrod S, Humpher E, Connor SC, Connelly JC, Spraul M,Nicholson JK, Holmes E. High resolution 1H NMR and magicangle spinning NMR spectroscopic investigation of the bio-chemical effects of 2-bromoethanamine (BEA) in intact renalhepatic tissue. MRM 2000; 45(5):781–790.

22. Nicholson JK, Hingham DP, Timbrell JA, Sadler PJ. Quantita-tive high resolution 1H NMR urinalysis studies on thebiochemical effects of cadmium in the rat. Mol Pharmacol1989; 36:398–404.

23. Griffin JL, Troke J, Walker LA, Shore RF, Lindon JC,Nicholson JK. The biochemical profile of rat testicular tissueas measured by magic angle spinning 1H NMR spectroscopy.FEBS Lett 2000c; 486:225–229.

24. Weybright P, Millis K, Campbell N, Cory DG, Singer S. Gradi-ent high-resolution magic angle spinning IH nuclear magnetic

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resonance spectroscopy of intact cells. Magn Reson Med 1998;39:337–344.

25. Carmichael PL. Cancer Invest 1998; 16(8):604–611.

26. Pole JCM. Ph.D. thesis, Imperial College of Science, Technol-ogy and Medicine, London, UK, 2002.

27. Bollard ME, Xu J, Purcell W, Griffin JL, Quirk C, Holmes E,Nicholson JK. Metabolic profiling of the effects of d-galactosa-mine in liver spheroids using 1H NMR and MAS-NMR spec-troscopy. Chem Res Toxicol 2002; 15:1351–1359.

28. Florian CL, Preece NE, Bhakoo KK, Williams SR, Nobel MD.Cancer Res 1995; 55(2):420–427.

29. Florian CL, Preece NE, Bhakoo KK, Williams SR, Nobel MD.NMR Biomed 1995; 8(6):253–264.

30. Griffin JL, Mann C, Scott J, Shoulders C, Nicholson JK. Cho-line containing metabolites during cell transfection: an insightinto magnetic resonance spectroscopy detectable changes.FEBS Letts 2001; 509:263–266.

31. Gavaghan CL, Holmes E, Lenz E, Wilson ID, Nicholson JK. AnNMR-based metabonomic approach to investigate the bio-chemical consequences of genetic strain differences: applica-tion to the C57BL10J and Alpk:ApfCD mouse. FEBS Lett2000; 484(3):169–174.

32. Wind RA, Zhi Hu J, Rommereim DN. High resolution (1)HNMR spectroscopy in organs and tissues using slow magicangle spinning. Magn Reson Med 2001; 46:213–218.


6

The Application of Metabonomics asan Early In Vivo Toxicity Screen

GREGORY J. STEVENS

Drug Safety Evaluation Pfizer GlobalResearch and Development,

La Jolla, CA, U.S.A.

ALAN J. DEESE

Analytical Research andDevelopment, Pfizer GlobalResearch and Development,

La Jolla, CA, U.S.A.

DONALD G. ROBERTSON

Drug Safety Evaluation Pfizer GlobalResearch and Development,


1. INTRODUCTION

1.2. Why Use Metabonomics as a ScreeningTool?

High-throughput screening efforts in early drug discovery,combined with combinatorial and computational chemistryproduce ever-increasing numbers of potential leads for

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further testing. On the other end of the spectrum in drugdevelopment, increased regulatory hurdles and complex clin-ical programs have significantly raised the cost of drug devel-opment. Drugs withdrawn from the market due to safetyissues have also highlighted the need for better safety testingprior to marketing new drugs. This presents a uniquechallenge to the pharmaceutical and biotechnology industriesto reduce attrition by bringing forward safe and efficaciousdrugs with greater survival rates. A significant bottleneckin the drug discovery and development process is the non-clinical efficacy and safety evaluation, in particular the con-duct of in vivo studies. While genomic and proteomictechnology will play a part in alleviating this bottleneck inthe future, they are not well positioned to assess in vivo toxi-city of new chemical entities. Which tissue do you profile?When?What dose? Not to mention the low-throughput natureof these technologies. Metabonomic technology can helpbridge this gap by providing a more rapid throughput methodto identify target organ effects, as well as dose and timerelationships early within a drug discovery program.

The subject of recent reviews (1–3), metabonomic tech-nology has evolved into a novel tool that can rapidly evaluatethe metabolic consequences of disease and drug-induced toxi-city through the use of 1H-NMR spectroscopy of biofluidscoupled with pattern recognition (4). This new tool allowsfor the non-invasive evaluation of toxicity by monitoringchanges in endogenous biochemicals from a single animal,and if needed these findings can be confirmed with traditionaltoxicological endpoints such as clinical and histopathology.Changes in the levels of endogenous biochemicals are causedby toxicant-induced perturbation in homeostasis, disruptingthe normal composition of key cellular metabolic pathwaysin targeted tissues. These disruptions result in changes inthe composition and quantity of biochemicals in various bio-fluids such as urine and blood. The biochemical compositionof the biofluid reflects, in part, specific target organ dysfunc-tion and host response. While individual biochemicals presentin biofluid may offer clues to cellular pathways perturbed by aparticular toxicant, a simple visual comparison of changes

196 Stevens et al.

associated with treatment is better suited for screening. Inaddition, high resolution NMR could provide specific biomar-kers of toxicity, however, these concepts are outside the scopeof this chapter.

This chapter will focus on the logistics of metabonomicsin rodent models and provide examples of metabonomic profil-ing in rats and mice. The objective of this chapter is to providetoxicologists, who may be unfamiliar with the technology,with information required to assess the practical applicabilityof the technology as a screening tool in support of drugdiscovery.

2. EXPERIMENTAL CONSIDERATIONS

2.1. Urinary NMR: Why? and How?

2.1.1. Urine is the Biofluid of Choice

One of the many advantages of the metabonomic technology isthat almost any biological fluid can be measured. Wholeblood, plasma, serum, saliva, spinal fluid, seminal fluid, aqu-eous humor, etc., can all be used for metabonomic studies(1,5). With the use of microflow probes, even small amounts(2 mL) of cerebral spinal fluid can be monitored by 1H-NMR(6). Profiling of these fluids can provide powerful mechanisticinformation; however, to be applied in a screening paradigmthe biofluids used need to be simple to obtain, require fewprocessing steps and of sufficient quantity for analysis.Therefore, blood and urine are the logical samples forscreening applications.

Urine has significant advantages over blood for screen-ing applications. Refrigerated serum or plasma can be viscousdue to high protein content, which can be problematic withauto-injectors. Excess protein can also stick to instrumenta-tion leading to problems when hundreds of samples are ana-lyzed. Another concern is that withdrawing blood is aninvasive procedure that can induce some stress in animals(7). This stress can add additional variability due to samplecollection. Blood volume in rodents (particularly mice) is lim-ited; therefore, repeated analysis is not always possible and

Metabonomics as an Early In Vivo Toxicity Screen 197

adds systemic variability due to blood loss. Perhaps the big-gest problem with blood as a sample, is timing the samplefor collection. The volume limitations mentioned above as wellas practical and ethical considerations limit the number ofsamples that can be obtained in any 24-hr period. Whileselecting time-points relative to dose is standard practice,there is no guarantee that any individual animal will respondto meet a preestablished timetable of sampling. Unlike bloodcollection, collecting urine from animals housed in metabo-lism cages is non-invasive, and comprehensive, avoidingmany of these complications. Thus, the biofluid of choice forthe application of metabonomics as an in vivo screen inrodents is urine.

The biochemical analytes present in urine provide aunique ‘‘fingerprint’’ of the host physiology. Within a givenurine sample, multiple endogenous analytes can easily bedetected by 1H-NMR in both rats and mice (Fig. 1). These

Figure 1 Representative 500MHz 1H NMR spectrum of urinefrom an untreated male rat and mouse. Common urinary analytesare highlighted.

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analytes are consistent in control animals and do not varysubstantially in healthy animals over a short period of time(Fig. 2). Repeated sampling of urine from a single animal dur-ing the course of a toxicological or pathological event providesa rich source of information. These data could be used notonly to follow toxicological consequences of a given treatmentbut also provide the opportunity to monitor reversibility.

2.1.2. Urine Collection

One of the most important components in applying metabo-nomics as a screening tool is the experimental design andproper collection of urine. Bacterial contamination can signif-icantly alter the 1H-NMR profile. Therefore, urine should becollected under refrigerated (0–4�C) conditions to diminishbacterial growth. A small amount of 1% sodium azide is addedto the collection tube to control bacterial contamination. The

Figure 2 1H NMR urinary profile of a single male B6C3F1 mouseover a 5-day period.


preferred method of collection is the use of refrigerated meta-bolism racks (VWR, West Chester, PA). Each rack allows forthe simultaneous housing of 12 individual metabolism cages,equipped for automatic timed collection of urine in conicaltubes situated in a refrigerated unit. Nalgene cages come withinserts that allow for simple conversion for housing mice andrats. One downside to this rack system is the high cost; over$72,000 per rack (with cages) and an estimated 6-month delayin delivery of the units upon ordering.

While metabolism cage collection is preferred for thereasons stated above, any urine collection procedure that iscapable of collecting clean (non-bacterially contaminated)urine in sufficient volume (e.g., >100mL) can be utilized.Addition of sodium azide or other ‘‘NMR friendly’’ (i.e.,without 1H-NMR peaks) bacterial-static compound shouldbe considered since samples will frequently thaw and cometo room temperature at some time during processing or ana-lysis. A final concentration of approximately 0.1% sodiumazide is adequate for this purpose.

2.1.3. Sample Handling

One of the greatest hurdles in applying metabonomics as anearly screen is the large numbers of samples that require ana-lysis. Therefore, sample preparation requirements should bekept to a minimum. Several factors related to sample hand-ling could impact the outcome of NMR results. The designof flow probes require that samples be free of solids to avoidclogging. Therefore, samples should be clarified via gravityprecipitation or centrifugation prior to placing into 96 wellplates. Osmolarity of the sample can alter the efficiency ofenergy transfer from the probe and sample pH can influencethe chemical shifts of molecules with ionizable groups suchas amines and carboxylic acids. Both of these effects can bediminished by diluting urine using a strong buffer (0.2Msodium phosphate, pH 7.4) in a 1:2 (buffer:urine) ratio. Forrats and mice, the urine can be diluted using a 1:2 ratioof urine to buffer without significant perturbations ofthe spectrum. The dilution of the urine does not impact the

200 Stevens et al.

interpretation of NMR data, since the endogenous analytesare present at reasonably high concentrations. Also, the dataare normalized to account for variability in metabolite concen-tration from sample to sample. As an internal chemical shiftstandard, sodium 2,20,3,30deutero-3-trimethylsilylpropionate(TSP) provides a single resonance, which defines 0 ppm inan NMR spectrum. A lock solvent such as D2O should alsobe added. Both steps are efficiently done by adding 1.0mMsolution of TSP dissolved in D2O to the buffered sample to afinal D2O concentration of 5–10%. Samples should be storedat �20�C after dilution and protected from light. Upon addi-tion of buffer and TSP, a small amount of precipitation, possi-bly calcium salts, usually occurs. Therefore, to avoid problemswith sample loading care should be taken in setting up theneedle depth to avoid aspirating solid material. If a liquidhandling system is used, samples waiting in the queue shouldbe maintained at �10�C. The minimum sample volume afteraddition of buffer and TSP is approximately 340mL. For flowprobes with 60 mL flow cells and 500mL for probes with 120mLflow cells.

2.1.4. Typical NMR Analysis

Other chapters in this volume provide a greater level of detailaround analyzing samples by 1H-NMR. This brief descriptionoutlines the processes used in the data provided in this chap-ter and options for conducting these studies in a screeningmode. As described above, urine is processed in 96 well plates,assisted with liquid handling systems equipped with autoin-jectors. Each sample is loaded into an NMR flow cell andallowed to come to probe temperature. For spectrum acquisi-tion a minimum of 64 pulses are recommended. The 1H-NMRcan be derived from a variety of instruments, however, forthis chapter they were generated from either a 500 or600MHz instrument equipped with a 60mL flow probe usinga NOESY one-dimensional (1D) preset sequence (13).Although two-dimensional (2D) 1H-NMR (8,9) and solid tissueNMR via magic angle spinning can provide additional insightinto interpretation of biochemical changes (10–12), these


more labor-intensive methodologies are not suitable for initialscreening but extremely powerful as follow-up to observedfindings. Free induction decays (FIDs) are multiplied by anexponential decay function (LB¼ 1.0Hz), zero filled from64K to 96K data points, and then converted to frequencydomain spectra using fast Fourier transformation.

The amount of data present within a single 1D 1H-NMRspectrum is quite extensive and magnified in the context of ametabonomic study; repeated sampling over time from multi-ple animals creates a dilemma in rapid data interpretation.One area that slows the data analysis is the base line correc-tion and phasing that is often done manually for each spec-trum. Automated phasing and base-line distortion correctionalgorithms will help speed up this portion of spectraldata analysis (13). Newer software and methodologies arecurrently being developed to address this issue.

In order to deal with the complex data sets derived fromthese studies, the spectra are data reduced using AMIX soft-ware (Analysis of MIXtures, Bruker GmbH, Karlsruhe,Germany). This reduction method allows for multivariatestatistical analysis of the data, reducing the data to compre-hensive plots and the generation of models for classification(4). For a given 1H-NMR spectrum within the chemical shiftrange of 0.2–10.0, spectral integrals are measured over0.04 ppm contiguous regions. Areas devoid of endogenouspeaks at either end of the spectrum and the region containingurea and water resonances (6.0–4.50 ppm) are excluded fromdata reduction. All data are normalized in AMIX by dividingeach integrated segment by the total area of the spectrum(minus the excluded region). The resulting integrals areexported as ASCII files into Microsoft Excel (MicrosoftCorporation) prior to performing principal component analysis.

2.1.5. PCA Analysis

A single spectrum may contain over 250 spectral integralregions following data reduction. One approach in dealingwith these complex data sets is through the use ofpattern recognition methodologies. One widely used pattern

202 Stevens et al.

recognition method for metabonomic analysis is the use ofmultivariate analysis and principal component analysis(PCA) (4,14). Pirouette (V2.7, InfoMetrix, Inc., Woodenville,WA, USA) and SIMCA-P (UMETRICS, Inc., Kinnelon, NJ)are two software packages commonly used for analysis ofmetabonomic data (14). These statistical software packagesallow for graphical representation of independent indices(e.g., principal components, PCs) derived from the reduceddata set. The integral spectral regions represent linear combi-nations of variables or PCs, which do not correlate to eachother, but do reflect the variance in the original data set.The first few PCs describe the greatest variation in the data.When plotted against each other to display patterns or group-ings, the difference in NMR spectral patterns can easily bevisualized. A good example of this is presented in Fig. 3. Mice

Figure 3 Effect of animal feed on non-treated mouse urinary pro-file. Male B6C3F1 mice were maintained on standard rodent chow(Purina #5002; circles) or folate deficient chow (triangles) for twoweeks prior to urine collection. A 24hr urine collection was per-formed and resulting 1H NMR was reduced and PCA plotconstructed.


fed two different types of food significantly changed the urin-ary output of endogenous biochemicals. These changes aredifficult to discern from an NMR plot but can easily be distin-guished using PCA plots. A detailed discussion of statisticalmethods appropriate for use in metabonomic studies can befound in Chapter 8.

2.1.6. Sample Variability

Variability is inherent to any experimental test systemand this variability is often magnified when experimentaldata are derived from highly sensitive techniques. This is par-ticularly true in the context of metabonomic profiling usinghigh-resolution 1H-NMR and pattern recognition. Holmes etal. (15) demonstrated that different strains of rats are easilydistinguishable by PCA. Others have shown differences indiurnal rhythm and estrus cycle (16). Gender differences, ani-mal health, and stress may all affect urinary analyte profiles.Age has also been shown to have a significant impact on thebiochemical pattern in rats (17). In mice, differences in typesof chow demonstrate a dramatic difference in the PCA plots ofnon-treated animals solely based on the type of food the ani-mals are provided (Fig. 3). One additional variable that canconfound the interpretation of the NMR spectrum duringthe conduct of toxicology studies is the type of vehicle usedin an in vivo study (18). Oral administration of 0.5%carboxymethylcellulose=0.2% tween, 0.5% 0.5% hydroxypro-pyl methylcellulose (methocel), and 0.1M sodium phosphatebuffered water have demonstrated to be suitable for metabo-nomic studies. PEG 200=300=400, microemulsions containingpropylene glycol and labrafil=corn oil, induce significantchanges in the urinary spectrum and could be problematic ifused for metabonomic studies. Thus, careful consideration ofstudy design should be considered prior to the conduct ofmetabonomic studies.

A major advantage in the use of urine as a sample is thatcontinuous sampling from pretest through termination can beperformed. This allows for the observation of urinary changesincluding onset of effects, peak changes, and regression at the

204 Stevens et al.

individual animal level. What may at first appear to be inter-nal variability in response may actually be a temporal differ-ence at the individual animal level. Despite our intentions,animals tend not to respond to toxic insult at the same rate.Unfortunately, when we employ standard toxicity endpoints,we have to guess at when the peak toxicity will occur andsample accordingly, with the assumption that all animals willrespond in more or less the same time course. In reality, peakeffects for one animal may not be the same for another animalleading to temporal heterogeneity in the data. To compensatefor this variability, we typically use larger ‘‘N’’s so that themean response will be more likely to reflect the severity oftoxicity at any given time. This effect is demonstrated inFig. 4, which shows metabonomic data, extracted from astudy looking at several nephrotoxins. The data presented

Figure 4 PCA plot from rats treated with 150mg=kg PAP.Twenty-four hour urine samples were collected from four rats(a–d) over a period of 4 days after treatment. The number inparentheses indicated total urinary protein levels (mg=24hr).


were collected from a group of four rats (a–d) administered150mg=kg of the tubular nephrotoxin, paraaminophenol(PAP) with 24hr urine samples collected pretest and dailythrough Day 4 (96h post dose). The numbers in parenthesesnext to the animal identification are the total urinary proteinmeasurements (in mg=24hr) from those same samples. Anovert effect is obvious from all treated animals with a markednorthwest trajectory change on Day 1 with subsequentregression of the samples toward pretest metabolic space.Concurrently, total urinary protein increased as much as50-fold in some treated animals. It is readily apparent thatthe metabonomic data correlate with peak changes in totalurinary protein based on an individual animal basis, but thechanges were not absolutely proportional to urinary total pro-tein with proteins ranging from 14.4 to 150.6mg=24hr inclose proximity in metabolic space. Two observations can bemade from these data. First, metabonomic data are more thanjust a surrogate measure of urinary total protein (if it werenot why do it?). This is consistent with the concept that meta-bonomics is a systems evaluation of the whole animal not justa tubular or glomerular functional marker. Second, one ani-mal (animal b) had a distinctly different temporal responsewith a peak metabonomic effect on Day 1 consistent withthe other three animals, but a much more rapid return to con-trol. These data are consistent with the urine protein data,with peak protein elevation, evident on Day 1 that was only67% of the next closest Day 1 sample with the PC patternback to pretest space by Day 3. Evaluation of single time pointsamples from Day 3 may have lead to the incorrect conclusionthat the compound affected only three of four animals. Addi-tionally, a single time point evaluation on Day 1 could havebeen interpreted correctly as a lesser response by animal Bor it may have been considered typical functional responsevariation to an identically severe lesion. Only when the entiretemporal data set is observed can the conclusion be made as toa difference in severity. Since continuously monitored urinaryprotein was the ‘‘standard’’ tox endpoint in this example, wecould have arrived at this conclusion without metabonomicdata. However, standard urinary measurements are typically

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only useful as markers of nephrotoxicity. The systems biologi-cal approach of metabonomics allows for potential monitoringof many target organs. The temporal advantage of metabo-nomics technology was also demonstrated in rats treated withthe hepatotoxin carbon tetrachloride (17).

2.2. Rodent Models

Theoretically, any species can be used as a predictive model oftoxicity, however, in the context of screening for drug discov-ery, rodents are the species most often employed.

2.2.1. Rat

The majority of metabonomic literature data exists solely forrats. Their size and 24hr urinary output makes the rat, a spe-cies of choice for metabonomic studies. Both Wistar and Spra-gue–Dawley rats weighing from 200–250g eliminate over13mL of urine over a 24hr period (Table 1). As discussedrelated to variability, metabonomics is a very sensitive techni-que for measuring subtle changes in urinary analytes. There-fore, the same gender, strain, age should be used within agiven experiment, with the animals housed under controlledconditions with access to the same water and chow. Theseimportant considerations are no different than those

Table 1 Average 24-hr Urinary Output from Different of Male Ratand Mouse Strains

Species Strain NWeightrange (g)

Collectionperiod (hr)

Volume (mL)mean � SD

Rat Sprague–Dawley 30 200–250 8 4.0 � 1.2Rat Sprague–Dawley 30 200–250 24 13.5 � 2.5Rat Wistar 36 200–250 24 15.5 � 5.7Mouse B6C3F1 30 19–26 24 0.81 � 0.45Mouse C3H 4 19–26 24 0.74 � 0.28Mouse CD-1 4 19–26 24 0.87 � 0.19Mouse C57BL=6 4 19–26 24 0.77 � 0.18Mouse B6D2F1 4 19–26 24 0.76 � 0.20Mouse A=J 4 19–26 24 0.36 � 0.11


considered in designing in vivo toxicity studies using conven-tional toxicological methods such as clinical chemistry or his-topathologic assessment. However, predictive metabonomicstechnology will rely heavily on a database of spectral datathat will probably be generated in a single strain, singlegender under specific control conditions. To date, mostmetabonomic data have been generated in either theSprague–Dawley or Wistar rat. Male rats and mice arepredominately used to avoid urinary changes associated withthe estrous cycle (16). COMET consortium has elected to usethe Sprague–Dawley (Crl:CD(SD)IGS BR) rat as the animalmodes for developing a database of liver and kidneytoxicants (19). Strain differences and variations in responseto treatment can limit the broad utility of such a database;however, such a database may not be needed in the contextof applying metabonomics as an early screen. Where a simpleassessment of changes with respect to an appropriate controlmay be all that is required.

2.2.2. Mouse

The mouse is the ideal screening species given their smallsize. Early in the drug discovery process, the amount of testmaterial is often less than a gram and considered very pre-cious indeed due to the large number of tests needed toadvance a compound through the discovery process. Mice willtypically use one-tenth the amount of material required for arat study and the potential exists to conduct early in vivo tox-icology studies with as little as 50–500mg of test material(dependent on the screening study design).

In addition to their small size, numerous pharmacologi-cal and disease models exist in mice including a host of geneti-cally modified mice. The unique nature of biochemicalprofiling using metabonomics affords this technique as a use-ful phenotyping tool. The urinary NMR profile has been usedto investigate genetic strain differences between C57BL10Jand Alpk:ApfCD mice (20). The major differences observedbetween these two phenotypically normal mice were observedin the tricarboxylic acid cycle intermediates and methylamine

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metabolism intermediates (20). Metabonomics has also beenused in a mouse model of neuronal ceroid lipofuscinosis (21).The biochemical changes that occur with vitamin E treatmentwere used to characterize the pathological response in the dis-ease model (21). Other potential metabonomic studies in micecould include monitoring responses to pharmacological treat-ment of disease models early in the discovery process; poten-tially providing a non-invasive surrogate of efficacy. Oneexample of such a study was shown with an antidiabeticagent, BRL 49653, in a diabetic mouse model (22). This studyshowed improved urinary glucose excretion upon treatmentwith BRL 49653, demonstrating the utility of metabonomicsas a non-invasive method for measuring efficacy in a mousemodel.

Unfortunately, there are few reports of metabonomic stu-dies in mice treated with known toxicological agents. Onerecent example shows the utility of 1H MAS NMR of hepatictissues derived from mice treated with acetaminophen (23),however, urinary NMR profiling was not performed. One pos-sible reason for few urinary metabonomic investigations inmice may be due in part to the limited amount of urine pro-duced by mice in a 24-hr period. On average, the 24-hr urin-ary output of a single mouse is less than 1.0mL (Table 1) withsome variability in output among several mouse strains. Outof six different mouse strains examined, A=J mice appeared tohave the lowest urinary output, while the other strainstended to produce sufficient quantity of urine for analysis.The availability of flow probes requiring less than 100 mLand the fact that mouse urine tends to be more concentratedthan rats overcome the limitation of detecting analytes insmaller injection volumes.

Mouse urine is collected in a similar fashion to the rat,using the same refrigerated rack system but with insertsdesigned for housing mice (which are commercially available).To avoid extensive dilution of urine, the amount of sodiumazide used is 100mL instead of the 1mL that is typical forthe rat. Care should be given to the cages with daily washingof the collection pan during the course of a study. Smallamounts of animal feed and feces can block or absorb urine


prior to reaching the collection tube. Powdered chow seems todiminish the amount of food particulates. Mice also tend toplay with their watering mechanisms so a trap under thewater outlet helps to divert the water from the collection tube.Although a small amount of water does not impact the NMRspectrum due to the water suppression procedure, excesswater will result in dilution of the sample and possibly limitanalyte detection.

One additional complication in using the mouse as a testsystem is the variety of mouse strains commonly used in sup-port of drug discovery. Similar to the strain differences notedin the rat, mouse strains also exhibit differences in urinaryNMR profile (Fig. 5). The PCA plot shows that A=J mice arequite different than the other strains tested. Given that

Figure 5 Differences in urinary metabonomic profile of variousmouse strains presented as a principal component map. A total of4–6 untreated mice from each strain were placed in metabolismcages for urine collection over a 24-hr period. Data derived fromurinary 1H NMR were used to construct the principal componentmap.

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C57BL=6, C3H and the various B6 strains have commonlineages, it is not surprising to find that A=J mice differ fromthese other strains. These data suggest that metabonomicscan be used to study phenotypic differences among variousanimal models. In theory, any mouse strain could be used inthe course of a metabonomic study as long as an appropriatecontrol group is included.

3. EXAMPLES

3.1. Examples of Metabonomics for Rat-InducedToxicities

A number of reports demonstrate the utility of metabonomicsto identify biochemical changes in rats after treatment withknown toxicants. In addition to examples provided elsewherein this volume, two examples are provided below.

3.1.1. Liver

Application of metabonomic technology to assess liver injuryin the rat has been extensive (12,17,24–28). Known biliarytoxicants have been shown to increase urinary excretion ofbile acids in rats and exhibit unique patterns of toxicity asevident in the NMR profile (17,24). Common finding in ratstreated with liver toxicants are trajectories within PCA maps.Within the PCA plot, the greatest distance from control spaceoften corresponds to the time of greatest cellular injury asdetermined by clinical or histopathology (24,25).

3.1.2. Kidney—BEA

As described above for PAP, metabonomics is quite sensitivein identifying kidney toxicity in rats. However, the type of tox-icant and lesions will alter the urinary profile. A good exam-ple of these differences is data derived from rats treatedwith 2- bromoethylamine (BEA). 2- Bromoethylamineproduces distinctive renal papillary pathology in rats (26).In a metabonomic study, eight rats were administered a sin-gle dose of 150mg=kg BEA with 24hr urine samples collectedpretest, and daily thereafter. Four animals were euthanized


96hrs after dosing (Day 4) and the remainder euthanized240hr after dosing (Day 10) for histopathologic assessment.Urine total protein was measured concurrently. The data fromthe study are summarized in Fig. 6. The pathology wasprogressive with moderate papillary effects noted 96hr afterdosing with marked effects evident 240hr postdose. Interest-ingly, the metabonomic data suggested the most pronounced

Figure 6 Metabonomic and histopathology data obtained fromeight rats treated with 150mg=kg BEA. Four rats were euthanized96hr after dose with the remainder euthanized 10 days postdose.Twenty-four hour urine samples were collected pretest and dailythrough Day 4 with an additional sample collected on Day 10. Totalurine protein is also presented. Ninety-six hours after dosing thepapillary region of the kidney had minimal tubular dilatation, withextensive papillary necrosis evident by Day 10. The metabonomicdata revealed a marked effect on Day 1 followed by regressiontowards control such that samples were nearly back to pretestmetabolic space by Day 10. This trajectory correlated nicely withthe urinary protein data but appeared discordant with the histo-pathology. See text for further explanation.

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affect on Day 1 with subsequent regression towards control,such that the Day 10 samples were nearly completelyreversed. This would appear to indicate a significant discre-pancy between the metabonomic data and the histopathology.However, total urinary protein, as an indicator of renal func-tional patency, was similarly affected, with approximately a63-fold increase in mean urinary protein evident 24hr afterdose, but protein levels were essentially normal on Day 10.These data suggest that while the morphology of the kidneywas tremendously altered, it was functioning fairly normally.Two observations can be made from these data. First, metabo-nomics cannot be seen as a simple surrogate for histopathol-ogy. Second, while the 240hr sample NMR patterns werenearly back to pretest space, they were still distinct from pret-est. It is always dangerous to relate trajectory distance to sys-temic severity when using principal component analysis inthis fashion. The data simply indicate that the spectra onDay 10 had many more features in common with pretest thanwith the Day 1 samples, but those subtle differences that stillwere evident on Day 10 may have tremendous biologic impor-tance and hence toxicological significance. A PCA plot does notspeak to the relative importance of the spectral differences. Todefine the importance of the spectral differences requires bio-logical and toxicological interpretation of the entire data set(clinical pathology, histopathology, and renal function) toestablish importance of any experimental differences.

3.2 Example of Metabonomics for Mouse-Induced Toxicities

3.2.1 Liver—CCl4

Carbon tetrachloride is a classical hepatotoxin that inducescentrilobular hepatotoxicity in a variety of species includingmice (27). In effort to assess the utility of metabonomics inmice as a sensitive tool to measure hepatotoxicity, mice weretreated with CCl4. A total of eight B6C3F1 male mice pergroup were treated orally with either vehicle control (cornoil) or 2400mg=kg CCl4. Four animals from each group wereeuthanized 48hr after dosing (Day 3) and the remainder


euthanized 168hr after treatment (Day 8). Terminal bloodcollections were performed for analysis of clinical chemistrychanges and target tissues (liver and kidney) were collectedfor histological analysis.

At the 48-hr time point, a marked increase (170-fold) inserum ALT was present which corresponded to diffuse centri-lobular hepatocellular necrosis (Table 2). Mild to moderateincreased incidence of hepatocellular mitosis and moderatehepatocellular vacuolation was present in hepatocytes inter-spersed between the necrotic zones. Reversibility of thesechanges was evident in animals euthanized 168hr after treat-ment. ALT values were at control values and pathologicalchanges were limited to diffuse necrosis with minor evidenceof centrilobular mononuclear infiltrates. Complicating inter-pretations of a true hepatic effect, kidney lesions were alsoapparent. Lesions included mild to moderate cortical tubularepithelial necrosis, mild increase in presence of mitotic fig-ures, and eosinophilia or vacuolation of tubular epithelialcells. By Day 8, these changes were still present however, aregenerative response was evident by a marked, multifocaltubular basophilia and nuclear hyperplasia.

The metabonomic data were consistent with thehistopathologic changes. Twenty-four hours after dosing, a

Table 2 Clinical and Histopathology Findings from Mice Treatedwith BEA and CCl4

TreatmentDose

(mg=kg)Necropsyfindings

ClinPatheffects Histopath

BEA 100 #BW (5%),#KW, #LW

#AST, #ALT,#AlkP, #Phos

Renal tubular cell death,lymphoid depletion (bonemarrow, spleen) intestinalcrypt cell death

CCl4 3000 #BW (7%)"LW

"ALT, "AST,"AlkP, "TB

Multifocal degeneration andnecrosis of cortical tubularepithelium; centrilobularhepatocellular necrosis

Abbreviations: Body weight (BW), kidney weight (KW), liver weight (LW), spleenweight (SW), alanine aminotransferase (ALT), aspartic aminotransferase (AST),alkaline phosphatase (AlkP), total bilirubin (TB), phosphorus (Phos).

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considerable difference in urinary NMR profile was evident. Acircular trajectory was apparent in the PCA plot (Fig. 7), sug-gesting a unique glimpse into the changes associated with theinitial insult followed by a repair process. The PCA plot alsoshows that by Day 8 most animals are still not completelyreturned to control values, suggesting that recovery was notentirely complete a week after treatment. This correlated tothe pathological changes remaining in the animals. Althoughthe clinical chemistry changes return to control values, thehistological examinations show hepatic and renal lesionsremaining by Day 8 possibly resulting in the observed changesin urinary metabolites (Fig. 8). Many of the analytes responsi-ble for these changes appear to be related to the Krebs cycle.Although outside the scope of a screening paradigm, lookingwithin the data at individual analytes, may provide unique

Figure 7 PCA plot derived from control and CCl4 treated mice.Male B6C3F1 mice were treated with a single oral dose of CCl4and urine collected over a period of 8 days. Each point within theplot represents an individual animal with the number representingthe study day. Each group is represented as PD (predose), C,(control) and H, (high).


urinary biomarkers of target organ effects. Other chapters inthis volume and several reports highlight these possibilities (28).

The metabonomic effects of CCl4 in the mouse are com-parable to those observed in rats. Rats treated with0.5mL=kg CCl4 (17) showed a maximal response 24hr aftertreatment and a similar circular trajectory during the courseof recovery. Therefore, the rat and mouse may be used inscreening for assessing hepatic injury.

3.2.2 Kidney—BEA

In effort to evaluate the urinary changes induced followingrenal damage, mice were treated with BEA. Similar to rats,BEA is also a renal toxicant in mice; however, BEA has beenshown to target both tubular and papillary epithelium (29).Male mice (eight=group) were treated with a single IP dose

Figure 8 Representative 1H-NMR profile of a mouse treated witha single acute dose of CCl4 (3000mg=kg). Data represent a typicalpretest, Day 3 and Day 8 spectrum with several of the analytesaccounting for differences over time outlined in the spectrum.

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of 100mg=kg BEA or vehicle (saline). Four per group wereeuthanized 48hr after dosing (Day 3) and the remaining ani-mals euthanized 192hr after treatment (Day 8). There wereno changes in clinical chemistry parameters indicative of kid-ney toxicity despite the presence of lesions on Day 3 and 8.Histological changes in the kidneys included the presence ofproteinaceous material in the tubules of the mid-cortex andmedulla, tubular dilation, and mild tubular epithelial necro-sis. Lesions identified on Day 3 were also present on Day 8but were less severe.

Metabonomic changes were consistent with the patholo-gical observations in the kidney. Figure 9 shows changes inthe PCA occurring as early as Day 2 with progression throughDay 8. Unlike the CCl4 PCA, there was no apparent trajectoryback to controls by Day 8. This could be due in part to thecontinued presence of the same histological changes as thoseobserved on Day 3.

Of particular importance in this study was the findingthat metabonomics could idendtify a change in BEA treated

Figure 9 PCA plot derived from mice treated with a single IP doseof 100mg=kg BEA. Each point represents an individual animal withthe number representing the day postday, H¼high dose, andC¼ control.


animals in the absence of significant changes in clinical chem-istry parameters. Both BUN and creatinine serum levels wereunchanged despite the pathological changes observed withinthe kidneys. These pathology changes were apparent in theurinary NMR spectra as shown in the PCA plot (Fig. 8). Thesedata highlight the sensitivity of metabonomics as a diagnostictool for assessing kidney toxicity.

The effect of BEA in the mouse differed from those shownabove in the rat. In the rat, BEA produced profound papillarynecrosis, while in the mouse the lesion was mixed betweentubular and papillary effects. In addition, the urinary NMRprofile for rats appeared to reverse back to control valuesdespite the appearance of continued pathological changeswithin the papillary region of the kidney (Fig. 6). While inmice the trajectory did not appear to exhibit a reversal typeof response.

4. SCREENING MODELS

Although the data from the few examples presented here aretoo limited to draw definitive conclusions regarding the predic-tive nature of the technology, the utility of the metabonomicsapproach should be readily apparent. The technology can notonly demonstrate onset of a toxic event, but also severityand reversal of toxicity can be monitored from a peripheralsample even at the individual animal level. The techniqueappears to, at least in some cases, exhibit greater sensitivitythan traditional clinical chemistry indices. These data, in con-junction with the temporal sequence of events, can providemechanistic insights into the etiology of observed lesions.Taken together, these data demonstrate the enormous poten-tial this technology has in a screening environment.

Metabonomics overcomes many of the limitations in con-ducting in vivo safety studies within a drug discovery-screen-ing paradigm. Due to limited blood volume, repeated samplingfor clinical pathology prevents the early acquisition of a time–response relationship. Many of the traditional clinical pathol-ogy biomarkers are transient and short lived in the serum;

218 Stevens et al.

therefore, significant changes can easily be missed in the con-text of a screening study where animal numbers may be limit-ing. The non-invasive nature of urine collection provides aneasy way to capture a time profile following treatment witha potential drug candidate. Metabonomics may also providegreater sensitivity to changes induced by xenobiotics thanthose observed with traditional clinical pathology. Moreover,a major limitation to early in vivo testing is the resource andtime commitment it takes to conduct histological examinationof tissues. Although metabonomics will not replace pathologi-cal examination of potential target tissues, urinary profilingwill provide an early read of possible changes that could beused to accelerate early decision making.

Outlined in this chapter, were examples from toxicantsthat produce effects following an acute dose. However, manycompounds may require repeat dosing to observe an adverseeffect. Metabonomics can easily accommodate these repeatdose studies. Nothing differs in regards to urine collectionor analysis. In some cases, drug metabolites within the urinecan confound the NMR profile. This can be corrected by sub-tracting those peaks from the spectrum prior to data reduc-tion. Further, collecting urine several days after post dosecan often shed light on treatment related effects after severaldays of treatment, and after which drug related metabolitesin the urine have typically cleared.

So how would metabonomics be used within a screeningparadigm supporting final compound selection? In general,metabonomics could be deployed in two different scenarios.The first approach is to apply metabonomics within programswith known target organ effects (e.g., back-up programs).This is the ideal situation in which to deploy metabonomicsfor the first time; allowing time to build confidence withinmanagement and discovery teams by demonstrating howmetabonomics can be used to improve safety. Having a com-pound with a known toxic effect, such as in a back-up pro-gram, and the ability to monitor these effects using simplePCA plots could function as a nice positive control. Once con-fidence in the utility of metabonomics as an early screen isestablished, a second scenario could include the use of


metabonomics as an early in vivo screen with 5–10 represen-tative compounds from one or two different chemical series.These early assays would help to nominate the final leadcompounds that exhibit minimal changes within the PCA plot.

Another important question to ask is how well will meta-bonomics detect other target organ effects such as bone mar-row, heart, or intestines? Recently, metabonomics was shownto identify rats that exhibit vasculitis (30) and reversal withanti-inflammatories (31). This complicated lesion in animalsmakes it difficult to identify biomarkers and screen for com-pounds that induce these effects. However, metabonomicsmay be the sensitive diagnostic to identify animals with vas-culitis without actually euthanizing animals. Other potentialtargets organs (testes, ovary, pancreas, spleen, and adrenals)that also suffer from poor diagnostics could benefit from thisnew technology.

5. CONCLUSION

Metabonomic technology has great potential to offer research-ers a sensitive tool to non-invasively identify toxicologicalevents that could be applied as an early screen for drug discov-ery. Currently the realization of this potential is underway andthe literature on novel applications will undoubtedly expandexponentially in the next few years. Biological fluids representa vast reservoir of information that can be sampled andassessed using metabonomics technology. Clearly one of ourbiggest hurdles in the near future will be designing approachesto collect, collate, and comprehend this wealth of information.The task for this technology will be similar to those toxicoge-nomic and proteomic initiatives. Metabonomics will not replacethese sister technologies, but should serve as an extension ofthem, aiding in placing data gleaned from these approachesin proper context. One can easily envision a combinedgenomic=proteomic=metabonomic tactical approach to addres-sing etiology and pathology from the gene through the proteinto the phenotype. A key to the success of these endeavors willbe a bioinformatics tool that allows visualization and querying

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of data sets regardless of the source. When combined withmetabolic pathway maps annotated with gene, protein, andmetabolite identification, the tremendous synergy of the tech-nologies will be realized to its fullest potential.

Animal Use Disclaimer

All animal experimentation reported in this chapter wereapproved and conducted in compliance with the AnimalWelfare Act Regulations (9 CFR Parts 1, 2 and 3), the Guidefor the Care and Use of Laboratory Animals (ILAR, 1996), aswell as all internal corporate policies and guidelines.

REFERENCES


2. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: under-standing the metabolic responses of living systems to pathophy-siological stimuli viamultivariate statistical analysis of biologicalNMR spectroscopic data. Xenobiotica 1999; 29(11):1181–1189.

3. Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabo-nomics: a platform for studying drug toxicity and gene func-tion. Nat Rev Drug Discov 2002; 1(2):153–161.

4. Holmes E, Nicholls AW, Lindon JC, Connor SC, Connelly JC,Haselden JN, Damment SJ, Spraul M, Neidig P, Nicholson JK.Chemometric models for toxicity classification based on NMRspectra of biofluids. Chem Res Toxicol 2000; 13(6):471–478.

5. Lindon JC, Nicholson JK, Everett JR. NMR spectroscopy ofbiofluids. Annu Reports NMR Spectrosc 1999; 38:1–88.

6. Griffin JL, Nicholls AW, Keun HC, Mortishire-Smith RJ,Nicholson JK, Kuehn T. Metabolic profiling of rodent biologicalfluids via 1H NMR spectroscopy using a 1mm microlitre probe.Analyst 2002; 127(5):582–584.

7. McGuill MW, Rowan AN. Biological effects of blood loss: impli-cations for sampling volumes and techniques. ILAR News1989; 4:5–20.


8. Willker W, Flogel U, Leibfritz D. A 1H=13C inverse 2D method forthe analysis of the polyamines putrescine, spermidine and sper-mine in cell extracts andbiofluids.NMRBiomed1998; 11(2):47–54.

9. Foxall PJ, Parkinson JA, Sadler IH, Lindon JC, Nicholson JK.Analysis of biological fluids using 600MHz proton NMR spectro-scopy: application of homonuclear two-dimensional J-resolvedspectroscopy to urine and blood plasma for spectral simplificationand assignment. J Pharm Biomed Anal 1993; 11(1):21–31.

10. Garrod S, Humpfer E, Spraul M, Connor SC, Polley S,Connelly J, Lindon JC, Nicholson JK, Holmes E. High-resolution magic angle spinning 1H NMR spectroscopic studieson intact rat renal cortex and medulla. Magn Reson Med 1999;41(6):1108–1118.

11. Tate AR, Foxall PJ, Holmes E, Moka D, Spraul M, NicholsonJK, Lindon JC. Distinction between normal and renal cell car-cinoma kidney cortical biopsy samples using pattern recogni-tion of (1)H magic angle spinning (MAS) NMR spectra. NMRBiomed 2000; 13(2):64–71.

12. Bollard ME, Xu J, Purcell W, Griffin JL, Quirk C, Holmes E,Nicholson JK. Metabolic profiling of the effects of d-galactosa-mine in liver spheroids using (1)H NMR and MAS-NMR spec-troscopy. Chem Res Toxicol 2002; 15(11):1351–1359.

13. Holmes E, Foxall PJ, Nicholson JK, Neild GH, Brown SM,Beddell CR, Sweatman BC, Rahr E, Lindon JC, Spraul M.Automatic data reduction and pattern recognition methods foranalysis of 1H nuclear magnetic resonance spectra of humanurine from normal and pathological states. Anal Biochem1994; 220(2):284–296.

14. Holmes E, Antti H. Chemometric contributions to theevolution of metabonomics: mathematical solutions tocharacterising and interpreting complex biological NMRspectra. Analyst 2002; 127(12):1549–1557.

15. Holmes E, Nicholson JK, Tranter G. Metabonomic characteri-zation of genetic variations in toxicological and metabolicresponses using probabilistic neural networks. Chem ResToxicol 2001; 14(12):182–191.

16. Bollard ME, Holmes E, Lindon JC, Mitchell SC, Branstetter D,Zhang W, Nicholson JK. Investigations into biochemical

222 Stevens et al.

changes due to diurnal variation and estrus cycle in femalerats using high-resolution (1)H NMR spectroscopy of urineand pattern recognition. Anal Biochem 2001; 295(2):194–202.

17. Robertson DG, Reily MD, Sigler RE, Wells DF, Paterson DA,Braden TK. Metabonomics: evaluation of nuclear magneticresonance (NMR) and pattern recognition technology for rapidin vivo screening of liver and kidney toxicants. Toxicol Sci2000; 57(2):326–337.

18. Beckwith-Hall BM, Holmes E, Lindon JC, Gounarides J,Vickers A, Shapiro M, Nicholson JK. NMR-based metabonomicstudies on the biochemical effects of commonly used drugcarrier vehicles in the rat. Chem Res Toxicol 2002;15(9):1136–1141.

19. Lindon JC, Nicholson JK, Holmes E, Antti H, Bollard ME,Keun H, Beckonert O, Ebbels TM, Reily MD, Robertson D,Stevens GJ, Luke P, Breau AP, Cantor GH, Bible RH,Niederhauser U, Senn H, Schlotterbeck G, Sidelmann UG,Laursen SM, Tymiak A, Car BD, Lehman-McKeeman L, ColetJM, Loukaci A, Thomas C. Contemporary issues in toxicologythe role of metabonomics in toxicology and its evaluation bythe COMET project. Toxicol Appl Pharmacol 2003;187(3):137–146.

20. Gavaghan CL, Holmes E, Lenz E, Wilson ID, Nicholson JK.An NMR-based metabonomic approach to investigate thebiochemical consequences of genetic strain differences: appli-cation to the C57BL10J and Alpk:ApfCD mouse. FEBS Lett2000; 484(3):169–174.

21. Griffin JL, Muller D, Woograsingh R, Jowatt V, Hindmarsh A,Nicholson JK, Martin JE. Vitamin E deficiency and metabolicdeficits in neuronal ceroid lipofuscinosis described by bioinfor-matics. Physiol Genomics 2002; 11(3):195–203.

22. Connor SC, Hughes MG, Moore G, Lister CA, Smith SA. Anti-diabetic efficacy of BRL 49653, a potent orally active insulinsensitizing agent, assessed in the C57BL=KsJ db=db diabeticmouse by non-invasive 1H NMR studies of urine. J PharmPharmacol 1997; 49(3):336–344.

23. Coen M, Lenz EM, Nicholson JK, Wilson ID, Pognan F,Lindon JC. An integrated metabonomic investigation of


acetaminophen toxicity in the mouse using NMR spectroscopy.Chem Res Toxicol 2003; 16(3):295–303.

24. Beckwith-Hall BM, Nicholson JK, Nicholls AW, Foxall PJ,Lindon JC, Connor SC, Abdi M, Connelly J, Holmes E. Nuclearmagnetic resonance spectroscopic and principal componentsanalysis investigations into biochemical effects of three modelhepatotoxins. Chem Res Toxicol 1998; 11(4):260–272.

25. Nicholls AW, Holmes E, Lindon JC, Shockcor JP, Farrant RD,Haselden JN, et al. Metabonomic investigations intohydrazine toxicity in the rat. Chem Res Toxicol 2001; 14(8):975–987.

26. Holmes E, Bonner FW, Sweatman BC, Lindon JC, Beddell CR,Rahr E, Nicholson JK. Nuclear magnetic resonance spectro-scopy and pattern recognition analysis of the biochemical pro-cesses associated with the progression of and recovery fromnephrotoxic lesions in the rat induced by mercury(II) chlorideand 2-bromoethanamine. Mol Pharmacol 1992; 42(5):922–930.

27. Mansour M. Protective effects of thymoquinone and desfer-rioxamine against hepatotoxicity of carbon tetrachloride inmice. Life Sci 2000; 66:2583–2591.

28. Clayton TA, Lindon JC, Everett JR, Charuel C, Hanton G,Le Net JL, Provost JP, Nicholson JK. An hypothesis for amechanism underlying hepatotoxin-induced hypercreatinuria.Arch Toxicol 2003; 77(4):208–217.

29. Gregg NJ, Bach PH. 2-Bromoethanamine nephrotoxicity inthe nude mouse: an atypical targetting for the renal cortex.Int J Exp Pathol 1990; 71:659–670.

30. Robertson DG, Reily MD, Albassam M, Dethloff LA. Metabo-nomic assessment of vasculitis in rats. Cardiovasc Toxicol2001; 1(1):7–19.

31. Slim RM, Robertson DG, Albassam M, Reily MD, Robosky L,Dethloff LA. Effect of dexamethasone on the metabonomicsprofile associated with phosphodiesterase inhibitor-inducedvascular lesions in rats. Toxicol Appl Pharmacol 2002;183(2):108–109.

224 Stevens et al.

7

Strategies and Techniques for theIdentification of Endogenous and

Xenobiotic Metabolites Detected inMetabonomic Studies

JOHN SHOCKCOR

Metabometrix, South Kensington,London, U.K.

IAN D. WILSON

Department of Drug Metabolism andPharmacokinetics, AstraZeneca,

Mereside, Alderley Park,Macclesfield, Cheshire, U.K.

1. INTRODUCTION

The metabolic profiles of biological fluids from normal indivi-duals contain a plethora of endogenous low mass metabolites,the composition of which depends upon the sample type(plasma urine, bile, etc.) and factors such as the species,strain, age, gender, diet, and gut microfloral composition ofthe organism fromwhich the sample derives and, indeed, even

225

the time of day at which the sample was taken. To this can beadded the changes brought about in these endogenous profilesdue to disease or toxicity, and the presence of the drugs andxenobiotics (and their metabolites) used to treat the condition,or in the case of toxicity, that caused it. To understand andinterpret the changes in profiles observed during metabo-nomic studies, it is vitally important to identify and charac-terize these metabolites, both known and unknown, whenthey are observed. It is the authors’ intent to illustrate howboth endogenous molecules and the metabolites of xenobioticscan be identified using modern spectroscopic techniqueseither alone or combined with either off-line methods or fullyon-line hyphenated techniques. Whilst there is some overlapbetween the techniques used for exogenous and endogenouscompounds, the strategies adopted for them do show somedifferences and these will be highlighted by the use of suitableillustrative examples. In general, we make the assumptionthat metabonomic analysis of biofluids will begin with 1HNMR of the unprocessed biofluid, complemented as requiredwith HPLC–MS and work with extracts.

2. XENOBIOTIC AND ENDOGENOUSMETABOLITE IDENTIFICATION DIRECTLYFROM BIOFLUIDS

2.1. NMR-Based Techniques

2.1.1. Endogenous Compounds

Clearly, the simplest and least time-consuming strategy forthe identification of endogenous metabolites of interest inmetabonomic work is to use the already very high informationcontent available in the 1H NMR spectrum of the neat biofluiditself. This is often possible because biofluids are composedprimarily of known biochemicals many of which have charac-teristic 1H NMR spectra. Having reduced the number of pos-sible candidates, confirmation of these assignments can bemade by using the spectral data of compounds typically foundin biofluids. Spiking, and overspiking, of the biofluid samplewith a standard to confirm an assignment is also a useful

226 Shockcor and Wilson

technique and this is particularly the case where the reso-nances for a compound are affected by small variations inthe pH of the sample. In such circumstances, overspiking thesample provides a rapid and simple approach to identification.As an example, Fig. 1A–C shows an expansion of spectra for (A)control rat urine, (B) a dosed animals urine, apparently withelevated concentrations of phenylacetylglycine (PAG), and (C)control urine spiked with PAG, confirming the assignment.

However, because of the large numbers of compoundsgiving rise to resonances in biofluids, it is often not possible

Figure 1 (A) a partial 600MHz 1H NMR spectrum of control raturine, (B) urine from a dosed animal with resonances correspondingto the presence of PAG and (C) control urine spiked with PAG.

Endogenous and Xenobiotic Metabolites in Metabonomics 227

to assign many low concentration peaks directly from thestandard one-dimensional (1D) 1H NMR spectrum. In thesecircumstances, a two-dimensional (2D) spectrum can beemployed to spread out the spectral information over bothdimensions. TOCSY (TOtal Correlated SpectroscopY) is theprincipal experiment for this purpose. Protons within a spinsystem, especially when there are overlapping multiplets orthere is extensive second-order coupling, can be observed asoff-diagonal elements in the TOCSY spectrum. TOCSY pro-vides long-range as well as short-range correlations and isespecially useful when coupling constants are small. How-ever, not all correlations necessarily appear.

Often multiple experiments, with variation to the mixingtime parameter, are required to observe all correlations. Fig. 2shows a TOCSY on a urine sample with the spin systemsannotated and assigned in order to illustrate the utility ofthe experiment. The pattern of the off-diagonal elements ofmany endogenous compounds in biofluids is often uniqueenough to use them to identify a metabolite.

Where such strategies fail then recourse must be made tothe techniques outlined below.

2.1.2. Xenobiotic Metabolites

If dosed in sufficient quantity, xenobiotics and their metabo-lites can often be observed directly in the sample. Their iden-tification as xenobiotic-related compounds can be importantfor a number of reasons not least of which is to eliminate themfrom consideration in the metabonomic study itself. However,sometimes such metabolites can provide important clues asto, e.g., the nature of the toxic insult if they should be discov-ered to be mercapturates, etc. resulting from the production ofa chemically reactive species. An example of the ready detec-tion of drug metabolites by 1H NMR is shown in Fig. 3A and Bby the partial spectra of the aromatic portion of a control raturine and sample obtained following paracetamol (acetamino-phen) administration with various compound-related signals,including unchanged parent, indicated. The assignment ofthese metabolites is straightforward because there is a great


deal of background literature. Had this been a compoundwhose metabolism was unknown, it would still have been pos-sible to deduce the presence of a number of metabolites,including the probable glucuronide from the resonances visi-ble in the spectrum. If a compound contains a fluorine (orfluorines, e.g. CF3) then quantitative metabolite profiles canalso be obtained using 19F NMR spectroscopy (e.g., see Ref.1 and references cited therein), and this can provide a usefuladditional source of information on the number and nature ofthe xenobiotic metabolites in a sample.

Figure 2 A 600MHz1H NMR TOCSY spectrum for nicotinamideN-oxide with the spin systems annotated and assigned.


For a novel compound, should the direct NMR approachprove inadequate for the identification of the metabolites inthe samples then, further characterization would be requiredas described in the next section.

2.2. Liquid Chromatography–MassSpectrometry-Based Methods

2.2.1. Endogenous Compounds

Whilst much of the metabonomic literature is based on NMR-centered strategies, the use of HPLC–MS for metabolite

Figure 3 A partial 500MHz 1H NMR spectrum (aromatic region)of (A) control rat urine and (B) a sample showing the resonances forthe major metabolites of paracetamol following administrationof 300mg of the drug. Key: a¼ glucuronide, b¼ sulfate, and c¼paracetamol.


profiling is currently in a state of rapid development (2–5).Metabolite profiling by HPLC–MS usually involves a ‘‘gen-eric’’ gradient separation on a reversed-phase column withprofiling accomplished using both positive and negative elec-trospray ionization (ESI). A typical example, obtained forthe HPLC–MS of control mouse urine, is shown in Fig. 4showing the different results obtained for negative and posi-tive ESI. The strategies for metabolite identification inHPLC–MS are essentially the same as those described abovefor NMR-based investigations. Thus, the retention time andmass spectral data can be compared with those of known bio-chemicals, and overspiking can then be used to confirm iden-tity. Where the compound detected is an unknown thenexamination of its mass spectrum and atomic composition (ifaccurate mass data have been acquired) may provide thebasis for a provisional identification that can subsequentlybe confirmed if the appropriate standard can be obtained.Where such approaches fail then the isolation of the

Figure 4 A typical example of the total ion current chromato-grams obtained for mouse urine for a sample obtained using gradi-ent HPLC–MS with positive electrospray and negative ESI.


compound for further spectroscopic characterization by NMRspectroscopy must be performed (see below).

2.2.2. Xenobiotic Metabolites

The detection of xenobiotic metabolites in biofluid samples byHPLC–MS is now a well-established technique (e.g., see Refs.6, 7). The easiest procedure is to examine the total ion currentobtained for a postdose sample with one from a predose orcontrol animal. Examination of the mass spectra of the peaksthat appear as a result of xenobiotic administration will oftenreveal a compound-related metabolite and, with luck, enoughinformation will be derived from this to provide a reasonablygood idea of the structure. Certainly, phase I reactions suchas hydroxylations, hydrolysis of esters, oxidations and reduc-tions should be readily apparent. In addition, phase II conju-gations to amino, acetic, glucuronic, and sulfuric acids shouldalso be readily detected by the appropriate MS experiments.Classically, metabolism studies are performed using radiola-beled compounds (most commonly 14C or 3H) and an in-lineradioactivity monitor can be used to direct MS investigationsto particular peaks. If a suitable radiolabeled form of the com-pound under study is not available, the detection of metabo-lites can be assisted by looking for characteristicfragmentation patterns associated with the parent compound.If bromine or chlorine are present as substituents, the result-ing isotope patterns can provide diagnostic signals in themass spectrum [e.g., see Fig. 5A for a mass spectrum of themajor hydroxysulfate metabolite of 4-bromoaniline (8)]. Alter-natively, the deliberate use of a mixture of isotopically-labeledcompounds (i.e., 12C=13C or 14N=15N, etc.) can be used to gen-erate a suitable isotopic fingerprint. Almost inevitably, how-ever, it will not be possible to fully define the metabolic fateof the compound under study by HPLC–MS as there are oftendifficulties in, e.g., distinguishing between positional isomersby MS alone (e.g., the position of hydroxylation on an aro-matic ring, etc.). At this point, isolation for NMR spectro-scopic characterization, or simply HPLC–NMR, is oftenrequired for unambiguous assignment of structure.


3. LOW RESOLUTION, OFF-LINE,TECHNIQUES FOR THE ISOLATIONOF UNKNOWNS

3.1. Solid Phase Extraction=Chromatography(SPEC)-NMR

One of the simplest methods for the extraction and concentra-tion of analytes from biofluids is the technique of solid phaseextraction (SPE). In this technique, the sample is applied to a

Figure 5 (A) The mass spectrum and (B) the stopped-flow 1HNMR spectrum of 2-amino-5-bromophenyl sulfate obtained duringthe reversed-phase HPLC–NMR=MS analysis of a sample of raturine obtained following administration of 4-bromoaniline.


suitably activated SPE cartridge which contains a quantity,usually several hundred milligrams, of a chromatographicstationary phase such as a C18-bonded silica gel that actsas a sorbent. Cartridges packed with other polymeric materi-als such as anion or cation exchangers, or with different car-bon chain lengths or loadings (C2, C8, etc.) have also beenutilized. Examples of the use of SPEC are numerous and typi-cal examples can be found in Refs. 9–11. The particularadvantage of the SPEC approach is that it is simple, readilyimplemented and requires no special equipment. The techni-que of SPEC has been used for both endogenous and xenobio-tic metabolites, and also as a preconcentration step prior toother techniques such as HPLC–NMR, etc.

In SPEC, the sorbent extracts the metabolites from thesample matrix, from which they can then be recovered by elu-tion with a suitably eluotropic solvent such as, e.g., methanol.In this way, several milliliters of sample (depending upon theweight of sorbent used) may be extracted, desalted, and con-centrated into a few hundred microliters very rapidly. Theurine and bile samples are usually loaded onto a cartridgepreconditioned with an organic solvent such as methanol fol-lowed by a buffer (and eluted under gravity or a low appliedpressure depending upon sample viscosity). After the sampleshave been passed through the cartridge, a simple wash withdeionized water (at an appropriate pH) is performed toremove any inorganics and salts. If all that is being performedis the concentration of the sample, elution can be performedwith a strongly eluotropic solvent such as methanol. How-ever, when the SPE is combined with a stepwise gradient elu-tion protocol (e.g., using sequential washes of a few millilitersof 20%, 40%, 60%, 80%, and 100% organic solvents), to giveessentially a low resolution chromatographic separation, itis often possible to obtain a fraction enriched in the targetanalyte and, on occasion, it has proved to be possible to actu-ally isolate them in a spectroscopically pure form. A particu-lar advantage of the SPEC approach is that, even if it doesnot provide the required clean-up of the target analyte(s),the stepwise gradient elution steps employed do enable theinvestigator to get a feel for the relative chromatographic


properties of the compounds under study. For example,unretained compounds, or those eluting in the earlier, highlyaqueous fractions from the SPE column will be ‘‘polar’’ andrequire chromatographic eluents for HPLC of low eluotropicstrength. Conversely, metabolites eluting in organic-richfractions will require strongly eluotropic solvents.

If desired, elution from the SPE cartridges can also beperformed with deuterated solvents in order to provide a con-centrated sample in a form suitable for NMR spectroscopy.Once collected the fractions are then analyzed directly byNMR and MS (if appropriate) or by HPLC–MS in order tolocate the fraction(s) containing the metabolite of interest. Ifsufficient quantities of the metabolite are present and purityis adequate, identification can be made rapidly. If, on theother hand, there is not enough material, the process can berepeated several times and fractions combined or the SPEcan be scaled up to increase the yield.

3.2. Examples of SPEC for Unknown EndogenousMetabolites

An illustration of the use of this simple SPEC-based approachto isolation and identification is provided by the case of someunusual resonances detected in urine 3weeks after the com-mencement of the administration of acetaminophen to ratsat 1% of the diet (12). The 1D proton NMR spectra of theurine of these animals showed four large multiplets at 2.05,2.41, 2.51 and 4.18ppm (1:2:1:1 ratio, respectively). Basedon 2D NMR, the connectivities between these separate sig-nals were demonstrated showing that all four belonged tothe same molecule. Based on the chemical shifts and relativeintensities of the signals, it was possible to suggest that onebelonged to a methylene (CH2, triplet, 2.41 ppm) group adja-cent to a carbonyl function. This was coupled to two other,strongly coupled highly non-equivalent methylene protons(a second-order spin system at 2.05 and 2.51ppm). These pro-tons were also coupled to a single methine proton (4.18ppm),with a chemical shift similar to that of an alpha-CH proton ofan amino acid, that formed the X of an ABX spin system.


However, detailed as this information was, it was insufficientto provide an identity. Solid phase extraction chromatogra-phy was therefore undertaken on a mixed mode cartridgewith cationic, anionic, and reversed-phase properties, using1H NMR to monitor the fractions. The partially purified frac-tion containing these unknown resonances was then sub-jected to fast atom bombardment (FAB) MS. This gave theessential further information that the unknown had a mole-cular mass of 129Da. Based on the ‘‘nitrogen rule,’’ thismeant that it must contain, in addition to two methyleneand a methine groups at least one nitrogen. Based on thisinformation, only a limited number of structures were consid-ered to be possible, and the unknown was rapidly identifiedby reference to standards to be 5-oxoproline (5OXP). Thismolecule is an intermediate in the gamma-glutamyl cyclethat is involved in the biosynthesis of glutathione which ispresumably disrupted following the chronic administrationof acetaminophen in this type of experiment leading to a buildup and then excretion of 5OXP. Coadministration of methio-nine (1%) with acetaminophen in the diet completely pre-vented the appearance of 5OXP, presumably by providing asource of sulfur-containing amino acids for glutathione bio-synthesis. The identification of 5OXP was, in the firstinstance, an analytical challenge requiring both NMR andMS data in order to achieve a successful outcome. However,once characterized its identification when encountered insubsequent, human-derived, samples (where it was presentas an inborn error of metabolism) proved to be trivial bycomparison (13).

Another example of the use of SPEC for the identifica-tion of a major unknown resonance detected in the urineof Han–Wistar and Zucker rats is the identification of3-(3-hydroxyphenyl)propionic acid (3-HPPA) (14,15). Thiscompound is derived from dietary chlorogenic acid via thegut microflora. Normally, the metabolism of chlorogenic acidresults in the production of benzoic acid which is subse-quently conjugated with endogenous glycine and excretedas hippuric acid. However, changes in the gut microfloralcomposition can result in a change in the fate of chlorogenic


acid and 3-HPPA (amongst other things) is excreted in theurine instead. The 3-HPPA was partially characterized froman examination of the urinary spectrum which showed fouraromatic multiplets between 7.3 and 6.7 ppm and two tripletsat 2.84 and 2.48 ppm integrating to two methylene and fouraromatic protons, respectively. The coupling interactions ofthe aromatic protons were consistent with 1,3-disubstitutionwhilst the chemical shifts suggested that a phenolic OHmight be present. In the case of the methylene groups, thechemical shifts were consistent with the presence of a car-boxylic acid. Solid phase extraction chromatography onC18-bonded silica gel was used to obtain a concentratedand essentially pure fraction for 13C NMR which indicatedthat the unknown contained nine carbon atoms. The likelyidentity of this compound as 3-HPPA was then confirmedby comparison with an authentic standard.

3.3. An Example of SPEC for XenobioticMetabolites

Applications of SPEC approaches to xenobiotic metabolitesare now very numerous and in principle the general proce-dures are identical to those used for endogenous metabolites.The greatest practical difference when attempting to identifyxenobiotic metabolites is that the investigator at least startsfrom the position that the structure of the starting materialis known. In addition, many potential metabolites can be pre-dicted from the structure of the parent (although this does notmean that they will be produced!), and these can be activelysought in the SPEC extracts using both NMR an MS. A fairlytypical example of the use of SPEC combined with 1H NMR inthis area is the isolation and identification of the glucuronideconjugates of the non-steroidal anti-inflammatory drug(NSAID) naproxen and its O-desmethyl metabolite (9). Here,the urine sample was acidified to ensure that the glucuronideswould be stabilized against alkaline hydrolysis and retainedon the C18-bonded SPE phase. The bulk of the endogenouscontaminants were eluted in the wash and 20% methanolic


eluents with the O-desmethyl naproxen glucuronide andnaproxen glucuronide recovered in the 40% and 60%methano-lic fractions, respectively.

3.4. Solvent Extraction

Whilst not as widely applied for isolation in metabonomic stu-dies as SPE, simple liquid–liquid extractions (LLE) do havethe potential to provide a means of isolating compounds frombiological samples. An example of the use of LLE is providedby studies on the compound dimethylformamide (16). Here,the metabolite was clearly visible in the 1H NMR spectra ofrat urine and a good extraction and clean-up were achievedby extracting into ethyl acetate under acidic conditions (pH2). Identification of the metabolite as the N-acetylcysteinylconjugate was then performed using a combination of theuse of chromatography (TLC), NMR and MS, and comparisonwith a synthesized standard.

In general, it has to be said that, in the authors’ opinion,SPE-based approaches are probably more versatile that LLE,though no doubt the latter may offer advantages in certaincircumstances.

3.5. Characterization and Identification ofCompounds in SPEC Fractions

If a suitably purified fraction is obtained from these lowresolution methods then further NMR experiments can beperformed in order to characterize the unknown. In additionto these NMR methods, both MS and LC=MS should beemployed to provide critical information on the mass of theunknown. The introduction of low cost, high-resolution,instruments like the Quadrapole-Time-Of-Flight (Q-TOF)mass spectrometer has made acquisition of elemental compo-sition possible, which further enhances the assignment pro-cess. The unique accurate mass and elemental compositionof known endogenous metabolites can provide rapid identifi-cation of these compounds. In the case of unknown and novelmetabolites MS2 or MSn experiments can provide informationon fragmentation that may allow their assignment.


4. DIRECT ON-LINE METHODS OFIDENTIFYING UNKNOWNS

Whilst the SPEC and solvent extraction procedures can oftenprovide a useful route toward identification of the unknowns,there is no doubt that they represent low resolution techni-ques and are really only well suited to the characterizationof major sample components. In cases where the unknownis a more minor component, or the extraction techniquesdescribed above provide too little purification to obtain anunambiguous assignment then either a full-blown prepara-tive chromatographic isolation with subsequent NMR andMS may need to be performed or, alternatively, fully on-linemethodologies (HPLC–NMR, HPLC–MS) will need to beemployed. Even where SPEC fails to provide a pure enoughsample for identification purposes, it often provides a usefulmethod for providing a partially purified concentrate onwhich these fully on-line techniques can be performed.

4.1. HPLC–NMR

The on-line hyphenation of NMR spectrometers to HPLC hasresulted from a series of technical advances over many years.In particular, robust NMR flow-probes and efficient methodsfor solvent suppression were required to turn HPLC–NMRfrom a novelty into a routine analytical tool (e.g., see Ref.17 and references therein). To be able to perform effectiveHPLC–NMR requires the detection of the signals of low con-centrations of the compounds of interest in the presence ofvery much larger resonances resulting from the HPLC sol-vents. This is somewhat problematic for NMR spectroscopyas the analog-to-digital signal converter has a finite dynamicrange. The solution has been to develop methods that eithersuppress the unwanted signals for the solvent or simply donot detect them. These methods are very effective and candeal with the problems resulting from the use of the commonHPLC solvents, such as methanol=water and acetonitrile=water (as well as even more complex solvent combinations)and gradient separations. These methods are sufficiently


effective that in many cases the need for deuterated solvents,with the increased expense associated with their use, hasbeen greatly reduced. However, for practical reasons, D2O isstill preferred over H2O simply because this results in a sim-plification of the solvent suppression. As D2O is relativelyinexpensive, compared to the operating costs of the NMRspectrometer, its use represents a good compromise betweeneconomy and the effective use of the instrument. Similarly,acetonitrile-d3 and methanol-d4 are also being used increas-ingly in the pharmaceutical industry because the cost of thesesolvents is far outweighed by the substantial gain in quality ofthe results.

Another problem currently associated with HPLC–NMRis the stray magnetic field from the spectrometer which placesa limit on how close the HPLC system can be positioned with-out adversely affecting performance. The need to keep theHPLC equipment at a distance thus necessitates the use oflong column-to-NMR flow-probe transfer lines which canresult in peak broadening. The latter is compounded by therelatively high volume of the NMR flow cells. However, inpractice, with sufficient care and attention to minimizingthe lengths and diameters of the tubing used to connect theend of the columns to the flow-probes, very satisfactoryresults can be obtained. A detailed analysis of the flow andNMR requirements for optimum operation of HPLC–NMRhas been described [see Ref. 17] and a number of reviews havedescribed applications in drug metabolism studies, e.g., Refs.18,19.

Compared to mass spectrometry, NMR spectroscopy isoften relatively insensitive and, where low concentration ana-lytes of the type encountered in metabonomic or xenobioticmetabolism studies are encountered, the analytical separationand spectroscopic strategy have to be designed accordingly.There are a number of recognized modes of HPLC–NMR thatcan be used depending upon the nature of the sample. In caseswhere the biofluid sample (or SPEC extract) is reasonablyconcentrated spectroscopy can be performed on-flow withspectra acquired continuously through the run. However, ingeneral, analytes are present at concentrations that require


the use of a stopped-flow technique of one sort or another. Inthe simplest of these, the flow from the column is stoppedwhen the peak corresponding to the analyte (observed using,e.g., UV, MS or some other conventional HPLC detector)reaches the NMR flow-probe. It is then held there untilsufficient FIDs have been acquired to obtain a satisfactoryspectrum (minutes to hours depending upon the concentrationof the analyte). After the spectrum has been acquired, chroma-tography can be restarted and continued until the next peakof interest is eluted. Whilst it might be thought that suchpractices would lead to excessive chromatographic bandbroadening and a consequent loss of resolution, in practice,it has been found that stopped-flow HPLC–NMR can beperformed on many peaks in a separation without degradingthe separation.

As the analyte is stationary in the flow-probe for as long asthe investigator wishes more complex (and time consuming),NMR experiments can be performed such as 2D NMR (e.g.,TOCSY, COSY, etc.). Where the peak is composed of morethan one partially resolved component, the technique of ‘‘timeslicing’’ can be used wherein the flow is restored for a few sec-onds to move the peak a little further through the flow-probeand then acquiring a further spectrum. By taking a numberof spectra across an eluting chromatographic peak in thisway, it may be possible to obtain spectra of the individual com-ponents. An example of the usefulness of time slicing was seenwith, e.g., the partially resolve diaseteroisomers of RS-flurbi-profen and RS-hydroxyflurbiprofen glucuronides. Here, theleading and tailing edges of the metabolite peaks were essen-tially composed of one individual diastereoisomer whilst themiddle of the peak was a mixture (20). An extreme, but verypowerful, approach employing time slicing is where the entireseparation is examined using in this way (usually using anovernight run) andanexample of this is discussedbelow.Averysimilar alternative to this ‘‘continuous time slicing’’ is the use ofon-flow HPLC–NMR at very low flow rates. Both of these tech-niques are valuable when information is required on all of thepeaks in a separation, a situation that could arise when theretention time of the compounds of interest is not known.


An alternative to stopped-flow HPLC–NMR is the collec-tion of the peaks of interest as they elute from the column insample loops (‘‘peak parking’’ or ‘‘peak picking’’). At the end ofthe run, the selected chromatographic peaks can then betransferred from the sample collection loops into the NMRflow-probe for subsequent spectroscopy. The only limitationto the number of peaks that can be collected is the numberof available loops for storage, and current designs allow forup to 36 individual peaks to be collected. It is also possibleto collect peaks via an on-line extraction process. In principle,several runs can be performed, collecting the required peakfrom each run, to provide a concentrate. The peak can thenbe eluted into the NMR flow-probe using a fully deuteratedorganic solvent to give the best possible NMR data (21).

In principle, it is possible to effect NMR detection for anyof the magnetically active nuclei. In the case of endogenousmetabolites, the most important nuclei to consider are 1Hand 13C (possibly 31P) whilst for xenobiotics 19F is also oftenencountered. However, because of the low concentration pre-sent in samples only 1H and 19F, the most sensitive nuclei,have been used to any great extent. In the absence of a speci-fic 13C-isotopically labeled compound, 13C NMR detection inHPLC–NMR can be facilitated through indirect detection of13C resonances via the much more sensitive 1H NMR signalsof attached protons using 2D methods such as 1H–13C hetero-nuclear single quantum coherence (HSQC). In xenobioticmetabolism studies, the ability to use 19F NMR spectroscopyfor the detection of fluorine-containing molecules is a greatadvantage in that the background is negligible (unlike thatfor 1H NMR spectroscopy).

A typical example of the use of HPLC–NMR for the iden-tification of diet-derived compounds is provided by metabo-nomic studies on differences in the metabolism of chlorogenicacid in two populations of rats by examining the compoundsexcreted in urine (22). In this study, a freeze–dried urine sam-ple was separated on a C18-bonded reversed-phase HPLC col-umn using a gradient separation based on D2O-acetonitrile(with the D2O acidified with deuterated formic acid). Theseparation was effected using a simple linear gradient from


0% to 50% ACN over 25min and then to 100% ACN over theperiod 25–35min. Spectra were acquired in the stopped-flowmode with peaks selected for analysis on the basis of theUV chromatogram. This investigation provided spectral infor-mation on three chromatographic peaks, eluting at 16.3, 19.9and 20.9min, respectively, that were significant in separatingthe two groups of animals when pattern recognition was usedto analyze urine spectra. From the spectra obtained, thepeaks were identified as hippuric acid (16.3min), 3-HPPA(19.9min) and 3-hydroxycinnamic acid (20.9min). In addi-tion, a further metabolite was detected (possibly a conjugateof some sort) that remained unidentified. The chromato-gram obtained for this study and the 1H NMR spectrum of3-hydroxycinnamic acid are shown in Fig. 6.

Applications of HPLC–NMR for the detection and identi-fication of xenobiotic metabolites are numerous, and an

Figure 6 A UV-detected reversed-phase HPLC chromatogramobtained for the urine of a male rat. Inset, the stopped-flow 1HNMR spectrum of 3-hydroxycinnamic acid (contaminated by a smallamount of 3-HPPA from the preceding peak).


illustrative example of the sort of result that can be obtainedis shown for the major hydroxymetabolite of 4-bromoanilineobtained following reversed-phase HPLC of rat urine (8) inFig. 5B (the mass spectrum obtained at the same time isshown in Fig. 5A). Another good example of the power of thistechnique is provided by the separation and identification ofthe positional isomers of the ester glucuronide metabolite of6,11-dihydro-11-oxodibenz[b,e]oxepin-2-acetic acid (23). Inthis application, an aliquot of human urine containing themetabolites was concentrated by freeze drying and the compo-nents were separated using reversed-phase HPLC. Spectrawere obtained using the stop-flow technique and the elutionorder of the isomers was determined to be 1-O-acyl, 4-O-acyl,3-O-acyl, and 2-O-acyl, with the alpha anomer eluting beforethe beta anomer in all cases. This is an interesting exampleas, because all of the isomers have exactly the same mass,HPLC–MS could not have been used to provide these data.

Another typical example of the use of HPLC–NMR forthe characterization of drug metabolites is the analysis ofhuman urine following oral dosing with antipyrine. Freezedrying enabled a 2.5-fold concentration of the sampleswhich were separated by reversed-phase gradient HPLC.UV-detected peaks were subjected to stopped-flow NMRspectroscopy leading to the firm identification of the etherglucuronide of 4-hydroxyantipyrine, norantipyrine glucuro-nide, and 4-hydroxyantipyrine. A fourth drug-related compo-nent was also observed that was tentatively identified as3-hydroxymethyantipyrine glucuronide (24).

As well as biofluid samples HPLC–NMR, like HPLC–MS,can also be used to analyze samples obtained from varioustypes of in vitro techniques such, e.g., tissue slices, cell sus-pensions, and subcellular fractions. For example, directlycoupled, stop-flow, 750MHz HPLC–1H NMR spectroscopywas used for the detection and identification of minor metabo-lites produced by rat microsomes from 3-nitro-2-(2-fluorophe-noxy)pyridine and 3-amino-2-(2-fluorophenoxy)pyridine (25).

The metabolism of the mono-amine oxidase-A inhibitor1-ethyl-phenoxathiin-10,10-dioxide has been studied inhuman liver microsomes using 600MHz 1H HPLC–NMR in


stop-flow mode (26). The peaks of interest were detected bymonitoring the eluent by UV and a 1H NMR spectrumobtained was obtained for six compound-related peaks. Simi-lar work has been undertaken on the multidrug resistanceinhibitor LY335979 and 7-ethoxycoumarin using human livermicrosomes (27).

4.2. HPLC–NMR–MS

It is often necessary to have both NMR and mass spectraldata to determine the structure of these compounds. Toachieve this correlation of MS and NMR without having toisolate the metabolite is a challenge for which the applicationof LC–NMR–MS provides an elegant solution. A typical sys-tem is illustrated in Fig. 7. In the same way that care hasto be taken with the location of the HPLC system in relationto the NMR spectrometer, it is also necessary to exercisecare in positioning the mass spectrometer. However, theincreasing availability of actively shielded magnets, withtheir greatly reduced magnetic footprint, is reducing thisproblem.

In HPLC–NMR–MS, the NMR and mass spectrometerscan be arranged either in parallel or in series. As the NMRspectrometer is generally the least sensitive instrument inthis combination, high concentration samples are analyzedwherever possible to ensure the best chance of success andto reduce analysis time. As a result, it is usual to employ4.6mm i.d. HPLC columns which have a good sample capacity(several milligrams of sample can often be loaded if the chro-matography is robust) with flow rates of the order of 0.5–1.0mL=min. These flow rates are also compatible with MS.Another reason for the parallel configuration is that MS isdestructive. Placing the NMR and MS in parallel, e.g.,see Ref. 28, and thus splitting the flow such that a minor frac-tion goes to the MS, enables the bulk of the peak of interest tobe collected for further testing if required. If the flow is splitprior to the NMR spectrometer (typically 20:1), with thelength of the capillary to the MS adjusted such that theanalyte peak is detected by the MS as it fills the NMR flow


cell, the MS can be used to select peaks for subsequent NMRexperiments.

Operating the spectrometers in series (with MS afterNMR) has been demonstrated (29), but can cause the NMRflow cell and its connections to be operated at higher pres-sures than they were designed for, with the consequent possi-bility that leaks are more likely. Series operation also fails totake advantage of the mass spectrometrs ability to flag uppeaks of interest quickly.

Correct solvent selection for HPLC–NMR–MS is a keyissue for succesful results and has to be a compromisebetween the ideal requirements of each instrument. In thecase of HPLC–NMR, the use of inorganic buffers such as

Figure 7 A schematic diagram showing a typical layout for anHPLC–NMR–MS system. Also included are UV and radioactivitydetectors for monitoring the eluent.


sodium phosphate, to modify the pH of the eluent is often theoptimum solution as it introduces no new signals into theresulting NMR spectrum. The problem is that the inorganicbuffers are quite unsuitable for this role in HPLC–MS andan alternative acidic modifier, that is also suitable for NMRspectroscopy, is required. For NMR, an ideal alternative is tri-fluoroacetic acid (TFA), which has no protons to cause inter-ferences in the NMR spectrum. However, whilst preliminaryexperiments [using acetaminophen metabolites (28) or pro-pranolol (30) as models] showed that 0.1% TFA could be usedin HPLC–NMR–MS, this proved to be true only for a limitedrange of analytes present at high concentration (>1mg on col-umn) in positive ion mode. With acidic analytes, such as theNSAID ibuprofen and its metabolites, ion suppression was com-plete when TFA was used, even when concentrated sampleswere studied, and no MS data could be obtained (30). The bestcompromise seems to be formic acid as the single proton of for-mic acid, which has a sharp, readily suppressible NMR singletnear d8.5, gives minimal interference in the resulting NMRspectra whilst MS data can also be acquired for acidic analytes.

The first application of HPLC–NMR–MS to drug meta-bolism, the detection and identification of the sulfate and glu-curonide conjugates of acetaminophen (as well as thedetection of the parent compound itself), also provides anexample of how endogenous metabolites can also be charac-terized (28). Thus in addition to the drug metabolites, hippu-ric acid was readily identified as well as a rather moreunexpected endogenous compound eluting shortly after hip-purate, namely phenylacetylglutamine. This compound hasbeen found as a component of human plasma in uremicpatients. Here, the urine extract injected onto the columnhad been made sufficiently concentrated by freeze drying asto make it possible to obtain all the required spectra on-flow,utilizing a linear reversed-phase gradient separation from 0%to 50% acetonitrile over 30min. The spectra shown in Fig. 5Aand B for the hydroxysulfate of 4-bromoaniline were alsoobtained using an integrated HPLC–NMR–MS system (8).

Another typical example of the use of HPLC–NMR–MSfor xenobiotic metabolite identification is provided by studies


on the b-blocker practolol (31). In this instance, the compoundwas radiolabeled with 14C enabling specific detection by on-flow scintillation counting of the chromatographic eluent.Stopped-flow 1H NMR was then performed on the three majorradiolabeled peaks detected using the radio-flow cell. Theradiochromatogram for this experiment is shown in Fig. 8A,together with HPLC–MS total ion current chromatogram(Fig. 8B). These compounds were identified as the parentcompound, the ring-hydroxylated metabolite and its corre-sponding phenolic glucuronide (Fig. 9A–C). An interesting

Figure 8 (A) The [14C]-detected HPLC-radiochromatogramobtained for a urine sample following administration of radiolabeledpractolol to a male rat and (B) the total ion current obtained simul-taneously. The MS data identified the peaks as (A) the glucuronideof hydroxypractolol (B), hydroxypractolol, and practolol itself.


feature of this study was the use of a 13C-label in the N-acetylgroup of the practolol. As a result of spin–spin couplingbetween the 13C and the 1H on the CH3 of the acetyl methylgroup, this label provided a characteristic doublet in the 1HNMR spectrum of the drug and its metabolites. The presenceof this label allowed an assessment of the degree of de- andreacetylation (so called futile acetylation) that had occurredprior to excretion in the urine showing that some 7–10% ofthe ‘‘unchanged’’ parent compound had undergone this sortof metabolism.

As indicated above, in the schematic showing the typicalHPLC–NMR–MS system shown in Fig. 7, the system is con-figured in such a way as to allow the MS to act as an intelli-gent detector for the NMR so that it is possible to select a peakfor NMR analysis based on its mass. In the case of anunknown endogenous metabolite one can simply allow theLC–NMR–MS control software to detect the mass of interestand stop the flow from the chromatograph when that peakis in the NMR probe. Normal NMR analysis can now be car-ried out on the sample yielding the spectroscopic data neededto complete the assignment.

However, the situation can arise when the unknown sig-nal is seen in the NMR spectrum but nothing is known aboutits mass. It is therefore necessary to resort to on-flow NMRmethods. In on-flow LC–NMR–MS a series of 1D spectra areacquired for 16–32 transients into 2–8K data points. Totalacquisition time for each transient is typically around 1 sec.The data are multiplied by a line-broadening function of1–3Hz to improve the signal-to-noise ratio and zero-filled bya factor of 2 before Fourier transformation in the F2 domainonly. This results in a contour plot of intensity with 1H or19F NMR chemical shift on the horizontal axis and chromato-graphic retention time on the vertical axis (Fig. 10). If on-flowdetection is required during a solvent gradient elution, theNMR resonance positions of the solvent peaks will shift asthe solvent proportions change (see solvent resonance inFig. 12). For effective solvent suppression, it is thereforenecessary to determine these solvent resonance frequenciesas the chromatographic run proceeds. This is accomplished


by measuring a single exploratory scan as soon as a chromato-graphic peak is detected in real time during the chromato-graphic run and then applying solvent suppressionirradiation at these frequencies as the peak elutes.

While the data are being collected by the NMR spectro-meter, the mass spectrometer is collecting the data on the samechromatographic run so that it is possible to correlate the massfroma specific retention time to theNMRspectrumat that sameretention time. The major problem with this approach is the

Figure 10 On-flow 19F LC–NMR–MS data displayed as a plotpseudo-2D plot with 19F chemical shift on the horizontal axis andretention time on the vertical axis. Metabolites of interest: the8-O-glucuronide conjugate (MW 507Da), a novel hydroxylatedcyclopropyl ring, 8-O-sulfate cysteinylglycine di-conjugate (MW605Da), and the N-glucuronide (MW 491Da).

Figure 9 1H NMR spectra of (A) Practolol, (B), hydroxypractolol,and (C) hydroxypractolol glucuronide obtained by stopped-flowNMR on the [14C]-detected peaks of the radiolabeled practolol-related compounds detected in Fig. 8A.

J


NMR spectrometers low sensitivity compared to the MS. Thisproblem can be overcome by stopping the flow at intervals overa chromatographic peak and collecting NMR data. This techni-que is referred to as ‘‘time slicing’’. The time-slicingmethodmayalso be useful if there is poor chromatographic separation, if thecompounds under study haveweak or noUV chromophores or ifthe exact chromatographic retention time is unknown. By timeslicing through an entire chromatographic run, one producesthe equivalent of a continuous-flow experiment with higher sig-nal-to-noise (Fig. 11) thus overcoming the sensitivity problem tosome degree, however, the need to stop the flow for fairly longperiods of time causes problems for the mass spectrometerwhich are often not easily resolved.

5. MINIATURIZATION

It is already the case thatminiaturizedHPLC–MS systems areavailable where the separation is performed on narrow, microor evennanobore columns. These systemsprovide the potential

Figure 11 Typical time-slice LC–NMR data on a rat bile fraction.


for very high resolution, if required, coupled to very modestsample requirements. Other high resolution miniaturizedseparations based on capillary electrophoresis–MS are alreadyavailable but as yet have not been applied to metabonomicsamples. In the case of NMR, miniaturization is happeningwith respect to both the separation and the spectrometersdetection system [see Ref. 17].

Capillary LC–NMR systems (CapLC–NMR) are based onan NMR probe which has a very small (5 ml) flow cell coupledto a capillary-HPLC. The reduction in RF coil size results in adirect sensitivity enhancement which can be as high as a fac-tor of four over a conventional LC–NMR system when thesame mass is in the flow cell. The use of a capillary HPLC,with its low flow rates, decreases the amount of expensivedeuterated solvents consumed during the separation andallows the use of deuterated solvents in both the aqueousand organic phases. It is, however, often impossible to achievethe level of concentration in a CapLC–NMR system that onemight have in traditional LC–NMR and thus the fullenhancement in sensitivity is not always achieved. Addition-ally, because of the capillary tubing needed to plumb the sys-tems, they are often tedious to use. The primary application ofthese systems is thus analysis of mass limited samples. It hasalso been shown that CapLC–NMR excels in obtaining on-flow LC–NMR. An example of such data is shown in Fig. 12,the pseudo-2D spectrum obtained following LC–NMR of anSPE concentrated human urine after dosing with phenacetin.Here, most of the major metabolites of phenacetin aredetected easily with excellent chromatographic resolution.

The introduction of cryogenic NMR flow-probes is arecent development and has significantly advanced the sensi-tivity of NMR. In these NMR probes, the electronic compo-nents are cryogenically cooled to �20K, while the sampleremains at ambient temperature, resulting in a dramaticreduction in the electronic noise. As a result, the signal-to-noise ratio for cryoflow-probes is increased on averagefourfold over that of conventional probes. This increase insignal-to-noise ratio provides a fourfold increase in sensitivityand yields a fourfold lower detection limit for a given amount


of sample thus reducing experiment time is reduced by a fac-tor of 16 over that of a conventional probe. These enhance-ments are ideal for the detection of xenobiotic andendogenous metabolites in biofluids, where the analyte in asample is often mass limited, NMR experiments of low intrin-sic sensitivity are required, experimental time is necessarilyshort such as in a high-throughput analytical regime or ana-lytes are chemically unstable. An application of cryoflow-probes to the analysis of xenobiotic metabolites in humanurines has been described by Spraul et al. (32).

In addition, a number of studies have demonstrated theuse of microcoils for HPLC–NMR applications such as capil-lary HPLC with microcoil NMR for the detection of terpe-noids. This system had an observation volume of 1.1 mL andenabled the detection of 37ng of a-pinene (33). The detectionof low nanogram amounts of compounds has been shown in3–4min under stop-flow conditions.

Figure 12 Typical on-flow 1H LC–NMR data with chemical shifton the horizontal axis and retention time on the vertical axis. Thesedata were obtained using a capillary LC flow-probe and show themetabolites of phenacetin in human urine at low microgram levels.


In addition to HPLC-based separations capillary electro-phoresis (CE), and the related hybrid technique capillaryelectrochromatography (CEC), coupled to NMR provides apotentially powerful approach, with impressive separationefficiencies. Both CE–NMR and CEC–NMR, at an observationfrequency of 600MHz, have been applied to the identificationof acetaminophen metabolites in a SPEC extract of humanurine (34,35). This experiment produced results of the typeshown in Fig. 13, which shows the on-flow CEC–NMR

Figure 13 The pseudo-2D on-flow results obtained for the CEC–NMR of an SPEC extract obtained from the urine of a human volun-teer following the oral administration of a normal therapeutic doseof 600mg of paracetamol (acetaminophen). Key: A¼paracetamolglucuronide, B¼paracetamol sulfate, and C¼hippuric acid (forspectra see figure 14).


spectrum obtained for the extract. These data were acquiredwith eight scans per row (i.e., about 10 sec acquisition time)and the contours seen correspond to the glucuronide and sul-fate conjugates of the drug together with signals for the endo-genous hippuric acid. Spectra extracted from individual rowsfor these three substances are shown in Fig. 14. The detectionlimit for this type of on-flow experiment was in the order of300ng of paracetamol glucuronide on column.

A major problem with these capillary systems is the lim-ited amount of material that can be applied to the column.

Figure 14 Typical on-flow NMR spectra obtained for the metabo-lites of paracetamol (acetaminophen) following separation by CECas shown in Fig. 13. Key: A¼paracetamol glucuronide, B¼parace-tamol sulfate, and C¼hippuric acid.


The use of capillary isotachophoresis (cITP) prior to NMRdetection enables sample focusing method, and thus the load-ing of larger amounts of sample. The technique depends onthe use of leading and terminating electrolytes. The leadingelectrolyte has a high electrophoretic mobility, the sample fol-lows and the terminating electrolyte, which has a low electro-phoretic mobility, brings up the rear. The application of anelectric field causes the components to separate into discretebands, with the sample components focused as a function ofthe ion concentration of the leading electrolyte. Such techni-ques can result in a 100-fold increase in NMR signal-to-noiseratio when comparing non-focused samples (36).

6. CONCLUSIONS

Analytical tools of great power are now available to enablethe rapid and efficient identification of both endogenouscompounds and drug metabolites detected as part of metabo-nomic studies. However, as the authors have learned, overmany years, no amount of technology can compensate for apoorly designed experiment. Therefore, we recommend a step-wise strategy, whereby the available analytical data arecarefully scrutinized to obtain the maximum amount of infor-mation on the unknown(s) detected in the sample. This, com-bined with good experimental design, before proceeding to thenext stage, will yield the best results. The greatest confidencein the conclusions derived from studies on the identification ofunknowns will be obtained when more than one spectroscopictechnique, (e.g., bothNMRandMS) support the proposed struc-ture, or comparison with an authentic standard has beenmade.

REFERENCES

1. Wilson ID, Nicholson JK, Lindon JC. The role of nuclear mag-netic resonance spectroscopy in drug metabolism. In: WoolfTF, ed. Handbook of Drug Metabolism. New York: MarcelDekker, 1999:523–550.


2. Plumb R, Granger J, Stumpf C, Wilson ID, Evans JA, Lenz EM.Metabonomic analysis of mouse urine by liquid chromatogra-phy—time of flight mass spectrometry (LC-TOFMS): detectionof strain, diurnal variation and gender differences. Analyst2003; 128:819–823.

3. Plumb RS, Stumpf CL, Gorenstein MV, Castro-Perez JM, DearGJ, Anthony M, Sweatman BC, Connor SC, Haselden JN.Metabonomics: the use of electrospray mass spectrometrycoupled to reversed-phase liquid chromatography shows poten-tial for the screening of rat urine in drug development. RapidCommun Mass Spectrom 2002; 16:1991–1996.

4. Lafaye A, Junot C, Gall BR, Fritsch P, Tabet JC, Ezan E.Metabolite profiling in rat urine by liquid chromatogra-phy=electrospray ion trap mass spectrometry. Application tothe study of heavy metal toxicity. Rapid Commun Mass Spec-trom 2003; 17:2541–2549.

5. Idborg-Bjorkman H, Edlund P-O, Kvalheim OM, Schupe-Koistinen I, Jacobsson SP. Screening for biomarkers in raturine using LC=electrospray ionisation-MS and two-way dataanalysis. Anal Chem 2003; 75:4784–4792.

6. Oliveira EJ, Watson DG. Liquid chromatography–mass spec-trometry in the study of the metabolism of drugs and otherxenobiotics. Biomed Chromatogr 2000; 14:351–372.

7. Clarke NJ, Ringden D, Kormacher WA, Cox KA. SystematicLC=MS metabolite identification in drug discovery. Anal Chem2001; 73:430A–439A.

8. Scarfe GB, Nicholson JK, Lindon JC, Wilson ID, Taylor S,Clayton E, Wright B. Identification of the urinary metabolitesof 4-bromoaniline and 4-bromo-[carbonyl-13C]acetanilide inrat. Xenobiotica 2002; 32:325–337.

9. Wilson ID, Ismail IM. A rapid method for the isolation andidentification of drug metabolites from human urine usingsolid phase extraction and proton NMR spectroscopy. J PharmBiomed Anal 1986; 4:663–665.

10. Wilson ID, Nicholson JK. Solid-phase extraction chromatogra-phy and NMR spectroscopy for the identification and isolationof drug metabolites in urine. Anal Chem 1987; 59:2830–2832.


11. Wilson ID, Nicholson JK. Solid-phase extraction chromatogra-phy and NMR spectroscopy (SPEC–NMR) for the rapid identi-fication of drug metabolites in urine. J Pharm Biomed Anal1998; 6:151–165.

12. Ghauri FYK, Mclean AEM, Beales D, Wilson ID, NicholsonJK. Induction of 5-oxoprolinura in the rat following chronicfeeding with N-acetyl 4-aminophenol (paracetamol). BiochemPharmacol 1993; 46:953.

13. Ghauri FYK, Parkes HG, Nicholson JK, Wilson ID. Asympto-matic 5-oxoprolinuria detected by proton magnetic resonancespectroscopy. Clin Chem 1993; 30:1341.

14. Phipps AN, Wright B, Stewart J, Wilson ID. Use of protonNMR for determining changes is metabolite excretion profilesinduced by dietary changes in the rat. Pharm Sci 1997; 3:143–146.

15. Phipps AN, Stewart J, Wright B, Wilson ID. Effect of diet onthe urinary excretion of hippuric acid and other dietary-derived aromatics in rat. A complex interaction between diet,gut microflora and substrate specificity. Xenobiotica 1998;28:527–537.

16. Tulip K, Timbrell JA, Nicholson JK, Wilson ID, Troke J. A pro-ton magnetic resonance study of the metabolism of N-methyl-formamide in the rat. Drug Metab Dispos 1986; 4:663–665.

17. Albert K. ed. On-line LC-NMR and Related Techniques.Chichester: Wiley, 2003.

18. Lindon JC, Bailey NJC, Nicholson JK, Wilson ID. Biomedicalapplications of directly coupled-nuclear magnetic resonance(NMR) spectroscopy and mass spectrometry. In: Wilson ID,ed. Handbook of Analytical Separations, Amsterdam: Elsevier.2003:293–329.

19. Shockor J. Application of on-line LC–NMR and related techni-ques to drug metabolism studies. In: Albert K, ed. On-lineLC–NMR and Related Techniques. Chichester: Wiley, 2003:89–108.

20. Spraul M, Hofmann M, Wilson ID, Lenz E, Nicholson JK,Lindon JC. Coupling of HPLC with 19F- and 1H-NMR spectro-


scopy to investigate the human urinary excretion of flurbipro-fen metabolites. J Pharm Biomed Anal 1993; 1:1009–1015.

21. Griffiths L, Horton R. Optimisation of LC-NMR III—increasedsignal-to-noise ratio through column trapping. Mag Res Chem1998; 36:104–109.

22. Gavaghan CL, Nicholson JK, Connor SC, Wilson ID, Wright B,Holmes E. Directly coupled high performance liquid chromato-graphy and nuclear magnetic resonance spectroscopic withchemometric studies on metabolic variation in Sprague–Dawley rats. Anal Biochem 2001; 291:2245–252.

23. Lenz EM, Greatbanks D, Wilson ID, Spraul M, Hofmann M,Troke J, Lindon JC, Nicholson JK. Direct characterisation ofdrug glucuronide isomers in human urine by HPLC–NMRspectroscopy: application to the positional isomers of 6,11-dihydro-11-oxodibenz[b,e]oxepin-2-acetic acid glucuronide.Anal Chem 1996; 68:2832–2837.

24. Wilson ID, Nicholson JK, Hofmann M, Spraul M, Lindon JC.Investigation of the human metabolism of antipyrine usingcoupled liquid chromatography and nuclear magnetic reso-nance spectroscopy of urine. J Chromatogr Biomed Appl1993; 617:324–328.

25. Corcoran O, Spraul M, Hoffman M, Ismail IM, Lindon JC,Nicholson J. 750MHz HPLC–NMR spectroscopic identificationof rat microsomal metabolites of phenoxypyridines. J PharmBiomed Anal 1997; 16:481–489.

26. Shockcor JP, Silver LS, Wurm RW, Sanderson PN, FarrantRD, Sweatman BC, Lindon JC. Characterisation of in vitrometabolites from human liver microsomes using directlycoupled HPLC–NMR: application to a phenoxanthinin monoa-mine oxidase-A inhibitor. Xenobiotica 1996; 26:41–48.

27. Ehlhardt WJ, Woodland JM, Baughman TM, Vanderbranden-den M, Kroin JS, Norman BH, Maple SR. Liquid chromatogra-phy=nuclear magnetic resonance spectroscopy and liquidchromatography=mass spectrometry identification of novelmetabolites of the multidrug resistance modulator LY335979in rat bile and human liver microsomal incubations. DrugMet Disp 1998; 26:42–51.


28. Shockor JP, Unger SE, Wilson ID, Foxall PJD, Nicholson JK,Lindon JC. Combined HPLC, NMR spectroscopy, and ion-trapmass spectrometry with application to the detection and char-acterisation of xenobiotic and endogenous metabolites inhuman urine. Anal Chem 1996; 68:4431–4435.

29. Plumb RS, Ayrton J, Dear GJ, Sweatman BC, Ismail IM. Theuse of preparative high performance liquid chromatographywith tandem mass spectrometric directed fraction collectionfor the isolation and characterisation of drug metabolites inurine by nuclear magnetic resonance spectroscopy and liquidchromatography=sequential mass spectrometry. Rapid Com-mun Mass Spectrom 1999; 13:845–854.

30. Taylor SD, Wright B, Clayton E, Wilson ID. Practical aspectsof the use of high performance liquid chromatography com-bined with simultaneous nuclear magnetic resonance andmass spectrometry. Rapid Commun Mass Spectrom 1998; 12:1732–1736.

31. Scarfe GB, Lindon JC, Nicholson JK, Martin P, Wright B,Taylor S, Lenz E, Wilson ID. Investigation of the metabolismof 14C=13C-practolol in rat using directly coupled radio-HPLC–NMR–MS. Xenobiotica 2000; 30:717–729.

32. Spraul M, Freund AS, Nast RE, Withers RS, Maas WE,Corcoran O. Advancing NMR Sensitivity for LC–NMR–MSusing a cryoflow probe: application to the analysis of acetami-nophen metabolites in urine. Anal Chem 2003; 75:1536–1541.

33. Lacey ME, Tan ZJ, Webb AG, Sweedler JV. Union of capillaryhigh-performance liquid chromatography and microcoilnuclear magnetic resonance spectroscopy applied to theseparation and identification of terpenoids. J Chromatogr A2001; 922:139–149.

34. Pusecker K, Schewitz J, Gforer P, Tseng L-H, Albert K,Bayer E, Wilson ID, Bailey NJ, Scarfe GB, Nicholson JK,Lindon JC. On-flow identification of metabolites of paracetamolfrom human urine using directly coupled VCZE–NMR andCEC–NMR spectroscopy. Anal Comm 1998; 35:213–215.

35. Schewitz J, Gforer P, Pusecker K, Tseng L-H, Albert K, BayerE, Wilson ID, Bailey NJ, Scarfe GB, Nicholson JK, Lindon JC.Directly coupled CZE–NMR and CEC–NMR spectroscopy for


metabolite analysis: paracetamol metabolites in human urine.Analyst 1998; 123:2835–2837.

36. Wolters AM, Jayawickrama DA, Larive CK, Sweedler JV.Capillary isotachophoresis=NMR: extension to trace impurityanalysis and improved instrumental coupling. Anal Chem2002; 74:2306–2313.


8

Multi- and Megavariate DataAnalysis: Finding and Using

Regularities in Metabonomics Data

LENNART ERIKSSON andERIK JOHANSSON

Umetrics AB, Umea, Sweden

HENRIK ANTTI andELAINE HOLMES

Biological Chemistry, BiomedicalSciences Division, Faculty ofMedicine, Imperial College of

Science Technology and Medicine,South Kensington, London, U.K.

1. INTRODUCTION

1.1. General Considerations

Metabolites are the products and byproducts of the manycomplex biosynthesis and catabolism pathways that exist inhumans and other living systems. Measurement of metabo-lites in human biofluids has often been used for the diagnosisof a number of genetic conditions, diseases, and for assessing

263

exposure to xenobiotics. Traditional analytical approacheshave been limited in scope, in that emphasis was usuallyplaced on changes in the level of one or a few metabolites.For example, urinary creatinine and blood urea nitrogen arecommonly used as parameters of renal function.

Recent advances in (bio-)analytical separation and detec-tion technologies, combined with the rapid progress in bioin-formatics, have made it possible to measure much largerbodies of metabolite data (1). One prime example is the useof NMR-spectroscopy in the monitoring of complex time-related metabolite profiles that are present in biofluids,such as urine, plasma, saliva, etc. In addition to NMR-spectroscopy, there are several other analytical methods,which can produce highly characteristic metabolic signaturesof biological samples, including MS, HPLC, GC=MS. All thesemethods generate large amounts of metabolite data and havebeen used to characterize biofluids, tissues, or cell cultures (2–4).

The ongoing data explosion necessitates the use of appro-priate analytical tools for extracting meaningful informationfrom the large amounts of raw data. It is no longer efficientto analyze data by simply looking at them or by plotting themin simple graphs. More sophisticated, computer-based meth-ods are needed if the data analysis is to be accomplishedwithin a reasonable time.

In this chapter, we shall study methods for extractinginformation from large tables of data. This is called multivari-ate data analysis, or MVDA for short and is particularlyappropriate for mining and interpreting metabonomic, geno-mic, and proteomic data sets. More specifically, this chapterwill focus on two multivariate projection methods which areuseful: principal component analysis (PCA) (5) and partialleast squares projections to latent structures (PLS) (6).

1.2. Pattern Recognition

In the very many varied engineering, mathematical, andapplied professions, the term pattern recognition (PARC) isoften used in connection with MVDA to indicate how multi-variate data analysis finds the typical ‘‘data pattern’’ for one

264 Eriksson et al.

or several classes of observations (e.g., type of rat, type of toxi-city, etc.) (7). The ‘‘pattern’’ of one class represents informa-tion about the relations between the observations within theclass: discerning which are similar, which are diverse, andwhich are atypical outliers. Information is also obtained aboutsimilarities and dissimilarities among the variables (descrip-tors, in this case spectral integrals).

If the ‘‘patterns’’ between classes are different, they canbe utilized to assign new observations to the classes, i.e., toclassify new observations on the basis of the degree of similar-ity between their data and the manifested ‘‘class patterns.’’The PARC can be generalized to the problem of findingpatterns that express relations between blocks of variablesmeasured on the same set of observations (or samples). Inthe simplest case there are two variable blocks, X and Y. Thisis also a generalization of regression and correlation. Withvarious combinations of classification, discrimination, andblock-relations, most questions put to a data table can beadequately addressed. Thus, PARC is an empirical but gen-eral approach to the analysis of multivariate data, empiricalin the sense that few fundamental assumptions or modelsare needed to perform the analysis.

One of the first methods of analyzing multivariate 1HNMR biofluid spectra was simple cluster analyses, such ashierarchical cluster analysis (HCA).

1.3. Projection Methods

This article describes a remarkably simple approach to multi-variate analysis based on so-called projection methods. Thisapproach represents the observations (here: NMR-spectra ofrats) as a swarm of points in aK-dimensional space (K¼numberof variables), and then projects the point swarm down onto alower-dimensional plane or hyperplane. The co-ordinates ofthe points on this hyperplane provide a compact representationof the observations, and the direction vectors of the hyperplaneprovide a corresponding representation of the variables.

The projection approach can be adapted to a variety ofdata-analytical objectives, i.e., (i) summarizing and visualizing

Multi- and Megavariate Data Analysis 265

a data set, (ii) multivariate classification and discriminantanalysis, and (iii) finding quantitative relationships amongblocks of variables. This applies to any shape of multivariatedata, with many or few variables, many or few observations,and complete or incomplete (containing missing entries) datatables. In particular, projections handle data matrices withmore variables than observations very well, and can alsoaccommodate noisy or highly collinear data. Spectroscopy,gene arrays, and two-dimensional proteomic gels areall methods that tend to generate variable-heavydata sets.

With small modifications, projection methods can bemade robust to outliers, deal with nonlinear relationships,and adapt to drift in multivariate process data.

1.4. Transition from Multi- to Megavariate DataAnalysis

The analysis of large data tables containing several measure-ments on the same sample is often called multivariate dataanalysis (MVDA). Traditionally, multivariate data analysishas implied the use of methods like multiple linear regression(MLR), linear discriminant analysis (LDA), canonical correla-tion (CC), factor analysis (FA), and principal component ana-lysis (PCA) applied to independent variables, (i.e., variablesthat are totally independent and no underlying latent correla-tions between variables exist). In chemometrics, bioinfor-matics, metabonomics practice, however, we often assumethat our systems are driven by inherent, latent, variables(e.g., metabolic pathways), which are few compared with thenumber of observed variables, K. Methods used here arePCA for overview, soft-independent modeling of class analogy(SIMCA) and PLS-DA for classification, and PLS andprincipal component regression (PCR) for latent variableregression.

The latent variable models are philosophically differentin objectives and formulation from the traditional multivari-ate models with independent variables. To distinguishbetween these two types of situations (with related data,

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models, and data-analytical methods), we have started torefer to the latter as megavariate. Megavariate data analysismodels data in terms of multiple latent variables, to giveresults that are multivariate (8). This is a new nomenclaturedistinguishing between the situation where X is full rankand the more common megavariate situation where X has amuch lower rank than both the number of variables (K) andthe number of observations (N) as illustrated in Fig. 1.

2 DATA-ANALYTICAL METHODS

2.1. Pretreatment of Data

Prior to MVDA, data are often pretreated, in order to trans-form the data into a form suitable for analysis, but alsoto reshape the data such that important assumptions arebetter fulfilled. In fact, preprocessing can make the differencebetween a useful model and no model at all (8).

In this section, we will introduce different ways of scalingthe data. A more general discussion on pretreatment of datais given in Sec. 5.2.

Figure 1 Notation used in MVDA. The observations (rows) can beanalytical samples, biological individuals (e.g., rats), chemical com-pounds or reactions, process time points of a continuous process,batches from a batch process, trials of a design-of-experiments(DOE) protocol, and so on. The variables (columns) might be of spec-tral origin, of chromatographic origin, or be measurements fromsensors and instruments in a process.


2.2. Centering and Scaling

2.2.1. Rationale Behind Scaling

Variables (here: chemical shift region integrals) often havesubstantially different numerical ranges. A variable with alarge range has a large variance, whereas a variable with asmall range has a small variance. Since, for instance, PCAis a maximum variance projection method, it follows that avariable with a large variance is more likely to be expressedin the modeling than a low-variance variable. The PLS is alsosensitive to the choice of scaling.

A simple example will further illustrate the concept ofscaling. In connection with a preseason friendly game of foot-ball (soccer), the trainers of both teams decided to measurethe body weight (in kg) of their players. The trainers alsorecorded the body height (in m) of each player. These dataare plotted in two ways in Fig. 2(a) and (b).

When the two variables are plotted in a scatter plotwhere each axis has the same scale—the x–and y-axes bothextend over 30 units—we can see that the data points spreadonly in the vertical direction [Fig. (2a)]. This is because bodyweight has a much larger numerical range than body height.Should we analyze these data with PCA, without any prepro-cessing, the results would only reflect the variation in bodyweight.

Actually, this data set contains an atypical observation(individual). This is much easier to see when the two vari-ables are more appropriately scaled [Fig. (2b)]. Here, we havecompressed the variation along the body weight axis andzoomed in on body height. There is a strong correlationbetween body height and body weight, except for one outlier

Figure 2 (a) Scatter plot of body weight vs. body height of 23 indi-viduals. The data pattern is dominated by the influence of bodyweight. The two variables have been given the same scale. (b) Scat-ter plot of body weight against body height of 23 individuals. Now,the variables are given equal importance by displaying themaccording to the same spread. An outlier, a deviating individual,the referee of the game, is now discernible.

I

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in the data. This was impossible to see in the previous plotwhen body weight dominated over body height. We have,therefore, scaled the data such that both variables make thesame contribution to the model.

In order to give both variables, body weight and bodyheight, equal weight in the data analysis, we standardizedthem. Such a standardization is also known as ‘‘scaling’’ or‘‘weighting,’’ and means that the length of each co-ordinateaxis in the variable space is regulated according to a predeter-mined criterion. The first time a data set is analyzed it is oftena good choice to set the length of each variable axis to equallength.

2.2.2. Unit-Variance-Scaling

There are many ways to scale the data, but the most commontechnique is the unit-variance (UV) scaling. For each variable(here: NMR region integrals), one calculates the standarddeviation (sk) and obtains the scaling weight as the inversestandard deviation ð1=skÞ. Subsequently, each column (vari-able) of X (i.e., the matrix of NMR-data) is multiplied by1=sk. Each scaled variable then has equal (unit) variance.

A simple geometrical understanding of UV-scaling isbased on the equivalence between the length of a vector andits standard deviation (square root of variance) (8). Hence,the initial variance of a variable is interpretable as thesquared ‘‘size’’ or ‘‘length’’ of that variable. This means thatwith UV-scaling we accomplish a shrinking of ‘‘long’’ variablesand a stretching of ‘‘short’’ ones. By putting all variables on acomparable footing, no variable is allowed to dominate overanother because of its length. One example of the value ofUV-scaling can be seen in the analysis of NMR-spectra ofurine obtained from rats treated with certain liver toxins.The excretion of particular patterns of bile acids can be highlydiagnostic of cholestatic liver damage. However, withoutapplying UV-scaling to the data, metabolites such as citrate,2-oxoglutarate, and glucose will dominate the analysisbecause they are present in much greater concentrations thanbile acids but carry less diagnostic information.

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Like any projection method, PCA and PLS are sensitiveto scaling. This means that by modifying the variance of thevariables, it is possible to attribute different importance tothem. This gives the possibility of down-weighting irrelevantor noisy variables. However, one must not overlook the risk ofscaling subjectively to give you the model you want. Gener-ally, UV-scaling is the most objective approach, and is recom-mended if there is no prior information about the data.Sometimes no scaling at all would be appropriate, especiallywith data where all the variables are expressed in the sameunit, for instance, with spectroscopic data. Later on, whenmore experience has been gained, more elaborate scaling pro-cedures may be used.

2.2.3. Mean-Centering

Mean-centering is the second part of the standard procedurefor preprocessing. With mean-centering, the average valueof each variable is calculated and then subtracted from thedata. This improves the interpretability of the model andmay also in certain cases remove some numerical instability.

The mean-centering and UV-scaling procedures are oftenapplied by default in commercial software, and the joint name‘‘auto-scaling’’ is frequent (8). Note, however, that in somecases, such as multivariate calibration and classificationbased on spectral data, it is not necessarily advantageous touse this combination of preprocessing tools, and some otherchoice might be more appropriate (see further discussionbelow).

2.2.4. No Scaling and Pareto Scaling

Sometimes, no scaling (but mean-centering) is the desiredmethod for ‘‘scaling’’ the data. Usually, this option is deployedwhen all variables are expressed in the same unit, such aswith spectroscopic data (8).

Moreover, in recent years an alternative technique calledPareto scaling has become more common (9). Pareto scalinggives each variable a variance numerically equal to its initialstandard deviation instead of unit variance. Here, the scaling


weight is 1=psk. Hence, Pareto scaling is intermediate

between the extremes of no scaling and UV-scaling (8,9).

2.3. Principal Component Analysis

2.3.1. Introduction to PCA

Principal component analysis (PCA) forms the basis for multi-variate data analysis (7,8,10,11). As shown by Fig. 1, thestarting point for PCA is a matrix of data with N rows (obser-vations) and K columns (variables), here denoted by X. ThePCA is used here in both examples. In the first example(Sec. 3), the rows are the NMR-spectra, and the variablesare the chemical shift region integrals. When PCA is utilizedin the second example (Sec. 4), the rows are the rats and thecolumns are the time points at which spectral measurementswere carried out.

Generally, the observations (rows) can be analytical sam-ples, biological individuals (e.g., rats), chemical compounds orreactions, process time points of a continuous process, batchesfrom a batch process, trials of a DOE protocol, and so on. Inorder to characterize the properties of the observations, onemeasures variables. These variables may be of spectral origin(NIR, NMR, MS, IR, UV, X-ray, . . . ), chromatographic origin(HPLC, GC, TLC, . . . ), or theymay bemeasurements from sen-sors in a process (temperatures, flows, pressures, curves, etc.).

PCA goes back to Cauchy, but was first formulated instatistics by Pearson, who described the analysis as findinglines and planes of closest fit to systems of points in space(see Ref. 5 for a historical account of PCA). The most impor-tant use of PCA is indeed to represent a multivariate datatable as a low-dimensional plane, usually consisting of 2–5dimensions, such that an overview of the data is obtained.This overview may reveal groups of observations, trends,and outliers. This overview also uncovers the relationshipsbetween observations and variables, and among the variablesthemselves.

Operationally, PCA finds lines, planes, and hyperplanesin the K-dimensional space that approximate the data as wellas possible in the least squares sense. It is easy to see that a

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line or a plane that is the least squares approximation of a setof data points makes the variance of the co-ordinates on theline or plane as large as possible (Fig. 3).

We will now explain how PCA works: initially, using ageometrical approach, followed by a more formal algebraicaccount.

2.3.2. Setting up K-dimensional space

Consider a matrix X with N observations (e.g., NMR-spectraof rats) and K variables (e.g., chemical shift region integrals).For this matrix, we construct a variable space with as manydimensions as there are variables (the axes in Fig. 4). Eachvariable represents one co-ordinate axis. For each variable,

Figure 3 The PCA derives a model that fits the data as well as pos-sible in the least squares sense. Alternatively, PCAmay be understoodas maximizing the variance of the projection co-ordinates.


Figure 4 (a) K-dimensional variable space. For simplicity, onlythree variable axes are displayed. The ‘‘length’’ of each co-ordinateaxis has been standardized according to a specific criterion, usuallyunit-variance-scaling. The observations (rows) in the data matrix Xcan be understood as a swarm of points in the variable space (K-space). (b) In the mean-centering procedure one first computes thevariable averages. This vector of averages is interpretable as a point(here: in dark gray) in space. This point is situated in the middle ofthe point swarm (at the center of gravity). The mean-centering pro-cedure corresponds to moving the origin of the co-ordinate system tocoincide with the average point.

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the length has been standardized according to a scaling criter-ion, normally by scaling to unit variance.

2.3.3. Plotting the Observations in K-dimensionalSpace

In the next step, each observation (each row) of the X-matrixis placed in the K-dimensional variable space. Consequently,the rows in the data table form a swarm of points in this space[Fig. 4(a)].

2.3.4. The Effect of Mean-Centering

The mean-centering involves the subtraction of the variableaverages from the data. This vector of averages correspondsto a point in the K-space. The subtraction of the averages fromthe data corresponds to a re-positioning of the co-ordinate sys-tem, such that the average point now is the origin [Fig.4(b)].

2.3.5. The First Principal Component

After mean-centering and scaling to unit variance, the dataset is ready for the computation of the first principal compo-nent (PC1). This component is the line in the K-dimensionalspace that best approximates the data in the least squaressense. This line goes through the average point (Fig. 5). Eachobservation may now be projected onto this line in order to geta co-ordinate value along the PC-line. This new co-ordinatevalue is known as a score.

2.3.6. Extending the Model with the SecondPrincipal Component

Usually, one principal component is insufficient to model thesystematic variation of a data set. Thus, a second principalcomponent, PC2, is calculated. The second PC is also repre-sented by a line in the K-dimensional variable space, whichis orthogonal to the first PC (Fig. 5). This line also passesthrough the average point, and improves the approximationof the X-data as much as possible.


2.3.7. Two Principal Components Define aModel Plane

When two principal components have been derived, theytogether define a plane, a window into the K-dimensionalvariable space [Fig. 6(a)]. By projecting all the observationsonto this low-dimensional subspace and plotting the results,it is possible to visualize the structure of the investigated dataset. The co-ordinate values of the observations on this planeare called scores, and hence the plotting of such a projectedconfiguration is known as a score plot. The score plot willshow the similarities and dissimilarities between the observa-tions (e.g., NMR-spectra of rats).

Figure 5 The first principal component, PC1, is the line whichbest accounts for the shape of the point swarm. It represents themaximum variance direction in the data. Each observation maybe projected onto this line in order to get a co-ordinate value alongthe PC-line. This value is known as a score. The second principalcomponent, PC2, is oriented such that it reflects the second largestsource of variation in the data, while being orthogonal to the firstPC. PC2 also passes through the average point.

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Figure 6 (a) Two PCs form a plane. This plane is a window intothe multidimensional space, which can be visualized graphically.Each observation may be projected onto this giving a score for eachof the calculated dimensions (PC1, PC2). (b) The principal compo-nent loadings uncover how the PC-model plane is inserted in thevariable space. The loading is described by the angle (a) betweeneach variable and the principal component. Hence, for PC1 in athree-dimensional space, the loadings described are a1, a2, a3 andthese are used for interpreting the meaning of the scores.


2.3.8. The Loadings Show the Orientation of thePlane

In a PC-model with two components, that is, a plane inK-space, we wonder which variables (e.g., chemical shiftregions) are responsible for the patterns seen among theobservations (e.g., NMR-spectra of rats). We would like toknow which variables are influential, and also how the vari-ables are correlated. Such knowledge is given by the principalcomponent loadings. These loading vectors are called p1 andp2 (see further discussion in Sec. 2.3.10).

Geometrically, the principal component loadings expressthe orientation of the model plane in the K-dimensional vari-able space [Fig. 6(b)]. The direction of PC1 in relation to theoriginal variables is given by the cosine of the angles a1, a2,and a3. These values indicate how the original variables x1,x2, and x3 ‘‘load’’ into (¼contribute to) PC1. Hence, theyare referred to as loadings. Of course, a second set of loadingcoefficients expresses the direction of PC2 in relation to theoriginal variables. Hence, with two PCs and three originalvariables, six loading values (cosine of angles) are needed tospecify how the model plane is positioned in the K-space.

2.3.9. Extensions to Higher-order Components

Frequently, one or two principal components are not enough toadequately summarize the information in a data set. In suchcases, the descriptive ability of the PC-model improves by usingmore principal components. There are several approaches thatcan be used to evaluate how many principal componentsare appropriate (5,12). One of these, cross-validation (CV), isdiscussed below (see Sec. 2.3.11.3.).

2.3.10. Summary of PCA

By using PCA, a data table X is modeled as

X ¼ 1 � �xx0 þ T�P0 þE ð1ÞIn the expression above, the first term, 1�x0, represents

the variable averages and originates from the preprocessing

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step. The second term, the matrix product T�P0, models thestructure, and the third term, the residual matrix E, containsthe noise. The principal component scores of the first, second,third, . . . , components (t1,t2,t3, . . . ) are columns of the scorematrixT. These scores are the co-ordinates of the observationsin the model (hyper-)plane. Alternatively, these scores may beseen as new variables which summarize the old ones. In theirderivation, the scores are sorted in descending importance (t1explains more variation than t2, t2 explains more variationthan t3, and so on). Typically, 2–5 principal components aresufficient to approximate a data table well.

The meaning of the scores is given by the loadings.The loadings of the first, second, third, . . . , components(p1,p2,p3, . . . ) build up the loading matrix P.

The loadings define the orientation of the PC plane withrespect to the original X-variables. Algebraically, the loadingsinform how the variables are linearly combined to form thescores. The loadings unravel the magnitude (large or smallcorrelation) and the manner (positive or negative correlation)in which the measured variables contribute to the scores.

2.3.11. Additional PCA Diagnostics

2.3.11.1. Observation Diagnostics—Strong andModerate Outliers

PCA discovers strong outliers and moderate outliers. Concep-tually, outliers are observations that are extreme or that donot fit the PCA-model. Outliers are both serious and interest-ing, but easy to detect. Strong outliers are found in plots ofPC-scores and moderate outliers are found by inspecting themodel residuals (13). By the term residuals we mean the X-variation that was not captured by the PC-model, i.e., the var-iation which constitutes the matrix E in Eq. (1).

Strong outliers are found in the scores. They have highleverage on the model, i.e., strong ‘‘power’’ to pull the PC-modeltoward themselves, and may ‘‘consume’’ one PC just because oftheir existence. The term leverage derives from the Archime-dean principle that anything can be lifted out of balance as longas the lifter has a long enough lever. Leverage is a measure of


the influence of an observation and is proportional to the dis-tance of the observation from the center of the data.

A diagnostic tool showing strong outliers is given byHotelling’s T2 (5,8,13). This statistic is a multivariate general-ization of Student’s t-test, and provides a check for observa-tions adhering to multivariate normality. A definition ofHotelling’s T2 is given in Ref. 8.

A data set may also contain moderate outliers, which arenot powerful enough to shift the model plane and hence showup as outliers in a score plot. Moderate outliers are identifiedby the residuals of each observation. We here call the detec-tion tool for moderate outliers DModX, a short-hand notationfor distance to the model in X-space (8). DModX is based onconsidering the elements of the residual matrix E and sum-marizing these row by row.

A value for DModX can be calculated for each observa-tion. These values can be plotted in a control chart wherethe maximum tolerable distance (Dcrit) for the data set isgiven. Moderate outliers have DModX values larger thanDcrit. With process data, moderate outliers often correspondto temporary process upsets, but occasionally more persistenttrends or shifts can be diagnosed.

Finding outliers in metabonomics data implies that someNMR-spectra are different from the majority of spectra. Themost common reason behind outliers is variation in theexperimental conditions. Outliers can also be due to varyinghandling of the animals, and errors made during data trans-fer from one electronic device to another. However, mechanis-tically seen, the most interesting outliers are those which arerelated to unique metabolic profiles. For such animals, MVDAcan pinpoint which chemical shift regions reflect their uniquemetabolic profile.

2.3.11.2. Variable Diagnostics—Which Variablesare Well Explained?

Apart from pooling the elements of the E-matrix row-wise,these elements may also be summarized column-wise to pro-duce diagnostics related to the variables (here: chemical shiftregions). One such diagnostic tool is called the explained

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variation of a variable, a quantity which ranges from 0 (noexplanation) to 1 (complete explanation). It tells us the extentto which each variable is accounted for by the model. This isusually reported as the explained variation (R2) or explainedvariance (R2

adj). The explained variance is simply theexplained variation adjusted for the degrees of freedom (DF).The values of Rk

2 are related to the loadings. For each com-ponent, a, pak

2 is proportional to how much the kth variableis modeled by this component.

A more thorough description of these parameters isfound in Ref. 8. Also observe that it is possible to calculateR2- and R2

adj-values pertaining to the complete X-matrix,not just to the individual variables.

2.3.11.3. Model Diagnostics—How ManyPrincipal Components are Really Needed?

An important question is how many components should beincluded in the model? This question is linked to the differ-ence between the degree of fit and the predictive ability.The fit tells how well we are able to mathematically reproducethe data of the training set. A quantitative measure of thegoodness of fit is given by the parameter R2 (¼the explainedvariation). The problem with the goodness of fit is that withsufficiently many free parameters in the model, R2 can bemade arbitrarily close to the maximal value of one (1.0).

More important than fit, however, is the predictive abil-ity of a model. This can be estimated by how accurately wecan predict the X-data, either internally via existing data orexternally through the use of an independent validation setof observations. The predictive power of a model is summar-ized by the goodness of prediction parameter Q2 (¼thepredicted variation). Here, we use CV to estimate the predic-tive ability of the model with increasing number of compo-nents (see next section).

The R2- and Q2-parameters display entirely differentbehavior as the model complexity increases (Fig. 7). The good-ness of fit, R2, varies between 0 and 1, where 1 means a per-fectly fitting model and 0 no fit at all. R2 is inflationary andapproaches unity as model complexity (number of model


parameters, number of components, . . . ) increases. Hence, itis not sufficient to have a high R2. The goodness of prediction,Q2, on the other hand, is less inflationary and will not auto-matically come close to 1 with increasing model complexity.This is provided that Q2 is correctly estimated.

2.3.11.4. Cross-validation

The approach to finding the optimal model dimensionalityadvocated throughout this chapter is called CV (12). Cross-validation (CV) is a practical and reliable way to test the sig-nificance of a PC- or a PLS-model. This procedure has becomestandard in multivariate data analysis, and is incorporated inone form or another in most commercial software. However,CV is implemented differently in different packages, whichmay cause some confusion when comparing models developedby different packages.

With CV, the basic idea is to keep a portion of the dataout of the model development, develop a number of parallelmodels from the reduced data, predict the omitted data by

Figure 7 The trade-off between the goodness of fit, R2, and thegoodness of prediction, Q2. The vertical axis corresponds to theamount of explained or predicted variation, and the horizontal axisdepicts the model complexity (number of terms, number of latentvariables, etc.). At a certain model complexity, one gets the modelwith optimal balance between fit and predictive ability.

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the different models, and finally compare the predicted valueswith the actual ones. The squared differences between pre-dicted and observed values are summed to form the predictiveresidual sum of squares (PRESS), which is a measure of thepredictive power of the tested model. PRESS is computed as

PRESS ¼X

ðxik � xxikÞ2 ð2ÞIn this work, CV is conducted for each consecutive model

dimension starting with A¼ 0. For each additional dimension,CV gives a PRESS, which is compared with the residual sumof squares (RSS) of the previous dimension. When PRESS isnot significantly smaller than RSS, the tested dimension isconsidered insignificant and the model building is stopped.

Normally, the performance a PC-model is evaluated bysimultaneously considering the explained variation R2 (good-ness of fit) and the predicted variation Q2 (goodness of predic-tion). As shown by Eqs. (3) and (4), these two statisticsresemble each other:

R2 ¼ 1� RSS=SSXtot:corr: ð3Þ

Q2 ¼ 1� PRESS=SSXtot:corr: ð4Þ

and they are both dimensionless. In the expressions above,SSXtot.corr. represents the total variation in the X-matrix aftermean-centering.

In the evaluation of the parameters R2 and Q2, there area few noteworthy facts. The first is that without a high R2, itis impossible to get a high Q2. Generally, a Q2 > 0.5 isregarded as good and a Q2 > 0.9 as excellent, but these guide-lines are of course heavily application dependent. Finally,the difference between R2 and Q2 must not be too large, andpreferably not exceeding 0.2 – 0.3.

2.4. Partial Least Squares Projections to LatentStructures, PLS

2.4.1. Introduction to PLS

PLS is a method for relating two data matrices, X and Y,to each other by a linear multivariate model (4,14–16).


PLS is used here in both metabonomic examples, given at theend of this chapter. In the first example (Sec. 3), the rows arethe NMR-spectra, and the X-variables are the chemical shiftregion integrals. The Y-matrix is an artificial matrix describ-ing class membership of rats. In the second example, PLS isused to relate NMR-spectra (X) to urine sample times (Y),where each row corresponds to a spectrum (Sec. 4).

PLS stands for projections to latent structures by meansof partial least squares. It derives its usefulness from its abil-ity to analyze data with many, noisy, collinear, and evenincomplete variables in both X and Y. For parameters relatedto the observations (individuals, samples, compounds, objects,items), the precision of a PLS-model improves with theincreasing number of relevant X- and Y-variables. This corre-sponds to the intuition of most experimentalists that manyvariables provide more information about the observationsthan just a few variables do.

PLS can be seen as a particular regression technique formodeling the association between X and Y, but it can also beseen as a philosophy of how to deal with complicated andapproximate relationships.

2.4.2. Preprocessing of Data

As in any data-analytical application, data are usually pre-processed prior to using PLS. The PLS-modeling works bestwhen the data are fairly symmetrically distributed and havea fairly constant ‘‘error variance.’’ Hence, variables that varymore than ten-fold are often logarithmically transformedbefore the analysis.

In addition, data are usually centered and scaled to unitvariance before the analysis. This is because in PLS any givenvariable will have an influence on the model parameterswhich increases with the variance of the variable. Scalingall variables to unit variance corresponds to the assumptionthat all variables are a priori equally important. Note,however, that spectral data are a special case where thecombination Pareto scaling and mean-centering, or justmean-centering, is often employed (8).

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If a priori knowledge about the relative importance ofvariables (X or Y) is available, this should be used to scalethe variables accordingly, giving important variables aslightly higher scaling weight than that corresponding tounit-variance-scaling; analogously, unimportant variablesare given a slightly lower scaling weight.

2.4.3. Setting up the X- and Y-spaces

In order to illustrate how PLS operates, we will consider acase in which there are three X-variables (K¼ 3) and threeY-variables (M¼ 3). For each matrix, X and Y, we constructa space with K and M dimensions, respectively. In these twospaces, each X- and Y-variable represents a co-ordinate axiswith a length defined by its scaling, usually unit variance.

Every observation in a data set may be understood as onepoint in the X-space and another point in the Y-space. Thus,with many observations, point-swarms with many membersare formed in the X- and Y-spaces, as illustrated in Fig. 8.

Figure 8 A regression situation with K¼ 3 X-variables and M¼ 3Y-variables. The length of each co-ordinate axis has been standar-dized by scaling to unit variance. The mean-centering procedureimplies that the origins of the two co-ordinate systems will coincidewith the average point (dark gray) in each cloud of points. Eachobservation is represented by one point in the X-space and anotherpoint in the Y-space.


This figure displays the mean-centered data, and the originsof the two co-ordinate systems coincide with the averagepoints of the data swarms.

The two point-swarms have elongated shapes indicatingthe correlated distribution of points in each cluster. We wishto obtain a good description of these two point-swarms, and anunderstanding of the association between them. In otherwords, we would like to know whether there exists a relation-ship between the positioning of points in the predictor (X)space and the positioning of points in the response (Y) space.This can be elucidated by PLS-analysis.

2.4.4. Calculating the First PLS-Component

The first PLS-component is a line in the X-space and anotherline in the Y-space (Fig. 9). These two lines are calculatedsuch that they (i) well approximate the point-swarms in Xand Y, and (ii) provide a good correlation between the posi-tions of points along these lines in X and Y. The two lines

Figure 9 The first component of a PLS-model may be interpretedas two lines, one inserted in the X-space and the other in theY-space. The orientation of these lines is regulated by the require-ment that they should (i) well approximate the shapes of the twopoint-swarms and (ii) the scores t1 and u1 be maximally correlated.The observations projected onto the two lines give the projection co-ordinates (the ‘‘scores’’) t1 (for X) and u1 (for Y).

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intersect with the average points. By projecting the observa-tions onto the two lines one obtains the scores t1 and u1, forX and Y, respectively (Fig. 9).

The correlation between X and Y, in terms of the twoscore vectors t1 and u1, may be displayed in a scatter plot.The two score vectors are connected through the inner rela-tion ui1¼ ti1þhi, where hi is a residual (Fig. 10). The slopeof the dotted line in Fig. 20 is 1.0, and when there is perfectmatching between the X- and the Y-data all the points arelocated on this diagonal. Conversely, when there is a weakcorrelation structure between X and Y, there is a considerablespread of points around the dotted line.

The t1=u1 score plot in Fig. 10 is a visualization of the cor-relation structure between X and Y. In this score plot one cansee outliers in theX-data, outliers in theY-data, and outliers in

Figure 10 The projection co-ordinates, t1 andu1, in the two spaces,X and Y, are connected and correlated through the inner relationui1¼ ti1þhi (hi is a residual). The slope of the dotted line is one (1).


the relation between X and Y. Furthermore, when thereare non-linearities between the predictors and the responses,these may also be detected by a curved relation between t1and u1.

2.4.5. Adding the Second Component

The second PLS-component may also be represented by twolines, one in each space, which pass through the averagepoints (Fig. 11). In the X-space, this second line is orthogonalto the first one, whereas in the Y-space this may not necessa-rily be the case. These lines improve the approximation of,and correlation between, the positions of the X- and Y-planesas much as possible.

Geometrically, a two-component PLS-model can be inter-preted as planes in the X- and Y-spaces. By projecting theobservations onto these planes, the PLS-scores t1 and t2 inX and u1 and u2 in Y are obtained (Fig. 12).

Analogously to the first score vector pair (t1=u1), a plot ofthe second set of score vectors, t2 and u2, also visualizes thecorrelation structure (Fig. 13). Normally, the score vectorsof the second component correlate less well than the first pairof latent variables. In fact, this is logical, as the first PLS-com-ponent captures the strongest source of variation in the data,

Figure 11 The second PLS component can be represented by twolines, one in each variable space. These lines improve the descrip-tion and correlation of X and Y as much as possible.

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i.e., the strongest ‘‘signal.’’ After the removal of the variationaccounted for by the first component, weaker ‘‘signals’’ remainin the data and therefore the correlation between X and Y (interms of t2 and u2) is usually weaker and less distinct.

Figure 12 Two PLS components correspond to the insertion ofmodel planes in theX- andY-spaces. Upon projecting the observationsonto these planes, the PLS-score vectors of the first model dimension,t1 and u1, and the second model dimension, t2 and u2, are generated.

Figure 13 The second pair of score vectors, t2 and u2, correlates,but usually less well than the first pair of score vectors (t1 and u1).This is indicated by the broader ‘‘correlation band’’ around thesecond component.


2.4.6. Which Original Variables Contribute to theFormation of the Model Planes?

Once a PLS-model has been established, it is of interest tomake an interpretation of its meaning. This may be accom-plished by considering the variable-related PLS-modelparameters called weights. The weights for the X- and theY-variables, which are denoted w� and c, respectively,may be plotted together in the same plot. These weights areinterpreted in much the same way as the PCA loadings(see Secs. 2.3.8 and 2.3.9), and show which variablescontribute to the PLS-model, and which are not modeledat all.

In principle, this means that the PLS weights reflect therelationships among all variables at the same time, and tellwhich are associated and which contribute unique informa-tion. Thus, with PLS, one obtains information on what X givesY, or, how to ‘‘set’’ X to get a desired Y. This implies that in,for example, metabonomics modeling, it is possible to under-stand how the urinary profile has changed, and from thereonunderstand which organ(s) and physiological process(es) areinvolved.

2.4.7. Higher-order Components

It is possible to include more than two components in aPLS-model. When this is done, we are no longer fittingtwo-dimensional planes in the X- and Y-spaces, but ratherhyperplanes of three, four, . . . , dimensions. Conceptually,such hyperplanes are no different from uni-dimensionallines or two-dimensional planes, and the principles ofprojecting observations onto these hyperplanes andreading off the new co-ordinate values (the scores) arepreserved.

One may ask how many PLS-components are reallynecessary? One way to address this topic is through CV (seebelow). Another is plotting of successive pairs of latent vari-ables. Not only will such plots give a good appreciation ofthe correlation structure, but they will also aid in determiningthe appropriate model complexity.

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2.4.8. Summary of PLS-Projections

PLS-modeling of the relationship between two blocks of vari-ables can be described in different ways. Perhaps the moststraightforward way is that PLS fits two ‘‘PCA-like’’ modelsat the same time, one for X and one for Y, and simultaneouslyaligns these models. The objectives are (a) to model X and Y,and (b) to predict Y from X, according to:

X ¼ 1�xx0 þ TP0 þE ð5Þ

Y ¼ 1�yy0 þUC0 þ F ð¼ 1�yy0 þ TC0 þG;

due to inner relation) ð6ÞIn these expressions, the first terms, 1x0 and 1y0,

respresent the variable averages and originate from the pre-processing step. The information related to the observationsare stored in the score matrices T and U; the informationrelated to the variables are stored in the X-loading matrixP0 and the X-weight and Y-weight matrices W0 and C0. Thevariation in the data that was left out of the modeling formthe E and F residual matrices.

The difference between PCA and PLS is that the formeris a maximum variance least squares projection of X, whereasthe latter is a maximum covariance model of the relationshipbetween X and Y. A detailed account of PLS is given in Ref. 8.

The X-weight matrix W contains the X-weight vectorswa, which show how the X-variables are linearly combinedto form the score vectors ta. Hence, we understand which ori-ginal variables dominate the new, latent variable ta. X-variables that are highly correlated with the Y-variables gethigh weights. Similarly, the Y-weights ca inform us how theY-variables are summarized by the score vector ua. In addi-tion, one should observe that there are two versions of theX-weights, one denoted wa and the other w�

a. The w� valuesrelate directly to the X-matrix, whereas the w values referto the residuals calculated in the previous dimension, Ea�1,instead of the X-variables themselves.

In summary, PLS forms ‘‘new x-variables’’, ta, as linearcombinations of the old ones, and thereafter uses these new


t’s as predictors of Y. Only as many t’s (components) areformed as are predictively significant (estimated via cross-validation). For each component (a), the parameters, ta, ua,wa (and w�

a), pa, and ca are calculated by the PLS-algorithm.For the interpretation of the PLS-model, the scores,t and u, contain information about the observations (here:NMR-spectra of rats) and their similarities=dissimilaritieswith respect to the given problem and model. The weightsw� and c give information about how the variables (here:chemical shift regions) combine to form the quantitative rela-tion between X and Y. Hence, these weights are essential forthe understanding of which X-variables are important(numerically large w�-values), which X-variables providethe same information (similar profiles of w�

a-values), theinterpretation of the scores, t, etc.

2.4.9. PLS-Model Interpretation

PLS provides many diagnostics which help in the model inter-pretation, and in the assessment of model performance andrelevance. Foregoing sections have concerned the PLS-scoresta and ua, what they mean and how they can be used. In thecurrent section, we consider the variable-related parameters,notably weights, coefficients, and VIP. VIP is an acronym forvariable influence on projection.

2.4.9.1. PLS-Weights

The PLS-weights w�c give information about how the X-vari-ables combine to form the scores t, the basis of the quantita-tive relation between X and Y. For a given PLS-model, onevector of X-weights w�

a and one vector of Y-weights ca areobtained for each model component (a). The PLS-weightscan be plotted in scatter, line, or column plots. Moreover, itis possible to plot the X-weights (w�) alone, the Y-weights(c) alone, or both types of weights (w�c) in the same graph.The line plot representation is prevalent in spectroscopicapplications, since it displays the ‘‘peak-like’’ spectral struc-ture modeled by each component.

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2.4.9.2. PLS-Regression Coefficients

A PLS-solution given in the latent variable framework (withscores, weights, etc.) may be re-expressed as a regressionmodel consisting of PLS-regression coefficients, BPLS,according to

Y ¼ 1�yy0þXBPLS þ F ð7ÞThe relationship between the PLS-regression coefficients

and the PLS-weights is given by:

BPLS ¼ WðP0WÞ�1 ¼ W�C ð8ÞThe PLS-coefficients are of interest because they simplify

the model interpretation when there are several components(>4–5) in the model. Their advantage is that the analystobtains only one vector of concise model information perresponse, rather than several vectors of weights. The disad-vantage of the coefficients is that information regarding thecorrelation structure among the responses is lost. This infor-mation is preserved by the PLS-weights.

2.4.9.3. The Variable Influence on Projection, VIP,Parameter

Interpreting a PLS-model with many components and a mul-titude of responses can be a complex task. A parameter whichsummarizes the importance of the X-variables, both for the X-and Y-models, developed by Wold et al. in 1993 (15), is calledthe variable influence on projection, VIP. The details of VIPare given in Refs. 8,15. For the moment, it suffices to statethat VIP is a weighted sum of squares of the PLS-weights,w�, taking into account the amount of explained Y-variancein each dimension. Its attraction lies in its intrinsic parsi-mony; for a given model and problem there will always beonly one VIP-vector.

2.4.10. Additional PLS-Diagnostics

PLS offers a number of useful model parameters and diagnos-tic tools. Many of these tools are similar to those of PCA.


2.4.10.1. Strong and Moderate Outliers

Outliers may be either strong or moderate. In PLS (and PCA),the former are found by inspecting the scores, and the latterby looking at the residuals. An observation may be an outlierin X, in Y, and=or in the relationship between X and Y.

Moderate outliers are seen in plots of DModX=DModY,which show each observation’s (e.g., NMR-spectra of rats) dis-tance to the model in X=Y-space. DModX and DModY arebased on a row-wise summation of the elements of the resi-dual matrices E and F and are equivalent to the row residualstandard deviation. For a one-dimensional PLS-model thisleads to the formation of the ‘‘beer-can’’ like tolerance volumesin X and Y (Fig. 14).

2.4.10.2. Well Explained X- and Y-variables

If the X-residuals in matrix E are summed column by column,it is possible to compute the explained variation (R2VX) of a

Figure 14 A tolerance volume enclosing a point-swarm can beused as a diagnostic tool to evaluate whether new observations(depicted here as the large circles) are similar or dissimilar to thetraining set members. In the plot, the dark-gray circle falls withinthe model tolerance, but the light-gray observation would be consid-ered as deviating from the model and hence likely be a moderateoutlier.

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variable. This quantity is often denoted as just R2, but herewe use the notation R2VX. R2VX ranges from 0 (no explana-tion) to 1 (complete explanation), and reveals how predictorsare explained by the model. Similarly, the sizes of the Y-resi-duals show which responses are well accounted for by thePLS-model. Quantitatively, this information is given by theexplained variation, R2VY. For the necessary equations,please see Ref. 8.

2.4.10.3. Cross-validation

In order to determine the appropriate number of componentsin a PLS-model, the technique of cross-validation is useful. Asin PCA, CV is performed by dividing the data in a number ofgroups and then developing a number of parallel models fromreduced data with one of the groups deleted. It should benoted that increasing the number of CV groups to N, i.e.,the so-called leave-one-out approach, is not recommended,because the estimated Q2 then becomes too similar to R2.

After developing the reduced model, the omitted data areused as a test set, and the differences between actual and pre-dicted Y-values are calculated for these data points. The sumof squares of these differences from all the parallel modelsare used to form PRESS. This is a measure of the predictiveability of the model:

PRESS ¼X

ðyim � yyimÞ2 ð9ÞWhen CV is used in the sequential mode, PRESSa=SSa�1

is evaluated after each component, and a component is judgedsignificant if this ratio is smaller than around 0.9 for at leastone of the y-variables [sharper bonds can be obtained from theresults of Wakeling and Morris (17)]. Here, SSa�1 denotes the(fitted) residual sum of squares before the current component(index a). The calculations continue until a component is non-significant. Alternatively, however, one can calculate PRESSfor each component up to, say 10 or 15 components, and usethe model which gives the lowest PRESS=(N�A� 1). This‘‘total’’ approach is computationally much more taxing, andthe practical difference from the ‘‘sequential’’ CV results issmall.


Both with the ‘‘sequential’’ mode and the ‘‘total’’ mode, aPRESS is calculated for the final model with the estimatednumber of significant components. This is often re-expressedas Q2 (the ‘‘cross-validated R2’’), a statistic which is similarto R2:

R2ðYÞ ¼ 1� RSS=SSYtot:cor: ð10Þ

Q2ðYÞ ¼ 1� PRESS=SSYtot:cor: ð11Þ

In the expressions above, SSYtot.corr. represents the totalvariation in the Y-matrix after mean-centering and scaling.

As stated previously, without a high R2 it is impossible toobtain a high Q2. Generally, a Q2 > 0.5 is regarded as goodand a Q2 > 0.9 as excellent, but these guidelines are of courseheavily application dependent. Differences between R2 andQ2 larger than 0.2–0.3 indicate the presence of many irrele-vant model terms or a few outlying data points.

2.4.10.4. Standard Errors and Confidence Intervals

Numerous efforts have been made to derive confidence inter-vals for PLS-parameters (see, for example, Refs. 18,19). How-ever, most of these approaches have been based onconventional regression assumptions, treating PLS as abiased regression model. Only recently in the work of Burn-ham et al. (20–22) has the issue been investigated consideringPLS as a latent variable model.

One way to estimate standard errors and confidenceintervals directly from the data is to use jack-knifing (23).This was actually recommended by Herman Wold (24) in hisoriginal PLS-work, and has recently been revived by Martensand Martens (25). The objective of jack-knifing is to estimatevariability of model parameters.

Interestingly, cross-validation—where the objective is toestimate the model complexity giving the optimal predictivepower—produces results which can be fed directly to jack-knifing. In this way, the various submodels generated bycross-validation are used to calculate the standard errorsof the model parameters, which are then converted into

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confidence intervals via the t-distribution. Since the PLS-parameters (scores, loadings, etc.) are linear combinations ofthe original data, they are approximately normally distribu-ted and so jack-knifing works well in the estimation ofconfidence intervals.

3. RESULTS FOR EXAMPLE DATA SET I—AMETABONOMIC INVESTIGATION OFPHOSPHOLIPIDOSIS

3.1. Background to Data-set

Phospholipidosis is a condition which reflects derangement ofnormal phospholipid metabolism and can be induced by manydifferent classes of drug in tissues such as liver, lung, brain,kidney, and endocrine glands. The condition can be difficultto detect and biomarkers are scarce (26). The first data setdeals with male rats treated with the drugs chloroquine (anantimalarial) or amiodarone (an antiarrhythmic), both ofwhich are known to induce phospholipidosis (26), here codedas ‘‘c’’ or ‘‘a’’. The drugs were administered to two differentstrains of rat, i.e., Sprague–Dawley and Fisher 344, herecoded as ‘‘s’’ or ‘‘f ’’. Sprague–Dawley rats are a standardlaboratory animal model, whereas Fishers rats are more sus-ceptible to certain types of drug exposure and hence it is ofteneasier to detect drug effects.

In total, the data set contains N¼ 57 observations (rats)and K¼ 194 variables (chemical shift region integrals). Theseobservations are divided into six groups (‘‘classes’’):

� control Sprague–Dawley (s), 10 rats;� Sprague–Dawley treated with amiodarone (sa), 8 rats;� Sprague–Dawley treated with chloroquine (sc), 10

rats;� control Fisher (f), 10 rats;� Fisher treated with amiodarone (fa), 10 rats;� Fisher treated with chloroquine (fc), 9 rats.

Urine samples were obtained from the rats treated witheither chloroquine (dosed at 60mg=kg=day i.p.) or amiodarone


(dosed at 80mg=kg=day i.p.) on day 21 and injected into anNMR-flow probe using the Bruker BESTTM flow injection sys-tem. The samples were measured on a 600MHz NMR-spec-trometer using a standard one-dimensional pulse sequence.Suppression of the water region was attained by using the‘‘WET’’ pulse sequence (27). The acquired spectra were manu-ally phase and baseline corrected prior to data reduction.

The urine 1H NMR-spectra were reduced by summationof all the data points over a 0.04 ppm region. Data pointsbetween 4.5 and 6.0 ppm, corresponding to water and urearesonances, were excluded from data reduction and subse-quent data analysis. Regions in the spectra associated withdrug-related compounds (DRCs) were also exluded prior todata analysis, leaving a total of 194 NMR-spectral regionsas variables for the multivariate modeling.

A more elaborate account of the experimental conditionsis found in the original literature source.

3.2. An Overview PCA-Model

Generally, when working with spectral data it is recom-mended to work with Pareto-scaled data (8). This way of scal-ing the data can be seen as a compromise between UV-scaling(risk: noise is inflated for chemical shift regions of low signalamplitude variation) and no scaling (risk: only those chemicalshift regions with large variation in signal amplitude will beseen). Hence, when overviewing the information in thefirst data set, PCA was applied to Pareto-scaled and mean-centered NMR-data.

For an overview model, usually only the two first compo-nents are extracted. In this case, these showed the perfor-mance statistics R2X¼ 0.48 and Q2X¼ 0.38. Fig. 15 showsthe scores of these two components. We can see that all thechloroquine-treated animals are positioned in the top part ofthe plot, whereas the majority of the amiodarone-treated ratsare found in the bottom part. All controls are located in thecentral, predominantly right-hand part of the plot. Hence,the second principal component reflects differences in theeffects of the two drugs.

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Another very interesting aspect is that the ‘‘f ’’-groupsconstantly tend to be ‘‘right-shifted’’ along the first principalcomponent in comparison with the corresponding ‘‘s’’-groups.This make us interpret the first PC as a ‘‘difference-between-type-of-rat’’-scale.

In order to interpret the scores, we use the loadings.Fig. 16 displays a line plot of the second loading spectrum.This spectrum highlights the various chemical shift regionscontributing to the formation of the second score vector. Forinstance, the chloroquine-exposed rats generally tend to havehigher peaks at chemical shifts of succinate (2.42), taurine(3.26 and 3.42), etc., and lower peaks at shifts of creatine

Figure 15 Scores of the two first components of the phospholipi-dosis data set overview model. Filled circle¼ controls of Sprauge–Dawley; filled triangle¼SD treated with amiodarone; filled dia-mond¼SD treated with chloroquine; open circle¼ controls ofFisher; open triangle¼F treated with amiodarone; open diamonds¼ F treated with chloroquine.


(3.06), glucose (3.70), hippurate (3.98, 7.58, 7.66, 7.86), etc. Ifa similar loading spectrum is plotted for the first loading vec-tor, it is possible to identify which spectral variables reflectthe major differences in NMR data due to strain of rat (Fisheror Sprague–Dawley).

Moreover, it is of interest to examine the model residuals(see DModX plot in Fig. 17). The DModX plot reveals one verydifferent ‘‘sc’’-rat with a DModX value exceeding the criticaldistance by a factor of 2. When tracing this information backto the previous score plot (Fig. 15), we realize that this animalis the remotely positioned sc-rat (marked with the openframe). This is an observation with unique NMR-data andits spectrum should be more carefully inspected to under-stand where the differences arise. These differences could

Figure 16 Line plot of loading vector p2 for the overview PCA-model. This loading spectrum uncovers which chemical shift regionsare responsible for the separation between the Fisher and Sprague–Dawley rats.

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be due to some very interesting change in metabolic pattern,or be due to experimental variation in the handling of therats, or perhaps a data transfer error. One way to pinpointthe likely cause for this discrepancy in DModX is throughthe loading plot or a contribution plot, but that option is notfurther exploited here.

3.3. PLS-Discriminant Analysis (PLS-DA)

It is obvious from the above PCA model that the observations(rats) are grouped according to treatment in the score plot.However, knowledge related to class membership is not usedto find the location of the principal components. The PC-model

Figure 17 Distance to model in the X-data (DModX) for the over-view PCA model (Data Set I). There is one strongly outlying ratwhich displays a worryingly high DModX.


is calculated to approximate the observations as well as possi-ble. It must be realized that PCA finds the directions in multi-variate space that represent the largest sources of variation,the so-called principal components. However, it is not necessa-rily the case that these maximum variation directions coincidewith the maximum separation directions among the classes.Rather, it may be that other directions are more pertinentfor discriminating among classes of observation (here: NMR-spectra of rats).

It is in this perspective that a PLS-based technique,called PLS discriminant analysis (PLS-DA) becomes interest-ing (28,29). PLS-DAmakes it possible to accomplish a rotationof the projection to give latent variables that focus on classseparation (‘‘discrimination’’). The method offers a convenientway of explicitly taking into account the class membership ofobservations even at the problem formulation stage. Thus, theobjective of PLS-DA is to find a model that separates classes ofobservations on the basis of their X-variables. This model isdeveloped from a training set of observations of known classmembership.

In PLS-DA, the X-matrix consists of the multivariatecharacterization data of the observations. In order to encodea class identity, one uses as Y-data a matrix of dummy vari-ables, which describes the class membership of each observa-tion in the training set. A dummy variable is an artificialvariable that assumes a discrete numerical value in the classdescription. The dummy matrix Y has G columns (for Gclasses) with ones and zeros, such that the entry in the gthcolumn is one and the entries in other columns are zero forobservations of class g.

3.4. PLS-DA of Groups ‘‘s’’ and ‘‘sc’’

In order to illustrate the utility of PLS-DA we are going tofocus on the difference between group ‘‘s’’ (controls ofSprague–Dawley) and ‘‘sc’’ (SD rats treated with chloroquine).However, in so doing we must first eliminate the outlying‘‘sc’’-rat. The PLS-DA requires homogenous groups devoid ofoutliers, otherwise inconsistent patterns may result.

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A PLS-DA model was calculated based on the 19 rats inthe ‘‘s’’ and ‘‘sc’’-groups. All variables were mean-centered andPareto scaled. This model contained two very strong compo-nents showing the performance statistics R2X¼ 0.69,R2Y¼ 0.94, and Q2Y¼ 0.90. The X-score plot of t1 and t2 of thismodel is displayed in Fig. 18. Evidently, there is strongseparation (‘‘discrimination’’) between the ‘‘s’’- and ‘‘sc’’-groups. It is mainly the first component that is responsiblefor separating the two groups of rat from each other. The sec-ond model component picks up within class variation. Theloadings for the first component (Fig. 19) indicate that chlor-oquine induced an increase in the urinary excretion of crea-tine (3.02, 3.94) and taurine (3.26, 3.42), which would inferdamage to the liver. In addition, an increase in the regionscorresponding to phenylacetylglycine (3.62 and 7.62) suggeststhat chloroquine is also causing phospholipidosis. Both of

Figure 18 PLS-DA t1=t2 score plot for the model contrasting the‘‘s’’ and ‘‘sc’’ groups. Each point in the plot represents one rat. Thetwo classes are well resolved in component 1.


these lesions were independently confirmed by conventionalhistology.

Thus, there is really no doubt that the chemical treat-ment of the rats induces a substantial and characteristicchange in their NMR-profiles.

3.5. DisjointPCA-ModelingofGroups ‘‘s’’ and ‘‘sc’’

In this paragraph, we would like to draw the attention to analternative to PLS-DA, known as soft-independent modelingof class analogy, or SIMCA for short (7,8). SIMCA is a graphi-cally oriented technique, and is applicable when clear group-ings exist in the data, such as those seen among theobservations (rats) in the first example.

As discussed above, any data-analytical exercise usuallystarts with a PCA on the entire data set to get an overview.Provided that the data set is not pruned inappropriately,e.g., to artificially enhance a class separation, such an

Figure 19 Line plot of the X-loadings of the first component of thePLS-DA model. This loading plot indicate chemical shift regionsinfluential for the separation of the two classes along the horizontaldirection in Fig. 18.

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overview of the training set data gives valuable indications ofclass separation, trends, and outliers. Division into classes,accounting for time trends, and exclusion of outliers can thenbe carried out accordingly. It should be noted, however, thatin any resulting subclass, the data should be selected suchthat each class of observations contains homogeneous datamaterial.

Subsequent to the initial data overview, in the SIMCAmethod each class of observations is modeled separately bydisjoint PC-models. In order to unravel the appropriatedimensionality of each local PC-model, we recommend thatcross-validation be used. Based on the residual variation ofeach class, one can compute the distance to the model(DModX) of each observation. It is also possible to computea critical distance for each class model. This was discussedin Sec. 2.3.11.1.

After the separate modeling of each class, the models areused to predict a likely class membership (‘‘classification’’) fornew observations. An observation is classified in SIMCAaccording to the tolerance intervals of the different classes(as calculated from the residual distance to the model—DModX). Observations that do not fit any class are then con-sidered as outliers, or perhaps as founders of a new, hithertounseen, class. Furthermore, in regions where tolerance inter-vals overlap, the observations cannot be unequivocallyassigned.

These local PCA-models can be interpreted by inspectingloadings, scores, residuals, and contribution plots. Amongother things, this will indicate which variables contribute tomodeling class similarity (loading plot), and which variablesdo not (contribution plot in residuals of nonfittingobservations).

In order to illustrate the applicability of the SIMCAmethod, one local PCA-model was fitted to the ‘‘s’’-group andanother to the ‘‘sc’’-group. These two models were used toinfer class belongings of all the other rats. The results fromthe classification phase are summarized in Fig. 20.

The plot in Fig. 20 is known as a Coomans plot, namedafter the Belgian chemometrician Danny Coomans, who in


the mid-80s demonstrated its great applicability (7,30). Theessence of the Coomans plot is that class distances (DModX ’s)for two classes are plotted against each other in a scatter plot.

By plotting also the critical distance, DCrit, for eachmodel in the Coomans plot, four areas of diagnostic interestare created. In the lower left-hand part of Fig. 20 a regionwhere prediction set samples (rats) that fit both models arefound (no rats in this case). In the lower right-hand partand the upper left-hand part there are regions where thoseobservations predicted to fit the ‘‘sc’’-model or the ‘‘s’’-modelare found, respectively. Finally, we have the upper right-hand

Figure 20 Coomans plot of SIMCA of the ‘‘s’’ and ‘‘sc’’ classes. Thisplot represents a scatter plot of DModX to the ‘‘sc’’ class modelagainst DModX to the ‘‘s’’ class model. Open diamonds denote ‘‘s’’rats; open circles denote ‘‘sc’’ rats; solid triangles represent all otherrats.

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area where we find observations that do not conform to eitherof the models. These are all the ‘‘sa’’, ‘‘f ’’, ‘‘fc’’, and ‘‘fa’’, whichconsistently are found to be different from the ‘‘s’’ and ‘‘sc’’ rats.

3.6. Discussion of First Example

The first example shows the power of NMR-data in combina-tion with multivariate statistics to capture differencesbetween groups of rats. Methodologically, it is very practicalto commence the data analysis with an initial overview PCAof the entire data set. This will indicate groups, time trends,and outliers. Outliers are observations that do not conformwith the general correlation structure. One clear outlier wasidentified among the ‘‘sc’’ rats.

By way of example we have also shown how groupingsspotted by an initial PCA may be further studied on a moredetailed basis. Then techniques like PLS-DA and SIMCAare very useful. A necessary condition for PLS-DA to workreliably is that each class preferably is ‘‘tight’’ and occupiesa small and separate volume in the X-space. Also, the numberof modeled classes must not be too high. Experience showsthat PLS-DA is useful with 2–4 classes, but when the numberof classes exceeds four, it is usually more tractable to switchto SIMCA.

Thus, in this presentation, we have focused on the differ-ences between two classes, i.e., the ‘‘s’’ and ‘‘sc’’ rats. This is ananalysis that will pick up drug-related effects of the chloro-quine treatment. In order to find out exactly which variables(i.e., chemical shift regions) that carry class discriminatorypower, one may consult plots of PCA or PLS-loadings, or con-tribution plots. A few of these possibilities were hinted at.

It should be noted that one need not only compare ‘‘s’’with ‘‘sc’’. Other possible comparisons focusing on drug effectare ‘‘f ’’) ‘‘fa’’, ‘‘f ’’) ‘‘fc’’, and ‘‘s’’) ‘‘sa’’. However, there arealso other twists of the data analysis, which may reveal inter-esting information. For example, a comparison made as‘‘f ’’) ‘‘s’’ would indicate rat strain differences and perhapsdiet differences. And modeling arrangements like ‘‘fa’’) ‘‘sa’’and ‘‘fc’’) ‘‘sc’’ might suggest species-dependent drug effects.


Although both chloroquine and amiodarone induce phos-pholipidosis, they do not compare in Fig. 15 indicating thatNMR-profiles are different for animals exposed to these twodrugs. Biological data are complex, and toxicants or drugsrarely target a single organ or cell type. For example, in addi-tion to inducing phospholipidosis, chloroquine also causes cellu-lar necrosis in the liver, which accounts for the difference inmapping positions between amiodarone- and chloroquine-trea-ted samples. Data filtering methods such as orthogonal signalcorrection can be used to focus on a single factor, such as phos-pholipidosis, and to exclude systematic variation arising fromother biological phenomena. This has been described in otherpublications and is not covered in the present chapter (31).

4. RESULTS FOR EXAMPLE DATA SETII—DEFINING THE DYNAMIC SEQUENCEOF BIOCHEMICAL EVENTS FOLLOWINGTHE ONSET OF TOXICITY

4.1. Background to Data-set

The second data set is a toxin data set containing exposuredata for two compounds plus a control group.� Ten rats wereexposed to the hepatotoxin a-naphthylisothiocyanate (ANIT).Five rats were treated with a low dose (100mg=kg i.p.) andfive rats using a high dose (200mg=kg i.p.). A second groupof five rats was subjected to treatment by 200mg=kg i.p.thioacetamide (TA), which is a known liver and kidney toxin.Finally, there was a control group comprising six rats.

Urine samples were collected at six time points, includ-ing one predose measurement and measurements at 24, 31,55, 79, and 103hr after exposure. To account for changes inmetabolic profiles, 209 NMR-variables (chemical shiftregions) were acquired. The NMR-data were pretreated asdescribed above for the first data set.

In order to enable predictive validation of models, theparent data set was split into two parts. To get a sufficiently

� Data source for data set II.

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large foundation for the model, we will use the 10 ‘‘ANIT’’ ratsas training set, whereas the other two groups of rat will beused for external prediction. The advantage of this selection isthat it allows assessment of whether NMR-spectra can discrimi-nate between toxins going for different target organs, and alsoallows control rats to be compared with drug-exposed animals.

The second data set can be understood as a (N�J�K)three-dimensional matrix built up by the ‘‘directions’’ rats(N)� urine samples (J)� NMR variables (K) (Fig. 21). In orderto analyze this data table we will use methodology developedfor batch statistical process control, BSPC (32,33), where eachrat is regarded as an individual batch. The approach to BSPCused is reviewed in the next section. It is based on two levels

Figure 21 The three-way data table is unfolded by preserving thedirection of the NMR-variables (NMR-shifts). This gives a two-waymatrix with N�J rows and K columns. Each row contains datapoints xijk from a single batch observation (rat i, urine sample j,variable k). If regression is made against urine collection time,the resulting PLS scores usually reflect linear (t1), quadratic (t2),and cubic (t3) relationships to this time.


of batch monitoring, the lower observation level and theupper batch level. This leads to an easy and straightforwardway of accounting for time trajectories. Pathological lesionsare dynamic biochemical processes and metabonomic analysisof biofluids provides the unique opportunity of defining ormonitoring the evolution of a pathological lesion, since a ser-ies of urine or plasma samples can be obtained over a specifiedtime course with minimal damage to the organism.

As will be seen below, the data set was divided into twoparts, a training set and a test set. To train the BSPC models,we used the 10 ANIT-treated rats and to evaluate these mod-els, we used a combined test set of the 11 TA-treated rats andcontrol rats.

4.2. BSPC: A Method to Handle Three-way DataTables

At the lower observation level, the three-way data table isunfolded by preserving the direction of the NMR variables(Fig. 21). The resulting two-way matrix then has N (rats)�J(urine samples) rows (observations) and K columns (NMR-variables). Hence, in this matrix, X, the observations arethe individual urine samples collected for each rat at differentsubsequent time points, and not the whole rats. In the dataset there are altogether 21 rats mapped by using either fiveor six urine collection times, resulting in a total of 121observations. These observations are mapped by the 209NMR-variables. The lower observation level modeling is herecarried out using PLS to relate the NMR-spectral regions (X)to the urine collection time (single variable y). This analysiswill capture trajectories of metabolic evolution, which canbe interpreted in terms of PLS-model parameters (loadings,VIPs, etc.). Thus, the biomarkers contributing to the shapeand direction of the trajectories, which can reflect the natureof the pathology in terms of the site or mechanism of damage,can be uncovered.

At the batch level, the PLS-score vectors of the lowerlevel model are re-arranged and used to account for thetime-related metabolic properties of each animal. The three-

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way data table is unfolded by preserving the direction of theNMR-variables (NMR-shifts). This gives a two-way matrix withN�J rows and K columns. Each row contains data points xijkfrom a single batch observation (rat i, urine sample j, variablek). If regression is made against urine collection time, theresulting PLS-scores usually reflect linear (t1), quadratic (t2),and cubic (t3) relationships to this time. The score values foreach batch (rat) are arranged as row vectors underneath eachother, giving a new matrix X that has the number of rows equalto the number of rats in the reference data set. From this newmatrix, one calculates the averages and standard deviations(SDs) of the matrix columns, and subsequently control limitsas averages � 3 SD. The objective is to calculate a model overthe whole batch (i.e., model the entire sequence of biochemicalevents in an ANIT-treated rat over the given time course) andto allow comparison on a rat-to-rat basis. This analysis willreveal which rats exhibit similar and=or different metabolic tra-jectory profiles, and also which rats that show a deviating beha-vior compared with the majority of the population. Forexample, by using this type of analysis, animals that are ‘‘fast’’or ‘‘slow’’ responders to different drug treatments, or even thosethat display an idiosyncratic response can be easily detected (1).The bottom line is the ability to classify new rats as ‘‘conformingto’’ or ‘‘breaking’’ the correlation structure among the modeledrats, and to ascertain when deviation from ‘‘normality’’ occurs.In this work, the upper batch level is accomplished using PCAon the lower-level PLS-score vectors.

We emphasize that the results presented below should beseen as a guide for BSPC-based modeling of time-dependentwhole-animal metabolic processes. As such, we are focussingon the most central aspects of the data analysis. Much empha-sis is given to the classification situation, i.e., the phase inwhich a model is used for prognosis of the metabolic responsein new independent sets of rats.

4.3. Base Level PLS-Model

To accomplish the lower-level model, the data for the 10 ANITrats were reordered. The total number of observations (rows)


in the training set was 55 (five high-dose rats urine sampledat six occasionsþfive low-dose rats sampled at five timepoints). As the dependent y-variable, the sampling time wasused. Prior to the data analysis, all variables were mean-centered and Pareto scaled.

According to cross-validation, the lower-level model wasa five-component PLS-model describing 74% (R2X¼ 0.74)of the variation in the spectral data and 92% (R2Y¼ 0.92) ofthe variation in the time variable. The predictive powerof the model was 77% (Q2Y¼ 0.77).

The X-score plot of the first two components is displayedin Fig. 22. There are 10 lines in this plot connecting the timed

Figure 22 The PLS score plot of the base-level model (second dataset). This score plot represents 56% of the variation in NMR-data.Score lines indicating metabolic trajectory profiles of low-dose rats(dashed black lines) and high-dose rats (gray solid lines) are given.The left-hand ellipse indicates the region where all animals arepositioned prior to dosing. Ideally, the trace of each rat shouldrevert back to this area after exposure. It is clearly seen that withinthe investigated postdose time frame, none of the high-dose animalshave this ability to recuperate; they all end within the right-handellipse. The low-dose animals have either reverted back to the nor-mal area or are approaching it.

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urine samples of each rat. The left-most ellipse indicates theregion where all animals are positioned in the predose situa-tion. In other words, the model finds the low-dose (dashedlines) and high-dose (solid lines) animals metabolically simi-lar prior to toxin exposure.

After exposure, three of the low-dose animals havereverted back completely within the time frame of the inves-tigation, and the other two animals are on their way back tothe initial area describing the predose metabolic state. Con-versely, however, none of the five high-dose animals havereturned back to this area indicating a greater duration ofeffect in the high-dose rats. These differences show the powerof the multivariate information in the NMR-spectra.

The loading plot given in Fig. 23 indicates the most infor-mative chemical shift regions. For instance, the regions 2.46,2.58, and 2.74 (relating to urinary citrate and 2-oxoglutarate

Figure 23 Loading plot corresponding to Fig. 22. This plots showsthat variables such as increased acetate, taurine, and glucose aregood markers for high-dose ANIT-treated animals at 55 hr postdose.


levels) are very characteristic for the predose metabolicconfiguration, whereas regions like 1.90 (acetate), 2.38, 2.42(succinate), 2.50, 2.54, 2.66 (citrate), 3.22, 3.26 (trimethyla-mine-N-oxide), and 3.94, 7.54, 7.62, 7.82 (hippurate) are all indi-cative of conditions of the high-dose animals at 103hr afterexposure. The administration of ANIT to rats is known to inducea preliminary effect on the metabolism of bile acids, creatine,glucose, hippurate, and the tricarboxylic acid cycle intermedi-ates. Here, one can observe that although many of the prelimin-ary effects of ANIT have passed by 103hr postdose, thetricarboxylic acid cycle intermediates (with the exception ofsuccinate) and hippurate remain depleted. In addition, anincrease in the excretion of acetate indicates that although thereare no direct signs of hepatotoxicity remaining at this time, theanimals are still not metabolically ‘‘normal.’’ Another observa-tion from these data is that the citrate resonances appearslightly shifted in the samples from the high-dose ANIT groupwhich results from an ANIT-induced change in urinary pH.

4.4. Classifying Individual Urine Samples

The ability of this model to classify the timed urine samples ofthe 11 rats not used for model training was tested. It wasfound that these 11 rats did not separate from the 10 trainingset rats in the X-score space. However, in the predictionDModX plot (Fig. 24), there is no doubt whatsoever that theTA-treated animals are very different in their NMR-data com-pared with the ANIT-treated rats. Interestingly, the controlgroup rats show greater similarity with the ANIT-dosed ani-mals, as they generally tend to fit the lower-level model well.

To be of value, metabonomic analysis must complementand enhance the more traditional methods of toxicity screen-ing, either by providing biomarker information earlier, atlower levels of toxicity or more efficiently than other methods,or by enhancing knowledge of the time profile of toxicity.Therefore, a common practice, as followed in the currentexample, is to administer several doses of compound to thechosen animal model, ranging from an acutely toxic dose thatcan be confirmed by histopathology to a subtoxic dose. Here,

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the training set comprised animals treated with both a high(200mg=kg) and low (100mg=kg) dose of ANIT, thus therange of response between the high- and low-dose animalsgenerates a model incorporating a high degree of variance.This accounts for the apparent inability of the ANIT modelto distinguish between samples from control and ANIT-treated animals at the lower level. The test samples obtainedfrom TA-treated rats, on the other hand, were significantlydifferent from those used to build the ANIT model. Two fac-tors contribute to this observation; firstly, TA was adminis-tered at a high dose only (200mg=kg) and thus induced asevere renal cortical lesion that was easily detected by histo-pathological examination. Secondly, the biomarkers of renalcortical toxicants include the increased excretion of a largerange of amino acids, organic acids, and glucose, reflectingthe inability of the damaged tubules to absorb such

Figure 24 DModX-plot for the lower-level PLS-model. It can beseen that the animals subjected to TA treatment do not fit at allthe model based on the ANIT-treated animals. In other words,NMR-spectra for the TAs are radically different from the ANITs.Contribution plotting can then be used to understand how (i.e., inwhich spectral variables) a certain observation is different. Thearrow indicates an animal exposed to TA and its urine samplecollected 55hr postdose.


substances. Glucose alone generates many 1H NMR reso-nances covering approximately 8–12% of the spectrum(depending on how many regions are excluded on the groundsof drug metabolites or spectral artifacts), which exerts a highdegree of leverage on the model. Thus, even when dealingwith a comparable level of tissue damage in the liver and kid-ney, themarkers for hepatotoxicity (bile acids, creatine, taurine,etc.) occupy relatively few spectral ‘‘bins,’’ in comparison withthe markers for cortical nephrotoxicity (glucose, amino acids,and organic acids). Methods such as logical blocking, QUILT-PLS analysis (34), and hierarchical extensions of PCA andPLS (see Sec 5.3), can be used to further advance the data ana-lysis, but are considered beyond the scope of the current chapter.

As a next step in the analysis, contribution plotting canbe helpful to provide additional and more detailed informa-tion about previously observed patterns in scores and DModX.Hence, abnormally behaving observations can be placedunder more careful scrutiny. This procedure may actuallybe thought of as placing a magnifying glass over a strangeobservation to try to resolve why it is different. As an exam-ple, Fig. 25 relates to the contribution plot for a urine sample

Figure 25 Contribution plot indicating in which spectral regionsthe observations highlighted in the previous figure are different.As seen, this mostly relates to the regions 3.46, 3.38, 3.22, (glucose),1.48 (alanine), and 1.30 (lactate), which are present in higher con-centrations in this TA-treated sample, as this animal exhibits astrong response to toxicity.

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collected 55hr after exposure to TA. Chemical shift regionsbeing very different for this animal are 1.30 (lactate), 1.48(alanine), 2.1 (N-acetyl glycoprotein fragments), and 3.22,3.38, 3.46, 3.7, 3.82 (glucose), reflecting the increased excre-tion of these compounds in TA-treated rats.

4.5. Upper Level PCA-Model of PLS-Descriptors

On the upper level, the five PLS-score vectors of the lower-levelmodel were rearranged. In the new X-matrix, the training setconsisted of 10 rows, each corresponding to one ANIT-treatedrat, and 50 columns (corresponding to the 5 PLS-vector� 10time points).

A PCA of the training set gave a four-component modelaccounting for 79% of the variation. Some separation betweenthe low and high doses of ANIT is apparent in the score space,but from the DModX statistics all 10 rats fell well below thecritical distance indicating no outliers in the training set.

4.6. Classifying Whole Rats

Next, the upper level PCA-model was applied to the 11 controland TA rats in the test set. Figure 26 includes the resultsfrom these predictions. It is obvious from Fig. 26 that the con-trol group overlaps with the training set, while all TA-dosedrats are grouped outside the limit defined by the trainingset. Recall that the TA- animals were found to be differentfrom the ANIT-treated rats already in the lower-level model.This was also clearly shown at the upper level in the DModXplot.

However, also the controls are found different from theANIT rats on the upper level. This was seen in a DmodX plotwhere all the rats from the control group had values clearlyexceeding the critical limit defined by the ANIT-dosedanimals.

4.7. Discussion of Second Example

The second data set was included to exemplify how time-dependent changes in levels of metabolites in biofluids may


be monitored using a combination of NMR-spectroscopy andmultivariate batch projection methods. Multivariate analysisof batch-wise data enables the correlation structure amongmeasured variables to be explored. This is important not onlyfor gaining an understanding of the underlying propertiesthat dominate a batch process (here: time-dependent changesof metabolites in urine), but also for early fault diagnosis.However, not only is the relationship among all batch vari-ables (here: chemical shift regions) at any time point of primeimportance, but also so are the trajectories or time dependen-cies of all these variables. The development of various trajec-tories of batches may often serve as a fingerprint of eachbatch. Thus, by multivariately modeling average batch trajec-tory features, and deviations from normal batch evolution, itis often possible to separate good and bad batches from eachother.

Figure 26 Predicted t1=t2 score plot of the upper level PCA model.Each point corresponds to one rat. A¼ANITs used for model train-ing (� denotes high-dose animal); T¼TAs used for model testing;c¼ control group. The TAs are situated in an area outside the areacovered by the training set. Hence, they do not generally conformwell with the model, i.e., they are different from the training setrats.

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Moreover, in addition to diagnosing whether a batch willend up as ‘‘accepted’’ (i.e., a rat whose urinary profile matchesthe training data) or ‘‘rejected’’ (i.e., a rat which does not con-form to the model), it is of great significance in batch monitor-ing to predict ‘‘maturity’’ or ‘‘state’’ of new batches. In thiscontext, this means that the PLS evolution charts can indi-cate the presence of fast or slow responders to specific toxicinsult. In simple cases, the state of a batch is an uncompli-cated linear or nonlinear function of time. However, in mostcases batches develop (respond) differently due to varyinginfluences of uncontrollable external factors. Additionally,as pointed out in Ref. 1, a careful model interpretation mayalso ‘‘provide a scale of the magnitude of response for eachanimal’’ (batch).

To demonstrate the power of this technique, in the cur-rent example we chose to develop a model on the 10 ANIT-treated rats. On the lower level, using the predicted DModXchart of the PLS-model, we were able to classify the TA-treated rats as fundamentally different from the trainingset. In other words, the PLS-model detected significant differ-ences in the NMR data—which, in turn, point to the urinaryprofiles—induced by the treatment of a hepatotoxin or a toxinknown to be causing both liver and kidney damage.

Furthermore, on the top level, were also able to separate thecontrol from the ANIT-group, again using the predicted DModXchart. This points to the fact that the differences in NMR-databetween control and ANIT-treated rats are more subtle thanthose observed between animals dosed with TA and ANIT.Hence, operating on the different model levels means that‘‘amplifiers’’ of different strength can be mounted on the modelto detect strongly or weakly aberrant urinary profile behavior.

5. DISCUSSION

5.1. The Usefulness of Multivariate ProjectionMethods in Metabonomics

There is a steady development in all parts of science and tech-nology, including metabonomics, proteomics, and genomics, to


use more and more variables to characterize molecules, reac-tions, metabolic processes, animals, and other ‘‘systems.’’ Thereasons are obvious; firstly, we strongly feel that we knowand understand more about our ‘‘systems’’ when we havemeasured many properties (e.g., chemical shift regions inNMR spectra) rather than a few. Secondly, ‘‘instrumentalrevolution’’ with computers, spectrometers, chromatographs,imaging equipment, and other electronic devices, providethe opportunities to get information-rich data for any investi-gated object, sample, individual, reaction, process, etc.

It is remarkable that we only recently have learnt how tocope with this abundance of data. Traditional statistics hasincorrectly instructed us that we must always employ fewervariables (K), than observations (N), otherwise we should enterinto a jungle of unreal and spurious relationships. This alert-ness is correct if we are to treat each variable as precise andindependent, i.e., having some unique piece of information.

PCA, PLS, and similar projection methods, however, arebased on other assumptions, namely that variables are corre-lated (collinear) and possibly also noisy and incomplete. Thesecorrelations are, in turn, modeled as arising from a small setof latent variables, where all measured (manifest) variablesare modeled as linear combinations of the latent variables.In metabonomics science, the latent variables are often inter-pretable in terms of the systematic metabolic fluctuationscharacterized by high field NMR-spectroscopy of biofluidsand tissues. The success of PCA and PLS indicates that theseassumptions forming the basis of PCA and primarily PLS aremore realistic than those of regression.

Projection methods such as PLS and PCA have theadvantageous property that the precision and reliability ofparameters related to the observations improve with increas-ing numbers of relevant variables. This feature of projectionmethods is easily understood by realizing that the scores (taand ua) are estimated as weighted averages of the X and Yvariables, respectively. Any (weighted) average becomes moreprecise the larger the number of elements used as its basis.Analogously, the PCA and PLS variable-related parameters,i.e., loadings, weights, VIP, R2, Q2, regression coefficients,

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etc., become more precise the larger the number of observa-tions, N. This because the loadings and weights and para-meters derived thereof are linear combinations—weightedaverages—of the N observation vectors.

Hence, PCA and PLS can model data also when the num-ber of X variables, K, exceeds the number of observations, N.Provided that the number of model components (A) is sub-stantially smaller than N, and that the components all aresignificant according to cross-validation, PCA and PLS model-ing works well also in this situation.

It is important to note that PCA, PLS, and the like, arenot statically locked to the array of problems sketched in thispaper. On the contrary, many ‘‘add-ons’’ and ‘‘twists’’ to thesemethods exist and are aimed at facilitating=enriching boththe preprocessing and data-analytical phases. Some of theseamendments to the basic modeling set-up are discussed inSecs. 5.2. and 5.3.

5.2. Additional Preprocessing Tools

5.2.1. Block-Scaling

One limitation of the scaling procedures discussed in Sec. 3.1is that they do not consider whether variables are grouped inblocks of naturally related descriptors or the number of vari-ables in each such block. If, for example, UV-scaling is used, alarge block of variables (say, hundreds of NMR descriptors)will dominate over a smaller block of variables (say, exposureconditions of animals) for purely numerical reasons. This isoften not wanted.

One way of addressing this situation is to employ block-scaling (8). In this procedure, one may down weight blocks ofvariables in relation to a selected basis scaling procedure. Thebasis scaling method is generally UV-scaling, especially whenvariables are markedly different in nature and numericalrange. However, in multivariate calibration, procedures likeno scaling or Pareto scaling may well be used as the basisfor block-scaling.

Block-scaling can be done in many ways. In our experi-ence, it is convenient to distinguish between soft and hard


block-scaling (8). In soft block-scaling, each block of variablesis scaled such that the sum of the variable’s variances (aftercompleted scaling) equals the square root of the number ofvariables in that particular block. Here, the additional scalingweight used is 1=(kblock)

1=4—where kblock represents the num-ber of variables in a block—which is multiplied by the basisscaling weight. Hard block-scaling involves even furtherdown weighting. With this approach, the variables in a blockare scaled such that the sum of their variances is unity. Here,the additional scaling weight used is 1=(kblock)

1=2. Block-scaling can convey several advantages, the greatest of thesebeing to increase the ability to detect biomarkers of toxicityor disease that are present in lower levels than the metabo-lites that commonly dominate the NMR-spectra or assay inquestion.

5.2.2. Signal Correction and Compression

Signal correction and compression are of great interest inmultivariate classification and calibration, but may be usedin many other fields, as well. Spectral data are often prepro-cessed (‘‘corrected’’) prior to data analysis, in order to enhancethe predictive power of multivariate calibration models. Thisis because variation in X that is unrelated to Y may degradethe predictive ability of a multivariate calibration model.

Common approaches for preprocessing of spectral dataare first and second order derivation (35), multiplicative sig-nal correction (MSC, also referred to as multiplicative scattercorrection) (36), and standard normal variate (SNV) correc-tion (37). Also, Wold et al. (38) a few years ago developed anovel filtering technique called orthogonal signal correction(OSC). In this latter approach, the objective is to ‘‘peel-off’’from X (spectral data) variation that is mathematically inde-pendent of Y (response data). The OSC is a PLS-based solu-tion. In contrast to other common methods (MSC, SNV, etc.)OSC uses Y to construct a filter of X.

Furthermore, in the context of multivariate calibration,wavelet analysis is gaining more and more attention. Alsbergand coworkers highlighted a number of cases where wavelet

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analysis could be of interest, e.g., for denoising of IR spectra,for feature extraction in the classification of NIR-spectra, andfor noise suppression and data compression of NIR-data (39).Trygg and Wold (40) also reported on the use of wavelets andPLS for multivariate calibration of compressed NIR-spectra.

5.2.2.1. First and Second Order Derivation

A rapid and often used method for reducing scatter effects forcontinuous spectra is to use derivatives (35). The first deriva-tive spectrum is the slope at each point of the original spec-trum. It peaks where the original spectrum has maximumslope and it crosses zero where the original has peaks. Thesecond derivative spectrum is a measure of the curvature ateach point in the original spectrum. This derivative spectrumis more similar to the original spectrum and has peaksapproximately as the original spectrum, albeit with aninverse configuration (35). The effect of the first derivativeis usually to remove an additive baseline (‘‘offset’’), whereasthe effect of the second derivative involves removal of a linearbaseline.

A problem with the above approach is that differencingmay reduce the signal and increase the noise, thus producingvery noisy derivative spectra. Realizing this risk Savitsky andGolay (SG) (41) proposed an improvement based on a smooth-ing approach SG derivatives are based on fitting a low degreepolynomial model (usually of quadratic or cubic degree) piece-wise to the data, followed by calculating the derivative andsecond derivative from the resulting polynomial at points ofinterest.

5.2.2.2. Multiplicative Signal Correction

With multiplicative signal correction (MSC), each digitizedspectrum (xi

0, row-vector in X) is regressed against the aver-age spectrum (m), according to xik¼aiþ bimkþ eik. From eachspectrum, one subtracts the intercept (ai) and divides by theslope (bi) to get the corrected data, according toxi,corr

0 ¼ (xi0 �ai)=bi. The result of MSC is that each ‘‘corrected’’

spectrum has the same offset and amplitude. With this formu-lation, we should realize that there is a risk of obtaining


vectors a and b that are correlated to Y. Therefore, MSC mayremove from X, information that is relevant for modeling andpredicting Y. Nevertheless, this approach is particularly use-ful for NIR data and such like, where the baseline correctionis inherently weak.

5.2.2.3. Standard Normal Variate Correction

The mathematical formulation of Standard normal variate(SNV) is similar to that of MSC. In SNV, the parameters aiand bi are calculated as the average and the standard devia-tion of the ith row of X. Actually, this corresponds to row-cen-tering and scaling (compare with column-centering andscaling discussed in Sec. 2.1.3).

5.2.2.4. Orthogonal Signal Correction and SomeExtensions

Orthogonal signal correction (OSC) is usually used to removeone component at a time from X based on the NIPALS algo-rithm (a standard method for extracting latent variables)(38). This has the advantage that the approach will cope alsowith moderate amounts of missing data, as do ordinary PCAand PLS. Prior to calculations, X and Y can be transformed,mean-centered, and scaled according to standard procedures.Details of the OSC algorithm can be found in Ref. 38.

Recently, Trygg and colleagues have presented new waysto decompose the PLS solution into (a) components orthogonalto Y and (b) components correlated to Y. These approaches arecalled OPLS for orthogonal PLS (42) and O2-PLS for secondgeneration OPLS (43). It is shown that by using OPLS=O2-PLS one can derive the OSC solution in a more direct way,and hence OPLS=O2-PLS can be used to compute OSC in adifferent way from that described in Ref. 38. The results ofthese alternative OSC computations are, however, similar.Westerhuis et al. (44) have also recently proposed yet anotherOSC alternative called direct OSC. The above papers onOPLS and O2-PLS also contain an overview on the literatureregarding these methods.

Application of OSC to NMR-based metabonomic data hasproved to be particularly useful for removing instrumental

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drift and inherent physiological variation that confound pat-terns of toxicity and stress (45).

5.2.2.5. Wavelet Analysis

Wavelet analysis is useful for signal correction and compres-sion. The theory of wavelet analysis can be made very elabo-rate and only brief account is provided here. Wavelets looklike small oscillating waves, and they have the capability ofinvestigating a signal according to scale, that is, bandpassof frequencies (39,40). The characteristic features of thisapproach are good compression and denoising of complicatedsignals. It has been shown that process fluorescence datameasured on a sugar production plant could be compressedby 97% without loss of predictive power (from nearly 4000spectral variables to 120 ‘‘wavelet’’ variables) (46).

The wavelet transform uses a mother wavelet, that is, abasis function, with a certain scale (width of the analyzingfunction window) to investigate the time-scale properties ofan incoming signal. By varying the width of this window, bothsharp and coarse properties of the signal are captured. A nar-row wavelet is used for detecting the sharp features, and awider wavelet is useful for uncovering general signal proper-ties. The mother wavelet can be selected from many differentfamilies of filters. The shape of the wavelet filter depends onthe selected family and the order. More details are found inRefs. 39 and 40.

Wavelet analysis is particularly useful for spectra thatcontain a mixture of overlapped broad and sharp components.For example, standard 1H NMR-spectra of plasma comprise ofresonances from low MWmetabolites such as amino acids andpolyols (sharp resonances) overlaid with resonances derivingfrom higher MW metabolites such as triglycerides and lipo-proteins (broad signals).

5.2.3. Transformation

When a variable contains one or a few extreme measure-ments, which may influence model building unduly, theremay be reason for transforming the raw data. Consider


Fig. 27(a), which shows the histogram of a variable calledVar1. One out of the 40 measurements in this variable is sub-stantially larger than the others. If this extreme measure-ment is not manipulated in some way prior to data analysis,it will exert a large influence on the model and dominate overthe other measurements.

One way to make such a non-normal distribution morenearly normal is by transformation, for example, the log-transformation (47,48). Figure 27(b) displays the result oflog-transforming Var1. Apparently, Var1 is approximatelynormally distributed after log-transformation. It is noted thata distribution of log (x) will not necessarily be perfectly nor-mal, but will usually be much closer to normality than isthe case for the untransformed data.

Also note that sometimes data may contain very extremeobservations (‘‘outliers’’), which may not be addressed satis-factorily by transformation. Then, other approaches such astrimming or winsorizing, whereby a percentage of the obser-vations for a particular variable are excluded from one or bothextremes of the range, may have to be tested (8).

The log-transformation is not the only transformationone can think of. Other often used transformations are nega-tive logarithm (‘‘neglog’’), logit, square root, fourth root,inverse, and power transformations (8). There is no doubt,however, that the log-transform is the most frequentlyapplied transformation. This is because log-normal distribu-tions are often encountered in nature, particularly whenthe variable studied has a natural zero, such as, retentiontimes, weight, height, concentrations, etc., and ranges overone order of magnitude.

5.3. Some Extensions of PCA and PLS

5.3.1. Nonlinear PLS

When any system or process is subjected to large changes, itappears nonlinear. In the present context, this means thatthe relation between X and Y becomes nonlinear. Also therelations between the X-variables may become nonlinear, aswell as the relations between the Y’s. Even so, the X- and

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Figure 27 (a). Histogram of a nontransformed variable Var1.(b). Histogram of variable Var1 after log-transformation.


Y-matrices can always be approximated by the bilinear PLS-model. Hence, nonlinear situations can be described by PLS-models, where the nonlinearities are expressed as nonlinearassociations between the X-scores (ta) and the Y-scores (ua).These nonlinearities can be modeled as polynomials (quadra-tic, cubic, etc.), spline functions, or other nonlinear forms,e.g., bi-exponential.

Numerous approaches have been published for thesimultaneous estimation of the X-scores (T) and the para-meters in a given type of nonlinear inner relation (49). Thesimplest polynomially nonlinear approach is to just expandthe X-matrix with the squares or cubes of its columns, andthen use this expanded matrix to model Y by PLS (50).

A recently introduced method, GIFI-PLS, shows greatpromise for the future (34,51). It is based on the binning ofcontinuous variables into categorical variables, followed bythe expansion of the latter into sets of concatenated 1=0dummy variables. This creates a flexible modeling set-upwhereby nonlinearities, discontinuities, and other anomaliesin the data are easily discovered.

In general, however, great caution must be used in anytype of nonlinear modeling, including that of nonlinear PLS.Since nonlinear models are much more adaptable and flexiblethan linear models, they easily fit outliers, noise, separateclusters, and the like, which results in very low predictivepower of the model. A prudent use of cross-validation to avoidtoo many terms in the model is strongly recommended. Also,XY-score plots (ta vs. ua) provide diagnostics for the presenceof nonlinearities (cf. Fig. 21). A nonlinear model is warrantedonly when strong curvature is seen in these score plots.

5.3.2. Hierarchical PCA and PLS Models

In PCA and PLS models with many variables, plots and listsof loadings, coefficients, VIP, etc. become messy, and resultsare difficult to interpret. An interesting approach is to dividethe variables into conceptually meaningful blocks, and thenapply hierarchical multiblock PCA- or PLS-models. For exam-ple, in metabonomics such blocks may correspond to different

328 Eriksson et al.

spectral techniques, or, within the same technique, differentspectral regions.

The idea with hierarchical modeling is very simple. Takeone model dimension (component) of an existing projectionmethod, say PLS (two-block), and substitute each variableby a score vector from a block of variables. We call these scorevectors ‘‘supervariables.’’ On the ‘‘upper’’ level of the model, asimple relationship, a ‘‘supermodel,’’ between rather few‘‘supervariables’’ is developed. In the lower layer of the model,the details of the blocks are modeled by block models as blockscores time block loadings. Conceptually, this corresponds toseeing each block as an entity, and then developing PLS mod-els between the ‘‘superblocks.’’ The lower level provides the‘‘variables’’ (block scores) for these block relationships.

This blocking leads to two model levels; the upper levelwhere the relationships between blocks are modeled, andthe lower level showing the details of each block. On eachlevel, ‘‘standard’’ PLS- or PC-scores and loading plots, as wellas residuals and their summaries such as DModX, are avail-able for the model interpretation. This allows an interpreta-tion focussed on pertinent blocks and their dominatingvariables. For further details, reference is given in the litera-ture (52,53).

5.4. Related Methods

Before PLS, two methods were available for regression-likemodeling with many and collinear X-variables, namely princi-pal components regression (PCR) and ridge regression (RR).Naturally, one can also use variable selection and try toreduce the problem to one of ordinary multiple linear regres-sion (MLR). The latter is, however, a poor approach thatgreatly increases the risk for spurious invalid models andvery poor predictions of Y for new observations (54).

In PCR, a principal component analysis (PCA) is firstmade of the X-matrix (properly transformed and scaled), giv-ing as the result the score matrix T and the loading matrix P0.Then, in a second step, a few of the first score vectors (ta) areused as predictor variables in a multiple linear regression


with Y as the response matrix. In the case that the few firstcomponents of PCA indeed contains most of the informationof X related to Y, PCR indeed works as well as PLS. This isoften the case in spectroscopic data, and here PCR is an oftenused alternative. In more complicated applications, however,such as QSAR and process modeling, the first few principalcomponents of X rarely contain a sufficient part of the rele-vant information, and PLS then works better than PCR.

RR uses another approach to cope with near singularitiesof X in the regression problem. Here a small number, d, isadded to all the diagonal elements of the variance covariancematrix of X (i.e., X0X) before its inversion in the regressionalgorithm. This closely corresponds to the discarding of allprincipal components with singular values smaller than d,and indeed RR and PCR show very similar performance.Hence also RR has problems in complicated applications(14), such as QSAR, metabonomics, and process modeling,and is mainly useful in situations where ordinary regression‘‘almost’’ works, i.e., rather few but correlated X-variables.Also, the RR solution often has a serious bias in the coeffi-cients even at small values of d, making the interpretationof RR coefficients problematic.

In addition, other methods such as neural networks (NN)are often tried in the analysis of chemical data. Since NNs areequivalent to a certain type of nonlinear regression, however,these are often less suitable for problems with many and col-linear variables. Either a prereduction of the variables byselection or a PCA is needed for such problems, resultingin the same difficulties as discussed above for PCR andRR. However, probabilistic neural networks based on aBayesian calculation of the probability distribution of objectsovercomes some of these inherent problems, as described inthe literature (55,56).

6. CONCLUDING REMARKS

We have shown the ability of PCA and PLS to develop quan-titative metabonomics models. PCA and PLS analysis of

330 Eriksson et al.

NMR-data creates one or several maps (i.e., score plots) thatshow trajectories of biochemical changes in biofluids inducedby toxin exposure or disease. Through this technology it ispossible (i) to detect target organs or pathways of dysfunction,(ii) to uncover likely chemical mechanisms of toxicity, and (iii)to identify useful biomarkers indicative of onset, develop-ment, and decay of abnormal animal health conditions.

Depending on the objective of the investigation, a multi-variate model can be tailored to ‘‘see’’ or ‘‘feel’’ different fea-tures hidden in the NMR-data. As shown by the first dataset, a model can be trained to discriminate between toxinsgoing for different target organs, but also taught to contrastcontrol rats with drug-exposed animals.

In order to facilitate the classification phase, the variousscore- and DModX-parameters of the PCA- and PLS-modelsmay be displayed in control charts. As pointed out in Refs. 1and 33, this will enable the identification of animals thatrespond slowly to intoxication compared with the majorityof the population, and also those that respond quickly. Also,such charts may suggest magnitude and directionality ofthe response of each animal.

Thus, in summary, there is really no doubt that the com-bination of urinary NMR-data and PCA=PLS offers a promis-ing approach to addressing the mechanism and nature ofpathological events. Within the next few years we foresee ageneral breakthrough for this rapidly developing disciplinein the areas of toxicological screening and disease diagnosis.

ACKNOWLEDGEMENTS

Figures 1–14 are reproduced from Ref. 8 and are used withpermission.

REFERENCES

1. Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabo-nomics: a platform for studying drug toxicity and gene func-tion. Nat Rev 2002; 1:153–161.


2. Freeman R, Goodacre R, Sisson PR,Magee JG,Ward AC, Light-foot NF. Rapid identification of species within the Mycobacter-ium tuberculosis complex by artificial neural network analysisof pyrolysis mass spectra. J Med Microbiol 1994; 40:170–173.

3. Halket JM, Przyborowska A, Stein SE, Mallard WG, Down S,Chalmers RA. Deconvolution of gas chromatography=massspectrometry of urinary organic acids—potential for patternrecognition and automated identification of metabolic disor-ders. Rapid Commun Mass Spectrom 1999; 13:279–284.

4. Ramos LS. Characterisation of Mycobacteria species by HPLCand pattern recognition. J Chromatogr Sci 1994; 32:219–227.

5. Jackson JE. A User’s Guide to Principal Components. NewYork: John Wiley, 1991 (ISBN 0-471-62267-2).

6. Wold S, Sjostrom M, Eriksson L. PLS-regression: a basic tool ofchemometrics. Chemometrics Intell Lab Systems 2001;58:109–130.

7. Wold S, Albano C, Dunn WJ, Edlund U, Esbensen K,Geladi P, Hellberg S, Johansson E, Lindberg W, Sjostrom M.Multivariate data analysis in chemistry. In: Kowalski BR,ed. Chemometrics: Mathematics and Statistics in Chemistry.Dordrecht, Holland: D. Reidel Publishing Company, 1984.

8. Eriksson L, Johansson E, Kettaneh-Wold N, Wold S. Multi-and Megavariate Data Analysis—Principles and Applications.Umetrics AB, 2001, ISBN 91-973730-1-X.

9. Wold S, Johansson E, Cocchi M. PLS. In: Kubinyi H, ed. 3D-QSAR in Drug Design, Theory, Methods, and Applications.Ledien: ESCOM Science, 1993:523–550.

10. Hoskuldsson A. A combined theory for PCA and PLS. J Che-mometrics 1995; 9:91–123.

11. Wold S, Esbensen K, Geladi P. Principal component analysis.Chemometrics Intell Lab Systems 1987; 2:37–52.

12. Wold S. Cross-validatory estimation of the number of compo-nents in factor and principal components models. Techno-metrics 1978; 20:397–405.

13. Wikstrom C, Albano C, Eriksson L, Friden H, Johansson E,Nordahl A, Rannar S, Sandberg M, Kettaneh-Wold N,

332 Eriksson et al.

Wold S. Multivariate process and quality monitoring appliedto an electrolysis process—Part I. Process supervision withmultivariate control charts. Chemometrics Intell Lab Systems1998; 42:221–231.

14. Hoskuldsson A. Prediction Methods in Science and Technol-ogy. Copenhagen, Denmark: Thor Publishing, 1996.

15. Wold S, Sjostrom M, Eriksson L., PLS in chemistry. In:Schleyer PVR, Allinger NL, Clark T, Gasteiger J, KollmanPA, Schaefer III, HF, Schreiner PR, eds. The Encyclopedia ofComputational Chemistry. Chichester: John Wiley & Sons,1999:2006–2020.

16. Wold S, Josefson M. (2000) In: Wold et al., eds. MultivariateCalibration of Analytical Data, Encyclopedia of AnalyticalChemistry. Wiley, 1999:1–27.

17. Wakeling IN, Morris JJ. A test of significance for partial leastsquares regression. J Chemometrics 1993; 7:291–304.

18. Denham MC. Prediction intervals in partial least squares. JChemometrics 1997; 11:39–52.

19. Martens H, Hoy M, Westad F, Folkenberg D, Martens M.Analysis of designed experiments by stabilised PLS-regressionand jack-knifing. Chemometrics Intell Lab Systems 2001; 58:151–170.

20. Burnham AJ, Viveros R, MacGregor JF. Frameworks forlatent variable multivariate regression. J Chemometrics1996; 10:31–45.

21. Burnham AJ, MacGregor JF, Viveros R. A statistical frame-work for multivariate latent variable regression methodsbased on maximum likelihood. J Chemometrics 1999; 13:49–65.

22. Burnham AJ, MacGregor J, Viveros R. Interpretation ofregression coefficients under a latent variable regressionmodel. J Chemometrics 2001; 15:265–284.

23. Efron B, Gong G. A leisurely look at the bootstrap, the Jack-knife, and cross-validation. Am Statisti 1983; 37:36–48.

24. Wold H. Soft modelling. The basic design and some extensions.In: Joreskog KG, Wold H, eds. Systems Under Indirect


Observation, , Vols. I and II Amsterdam, The Netherlands:North-Holland, 1982.

25. Martens H, Martens M. Modified Jack-knife estimation ofparameter uncertainty in bilinear modeling (PLSR). FoodQuality Preference 2000; 11:5–16.

26. Espina JR, Shockcor JP, Herron WJ, Car BD, Contel NR,Ciaccio PJ, Lindon JC, Holmes E, Nicholson JK. Detection ofin vivo biomarkers of phospholipidosis using NMR-basedmetabonomic approaches. Magn Reson Chem 2001; 295(39):559–565.

27. Smallcombe SH, Platt SL, Kaifer PA. J Magn Reson A 1995;117:2953.

28. Sjostrom M, Wold S, Soderstrom B. PLS discriminant plots.Proceedings of PARC in Practice, Amsterdam, June 19–21,1985. North-Holland: Elsevier Science Publishers B.V., 1986.

29. Stahle L, Wold S. Partial least squares analysis with cross-validation for the two-class problem: a Monte Carlo study. JChemometrics 1987; 1:185–196.

30. Coomans D, Broeckaert I, Derde MP, Tassin A, Massart DL,Wold S. Use of a microcomputer for the definition of multivari-ate confidence regions in medical diagnosis based on clinicallaboratory profiles. Comp Biomed Res 1984; 17:1–14.

31. Shockcor JP, Holmes E. Metabonomic applications in toxicityand disease diagnosis. Curr Topics Med Chem 2002; 2:35–51.

32. Wold S, Kettaneh N, Friden H, Holmberg, A. Modelling anddiagnostics of batch processes and analogous kinetic experi-ments. Chemometrics Intell Lab Systems 1998; 44:331–340.

33. Antti H, Bollard ME, Ebbels T, Keun H, Lindon JC, NicholsonJK, Holmes EB. Batch statistical processing of 1H NMR-derivedurinary spectral data. J Chemometrics 2002; 16:461–468.

34. Wold S, Trygg J, Berglund A, Antti H. Some recent develop-ments in PLS modeling. Chemometrics Intell Lab Systems2001; 58:131–150.

35. Naes T, Isaksson T, Fearn T, Davies T. A User-friendly Guideto Multivariate Calibration and Classification. Chichester,UK: NIR Publications, 2002 (ISBN: 0-95286662-5).

334 Eriksson et al.

36. Geladi P, MacDougall D, Martens H. Linearization and scat-ter-correction for near-infrared reflectance spectra of meat.Appl Spectrosc 1985; 3:491–500.

37. Barnes RJ, Dhanoa MS, Lister SJ. Standard normal variatetransformation and de-trending of near-infrared diffuse reflec-tance spectra. Appl Spectrosc 1989; 43:772–777.

38. Wold S, Antti H, Lindgren F, Ohman J. Orthogonal signal cor-rection of near-infrared spectra. Chemometrics Intell Lab Sys-tems 1998; 44:175–185.

39. Alsberg BK, Woodward AM, Kell DB. An introduction to wave-let transforms for chemometricians: a time-frequency approach.Chemometrics Intell Lab Systems 1997; 37:215–239.

40. Trygg, J, Wold S. PLS regression on wavelet compressed NIRspectra. Chemometrics Intell Lab Systems 1998; 42:209–220.

41. Savitzky A, Golay MJE. Smoothing and differentiation by sim-plified least squares procedures. Anal Chem 1964; 36:1627–1632.

42. Trygg J, Wold S. Orthogonal projections to latent structures,OPLS. J Chemometrics 2002; 16:119–128.

43. Trygg J. O2-PLS for qualitative and quantitative analysis inmultivariate calibration. J Chemometrics 2002; 16:283–293.

44. Westerhuis JA, de Jong S, Smilde AK. Direct orthogonal signalcorrection. Chemometrics Intell Lab Systems 2001; 56:13–25.

45. Beckwith-Hall BM, Brindle JT, Barton R, Coen M, Holmes E,Nicholson JK, Antti H. Application of orthogonal signal cor-rection to minimise the effects of physical and biological varia-tion in high resolution 1H NMR spectra of biofluids. Analyst2002; 1283–1288.

46. Eriksson L, Trygg J, Johansson E, Bro R, Wold S. Orthogonalsignal correction, wavelet analysis, and multivariate calibra-tion of complicated process fluorescence data. Anal Chim ActaAnal 2000; 420:181–195.

47. Davis OL, Goldsmith PL. Statistical Methods in Research andProduction. New York: Longman, 1986.

48. Massart DL, Vandeginste BGM, Deming SN, Michotte Y,Kaufman L. Chemometrics: A Textbook. Elsevier, 1988.


49. Wold S, Kettaneh-Wold N, Skagerberg B. Nonlinear PLS Mod-elling. Chemometrics Intell Lab Systems 1989; 7:53–65.

50. Berglund A, Wold S. INLR, implicit non-linear latent variableregression. J Chemometrics 1997; 11:141–156.

51. Eriksson L, Johansson E, Lindgren F, Wold S. GIFI-PLS: mod-elling of non-linearities and discontinuities in QSAR. Quanti-tat Struct–Activity Relationships 2000; 19:345–355.

52. Wold S, Kettaneh-Wold N, Tjessem K. Hierarchical multiblockPLS and PC models for easier model interpretation and as analternative to variable selection. J Chemometrics 1996; 10:463–482.

53. Eriksson L, Johansson E, Lindgren F, Sjostrom M, Wold S.Megavariate analysis of hierarchical QSAR data. J Computer-Aided Molec design. 2002; 16: 711–726.

54. Frank IE, Friedman JH. A statistical view of some chemo-metrics regression tools. Technometrics 1993; 35:109–135.

55. Holmes E, Nicholson JK, Tranter G. Metabonomic characteri-zation of genetic variations in toxicological and metabolicresponses using probabilistic neural networks. Chem ResToxicol 2001; 14(2):182–191.

56. Parzen E. On estimation of a probability density function andmode. Ann Math Statist 1962; 33:1065–1076.

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9

Use of Metabonomics to StudyTarget Organ Toxicity

CRAIG E. THOMAS

Investigative Toxicology, LillyResearch Laboratories, A Division of Eli

Lilly and Company,Greenfield, IN, U.S.A.

ELAINE HOLMES

Biological Chemistry, BiomedicalSciences Division, Imperial College

of Science, Technology andMedicine, University of London,South Kensington, London, U.K.

DONALD G. ROBERTSON

Drug Safety Evaluation, Pfizer GlobalResearch and Development,


1. INTRODUCTION

Identifying target organ toxicity remains a primary objectiveof drug safety assessment. The more efficiently this can bedone, the better. For example, can subchronic effects bedetected with acute dosing, can the toxicity be detected in a

337

less invasive manner than histopathologic assessment of col-lected tissues, etc. The bulk of themetabonomic literature overthe past 5–10 years is devoted to studying target organ toxi-city; either at the diagnostic or mechanistic level. Not surpris-ingly, the two most well-studied organs are the liver andkidney, while there exists a relative paucity of data on otherorgans as studied by NMR. This chapter will review the exten-sive literature on liver and kidney. Furthermore, recentefforts on using metabonomics to study drug-induced vasculo-pathies are discussed. This represents a unique opportunity toaddress a toxicity that hampers pharmaceutical drug develop-ment and for which current methods are intensive andinvasive, and for which no reliable, robust biomarkers exist.

2. HEPATIC TOXICITY

2.1. The Liver as a Target Organ

Hepatotoxicity continues to represent a major stumblingblock for advancement of new chemical entities in humanclinical trials (1). As the liver is exposed to absorbed xenobio-tics and new drug candidates via portal vein perfusion andserves as the central point for metabolism, it is not surprisingthat this organ can be susceptible to multiple mechanisms oftoxicity. Generally, acute liver injury manifests as cell death(necrosis) or lipid accumulation (steatosis). Direct injury toliver parenchyma can be readily detected by a combinationof clinical chemistry and histopathology and, thus, monitoredfor in preclinical development. It is also possible to screen forhepatocellular damage in a variety of in vitro systems includ-ing isolated hepatocytes, liver slices, and cell lines with thecaveat that these systems can have limitations with respectto metabolic capacity and, henceforth, bioactivation or detox-ification (2). Because the liver is also designed to synthesizeand secrete bile acids, drug-induced injury can lead to choles-tasis that in itself can propagate additional hepatotoxicity.Not surprisingly then, certain drugs are also known to pro-duce hepatic injury with features of both cholestasis andnecrosis or apoptosis (3).

338 Thomas et al.

In late stage preclinical development, or in clinicaltrials, termination of development due to hepatic injurybecomes much less predictable, and more challenging to dis-sect mechanistically. In some instances, it is simply a matterof a ‘‘shrinking’’ margin of safety that can result from drugaccumulation or enhanced metabolism to a reactive inter-mediate via enzyme induction. Chronic toxicity might alsorepresent an inability to efficiently repair acute injury inthe face of repeated insult. In man, liver injury is often idio-syncratic in nature and not always adequately predicted bystudies in rodent and nonrodent mammals (1). Therefore,the ability to more accurately and rapidly assess this majorliability early in the compound selection phase would repre-sent a significant advance in drug development. If a technol-ogy with appropriate throughput provided insight into themechanism of action driving the toxicity at an early stage,it is conceivable that an SAR can be developed around thehepatotoxicity.

Alternatively, the identification of more precise and sen-sitive biomarkers for hepatic injury, whether direct or indir-ect injury, could improve on our ability to advance drugsthrough clinical development and minimize the occurrenceof untoward effects. For purposes of this chapter, a biomarkerwill refer to a measurement that can be captured noninva-sively and which is linked to a pathologic change. For exam-ple, the classic ‘‘biomarker’’ employed to monitor hepaticinjury is serum transaminase levels, yet it is difficult to pre-dict if a slight elevation in transaminases portends a progres-sion to serious liver injury. In preclinical studies in animals, itis not unusual to see transient elevations in transaminases inblood with no histologic evidence of overt parenchymal injury.However, since it is of utmost importance to ensure patientsafety in clinical trials, any suggestion of a hepatic liabilitycan require that thousands of additional patients be moni-tored in order to adequately define the risk of exposure to acandidate drug, thereby greatly increasing the time and costof development. As described below, metabonomics is promis-ing as a tool to significantly increase the probability of identi-fying the liver as a target organ for drug-induced injury.

Metabonomics to Study Target Organ Toxicity 339

2.2. Using Metabonomics to Study AcuteHepatic injury—Comparison to ClinicalChemistry and Morphologic Pathology

Clearly, the pathogenesis of hepatic injury has multipleetiologies. However, in many instances, including microvesi-cular steatosis, nonalcoholic steatosis, and cytolytic hepati-tis, perturbation of mitochondria has been shown to occur(3). As described elsewhere in this volume, biofluid NMR isan excellent tool to monitor multiple metabolic intermediatesthat are directly affected by the state of the mitochondrion.While the kidney is the target organ most intensely studiedby high field NMR, metabonomic analyses of urine and tis-sue samples from animals treated with xenobiotics thatinduced varied types of liver injury have also been reported.One of the first published studies extending metabonomicsto the liver was from Beckwith-Hall et al. (4) who studiedthree distinct hepatoxicants: a-naphthylisocyanate (ANIT),d-(þ) galactosamine (GalN) and butylated hydroxytoluene(BHT) by NMR. These three agents cause intrahepatic cho-lestasis, acute hepatitis, and centrilobular and periportalnecrosis, respectively. In this study, the three toxicants wereadministered as a single dose to rats followed by 7 days ofmonitoring with conventional clinical chemistry, urinarymetabolites via 600MHz 1H-NMR, and histopathology ofliver tissue. Perhaps not surprisingly, the NMR spectraidentified a number of urinary metabolites that changedsimilarly amongst the compounds (Table 1). These includeddecreases in urinary excretion of citrate, 2-oxoglutarate,and succinate; changes that are often associated with toxi-city to other target organs and appear to signal generalizedtoxicity, irrespective of the organ (5,6). More unique to thethree hepatotoxicants was an increase in taurine, creatine,and acetate. The elevation in urinary taurine is consistentwith the findings of Timbrell et al. (7,8) who demonstrateda similar increase for a variety of liver toxicants using meth-ods other than NMR. Early 1H-NMR work also revealedhypertaurinuria in association with hepatotoxicity (9). Over-lapping, but not all-inclusive, changes included increased

340 Thomas et al.

bile acid excretion with GalN and ANIT, while glycosuriawas associated with BHT and ANIT.

The most significant distinction between the compoundswas best illustrated by using principle components analysis(PCA) to analyze time related changes in the NMR spectrafrom urine. The magnitude of the trajectory (position in PC1vs. PC2 relative to time) for BHT indicated a less severeinjury that was corroborated by histopathology evaluation.The position in movement along PC1 relative to PC2 for thecompounds also highlighted differences or similarities inmetabolic profile at various timepoints. For example, themean position (PC1 vs. PC2) for the rats treated at 24–96hrpostdose with ANIT mapped closely to the GalN treated ratsat 24–72hr postdose. Inspection of the PCA maps revealedthat bile acids were similar urinary components at those time-points and dissociated the effects of these two toxins fromBHT.

The ability to separate the compounds by pattern recog-nition provides an opportunity to identify unique biomarkers

Table 1 Major Metabolic Changes Identified for HepatotoxicityUsing ANIT, Galactosamine and BHT

Increased Decreased

Metabolite changes observedfor all three toxicantsAcetate CitrateCreatine 2-OxoglutarateTaurine SuccinateMetabolite changes observed fortwo of three toxicantsAlanine (A,G) N-methyl nicotinate (G,B)Bile Acids (A,G) Hippurate (G,B)Glucose (A,B)Lactate (A,G)

Table lists urinary metabolites identified as changed in response to ANIT (A), galac-tosamine (G), or BHT (B). Several metabolites were either increased or decreased incommon across all three toxicants. For metabolites affected by two toxins, thoseincreased were most similar for ANIT and galactosamine while those decreased wereparticular to galactosamine and BHT. (Adapted from Ref. 4.)


for the particular pathologic lesion observed. In the case ofGalN, there was increased urinary excretion of betaine,urocanic acid, tyrosine, threonine, and glutamate. While bileacid elevation was common to both GalN and ANIT, theamino aciduria was associated primarily with GalN. It is alsoimportant to note that the differences in metabolite profilewere consistent with differing pathologic injury as shown byhistologic examination. Correlations between histology, clini-cal chemistry, and urinary NMR spectra were also noted; forexample, with ANIT, the maximum yield of urinary bile acidscoincided with maximal elevation in plasma ALP and biliru-bin levels, bile duct proliferation, and cholangitis. Overall,this study provided strong evidence that even with simplepattern recognition methods, it is possible to separatetoxicants by unique groups or a ‘‘pattern’’ of biomarkers.

The work of Beckwith-Hall has been more recently con-firmed and extended by Waters et al. (10) who performedNMR analysis on liver, urine, and plasma of ANIT-treatedrats. Clinical chemistry=hematology and histopathology eva-luations were also performed to provide an overall integratedmetabonomics study of ANIT-induced liver injury. Critical tothe evaluation was a complete evaluation of all these par-ameters at 3, 7, 24, 31, and 168hr postdose allowing adetailed picture of the time dependence of the insult andrepair to emerge. The earliest noted changes were an increasein plasma and urinary glucose; in agreement with the corres-ponding drop in hepatic glucose and glycogen noted histolo-gically. Also emerging early were an elevation in liver andplasma lactate consistent with increased glycogenolysis andglycolysis. The commensurate depression of succinate, citrate,and 2-oxoglutarate implied a general increase in energy meta-bolism. There were also reported changes in lipid metabolismand storage. As early as 24hr postdose, there was an eleva-tion in lipids, such as triglycerides in liver, plasma, and urine,which fell below control levels by 168hr and could beexplained by either a drug-induced steatosis, triglycerideaccumulation as a response to toxic injury, or an impairmentof hepatic apolipoprotein formation. The slight lag in plasmalipid elevation relative to the liver agrees with the histo-

342 Thomas et al.

pathologic evidence of cholestasis. While the overall time-dependent trajectory of injury suggested a return toward a‘‘normal, control state’’ beginning at about 72hr postdose,there were specific changes in lipid profiles that were notedlate. The decrease in hepatic lipid content at 168hr postdosewas associated with increases in trimethylamine-N-oxide,betaine, phosphocholine, and choline. This can be explainedby catabolism of accumulated lipids to intermediate species.As these alterations occurred late, and coincident with bileduct hyperplasia, these components could be considered asbiomarkers for the bile duct cell proliferation.

As with the previously described work using ANIT (4),NMR evaluation of the urine revealed bile-aciduria andglycosuria; as well as marked elevations in taurine and crea-tine that are currently accepted as markers of hepatic injury.While the source of taurine is not established for many hepa-totoxicants, the known dependence of ANIT toxicity on intra-heptic recycling of a GSH–ANIT conjugate suggests a possiblemechanism. The increase in taurine may be a protectivemechanism that prevents cysteine buildup, in response tothe decrease in GSH biosynthesis that occurs following GSHliberation via dissociation of the drug conjugate. The induc-tion of hepatotoxicity in this study, as judged by urinaryNMR analysis, was also confirmed by the marked elevationsin plasma transaminases, glutamate dehydrogenase, and sor-bitol dehydrogenase. This concordance of NMR data with themethods of clinical chemistry and histology data was consis-tent throughout the study. Furthermore, the careful stagingof timepoints allowed a clear picture of the etiology of hepaticinjury and recovery. While this study utilized all the technol-ogies (clinical chemistry, histopathology, NMR) at all thetimepoints, it must be recognized the tremendous resourcesthat would be consumed to do this on a routine basis. The abil-ity to noninvasively monitor urinary changes easily, and forrelatively little cost, highlights one of the distinct advantagesof metabonomics. However, as for any new and unproventechnology, careful validation of the technology is requirednecessitating the conduct of detailed and laborious studies,such as that just described, in order to establish a strong link


between metabolite changes and the current ‘‘gold standards’’of morphologic pathology and clinical chemistry.

2.3. Metabonomic Studies of Dose-DependentHepatotoxicity

Most metabonomic studies, including the aforementioned,were conducted using a single dose. In the arena of drugdiscovery and development, important decisions require aclear understanding of dose-dependent effects and marginof safety based on systemic exposure to the drug candidate.Two relatively recent studies have addressed whether bio-fluid NMR can detect dose-dependent effects using severalwell-studied hepatotoxicants. Robertson et al. (11) evaluatedthe feasibility of using high field NMR and statistical para-digms for pattern recognition to screen for toxicants whichaffected the liver or kidney. In this study, both a highand a low dose of the hepatotoxicants ANIT and CCl4 wereemployed. Again, the metabonomic results were comparedto histopathology and clinical chemistry in an ongoing effortto validate the NMR technology. Clinical chemistry changeswere restricted to the high-dose animals only, peaked at24–48hr and returned to control levels by Day 4 for bothtoxicants. Microscopically, ANIT (10mg=kg) showed mini-mal bile duct proliferation while the dose of 100mg=kgresulted in additional features including increased numbersof Kupffer cells, fibrosis, and necrosis in two of four treatedrats (Fig. 1). At 0.1mL=kg CCl4, only minimal hepatocellu-lar vacuolation was noted, but 0.5mL=kg caused mild tomarked necrosis and Kupffer cell proliferation that was sig-nificant at Day 4, but had returned to normal at Day 10.Both CCL4 and ANIT showed the common feature of adecrease in urinary excretion of 2-oxoglutarate, citrate,and hippurate. On Day 1, the trajectory for the two dosesof ANIT was similar in magnitude and direction indicatingsimilar biochemical effects at the two doses (Fig. 1). Subse-quently, however, the low-dose animals returned rapidly tocontrol regions of the trajectory plot, while the high-doseanimals had maximal injury on Day 2 corresponding to

344 Thomas et al.

the maximum serum bilirubin elevation. Likewise, thetrajectory analysis for CCL4 at the two doses demonstrateda more severe and longer lasting metabolic disturbance atthe high dose.

Figure 1 Principle components analysis of urine spectra from ratstreated with ANIT. Male Wistar rats were treated with a single oralgavage dose of ANIT at 10 and 100mg=kg and urine collected pret-est and in 24hr intervals thereafter for 4 days. The numbers depictsample days and the letters denote an individual animal. Individualanimal data from the same sample days are grouped and high-lighted by the shaded polygons. Thus, each polygon represents theinter-animal variability for each day, while the distance of eachpolygon from the appropriate pretest polygon is a measure of themagnitude of the effect. Only pretest and Day 1 are shown for the10mg=kg group. The corresponding mean serum total bilirubinlevels for each group are also shown in parentheses. For the micro-graphs, ANIT at 10mg=kg showed minimal bile duct proliferationwhile at 100mg=kg additional features included increased numbersof Kupffer cells, fibrosis, and necrosis. (Figure adapted fromRef. 11.)


The findings reported by these investigators demon-strated that the time course of injury and recovery was simi-lar when comparing NMR to clinical chemistry data. It wasalso of interest to note that animals that appeared to be out-liers from the clinical chemistry data, based on severity ortemporal effect, were also judged to be outliers by PCA ofthe NMR spectra. This tight agreement between the NMRtechnique and the clinical chemistry was stronger thanbetween NMR and histology, but it must be considered thattissues were obtained for microscopic examination only onDays 4 and 10. Nonetheless, this study has several importantconnotations. First, it further solidifies the strong correlationsbetween conclusions based on clinical chemistry findings andthose from NMR data. As toxicology studies frequently useserum markers to monitor and quantify toxicity, and theseparameters are also often used clinically, this is further prooffor the potential application of metabonomics both in animalsand man. Secondly, it is one of the first illustrations of thesensitivity of the NMR to distinguish dose-dependent effectsin a manner reasonably consistent with histology. The PCAof ANIT effects demonstrated a separation from controls forboth doses, while the low-dose CCl4 animals could not be dis-tinguished from controls. Accordingly, the only detectable his-tologic change in the low-dose CCl4 treated animals was aslight depletion of glycogen. Finally, this work demonstratedthat it was possible to rapidly and easily distinguish the twohepatoxicants from the two renal toxicants, following a singledose, using NMR and simple pattern recognition techniques.

A second study, which also touched upon the issue ofdose, investigated hydrazine toxicity (5). Similar to whatwas described for ANIT, Nicholls et al. combined 1H-NMRanalysis of urine and plasma with traditional clinical chemis-try and histopathology of the liver. Again, this emphasizes thecritical need at this juncture to continue to test and validatemetabonomic technology against the currently acceptedmethods that are used to make critical decisions on safety ofdrug candidates. Hydrazine, a well-known hepatotoxicant,was dosed at 75, 90, and 120mg=kg and caused midzonalhepatic fat vacuolation at 48–72hr postdose, and which had

346 Thomas et al.

resolved by 7 days. The NMR analysis of the urine revealedclear dose-dependent effects as judged by the magnitude ofthe changes on PCA. At 75 and 90mg=kg, the trajectorieswere maximal at 24–32hr, but by 152hr were similar in posi-tion to those of control rats as judged by PC1 vs. PC2, thusindicating recovery. At 120mg=kg, the animals were removedfrom the study early owing to poor health, yet it was clearthat these animals were more affected, particularly in thedirection of PC2, as compared to the two lower dose groups.

Detailed examination of the loading plots showed theusual, toxicant-induced decreases in Krebs cycle intermedi-ates citrate, succinate, and 2-oxoglutarate, as well as tri-methylamine-N-oxide, fumarate,andcreatinine. Interestingly,the decrease in 2-oxoglutarate occurred earlier than changesin citrate and succinate suggesting an impact on pathwaysother than just the tricarboxylic acid cycle. Consistent withthe work with other hepatotoxicants (4,8), urinary levels oftaurine and creatine were elevated as was excretion ofthreonine, N-methylnicotinate, tyrosine, b-alanine, citrulline,Na-acetylcitrulline, and arginosuccinate. In the plasma, therewas a general trend of elevated amino acids including glycine,isoleucine, valine, lysine, arginine, histidine, and threonine.Low molecular weight substances elevated in both plasmaand urine included alanine, citrulline, tyrosine, and creatine.There was also an overall reduction in plasma lipids, parti-cularly in the region of the spectra associated with long chainCH2 groups of fatty acids and terminal methyl groups.

A specific examination of the dose–response relationshipfor specific metabolites was conducted. This is significant, asdemonstration of dose-dependency would help to solidify themetabolites as biomarkers of hydrazine toxicity, hepatotoxi-city, or both. To some extent, the elevations in both urinaryand plasma tyrosine were dose-dependent. The most strikingfinding was the dose-dependent effect of hydrazine on levelsof 2-aminoadipate (2-AA) in both biofluids. This potential bio-marker had been previously shown to be associated withhydrazine toxicity (12), but in this work 2-AA was followedover the entire 7 days of the experiment, and was shown tobe the major discriminant indicative of a compound-induced


effect. The effect of hydrazine on both 2-AA and tyrosine canmechanistically be explained via aminotransferase inhibitionand pyridoxal 50-phosphate sequestration. Increases in ala-nine in liver and plasma by hydrazine in earlier studies hadbeen suggested to reflect transaminase inhibition (13).Furthermore, 2-AA has been reported to be a neurotoxicantcausing seizures and convulsions (14) and its elevation mayexplain the reported neurologic effect of hydrazine. Shownin Fig. 2 are results from a study in which Sprague–Dawley(SD) rats were dosed with hydrazine at 30 and 90mg=kg.Data are presented for predose and at 24 and 48hr after dos-ing. It is apparent that there is a dose-dependent effect for anumber of metabolites. This includes 2-AA which is detectableat 24hr at 90mg=kg, but not until 48hr at 30mg=kg. Overall,

Figure 2 600 mHz 1H-NMR spectra of urine samples from hydra-zine-treated rats. Male, SD rats were given a single dose of hydra-zine at 30 or 90mg=kg. Urine was collected prior to, and in 24hrintervals after dosing. Dose-dependent effects for a number of meta-bolites are evident with respect to magnitude of the change or, inthe case of 2-amino-adipate, the effect is observed at an earlier time-point at the high dose.

348 Thomas et al.

these studies clearly demonstrate the potential power of highfield biofluid NMR to understand toxicity mechanistically andto provide biomarkers of intoxication.

2.4. Phospholipidosis

In addition to the aforementioned work on well-known hepa-totoxicants, several recent publications have focused on a par-ticular pathologic condition, rather than a specific toxicant,namely phospholipidosis (PLD). Phospholipidosis is generallyassociated with subchronic or chronic treatment and is char-acterized by the accumulation of phospholipid within cells;the most characteristic finding being the appearance of‘‘foamy’’ macrophages. Whilst observed with multiple chemi-cal structures, the classic inducers of this phenomenon arecationic amphiphilic drugs of various classes including anti-depressants, antiarrythymics, antianginals, and others(15,16). The current requirement to characterize this featureby electron microscopy and=or biochemical measurement oftissue phospholipid content does not lend itself to facile orearly identification of this issue early in drug safety evalua-tion. Thus, metabonomics has been investigated for its abilityto provide biomarkers of this condition. Nicholls et al. (17)studied five drug candidates by NMR; two of which had beenshown to cause mild PLD in lung and liver. After a single doseof the drug, urine was collected for 48hr, at which time ratswere sacrificed for histologic examination. Two of the com-pounds resulted in the appearance of foamy alveolar macro-phages with evidence of hepatic lipid accumulation. Theurinary profiles for the animals demonstrated a clear differ-ence between these two compounds and the three whichshowed no morphologic evidence of PLD. The spectra of thesetwo compounds were distinguished by decreases in urinarycitrate and 2-oxoglutarate, which have already been shownto be nonspecific markers of xenobiotic exposure. However,there was a clear elevation in phenylacetylglycine (PAG)which is not a commonly observed metabolite and it was sug-gested that these three metabolites together may represent aset of biomarkers signifying this condition. What is puzzling


is that it is difficult to understand the linkage between theformation of PAG and the mechanism of toxicity; this is inneed of further investigation. Nonetheless, another grouphas also reported on the appearance of PAG in associationwith PLD (18). We have also utilized metabonomics in aneffort to develop a ‘‘screening’’ method for this toxicity. In gen-eral, we observe an increase in PAG when compounds such asamiodarone are utilized (Fig. 3). While the endogenous levelof PAG in rats is generally low, there are variations in control,nontreated animals. Currently, our belief is that endogenouslevels of PAG reflect variations in diet and fed vs. fastedconditions, but this remains to be ascertained.

2.5. Magic Angle Spinning (MAS) of Liver Tissue

To date, the progress made in using biofluid NMR to charac-terize hepatotoxicity is encouraging. The ability to assign bio-markers specifically to hepatic injury will depend uponpowerful chemometric methods that compare and contrastamongst various target organ biofluid profiles. Another oppor-tunity to associate metabolites with liver damage is to directlymeasure metabolite changes in the liver using magic anglespinning-NMR (MAS-NMR) as described more fully inChapter 5. Bollard et al. (19) have demonstrated the abilityof MAS-NMR with an 800MHz instrument to identify variousclasses of biological molecules in intact liver tissue. Not sur-prisingly, the success of this technique is highly dependentupon tissue sample preparation (20). One-dimensional (1D)NMR was capable of resolving substances of generally lessthan 1000Da. Using multiple 2D methods, it was possible toassign resonances to low molecular weight substances suchas glucose, alanine, glutamate, glycine, and others; as wellas to observe signals from glycogen. As glycogen depletion isoften one of the more subtle manifestations of liver injury,these data suggest that NMR can detect at least one of thepathologic hallmarks of toxicity. It must be cautioned, how-ever, that as with any new technology, the results mustnot be over-interpreted. In many toxicity studies, glycogen

350 Thomas et al.

Figure 3 600 mHz 1H-NMR spectra (d 1.5–4.5) of urine samplesfrom amiodarone-treated rats. Fisher 344 rats were given a singleoral gavage dose of amiodarone at 500mg=kg and urine was col-lected prior to dosing and in 24hr intervals thereafter. Evident by24hr was a reduction in several metabolites, including the Krebscycle intermediates succinate, 2-oxoglutarate, and citrate. Phenyla-cetylglycine (PAG), which is less often observed in urine, wasdetectable at 24 hr postdose, with a greater concentration at 72hrpostdose.


depletion is a relatively short-lived finding and is not ulti-mately linked to any evidence of hepatocellular injury. Inmany instances, glycogen depletion can signify a decrease infood consumption associated with acute compound adminis-tration. As an aside, it must be considered that in acute tox-icity studies, inappetance is often noted which wouldnaturally be expected to influence many intermediary metabo-lites, in addition to glycogen. Thus, a future area of investigationshould be to study the impact of food and=or water depriva-tion on urinary metabolite profiles and how to model thesechanges in relation to a direct compound effect that resultsultimately in tissue injury.

A very recent study described NMR and pattern recogni-tion studies on liver extracts and intact livers from rats trea-ted with ANIT (21). Aqueous extracts of liver following ANITtreatment showed progressive decreases in glucose and glyco-gen, while signals for bile acids, choline, and phosphocholinewere elevated. Additionally, an increase in the cytoprotectiveagent glutathione was detected at 24hr postdose. The lipidfraction (chloroform:methanol extract) revealed elevated tri-glyceride levels. Using MAS-NMR, which required little sam-ple preparation, it was possible to distinguish similaralterations. What was not revealed by MAS-NMR was theincreases in GSH and lactate, and not all timepoints showedthe elevations in choline and phosphocholine. This may reflectthat extraction of the lipids from the membrane into solutionallowed isotropic motion enabling measurement similar toliquid 1H-NMR.

When PCA was performed with the combined data ofboth aqueous and organic extracts, the trajectory plot indi-cated a recovery by 168hr postdose; the same was not truefor the MAS-NMR. However, the variation in the data wasless for MAS-NMR as compared to 1H-NMR. In spite of thesedifferences, the late (168hr) changes accounting for theseparation from control animals by 1H-MAS-NMR were com-mensurate with the bile duct hyperplasia noted histologically.Thus, 1H-MAS-NMR provided an opportunity to screen poten-tial target organs with minimal resources devoted to samplepreparation.

352 Thomas et al.

While MAS-NMR is expected to further develop our abil-ity to study target organ toxicity, it must also be recognizedthat it is possible to see metabolite changes in tissues whichare not injured, as judged by morphologic pathology. This con-cept was nicely illustrated in the work of Garrod et al. (22). Inthis study, the renal papillary toxicant 2-bromoethanamine(BEA) was administered as a single dose to SD rats with kid-ney (cortex and papilla) and liver tissue obtained at 2, 4, 6,and 24hr postdose. The most pronounced finding was an ele-vation in glutaric acid which was attributed to an inhibition ofmitochondrial fatty acyl CoA dehydrogenases. While the renalpapilla is clearly the target organ for BEA, glutaric acid wasfound in all three tissue samples suggesting that BEAinduced an overall mitochondrial defect. The other remark-able change in the papilla included a marked depletion ofrenal osmolytes that may signal a homeostatic response topolyuria. The liver also had increases in triglycerides, lysine,and leucine in addition to glutaric acid. Thus, while it could beconsidered that glutaric acid represents a biomarker for renalpapillary injury, this study highlights that changes withina given tissue do not necessarily implicate that tissue as atarget organ; if a target organ is defined as only those organ(s)showing pathologic or functional alterations.

While it is prudent to interpret metabonomic changescarefully when rendering judgements on the safety or efficacyof drug candidates, it is clear that the attributes of biofluidNMR make it suitable for rapid evaluation of new chemicalentities at an early stage of assessment. In particular, theability to monitor for compound effects in a nonbiased fashionis desirable at an early stage where limited, or no, in vivo dataexist. While this same advantage can also be problematic inassigning metabolite alterations to a specific target organ asdiscussed above, it can be expected that this will become lessof an issue with further work. For example, the association ofelevations in taurine and creatine with liver injury is reason-ably well documented (4,5,7,8). During the course of a studywith a new chemical entity being evaluated as a potentialdrug candidate, we observed early elevations in taurine andcreatine by urinary NMR. Previous histopathology findings


with other members of this structural class had clearly shownrenal papillary necrosis (RPN) as the dose limiting toxicity.However, based on these NMR data, in a subsequent study,the livers from animals treated with this agent were exam-ined by light microscopy at 4 and 168hr postdose. Consistentwith the urinary NMR results, evidence of a drug effect waspresent at the early, but not late, timepoint. These datademonstrate nicely the linkage between the NMR findingsand histopathology and document the sensitivity of biofluidNMR for detecting target organ effects.

2.6. Chemometric Analysis of Hepatotoxin-Induced Urinary Metabolite Changes

The concepts of multivariate statistics and pattern recogni-tion methods are described in detail elsewhere within thisvolume. However, as it is clear that data analysis and thedevelopment of robust models to describe target organ toxicitywill be critical to the eventual acceptance of metabonomics asa tool for drug safety evaluation, several studies will betouched upon herein. From the work described in precedingsections, it is evident that there can be similarities in meta-bolic changes for differing toxicants when judged simply byPCA. This is certainly true for liver and kidney which areby far the most well-studied organs. In an effort to betterdescribe the metabolic changes associated with liver or kidneyinjury, Holmes et al. (23) used hydrazine and HgCl2 as modeltoxicants to provide data for chemometric analysis. In addi-tion, both SD and Han-Wistar (HW) rats were used with eachtoxicant. Chemometric analysis was able to distinguish con-trol urine spectra from the two strains with the HW rat urinehaving more acetate, lactate, and taurine, and less hippurate,as compared to SD rat. Drug treatment produced organicacids, amino acids, and sugars as biomarkers of HgCl2 admin-istration, while taurine, b-alanine, creatine, and 2-AA werefound following hydrazine treatment. Soft independent mod-eling of class analogy (SIMCA) data analysis was employedto build predictive models from a training set of 416 samples

354 Thomas et al.

according to toxicity type and strain. One hundred andtwenty-four samples were used to test the models and 98%of the samples were correctly classified as control, hydrazine-or HgCl2-treated. Furthermore, the method was sufficientlyrobust to correctly classify 79% of the time the control forthe strain. These data represented an important advance asthey demonstrated the great improvement afforded by modelbuilding, as compared to the more simplistic PCA, and builtupon other, earlier investigations by this group using PCAand SIMCA (24).

More recent work from the same investigators hasfurther demonstrated the power of appropriate data analysisin the use of metabonomic data (25). In this study, 13 toxins ordrugs affecting liver or the kidney were used. The toxins tar-geted the kidney cortex or glomerulus and induced hepaticinjury of varying etiology including cholestasis, steatosisand necrosis as judged by histopathology. Again, both HWand SD rats were treated with different doses depending uponthe dose required to elicit the toxicity for each compound,urine was collected over 7 days, and organs taken for histo-pathology at 48 and 168hr postdose. The 1H-NMR spectrawere data-reduced and analyzed using a probabilistic neuralnetwork (PNN) approach with 583 samples making up thetraining set and 727 used as the test set for validation. Usingthis method, the 13 classes of toxins could be distinguishedfrom one another, including strain difference, in greater than90% of the samples. Not only was the model able to classifythe samples, it was also able to delineate the metabolites thatdictated the classification. In some cases, there was a timedependence for classification. For example, the early phaseof BEA-induced changes is dominated by glutaric acid whichwas classified with other compounds as a mitochondrial poi-son. Classification for BEA at later timepoints was with com-pounds causing renal papillary toxicity as defined by changesin trimethylamine-N-oxide, dimethylglycine, and creatine.From these two studies, it can be surmised that there existstremendous promise for using biofluid NMR to rapidly andreliably identify and classify toxicants according to targetorgan, including regional effects, as well as mechanism or


pathologic features such as cholestasis. While this in and ofitself represents a significant competitive advantage for drughunting, it also presents the opportunity to subsequentlydevelop non-NMR methods to screen for particular toxicitiesduring preclinical lead optimization.

Additional methods for analyzing metabonomic data arealso currently being evaluated. Data obtained from study ofurine collected from ANIT-treated rats have been evaluatedusing batch statistical process control, otherwise referred toas batch processing (BP) (26). This method, normally usedto monitor industrial processes, is based on partial leastsquares (PLS) and has the advantage that each rat is consid-ered a batch. The technique provides two levels of data ana-lysis; at one level, PLS regression against time permits thetoxin-induced metabolic effects to be assessed. A second levelof analysis revolves around PC-based analysis of lower levelPC scores and allows a means for representing the totalsequence of metabolic effects for a particular rat (batch).The two levels of loadings are inter-related and generate amore complete picture of xenobiotic-induced metabolicderangements. In the ANIT study, a model defining themean urine profile for 7 days following a single 100mg=kgdose was generated and compared to the control group. AllANIT-treated animals could be shown to deviate from controlrats and changes were consistent with analysis by othermethods. The advantage of BP was that it provided a facileway to visualize the response to ANIT on an animal-by-animal basis, as well as the net variation in metaboliteexcretion profiles.

2.7. Metabonomic Studies of Hepatic Injury inMan

The most widespread use of NMR with humans has been inthe study of inborn errors of metabolism (27). While thepatient-to-patient variation in urine composition can makeinterpretation more problematic than in rats fed a consistentand well-defined diet, the method has been used successfully

356 Thomas et al.

(for a description of major differences in rodent vs. humanurine as judged by NMR, see Ref. 28). The NMR has also beenused to study the progress of patients receiving renal trans-plants (29) or diagnosed with glomerulonephritis (30,31).

The NMR technique has been used to study the metabo-lism of acetaminophen in man which is largely excreted in theurine in conjugated form. Overdosing with acetaminophenhas been shown to cause centrilobular necrosis, subsequentto glutathione depletion. While the NMR readily detects dif-ferences in the drug conjugate profile following ingestion oftoxic doses, evaluation of the spectra for endogenous metabo-lites is also informative. In both fatal and nonfatal cases,levels of lactate, tyrosine, alanine, and other amino acids wereelevated pointing to a perturbation of hepatic transaminationreactions (32). In a situation of the extreme overdose, eleva-tions in amino acids in plasma were likewise noted. There-fore, urinary NMR profiles can be used to detect drugeffects in human urine and can potentially identify targetorgans such as the liver. While there have been a few isolatedreports documenting NMR analysis of urine following acci-dental poisoning, there are at present no detailed investiga-tions specifically addressing the use of urinary meta-bonomics to study hepatotoxicity in humans.

The study of bile can provide an indirect assessment ofhepatic function and this has been done in several instancesin humans. NMR spectroscopy of bile is challenging owingto the complexity of the matrix and the broad resonancesdue to the presence of bile acids in mixed micelles with choles-terol and phospholipids (28). One approach to overcome this isto lyophilize and reconstitute the sample in water; however,loss of certain unstable components during processing cannotbe discounted. Powell et al. (33) have suggested the use of bileto monitor hepatic function in humans and have studied thebile composition from patients with primary biliary cirrhosisof the liver and with hepatobiliary diseases including cancer.It is reasonable to expect that as models, and associatedbiomarkers for hepatic injury in animals become moredeveloped; additional use of the technique in the clinic willfollow.


2.8. Current Limitations in the Use ofMetabonomics to Study Liver Injury

Relative to the kidney as a target organ, the available data forspecific hepatotoxins are scarce. It is readily apparent thatmost hepatotoxicants, and toxins in general, lead to distur-bances in metabolites influenced by mitochondrial functionsuch as citrate, succinate, and 2-oxoglutarate. This is perhapsnot wholly surprising considering the central role the mito-chondrion plays in cellular metabolism and in providing redu-cing equivalents that are essential in xenobiotic metabolismand detoxification. It is clear from several studies thatincreases in urinary taurine and creatine are often associatedwith hepatotoxicity and not with other target organs (4,5,7–9).At present, however, it remains somewhat tenuous to ascribechanges in these two metabolites solely to hepatic injury. Forexample, cadmium has been shown to decrease citrate, 2-oxo-glutarate, and succinate which occurs coincident with an ele-vation in creatine (6). At first glance, one might suspecthepatic injury, however, the liver is not affected as judged his-tologically and creatine was shown to arise via direct releasefrom the seminiferous tubules. An added complication mightbe that urinary taurine has been shown to increase withage in laboratory rodents (34). This would render control forage, a criticality in the study of hepatotoxicity.

Much success has been achieved in developing models forregional effect of toxicants in the kidney. In the liver, asdescribed in the introduction to this chapter, there are alsomarked differences in the location and nature of liverdamage. For drug development, it will be valuable to be ableto delineate site and mechanism as this may impact signifi-cantly on the level of concern and the safety margin that mustbe achieved to deliver success in the clinic. The recentdescribed studies with SIMCA and PNN offer hope thatrobust models that reliably predict liver injury can be devel-oped since Holmes et al. (24) were able to classify liver toxinsas cholestatic, steatotic, necrotic or ‘‘other.’’ Therefore, muchadditional effort to study hepatotoxins with multiple etiolo-gies and resultant pathologic sequela is warranted.

358 Thomas et al.

Another challenge, and potential pitfall, of metabo-nomics in the study of liver injury is that certain pathologiessuch as fibrosis would not be expected to be associated withmetabolic derangements until significant tissue damage hadoccurred. As most metabonomic investigations currently useonly a single dose, it can be anticipated that the expansionof the method to encompass more chronic pathologies willnecessitate the use of multidose studies. Inherent in this isthe continued need to link the metabolic changes with histo-pathology findings. In spite of these current challenges,considerable progress has been made in the use of meta-bonomics to study hepatocellular injury or liver functionand further experimentation will continue to affirm theimportance of this method in drug safety assessment.

3. RENAL TOXICITY

3.1. The Kidney as a Target Organ

The kidney is particularly susceptible to toxicants, andnephrotoxicity represents one of the major causes of attritionin drug discovery and development. The vulnerability of thisorgan to xenobiotic-induced toxicity lies in a combination ofcontributory factors. The kidneys receive 25% of the total car-diac output and are thus exposed to high concentrations oftoxins circulating in the blood, which are concentrated by tub-ular reabsorption and the counter current multiplier systemin the Loop of Henle. Additionally, the renal cortex possessesan extremely high level of metabolic activity places a highoxygen demand on the tissue, thereby rendering the tissuesusceptible to ischemic damage. Although many toxicantsaffect multiple tissues, others are specific to particularregions of the nephron. The kidney is a highly heterogeneousorgan and is comprised of structurally and functionally dis-tinct regions. Over 20 morphologically distinct cell types existwithin a single nephron accounting for the differential distri-bution of enzymes and other endogenous metabolitesthroughout the kidney. Although certain chemicals, such asmercury and lead, are directly toxic to the kidney, more


commonly a relatively inert parent undergoes biotransfor-mation into a reactive metabolite; e.g., acetaminophen whichis metabolized to para-aminophenol (35).

Conventional assessment of renal toxicity and functionincludes measurement of the glomerular filtration rate, urin-ary flow rate, blood urea nitrogen, plasma creatinine, inor-ganic urinary electrolytes, urinary glucose, and clearance ofpara-aminohippuric acid (36). These tests of renal functionare not specific, and therefore have limited diagnostic poten-tial. In addition, the kidney has a high capacity for compen-sating for tissue damage and can mask the effect of anephrotoxin until the onset of severe toxicity. Even aftermajor intervention such as surgical removal of one kidney,within a short time the remaining kidney will hypertrophyto such an extent that the conventional clinical assays of renalfunction appear normal (37). Detection of enzymuria providesa more specific measure of renal pathology. For example, anti-biotics are known to cause elevations in lysosomal enzymessuch as N-acetyl glucosaminidase (NAG) and b-galactosidase,whilst metals such as mercury and cadmium induce anincrease in urinary activity of the brush border membraneenzymes of the proximal tubule, including alkaline phospha-tase, lactate dehydrogenase, and g-glutamyl transaminase(38,39). However, enzymuria is at best transitory. Metabo-nomics offers a more efficient means by which to characterizerenal lesions, providing an effective screening tool for a widerange of low MW metabolites. Nephrotoxicity has been exten-sively studied using metabonomic technology and some of thetoxins studied to date by metabonomic technologies are listedin Table 2, together with the major characteristic biomarkersassociated with each nephrotoxin.

3.2. Proximal Tubular Toxicity

Traditionally the proximal tubule has been divided into thepars convoluta and pars recta, but more often the terms S1,S2, and S3 are now used to define the structurally and func-tionally distinct regions of the proximal tubule (Fig. 4). Byfar the most common type of nephrotoxicity is proximal

360 Thomas et al.

Table

2Examplesof

Metabon

omic

Studieson

Nep

hrotoxins

Reg

ion

Main

metabolic

perturbation

sTox

in(ref)

Glomerulus

Proteins("),glycoproteins("),

Adriamycin,

dicarbox

ylicacids("),

hippurate

(#),crea

tine("),

citrate

(#),2-oxog

lutarate

(#)

Puromycin(54)

Ren

alcortex

S1

Glucose

("),hippurate

(#)

Sod

ium

chromate

(40,42)

Cep

halosp

orins(44,63)

Ren

alcortex

S2=S3

Glucose

("),aminoand

organic

acids("),

Mercu

rych

loride(44)

hippurate

(#),crea

tinine(#),

citrate

(#),

Uranylnitrate

(64)

2-oxog

lutarate

(#)

Hex

ach

lorobutadiene(40)

Glucose

("),aminoand

organic

acids("),

1,1,2-Triflch

loro-3,3,3-fluoro-1-

propen

e(41)

hippurate

(#),

S-(1,2-dichlorovinyl)-l-cysteine(41)

crea

tinine(#),citrate

(#),

2-oxog

lutarate

(#)

Para

-aminop

hen

ol(11,40)

Cisplatin(42)

Ren

almed

ullary

TMAO

("),dicarbox

ylic

acids("),hippurate

(#),

crea

tine("),

citrate

(#),

2-B

romoe

thanamine

hydroch

loride(50)

2-C

hloroethanamine

hydroch

loride,

2-oxog

lutarate

(#)

Propyleneimine(58)


tubular toxicity since a high level of blood flow to the kidney isdelivered to the cortex. Moreover, the pars recta or S2=S3

region of the proximal tubule is generally most affected bytoxicants as this portion of the proximal tubule has a greatercapacity for active secretion of compounds than the convo-luted portion of the proximal tubule. Damage to the proximaltubule is often manifested by failure to reabsorb solutes fromthe lumen of the nephron leading to high urinary concentra-tions of glucose, amino acids, and organic acids, amongstother metabolites.

Toxins that predominantly target the S3 portion of theproximal tubule include HgCl2, hexachlorobutadiene, 1,1,2-trichloro-3,3,3-trifluoro-1-propene (TCTFP), maleic acid,para-aminophenol, and uranyl nitrate. The urinary finger-print generated by each of these toxins is remarkably similar(Fig. 5) and includes increased levels of glucose, amino acids

Figure 4 Schematic diagram of a nephron.

362 Thomas et al.

Figure 5 Stackplot of 600MHz urine spectra obtained from ani-mals 48hr after treatment with S3 renal cortical nephrotoxins.The spectra show the similarity of biochemical perturbationprofiles; mercury II chloride (HgCl2), uranyl nitrate (UN), hexa-chloro-1,3-butadiene (HCBD) and 1,1,2-trichloro-3,3,3-trifluoro-1-propene (TCTFP).


such as alanine, glutamate, glutamine, isoleucine, leucine,lysine, threonine, tyrosine, and valine, and organic acidssuch as b-hydroxybutyrate and lactate. These urinarychanges are indicative of reduced capacity of the proximaltubule to reabsorb such compounds. Other metabolic pertur-bations following the onset of renal cortical toxicity are morecharacteristic of mitochondrial effects or reduced muscleturnover and include a reduction in urinary concentrationsof citrate, succinate, 2-oxoglutarate, hippurate, and creati-nine (6,40–42). In particular, resonances from glucose,lactate, alanine, and b-hydroxybutyrate tend to dominatethe urine spectral profiles after the administration of S3 prox-imal tubular toxicants. However, although there is a general-ized pattern of dysfunction relating to S3 renal toxicity, theurinary perturbations observed also contain features thatare specific to individual toxins.

Because such a large portion of the 1D urine spectrumcontains resonances from glucose, amino acids and organicacids and the tricarboxylic acid cycle intermediates (>25%of the aliphatic integral region), it can be difficult to detectmultiple toxicity signatures in the urine profiles where astrong S3 proximal tubular response occurs. Metal speciessuch as Hg2þ, U2þ, and Pb2þ all induce renal tubular damageby a combination of ischemia and=or direct cytotoxicity, inaddition to targeting other organs and tissues. However, itis the tubular necrosis signature that usually dominates thespectral profile.

Fluoride has been shown to induce widespread damagein the proximal tubule and also gives rise to a typical S3 urin-ary metabolite pattern (42,25). Likewise, cisplatin, whichaffects both the proximal and distal tubule, produces a predo-minantly proximal tubular profile (40,42). Although mostproximal tubular toxins act upon the straight portion, certainnephrotoxins such as chromium and to some extent, cephalo-sporins, are more specific to the S1 or convoluted portion ofthe nephron (43). The metabolic pattern induced by chromateions is distinct from that of the S3 tubular toxins and is domi-nated by glucose in the absence of gross amino acid andorganic aciduria (42). In addition, although some depletion

364 Thomas et al.

in citrate and succinate resonances are evident, the effect isnot as marked as that observed with classic S3 toxins whichcan totally obliterate the tricarboxylic acid cycle intermedi-ates from the spectral profile within 24hr of a single treat-ment. A study investigating the nephrotoxicity of threecephalosporins also found a strong metabonomic signaturefor necrosis of the proximal convoluted tubule. Cephaloridine,cefoperazone, and cephalothin were administered to maleNew Zealand rabbits. All three compounds induced glycosuriaand resulted in a depletion of urinary hippurate concentra-tions within 48hr (44). However, only cephaloridine treatedanimals displayed histological evidence of necrosis, whichwould again indicate that metabonomic technology is, at leastin some instances, more sensitive than conventional mea-sures of renal toxicity. Similar conclusions have been drawnfor other target organs. In support of this observation, workperformed by Robertson et al. (11) showed that metabonomictechnology was able to detect similar biomarkers of toxicityfor the renal cortical toxin para-aminophenol at both high(150mg=kg) and low (15mg=kg) doses, whereas conventionalhistopathology and clinical assays were not able to differenti-ate the low dose from control. Although the focus of metabo-nomic technology originated from acute toxicity screeningstudies, recent studies have illustrated the scope of the technol-ogy in investigating more subtle pathologies such as those aris-ing from environmental pollution. One such study employedMAS-NMR spectroscopic techniques (see Chapter 5) in orderto directly investigate the effects of exposure to low levels ofcadmium in the kidneys (45). Early evidence of cadmium-induced nephrotoxicity was detected as altered renal lipid,glutamine, and glutamate, levels.

3.3. Renal Medullary Toxicity

Model toxins that specifically target the renal medulla arerare. Many analgesics, such as ibuprofen, aspirin, phenacetin,mefenamic acid, and indomethacin have been reported toinduce toxicity in the Loop of Henle (46) and are known tohave a synergystic action, particularly when combined with


caffeine. The chemical structures and physico-chemical prop-erties of these compounds are quite diverse but they are allthought to produce an analgesic effect via inhibition of pros-taglandin synthesis. A causal relationship exists betweenlong-term intake of analgesics and RPN, and interstitialnephritis (47,48). The diagnosis of analgesic nephropathy isdifficult, since the development of this condition is relativelysilent until advanced stages of renal failure have occurred.Clinical symptoms of RPN are nonspecific and include lossof urinary concentrating ability, electrolyte wastage, increasedblood urea nitrogen, and increased serum creatinine, whichmakes diagnosis of this condition difficult using conventionalrenal function assays.

Model compounds such as BEA, 2-chloroethanamine(CEA), ethyleneimine, and propyleneimine (PI) have beenused to model non-steroidal anti-inflammatory (NSAID)papillary damage since analgesics generally require long-term administration before papillary lesions develop. How-ever, as with analgesic mixtures, although these modelcompounds predominantly affect the renal papilla, very oftenthey also induce a secondary cortical damage that can obscurethe underlying papillary necrosis. Additionally, rats are mul-tipapillate, whilst humans are unipapillate, thereby furtherconfounding extrapolation of data from model studies tohuman RPN. Various mechanisms of analgesic-induced RPNhave been proposed including direct cellular toxicity,enhanced by the concentrating effect of the countercurrentmultiplier system in the Loop of Henle, ischemia, inhibitionof prostaglandin synthesis, free radical formation, and immu-nologic response. However, it is most likely that the patho-genicity of analgesic-induced RPN is multifactorial (48).BEA, CEA, and PI are believed to induce toxicity via theformation of the aziridine intermediate which is extremelyreactive (49). All these compounds cause a reduction incitrate, 2-oxoglutarate, and succinate, in conjunction with adecrease or perturbation in the levels of trimethylamine N-oxide and dimethylglycine, which are thought to act as non-perturbing renal osmolytes (Fig. 6). Additionally, an increasein creatine is also commonly a feature of drug-induced RPN.

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Figure 6 1H-NMR urine spectra obtained from a SD rat after asingle i.p. dose of 150mg=kg BEA.


Other notable effects of BEA administration includeincreased excretion of lactate, alanine, and glucose (Fig. 6)which result from a secondary insult to the renal corticaltubules. In the case of both BEA and CEA, a major contribu-tion to the spectral profile from 8 to 24hr p.d. derives fromresonances from dicarboxylic acids such as glutaric and sub-eric acid. Magic angle spinning NMR spectroscopy can beused to obtain metabolite profiles for small sections of intacttissue (10–20mg) and has been used to measure intact renalpapillary and cortical tissue following BEA administrationin order to connect urinary biomarkers to pathological events.A time course of spectra over a 24hr period (Fig. 7) shows thedepletion of renal osmolytes such as betaine, TMAO, myoino-sitol, sorbitol, and glycerophosphocholine in the papillatogether with an increase in creatine and glutamate levelsand a change in the composition of the triglyceride resonancesat d 1.26. Thus, perturbation of renal osmolytes and creatinecorrelated across both urine and papillary tissue profiles. Theappearance of dicarboxylic acids in the urine at 6–8hr post-dose was deemed more likely to be related to a generalizedmitochondrial dysfunction rather than a specific papillarylesion as these dicarboxylic acids were also found to be

Figure 7 1H-NMR-MAS spectra of renal papilla tissues wereobtained from (a) control, and (b) BEA-treated rats 24hr p.d. show-ing perturbation of the renal osmolyte profile.

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elevated in intact liver (Fig. 8) and renal cortical samples, aswell as in papillary tissue from BEA-treated animals.

3.4. Renal Glomerular Toxicity

The renal glomerulus is the primary site of action for manychemicals. However, compounds that specifically target theglomerular apparatus are also uncommon. Certain chemi-cals induce a change in the permeability of the glomerularbasement membrane resulting in the leakage of proteinsinto the nephron. Destruction of the glomeruli will inevita-bly result in a buildup of debris in the tubules, which theninduces a series of urinary perturbations similar to thecharacteristic proximal tubular signature. Typically, therenal glomerular signature includes a broadening of certainspectral features caused by proteinuria. Toxins such as pur-omycin aminonucleoside, adriamycin, penacillamine, andgold-based antiarthritic drugs have been reported to inducelesions in the glomeruli (50–53). Puromycin aminonucleosidereproducibly induces glomerular toxicity around 3–7 daysafter the administration of a single dose of 150mg=kg (54).A series of metabolic patterns can be observed in Fig. 9a,bcorresponding to initial effects of puromycin in the liver(manifested as increased levels of urinary creatine, taurine,and PAG). Also evident were effects in the renal tubules(slight glycosuria) followed by a marked spectral changeover the latter time periods reflecting the glomerular lesioncharacterized by a broad envelope of resonances fromproteins superimposed with sharper resonances derivingfrom glycoprotein fragments.

Inspecting the spectral profiles by PCA allows the simi-larity of spectra to be represented efficiently and can helpto unravel temporal patterns within the data. In the caseof puromycin treated animals, the mean PCA trajectoryshows three inflections corresponding to the metabolicstatus, or the stage of toxicity (Fig. 9). Metabonomicanalysis has also been used successfully to follow adriamy-cin and to identify markers of both cardiotoxicity and renalglomerular toxicity (42).


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370 Thomas et al.

Figure 9 Effect of puromycin aminonucleoside on urinary meta-bolites. (a) Stackplot of 600MHz 1H-NMR urine spectra obtainedfrom a rat after the administration of puromycin aminonucleoside,and (b) mean PCA trajectory plot showing deviation of trajectoryfrom predose and control position, and progression with time.


3.5. Species and Strain Differences in Responseto Nephrotoxins

Certain species and strains of laboratory animals are knownto be susceptible to nephrotoxicity. Literature suggests thatthe Fischer 344 rat strain is particularly sensitive to renaltoxicants (55), and that the Gunn rat is more susceptible toanalgesic models (56). In contrast, desert dwelling rodentssuch as Mastomys natalensis and the Mongolian gerbil haveproved to be more resistant to nephrotoxins, including HgCl2,BEA, PI, CdCl2, and phenylamine (49,57–60). Indeed, thebasal metabolic composition of biofluids from healthy controlanimals differs between species and even between strains(see Chapter 10). Therefore, it is not surprising that thereare significant differences in urinary profiles of Fisher 344rats and M. natalensis in comparison to the SD rat, a stan-dard laboratory strain, after the administration of a singledose of PI (58). Histopathology confirmed that Mastomys weremore resistant to PI in comparison with SD rats, but did notfind any differences in susceptibility between the SD and theFischer 344 strains (58). Metabonomic technology provides agood platform for comparing the response of different labora-tory species to xenobiotics. One such comparison employed

Figure 9 Continued

372 Thomas et al.

probabilistic neural networks to assess the response of SDand HW rats across several liver and kidney toxins. Althoughthe resultant response was found to be similar for bothstrains, the technology was sensitive enough to distinguishbetween the strains (25).

3.6. Direction of Metabonomic Research inNephrotoxicity Studies

One of the main strengths of metabonomic analysis is thatinformation is generated for a wealth of low MW metabolitessimultaneously; thereby facilitating the assessment of multi-ple biomarkers for specific pathologies. For example, as men-tioned earlier, creatinuria is commonly a feature of hepaticinjury and is particularly diagnostic in the presence ofperturbed taurine and PAG levels. However, creatinuria inassociation with changes in the renal osmolytes indicatesrenal medullary dysfunction. Hence metabonomic data canalso provide a framework for understanding the biochemicalconsequences of toxicity. Advances in computational algo-rithms and data filters are making metabonomic analysismore sensitive and hence more applicable across a wide rangeof biomedical disciplines (see Chapter 8). In terms of investi-gating nephrotoxicity, these tools can be used to ‘‘filter out’’extraneous biological effects in order to focus on more subtlepathologies, or to remove the dominating effect of S3 tubulartoxicity.

The use of trajectories or BP methods (61) allows thedynamic nature of toxic lesions to be taken into account. Theability to describe the evolution of lesions provides a moreaccurate mapping of the similarities and differences betweennephrotoxins with respect to site or mechanism of action.Following a single moderate dose of toxin, indices of onsetprogression and regression phases of lesions can be monitoredand metabolic markers of both degeneration and regenerationelucidated (62). A ‘‘simple’’ PCA trajectory plot is shown inFig. 10 for two renal cortical toxins, a renal medullary toxin,a renal glomerular toxin, and a hepatotoxin. The trajectoriesare constructed by connecting themean response (asmeasured


in the first three principal components) of a group of animals inchronological order for each compound. The direction and themagnitude of the trajectory from the origin, or predoseposition, can be translated into information pertaining to thenature and extent of pathology. Moreover, as with simplePCA maps, the corresponding loadings can be calculated toindicate which spectral metabolites have the highest leverage,or are the strongest biomarker set, for each toxicity type.

4. VASCULAR TOXICITY

4.1. Challenges for Assessing Vascular Toxicity

In the preceding sections, it has been described how meta-bonomics is complimentary to, or provides advantages over,

Figure 10 Three-dimensional plot showing the mean trajectoriesfor groups of animals treated with regional toxins. The regions andtoxins were: renal glomerular (puromycin aminonucleoside ;),renal medullary (2-bromoethanamine hydrochloride c), liver(hydrazine, �) and two proximal tubular (HgCl2 & and hexa-cholro-1,3-butadiene G). Note the different directions of the trajec-tories for each type of tissue lesion and the similarity of the tworenal cortical trajectories indicating a common metabolic state.

374 Thomas et al.

currently existing, well-accepted endpoints for renal andhepatic toxicity. Metabonomics may offer, in fact, its highestlevel of application for those toxicities that cannot be reliablyscreened noninvasively or by simple blood tests. In thesecases, the only reliable way to demonstrate toxic liability isby microscopic examination. By definition, these toxicitiesrequire the laborious effort of conducting in vivo toxicity stu-dies followed by histopathologic assessment. Most often thesestudies involve large numbers of animals to convince theinvestigator that the presence or absence of the lesion atany particular timepoint is representative of the animal’s trueresponse to the drug. Of course, one also has to be concernedabout the time course of the pathology itself. When is anadequate time interval to assess the toxicity? Should multiplesacrifice times be included in the study design? How manyanimals per timepoint need be examined? Is there a sex differ-ence, etc.? Clearly, it is these types of toxicities where meta-bonomics screening can present a tremendous advantageover traditional approaches. Vascular injury represents anexample of such a problematic target toxicity and recent workusing metabonomics as a new tool to study vasculopathies arediscussed below.

4.2. Drug-Induced Vascular Injury

Drug-induced vasculopathies, in particular those groups ofvascular toxicities frequently, but perhaps inappropriately,called ‘‘vasculitis’’ are one example of a toxicity of great inter-est to the pharmaceutical industry that is in dire need of atechnique for rapid, non-invasive assessment. The vasculi-tides are a significant problem because they are associatedwith several classes of pharmaceutical agents including phos-phodiesterase type 3 (PDE3) inhibitors (65), PDE4 inhibitors(66), endothelin receptor antagonists (67,68), adenosine ago-nists (69), dopamine (DA1) receptor antagonists (70), andpotassium channel openers (71). Beyond the range of thera-peutic classes, species difference in response to these agents,further complicates their evaluation. In dogs, the lesion tends


to primarily affect cardiac arteries, while in rat the mesen-teric vasculature appears to be especially sensitive. Figure 11presents a micrograph of a typical rat mesenteric vascularlesion. Not all compounds affect both species and, in thosethat do, one species may be more sensitive (based on systemicexposure) than the other. Clearly, much can be gained if theselesions can be assessed by something short of vascular micro-scopic assessment.

Although vascular lesions in dogs are of as much interestas vascular lesions in rats, early screening efforts havefocused on the rodent species simply because it is much easierfrom a logistics standpoint. Furthermore, certain rat mesen-teric vasculitides, particular those induced by PDE3 and

Figure 11 Cross-section of mesenteric artery from a rat treatedwith CI-1018 for 3 days. Animals dosed at 750mg=kg showingmarked necrosis of the media with hemorrhage and mixed inflam-matory cell infiltrates in the media, adventitia, and perivasculartissue.

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PDE4 inhibitors, are dose limiting for clinical studies. Thesefindings have become significant hurdles in the developmentof these compounds.

4.2.1. CI-1018

The initial evaluation of metabonomic technology for use inassessing vasculitis was conducted using the PDE4 inhibi-tor, CI-1018 (72), a compound that had previously beendemonstrated to produce a mesenteric vasculopathy in rats(73). Doses of at least 750mg=kg are required to inducelesions in males within a 4-day time period, and routinelyat least 1500mg=kg is administered to induce vascularlesions in males. Females are a bit more sensitive, probablydue to toxicokinetic considerations rather than a true sexdifference in sensitivity. Despite the massive doses requiredto induce these lesions, the compound is fairly well toleratedand typically 100% survival can be anticipated over a 4-daydosing period. Even at the high-dose levels, incidence of vas-cular lesions in males is generally less than 50% at the1500mg=kg dose level (females tend to have 75–100%incidence at doses of 750mg=kg and above) (74). Anotherdrawback to the compound is that when administeredorally, the vehicle typically contains polyethylene glycol(PEG) to ensure uniform suspensions. In practice, PEG isreadily eliminated in rat urine and typically produces amajor signal in the NMR spectrum in the region from 3.6to 3.8 ppm that must be eliminated for further statisticalanalysis (72). Despite these drawbacks, CI-1018 proved tobe a fairly useful model of drug-induced vasculopathybecause of, not despite; it has relatively low potency in indu-cing this untoward effect. When groups of Wistar rats wereadministered from one to four doses of CI-1018 ranging from250 to 3000mg=kg, 11 rats (across doses) were found to havemicroscopic evidence of mesenteric vasculopathy. Thirty-seven rats across all doses did not have any microscopicevidence of vascular lesions. Urine collected daily duringthe study (pretest though termination) was assessed forNMR spectral changes using PCA. Results of that data


clearly indicated that 8 of 11 rats, later identified as havingvascular lesions, had distinct urine NMR spectra as revealedby the principal component map. Importantly, 36 of 37 ratswithout lesions had no such distinction. The lack of potency(for producing the vasculopathy) worked in favor of thetechnology in this setting because some rats dosed as highas 3000mg=kg for 4 days, had no evidence of vascularlesions. Importantly, urine samples from those animals werenot significantly different from control samples indicatingthat the urine spectral changes induced in animals withvascular lesions were not simply a reflection of unrelatedhigh-dose effects of the compound (e.g., efficacy or othertarget organ toxicity). Figure 12 presents the PCA mapobtained from one of the experiments in that study.

4.3. Advantages of Metabonomics for AssessingDrug-Induced Vascular Toxicity

Some of the advantages of metabonomic technology havealready been identified. The fact that vascular toxicity canbe assessed noninvasively is certainly a significant attraction.The possibility of using the technology similarly in the clinicalsetting also makes the approach quite appealing. Less obvious

Figure 12 PCA analysis of urine from 36 of 48 rats treated withCI-1018. Data are plotted as the first three principal components(PC1, PC2, and PC3). Open circles represent control and pretestsamples. Solid circles represent samples from animals treated withCI-1018 (all doses combined). Letters indicate samples from theeight animals with vascular lesions (a–h). Numbers after lettersrefer to sample day. The large circle identifies clustering that sepa-rates animals with lesions from animals without lesions. X¼ samplefrom 1500mg=kg animal without vascular lesion falling outside thecontrol cluster. Y0 and Y1 represent pretest and Day 1 samplesfrom outlier animal—see text for explanation. (a) Plot oriented forbest visualization of all data. (b) Plot rotated 45� around PC3 axisshowing distinct pattern separation of samples from animals Gand H from other samples. Both animals had profound ketonuria.

I

378 Thomas et al.

is the fact that less bulk drug need be used to screen for thesetoxicities in vivo since each individual animal can be moni-tored from pretest through onset to peak and reversal of toxi-city, obviating the need for satellite groups for time coursestudies.

The data from the CI-1018 experiment described abovedemonstrated another advantage of metabonomics technol-ogy, specifically, the ability to identify concurrent effectswithin a group and even within an individual animal. Severalanimals within the study had clear evidence of ketonuria. Ori-ginally, the authors concluded that the ketonuria was simplya manifestation of toxicity-induced inappetence, a commoncause of ketonuria. Later findings led the authors to questionthat assumption (74), but the fact remains that an effectcompletely separate from the vascular effect was readilyapparent. The ketonuria observed in these animals may havemechanistic relevance and provide an avenue for furtherresearch that would have been otherwise unknown.

4.4. Metabonomics and Vascular Toxicity: Issueof Concern

4.4.1. The ‘‘Usual Suspect’’ Question

One of the pressing questions that almost universally arises,when evaluating PCA analyses of NMR spectral data, is whatexactly is driving the pattern separations. In other words,what are the biochemical changes in the urine responsiblefor the pattern separation. The hope, of course, is that thesebiomolecules may serve as biomarkers. Unfortunately, thisis seldom as easy as it sounds. In the study cited above,the authors concluded that the major changes in urinarybiochemical composition included the increases in the urinaryketone bodies, acetoacetate, 3-hydroxybutyrate, and acetonedue to the ketonuria previously mentioned. Changesassociated with pattern separation due to vascular lesionsin the absence of ketonuria included decreases in citrate, 2-oxoglutarate and succinate, fumarate and hippurate, as wellas increases in formate. Representative spectra from theexperiment are presented in Fig. 13. While these might be

380 Thomas et al.

considered biomarkers of an effect, it is unclear as to whatexactly they are biomarkers of. In particular, decreases inKrebs cycle intermediates have been noted with toxins as dif-ferent as ANIT and BEA. How specific can these changes thenbe? In fact, these and several other metabolic intermediatesare so frequently the drivers behind PCA separations of var-ious toxicants; they have been dubbed, somewhat tongue incheek, as ‘‘the usual suspects.’’ This example demonstrateshow one of the technology’s biggest strengths can also be a

Figure 13 600 mHz 1H-NMR spectra of urine samples fromnormal and CI-1018 treated animals. Vertical scales were manuallyadjusted to provide a constant urea peak height and key metabo-lites in the urine are labeled. Bottom trace: Normal pretest urine(from animal D on Fig. 2). Middle trace: Urine from same animalafter 3 days of treatment administration of 1500mg=kg CI-1018(D3 in Fig. 2). Top trace: Sample from animals treated for 3 dayswith 3000mg=kg CI-1018, with profound ketonuria (G3 in Fig. 2)Inset: The upfield region of the top trace, plotted with a 15-foldreduction in vertical scale.


significant weakness. The nondiscriminating nature of theanalysis means any aspect of an animal’s pathophysiologicresponse to an exogenous compound is potentially observable.While this is an extremely powerful advantage when screen-ing novel compounds with unknown toxicity profiles, it alsomeans that frequently changes will be reflected in the urinaryspectra that are unrelated to the toxicity of concern and whichcomplicate the ability to interpret the data from a mechanisticstandpoint. This should not be unexpected and the problem isequally applicable to both proteomic and toxicogenomicapproaches. What is the genotype of a ‘‘sick’’ animal? Whenan animal loses weight or is unkempt in its appearance (urinescald, fecal staining, etc.), exhibits tremors or is hypoactive;do these effects produce altered gene transcription? What cir-culating proteins are quantitatively changed? Unfortunately,the questions are somewhat ‘‘chicken and egg’’ since it is dif-ficult to discriminate cause from response with these nonspe-cific indices of toxicity—but that is precisely the point. Whenattempting to associate specific biomolecular changes (or genetranscript or protein changes for that matter) in urine orother tissue with target organ toxicity, these changes haveto be interpreted in light of any and all indirect effects ofthe toxicant. These not only include any secondary toxicitiesthe compound may have, but also include the indirect meta-bolic consequences of the toxicity of interest. For example,genes associated with oxidative stress may be mechanisticallyrelated to a vascular toxicity and the vascular effects them-selves then induce hypoactivity and inappetence, which sec-ondarily induce a gene response to these clinical effects.Clearly, unless you can temporally differentiate the geneticresponse, or differentiate based on severity, it becomes diffi-cult to determine which genes are linked directly to the toxi-city and which are secondary. The same holds true formetabolic responses; it should not be terribly surprising thatrats can have similar clinical responses to a variety of toxi-cants and this common response will be reflected by similarbiochemical flux through key metabolic pathways. However,toxicants having similar components driving PCA separationsclearly have distinct spectral differences such that though

382 Thomas et al.

different from control spectra, they are also different fromeach other (11).

4.4.2. Spectra=Toxicity Relationships

The discussion in the previous section raises the second mostcommon question asked when examining a series of com-pounds, all of which produce similar end stage pathologies.Many, if not all, PDE3 and PDE4 inhibitors induce mesen-teric vasculitis in rats. If we assume the mechanism of vascu-lar pathology is the same within this class of compounds,which seems a reasonable assumption, and the endpointpathology is the same then should not the metabolic responsebe the same and all PDE4 inhibitors produce similar urinaryspectral profiles? Figure 14 presents spectra obtained fromtwo PDE4 inhibitors, CI-1018, and rolipram. It does not takePCA analysis to demonstrate that not only are these urinaryNMR spectra produced by animals treated with these com-pound different from control, they are also quite differentfrom each other. How can this be? The answer is familiar toanyone who has run similar type studies assessing eitherpharmacological or chemical structure activity relationshipswith regard to class toxicities. Though compounds within achemical or pharmacological class frequently produce similartoxicities they are seldom identical with respect to all actionson animal physiology. Temporal response and severity fre-quently vary, clinical signs may differ, and sometimes second-ary toxicities vary within the class. Certainly toxicokineticresponses are usually different to some extent. Therefore, itshould not be surprising that at any given timepoint urinaryspectra may differ quite remarkably among toxins producing,what would otherwise be considered, a similar response. Thetrick of course is identifying those key components of the urin-ary spectra that are reproducibly associated with the toxicityof interest, while separating out and discounting those effectsthat are secondary and only indirectly related to the targetlesion. It is this arena where current efforts are focused inan effort to tease out those biomolecular changes that willhave utility as biomarkers of vascular toxicity.


4.5. Metabonomics and Mechanisms of VascularPathology

The initial work demonstrating the utility of metabonomictechnology to assess vascular toxicity raised a significantquestion. Were the biomolecular changes expressed in theurinary spectra a reflection of the mechanism of vascular toxi-city or were they simply a reflection of the concurrent inflam-mation that is the hallmark of these types of lesions? Thisquestion is of great significance as the search for biomarkers

Figure 14 600 mHz 1H-NMR spectra of urine from rats treatedwith rolipram or CI-1018. Shown are the spectra of pretest (control)urine sample (bottom trace) and urine from the rats treated for 3days with rolipram (middle trace) or CI-1018 (top trace). Despitesimilar mesenteric vascular lesions with both rolipram andCI-1018, the NMR spectral changes induced by rolipram are not-ably different than changes induced by CI-1018.

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for vascular toxicity has been largely restricted to markers ofinflammation, which may not be specific for drug-induced vas-cular lesions alone (75–77). Furthermore, many drugs thatinduce vascular toxicity are being developed for inflammatorydisease indications, which would greatly complicate the use ofinflammatory biomarkers for assessment of vascular lesions.

To address this particular question, an experiment wasundertaken in which rats were pretreated with dexametha-sone for a day and then concurrently administered CI-1018and dexamethasone for four consecutive days at a dose pre-viously demonstrated to produce vasculitis in 75–100% ofanimals receiving the compound (74). Additional groups ofrats were given either vehicle, CI-1018 or dexamethasonealone to serve as appropriate controls. The data are summar-ized in Fig. 15. In the study, 6=6 CI-1018 treated animals hada detectable pattern shift during the course of the study inaccordance with results noted previously (72). Interestingly,five=six animals treated with dexamethasone have vascularlesions characterized by minimal medial smooth musclenecrosis and degeneration without concurrent inflammatorycell infiltrates. One of the six dexamethasone=CI-1018 treatedanimals had no evidence of vasculitis. Perhaps most impor-tant was the fact that even in the absence of an inflammatorycomponent of the lesion, the NMR spectral patterns weresimilar to those observed with CI-1018 alone, shifted relativeto control with the five animals exhibiting medial lesions hav-ing shifted NMR spectral patterns, while the one unaffectedanimal had no observable pattern shift. These data suggestedthat the urinary spectral patterns induced by CI-1018 werenot simply a reflection of the concurrent inflammatory pro-cess, but rather more directly related to the etiology of thelesion. These data raise a whole series of interesting ques-tions; for example, how can a very focal lesion in one vascularbed induce micro- to milli-Molar changes in urinary biomole-cular components? Mechanistically, this may indicate ayet-to-be-described intermediate metabolic component ofthe lesion (perhaps associated with ketonuria?).

Another mechanistic question that arises when looking atthe metabonomic data is the temporal nature of the NMR


pattern shifts. Almost without exception, the onset of NMRspectral pattern shifts occur 48–72hr after initiating dosingeven if only a single dose is used to initiate the lesion. Further-more, the onset of these changes generally precedes overtmicroscopic evidence of vascular pathology. Additionally,NMR pattern shifts have been observed to occur at doses ofrolipram lower than those that induce overt vascular pathol-ogy (78). These data taken together have significant mechan-istic ramifications that would not otherwise be available. Thesignificance of these changes and the role they play in theetiology of the vascular lesions still need to be elucidated.

Figure 15 PCA plot from female rats treated with 750mg=kg CI-1018, with or without concurrent dexamethasone treatment. Num-bers above symbols indicate day of sample collection (Day1¼pretest). Note distinct spectral separation of Day 3–5 (48–96hrpostdose) samples from animals treated with CI-1018 from controlor pretest samples. Although dexamethasone markedly suppressedthe inflammatory component of vascular lesions, vascular pathologywas still evident and NMR spectral patterns differed little fromanimals treated with CI-1018 alone.

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4.6. Conclusions

Metabonomic technology has already made an impact in thearea of assessing and understanding drug-induced vascularpathology. As with any new technology, the questions gener-ated by any set of experiments are frequently more importantthan the questions answered. Future work will focus on iden-tifying those unique biomolecular changes associated withvascular toxicity that may serve as potential biomarkers.Furthermore, the biomolecules themselves will aid in the gen-eration of testable hypothesis with regard to the etiology ofthe lesion. Already ‘‘panomic’’ experiments are underwaywhich link transcriptomic, proteomic, and metabonomic tech-nologies to generate a complete picture form gene to protein tophenotype. It is hoped that utilization of all three technologiesin unison will be synergistic, rather than additive, withregard to our understanding assessment of drug-inducedvascular toxicity.

REFERENCES

1. Lee WM. Drug-induced hepatotoxicity. Med Prog 1995:1118–1127.

2. MacGregor JT, Collins JM, Sugiyama Y, Tyson CA, Dean J,Smith L, Andersen M, Curren RD, Houston JB, KadlubarFF, Kedderis GL, Krishnan K, Li AP, Parchment RE,Thummel K, Tomaszewski JE, Ulrich R, Vickers AEM,Wrighton SA. Forum: in vitro human tissue models in riskassessment: report of a consensus-building workshop. ToxicolSci 2001; 59:17–36.

3. Jaeschke H, Gores GJ, Cederbaum AI, Hinson JA, Pessayre D,Lemasters JJ. Forum: mechanisms of hepatotoxicity. ToxicolSci 2002; 65:166–176.

4. Beckwith-Hall BM, Nicholson JK, Nicholls AW, Foxall PJD,Lindon JC, Connor SC, Abdi M, Connelly J, Holmes E. Nuclearmagnetic resonance spectroscopic and principal componentsanalysis investigations into biochemical effects of three modelhepatotoxins. Chem Res Toxicol 1998; 11:260–272.


5. Nicholls AW, Holmes E, Lindon JC, Shockcor JP, Farrant RD,Haselden JN, Damment SJP, Waterfield CJ, Nicholson JK.Metabonomic investigations into hydrazine toxicity in therat. Chem Res Toxicol 2001; 14:975–987.

6. Nicholson JK, Higham DP, Timbrell JA, Sadler PJ. Quantita-tive high resolution 1H NMR urinalysis studies on the bio-chemical effects of cadmium in the rat. Mol Pharmacol l989;36:398–404.

7. Timbrell JA, Waterfield CJ. Changes in taurine as an indicatorof hepatic dysfunction and biochemical perturbations. Studiesin vivo and in vitro. Adv Exp Med Biol 1996; 403:233–245.

8. Waterfield CJ, Turton JA, Scales MD, Timbrell JA. Investiga-tion into the effects of varioushepatotoxic compounds onurinaryand liver taurine levels in rats. Arch Toxicol 1993; 67:244–254.

9. Sanins SM, Timbrell JA, Elcombe C, Nicholson JK. Hepato-toxin-induced hypertaurinuria: a proton NMR study. ArchToxicol 1990; 64:407–411.

10. Waters NJ, Holmes E, Williams A, Waterfield CJ, Farrant RD,Nicholson JK. NMR and pattern recognition studies on thetime-related metabolic effects of a-naphthylisothiocyanate onliver, urine, and plasma in the rat: an integrative metabo-nomic approach. Chem Res Toxicol 2001; 14:1401–1412.

11. Robertson DG, Reily MD, Sigler RE, Wells DF, Paterson DA,Braden TK. Metabonomics: evaluation of nuclear magneticresonance (NMR) and pattern recognition technology for rapidin vivo screening of liver and kidney toxicants. Toxicol Sci2000; 57:326–337.

12. Sanins SM, Timbrell JA, Elcombe C, Nicholson JK. ProtonNMR spectroscopic studies on the metabolism and biochemicaleffects of hydrazine in vivo. Arch Toxicol 1992; 66:489–495.

13. Sanins SM, Timbrell JA, Nicholson JK. High-resolution pro-ton-NMR studies of metabolism and effects of hydrazine inrat. Hum Toxicol (Suppl) 1986; 5:123.

14. Olney JW, de Gubareff T, Collins JF. Sterospecificity of thegliotoxic and anti-neurotoxic actions of alpha-aminoadipate.Neurosci Lett 1980; 19:277–282.

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15. Halliwell WH. Cationic amphiphilic drug-induced phospholipi-dosis. Toxicol Pathol 1997; 25:53–60.

16. Kodavanti UP, Mehendale HM. Cationic amphiphilic drugsand phospholipid storage disorder. Pharmacol Rev 1990;42:327–354.

17. Nicholls AW, Nicholson JK, Haselden JN, Waterfield CJ. Ametabonomic approach to the investigation of drug-inducedphospholipidosis: an NMR spectroscopy and pattern recogni-tion study. Biomarkers 2000; 5:410–423.

18. Espina JR, Shockcor JP, Herron WJ, Car BD, Contel NR,Ciaccio PJ, Lindon JC, Holmes E, Nicholson JK. Detection ofin vivo biomarkers of phospholipidosis using NMR-basedmetabonomic approaches. Magn Reson Chem 2001; 39:559–565.

19. Bollard ME, Garrod S, Holmes E, Lindon JC, Humpfer E,Spraul M, Nicholson JK. High-resolution 1H and 1H–13Cmagic angle spinning NMR spectroscopy of rat liver. MagnReson Med 2000; 44:201–207.

20. Waters NJ, Garrod S, Farrant RD, Haselden JN, Connor SC,Connelly J, Lindon JC, Holmes E, Nicholson JK. High-resolution magic angle spinning 1H NMR spectroscopy ofintact liver and kidney: optimization of sample preparationprocedures and biochemical stability of tissue during spectralacquisition. Anal Biochem 2000; 282:16–23.

21. Waters NJ, Holmes E, Waterfield CJ, Farrant RD, NicholsonJK. NMR and pattern recognition studies on liver extractsand intact livers from rats treated with alpha-naphthylisothio-cyanate. Biochem Pharmacol 2002; 64:67–77.

22. Garrod S, Humpher E, Connor SC, Connelly JC, Spraul M,Nicholson JK, Holmes E. High-resolution 1H NMR magicangle spinning NMR spectroscopic investigation of thebiochemical effects of 2-bromoethanamine in intact renal andhepatic tissue. Magn Reson in Med 2002; 45:781–790.

23. Holmes E, Nicholls AW, Lindon JC, Connor SC, Connelly JC,Haselden JN, Damment SJP, Spraul M, Neidig P, NicholsonJK. Chemometric models for toxicity classification basedon NMR spectra of biofluids. Chem Res Toxicol 2000; 13:471–478.


24. Holmes E, Nicholls AW, Lindon JC, Ramos S, Spraul M, Nei-dig P, Connor SC, Connelly J, Damment SJP, Haselden J,Nicholson JK. Development of a model for classification oftoxin-induced lesions using 1H NMR spectroscopy of urinecombined with pattern recognition. NMR Biomed 1998;11:235–244.

25. Holmes E, Nicholson JK, Tranter G. Metabonomic characteri-zation of genetic variations in toxicological and metabolicresponses using probabilistic neural networks. Chem ResToxicol 2001; 14:182–191.

26. Azmi J, Griffin JL, Antti H, Shore RF, Johansson E, NicholsonJK, Holmes E. Metabolic trajectory characterisation ofxenobiotic-induced hepatotoxic lesions using statistical batchprocessing of NMR data. Analyst 2002; 127:271–276.

27. Lindon JC, Nicholson JK, Everett JR. Annual Reports on NMRSpectroscopy. Vol. 38. Academic Press, 1999:1–88.

28. Lindon JC, Nicholson JK, Holmes E, Everett JR. Metabo-nomics: metabolic processes studied by NMR spectroscopy ofbiofluids. Concepts Magn Reson 2000; 12(5):289–320.

29. Le Moyec L, Pruna A, Eugene M, Bedrossian J, Idatte JM,Huneau JF, Tome D. Proton nuclear magnetic resonance spec-troscopy of urine and plasma in renal transplantation follow-up. Nephron 1993; 65:433–439.

30. Knubovets TL, Lundina TA, Sibeldina LA, Sedov KR. 1H NMRurinalysis in glomerulonephritis: a new prognostic criterion.Magn Reson Imaging 1992; 10:127–134.

31. Lundina TA, Knubovets TL, Sedov KR, Markova SA, SibeldinLA. Variability of kidney tubular interstitual distortions inglomerulonephritis as measured by 1H-NMR urinalysis. Clin-ica Chim Acta 1993; 214:165–173.

32. Bales JR, Nicholson JK, Sadler PJ. Two-dimensional protonnuclear magnetic resonance ‘‘maps’’ of acetaminophen metabo-lites in human urine. Clin Chem 1985; 31:757–762.

33. Powell JJ, Gartland KPR, Lombard M, Sallie R, Nicholson JK,Thompson RPH. Proton NMR spectroscopy of bile as a markerof liver function in liver-transplant patients. Clin Sci 1990;78:13.

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34. Nicholson JK, Wilson ID. High resolution proton magneticresonance spectroscopy of biological fluids. Prog NMR Spec-trosc 1989; 21:444–501.

35. Hook JB, Smith JH. Biochemical mechanisms of nephrotoxicity.Transplantation Proceedings 17 (supplement 1): 41–50, 1985.

36. Gabriel R. Postgraduate Nephrology. 3rd ed. Butterworths,1985.

37. Hook JB, Hewit WR. Toxic responses of the kidney. In: Klaas-sen CD, Amdur MO, Doull J, eds. Toxicology: The BasicScience of Poisons. New York: Macmillan, 1986:310–329.

38. Berndt WO. Renal methods in toxicology. In: Hayes WA, ed.Principles and Methods of Toxicology. New York: Raven Press,1982:447–474.

39. Halman J, Fowler JSL, Price RG. Urinary enzymes, protei-nuria and renal function tests in the assessment of nephrotoxi-city in the rat. In: Bach PH, Lock EA, eds. Renal Heterogeneityand Target Cell Toxicity. Chichester, U.K.: Wiley, 1985:295–298.

40. Gartland KPR, Bonner FW, Nicholson JK. Investigation intothe biochemical effects of region-specific nephrotoxins. MolPharmacol 1989; 35:242–250.

41. Anthony ML, Beddell CR, Lindon JC, Nicholson JK. Studieson the comparative toxicity of S-(1,2-dichlorovinyl)-l-cysteineand 1,1,2-triflchloro-3,3,3-fluoro-1-propene in the Fischer 344rat. Arch Toxicol 1994; 69:99–110.

42. Holmes E, Nicholson JK, Nicholls AW, Lindon JC, Connor SC,Polley S, Connelly J. The identification of novel biomarkers ofrenal toxicity using automatic data reduction techniques andPCA of proton NMR spectra of urine. Chemometrics IntellLab Syst 1998; 44:245–255.

43. Tandon SK. Organ toxicity of chromium in biologicaland environmental aspects of chromium. In: Langard S, ed.Amsterdam: Elsevier, 1982:209–220.

44. Halligan S, Byard SJ, Spencer AJ, Gray TJB, Harpur ES,Bonner FW. A study of the nephrotoxicity of three cephalos-porins in rabbits using 1H NMR spectroscopy. Toxicol Lett1995; 81:15–21.



46. Nanra RS. Renal effects of antipyretic analgesics. Am J Med1983; Nov 14: 70–80.

47. Sabatini S. Analgesic-induced papillary necrosis. SeminNephrol 1988; 8(1):41–54.

48. Shelley JH. Pharmacological mechanisms of analgesic nephro-pathy. Kidney Int 1978; 13:15–26.

49. Holmes E, Bonner FW, Nicholson JK. Comparative studies onthe nephrotoxicity of 2-bromoethanamine hydrobromide in theFischer 344 rat and the multimammate desert mouse (Mast-omys natalensis). Arch Toxicol 1995; 70:89–95.

50. Soose M, Gwinner W, Grotkamp J, Hansemann W, Stolte H.Altered renal fibronectin excretion in early adria-mycin nephrosis of rats. J Pharm Exp Ther 1991; 257(1):493–499.

51. O’Donnell MP, Michels L, Kasiske B, Raij L, Keane WF.Adriamycin-induced chronic proteinuria: a structural andfunctional study. J Lab Clin Med 1985; 106(1):62–67.

52. Grond J, Koudstaal J, Elema JD. Mesangial function andglomerular sclerosis in rats with aminonucleoside nephrosis.Kidney Int 1985; 27(2):405–410.

53. Hill GS. Drug associated glomerulopathies. Toxicol Pathol1986; 14:37–44.

54. Cutler P, Bell DJ, Birrell HC, Connelly JC, Connor SC,Holmes E, Mitchell BC, Monte SY, Neville BA, Pickford R,Polley S, Schneider K, Skehel JM. An integratedproteomic approach to studying glomerular nephrotoxicity.Electrophoresis 1999; 20(18):3647–3658.

55. Mazze RI, Cousins MJ, Kosek JC. Strain differences in meta-bolism and susceptibility to the nephrotoxic effects of methox-yfluorane in rats. J Pharmacol Exp Ther 1973; 184:481–488.

56. Henry MA, Sweet RS, Tange JD. A new reproducible experi-mental model of analgesic nephropathy. J Pathol 1983;139:23–32.

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57. Holmes E, Bonner FW, Nicholson JK. Comparative biochem-ical effects of low doses of mercury II chloride in the F344rat and the multimammate mouse (Mastomys natalensis).Comp Biochem Physiol C Pharmacol Toxicol Endocrinol1996; 114(1):7–15.

58. Holmes E, Bonner FW, Nicholson JK. 1H NMR spectroscopicand histopathological studies on propyleneimine-inducedrenal papillary necrosis in the rat the multimammate desertmouse (Mastomys natalensis). Comp Biochem Physiol C Phar-macol Toxicol Endocrinol 1997; 116(2):125–134.

59. Rehm S, Waalkes MP. Acute cadmium chloride-induced renaltoxicity in the Syrian hamster. Toxicol Appl Pharmacol 1990;104(1):94–105.

60. Lenz SD, Carlton WW. Diphenylamine-induced renal papil-lary necrosis and necrosis of the pars recta in laboratoryrodents. Vet Pathol 1990; 27(3):171–178.

61. Holmes E, Antti H. Chemometric contributions to the evolu-tion of metabonomics: mathematical solutions to characteris-ing and interpreting complex biological NMR spectra.Analyst 2002; 127:549–1557.

62. Holmes E, Bonner FW, Sweatman BC, Lindon JC, Beddell CR,Rahr E, Nicholson JK. Nuclear magnetic resonance spectro-scopy and pattern recognition analysis of the biochemical pro-cesses associated with the progression of and recovery fromnephrotoxic lesions in the rat induced by mercury (II) chlorideand 2-bromoethanamine. Mol Pharmacol 1992; 42:922–930.

63. Murgatroyd LB, Pickford RJ, Smith IK, Wilson ID, MiddletonBJ. 1H NMR spectroscopy as a means of monitoring nephro-toxicity as exemplified by studies with cephaloridine. HumExp Toxicol 1992; 11(1):35–41.

64. Gartland KP, Anthony ML, Beddell CR, Lindon JC, NicholsonJK. Proton NMR studies on the effects of uranyl nitrate on thebiochemical composition of rat urine and plasma. J PharmBiomed Anal 1990; 8(8–12):951–954.

65. Joseph EC, Jones HB, Kerns WD. Characterization of coron-ary arterial lesions in the dog following administration ofSK&F 95654, a phosphodiesterase III inhibitor. Toxicol Pathol1996; 24:429–435.


66. Larson JL, Pino MV, Geiger LE, Simeone CR. The toxicity ofrepeated exposures to rolipram, a type IV phosphodiesteraseinhibitor, in rats. Pharmacol Toxicol 1996; 78:44–49.

67. Albassam MA, Metz AL, Gragtmans NJ, King LM, MacallumGE, Hallak H, McGuire EJ. Coronary arteriopathy in monkeysfollowing administration of CI-1020, an endothelin A receptorantagonist. Toxicol Pathol 1999; 27:156–164.

68. Albassam MA, Metz AL, Potoczak RE, Gallagher KP, HaleenS, Hallak H, McGuire EJ. Studies on coronary arteriopathyin dogs following administration of CI-1020, an endothelin Areceptor antagonist. Toxicol Pathol 2001; 29:277–284.

69. Metz AL, Dominick MA, Suchanek G, Gough AW. Acute cardi-ovascular toxicity induced by an adenosine agonist- antihyper-tensive in beagles. Toxicol Pathol 1991; 19:98–107.

70. Kerns WD, Arena E, Macia RA, Bugelski PJ, Matthews WD,Morgan DG. Pathogenesis of arterial lesions induced by dopa-minergic compounds in the rat. Toxicol Pathol 1989; 17:203–213.

71. Mesfin GM, Robinson FG, Higgins MJ, Zhong WZ, DuCharmeDW. The pharmacologic basis of the cardiovascular toxicity ofminoxidil in the dog. Toxicol Pathol 1995; 23:498–506.

72. Robertson DG, Reily DR, Albassam M, Dethloff LA. Metabo-nomic assessment of vasculitis on rats. Cardiovasc Toxicol2001; 1:7–9.

73. Dethloff LA, Pegg DG, Metz AL. Preclinical toxicity studies ofthe phosphodiesterase IV inhibitor CI-1018 in rats. Toxicol Sci1999; 48(1S):1502a.

74. Slim RM, Robertson DG, Albassam M, Reily MD, Robosky L,Dethloff LA. Effect of dexamethasone on the metabonomicsprofile associated with phosphodiesterase inhibitor-inducedmesenteric vascular lesions in rats. Toxicol Appl Pharmacol2002; 183:108–116.

75. Weyand CM, Goronzy JJ. Pathogenic principles in giant cellarteritis. Int J Cardiol 2000; 75:S9-S19.

76. Igarashi H, Hatake K, Shiraishi H, Samada K, Tomizuka H,Momoi MY. Elevated serum levels of macrophage colony-stimulating factor in patients with Kawasaki disease

394 Thomas et al.

complicated by cardiac lesions. Clin Exp Rheumatol 2001; 19:751–756.

77. Merkel, PA. Drug-induced vasculitis. Rheum Dis Clin NorthAm 2001; 27:849–862.

78. Robertson DG, Reily MD, Albassam M, Dethloff LA, Wells DF,Braden TK. Metabonomic assessment of drug-induced vasculi-tis in rats. Toxicol Sci 2001; 60(1S):375.


10

Physiological Variation inLaboratory Animals and Humans

M.E. BOLLARD, E.G. STANLEY, Y. WANGJ.C. LINDON, J.K. NICHOLSON and

E. HOLMES

Biological Chemistry, Biomedical SciencesDivision, Imperial College, University of London,

South Kensington, London, U.K.

1. INTRODUCTION

Various physiological factors influence the metabolic composi-tion of the biofluids and tissues of living organisms. Bothinternal and external stimuli result in small metabolic adjust-ments in order to preserve homeostatic equilibrium in organ-isms. The metabolite profiles of the tissues and body fluidsprovide a fingerprint of the metabolic status of an animaland the expressed phenotype is a product of many genetic andenvironmental events. Factors such as diet, temperature,

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hydration state, hormonal cycles, metabolic rate, allostaticload, age, gender, and circadian rhythms all interact to influ-ence the metabolism of an organism in a dynamic manner. Inorder to interpret and understand the metabolic consequencesof pharmacology, pathology, or genetic modification, it is firstnecessary to define ‘normality’ in healthy organisms and toestablish the breadth of normal physiological variation. Ingeneral, pathological and toxicological effects on metaboliteprofiles are greater than pharmacological effects, with physio-logical variation causing even more subtle perturbations incomparison. Nevertheless, these subtle effects have a diverserange of both intrinsic and extrinsic sources, which affectmany biochemical pathways resulting in characteristic meta-bolite variation in biofluids and tissues. These biochemicalpathways may also be involved in toxification or detoxificationof xenobiotics making it necessary to understand their contri-bution to defining ‘‘normality’’.

Nuclear magnetic resonance (NMR) spectroscopytogether with pattern recognition (PR) techniques provide anefficient tool with which to investigate the inherent metabolicvariability in control populations of experimental animals andhumans. A 1H NMR spectrum of a biofluid is extremely com-plex, consisting of thousands of well-resolved signals, theintensities of which reflect the concentration of metabolitespresent in the sample. NMR spectroscopy enables the simulta-neous monitoring of a wide range of low molecular weightendogenous and exogenous metabolites, and provides amethod for identifying organic compounds by virtue of theinfluence of the global and local chemical environment of theproton moiety. Thus, the 1H NMR spectrum of a biofluid or tis-sue sample provides a multidimensional fingerprint of anorganism, which can be changed by a disease or toxin, or asan effect of the nutritional state or lifestyle of the animal.The PR methods can be used to reduce the complexity of thedata, allowing the examination of sequentially collected urineor other biological samples over a given time-course to estab-lish changes in profile and highlight the dynamic metabolicstatus of an organism (1). Previous chapters have describedsome of the principles and practices of analyzing NMR data

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with chemometric and bioinformatic tools to provide a meansof characterizing and predicting a range of pathologies (seeChapter 9). Here, we illustrate the use of NMR-based metabo-nomic analysis to characterize the more subtle metabolicperturbations associated with physiological variation.

The NMR spectra are exquisitely sensitive to detectingphysiological variability in a ‘‘normal’’ population and thesubsequent use of PR can generate characteristic patternsof biochemical perturbations describing a particular physiolo-gical or pathological state. The NMR–PR techniques havefacilitated investigations into a range of extrinsic factors inthe rat and mouse, such as diurnal variation, which is con-trolled by an artificial light–dark cycle in the laboratory(2,3). In addition, this metabolic profiling approach has beenused to relate the metabolic signature or metabotype of a bio-fluid to differences in genetic composition of organisms andcan be used to interpret the functional consequences ofgenetic modification (4). In order to differentiate between phy-siological and pathological responses in animal models andhumans, we must first construct multivariate boundaries ofnormality. An estimated 3–5% of experimental animals arenot healthy prior to inclusion in toxicological studies and asa result may show anomalous responses to toxins (5). Theidentification of these individuals can improve the sensitivityof the analysis and increase the interpretability of subsequentPR models incorporating toxicological or disease-related data.Such control models in humans, representing ‘‘normal’’ popu-lations, are particularly useful in identifying individuals, whowere non-compliant during clinical trials. The ability ofNMR–PR based techniques to distinguish between variousnormal physiological states illustrates the power and sensitiv-ity of this approach in detecting subtle changes in the endo-genous metabolite profiles of the urine, and for investigationinto biochemical and physiological rhythms.

Within a laboratory environment, many of the externalinfluences that may cause variability in caged animals, suchas food intake, room temperature, and light intensity can becontrolled. However, even in experiments where the animalsare genetically homogenous and the environmental conditions

Physiological Variation in Laboratory Animals and Humans 399

are carefully controlled, metabolic differences between ani-mals have been observed. Disparity in the microenvironmentof an animal, such as hormonal fluctuations during the estruscycle, level of activity and biological rhythms are more diffi-cult to control and are reflected in the urinary and plasma1H NMR spectral profiles. Humans are exposed to a muchgreater diversity of environmental conditions and thus pre-sent a significant analytical challenge in terms of characteriz-ing their normal metabolic profiles. Standard PR tools such asprincipal component analysis (PCA), hierarchical clustering,and neural network analysis have been effective in differen-tiating between pathological states and in indicating the pre-sence and nature of physiological variation. However, sincemultiple environmental and genetic factors contribute to theoverall metabolite profile of tissues and biofluids, the conse-quences of a single intended intervention can be difficult todisentangle from the other inherent sources of biologicalvariability that influence the spectral profile.

Urinary composition is able to change readily in responseto many parameters, such as diurnal variations in intermediarymetabolism, circadian rhythms, hormonal influences, stress,dietary components, the overall state of nutrition, stage of foodintake, or levels of exercise and does this without exerting adetrimental effect on an organism. Other biological matricessuch as plasma and tissues are under tighter homeostatic con-trol and although metabolic challenges to the organism willresult in a perturbation of the system, the changes inmetaboliteprofiles are generally more subtle and harder to detect. In addi-tion to the wide range of standard chemometric tools available,more sophisticated strategies including data filtering algo-rithms or data restructuring (e.g., logical blocking or QUILTanalysis) and the application of differential scaling factors canbe used to aid the deconvolution and removal of unwanted bio-logical variation or ‘‘noise.’’ This allows us to focus on the meta-bolic effects of a single stimulus such as a drug or disease process(6). Some of these techniques are described in Chapter 8.

This chapter aims to emphasize the importance of consid-ering the extent and nature of physiological variance priorto interpreting data from toxicological or clinical studies in

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animals and humans. It is essential to determine normality ifwe wish to facilitate differentiation between physiological andpathological responses and ascertain the degree of pathologi-cal response.

2. PHYSIOLOGICAL VARIATION INLABORATORY ANIMALS

2.1. Inter-Animal Variation

The metabolic composition of urine from same-sex animals isknown to vary according to the health status of a particularindividual, as a result of genetic variability or as a responseto stress. Such inter-animal differences can affect the meta-bolism of a drug or its toxicity. For instance, a significantdegree of inter-rat variability is observed in urinary profilesafter galactosamine treatment at toxic levels resulting in dif-fering magnitudes of response between animals (7,8). Moresurprisingly, however, are the inter-animal differencesfound in supposedly homogeneous populations of rats main-tained under identical environmental conditions. Recent tox-icological studies in the Sprague–Dawley (SD) control ratand B6C3F1 mouse documented greater inter-individualvariability of urinary profiles between rats than betweenmice (9).

In the work carried out on physiological variation infemale SD rats, urine from a group of 10 animals was sampledover a 10-day period (2). From PCA of the data, the scores fromthe first two PCs, which accounted for 66% of the variance inthe data were tabulated, and the mean values and standarddeviation ellipses for each rat were mapped using the meanintegral values of urinary spectral data as input variables(Fig. 1). In general, samples from individual animals over-lapped within one standard deviation. Several animalsshowed a higher degree of dissimilarity from other rats, forinstance, rat 8 excreted relatively low concentrations of tricar-boxylic acid cycle (TCA) intermediates and hippuric acid, andhigher concentrations of taurine, dimethylglycine, creatinine,and glucose than the other rats in terms of urinary spectral


profile. Studies have shown that citrate, taurine, hippuricacid, and the renal osmolytes have a high inter-individual var-iation in control animals (2,10–12). The normal physiologicallevels of hippuric acid in the urine are known to be highly vari-able due to alterations in the gut microbial contents as a resultof external factors such as stress or a change in diet (13,14).Citrate excretion is thought to be affected by numerous factorsincluding nutrition, alterations in acid–base balance, hor-mones, calcium, and renal metabolites (15,16).

2.2. Age-Related Differences

The age of experimental animals is known to influence theirsusceptibility to certain toxins and in many cases young

Figure 1 PC1 vs. PC2 scores plot of mean-centered urinary spec-tral data � standard deviation ellipses for each female SD ratsampled during several estrus cycles, illustrating the partialseparation of individual rat data.

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animals have been shown to be more sensitive and showincreased duration of drug action than more mature animals.The reason for this lies in the development of drug meta-bolizing enzymes, which can also be dependent upon the sub-strate, species, strain, and gender of the animal (17). In otherinstances neonates are less susceptible to drug toxicity due todiffering hepatic metabolism of drugs. For example, in a studycarried out by Moser and Padilla (18) into the biochemicaltoxicity of chlorpyrifos in young (postnatal Day 17) and adult(about 70days old) rats, the magnitude of the age-related dif-ferences decreased as the rats matured. In addition, the onsetof maximal effects was delayed in the young rats; recoveryoccurred more quickly and immature rats showed no gen-der-related differences in toxicity.

The ageing rat undergoes numerous physiologicalchanges that are reflected by physical and biochemicalchanges in the animal, resulting in a difference in the propor-tions of endogenous metabolites excreted in the urine. Forinstance, the quantity of aromatic metabolites in the urine isknown to vary according to age of the animal in addition tothe influences of diet and gut microflora (14,19). The effect ofaging on urinary profiles is clearly illustrated in Fig. 2whereby rats aged 12–13weeks could be clearly separatedfrom 7–8-week-old rats (20). Metabolite patterns in the urineare dependent on the stage of development of the animal,e.g., in the case of the rat, the kidneys do not reach full devel-opment, and therefore, a mature pattern of glomerular filtra-tion, until the third month of age (19).

The levels of sex hormones in the plasma are age-relatedin the rat. The mechanism of age-associated alterations inplasma sex hormone levels and their affect on drug metaboliz-ing enzyme activities were studied in male and female Fischer344 (F344) rats of ages ranging between 3 and 30months (21).Plasma testosterone levels, as well as the activity of the ratelimiting enzyme required for testosterone production in thetestes, decreased with senescence. Imprinting neonatalfemale and male rats with either testosterone or estrogens,respectively, has been shown to alter the activity of certainhepatic enzymes to reflect the male and female liver type (22).


2.3. Gender Differences

Studies have shown that the metabolite profiles of femaleurine samples differ significantly from those of males, forinstance, sex-related differences in the elimination of citratein the urine have been determined (10,23). In the work car-ried out on gender differences in the Han-Wistar (HW) rat,male urinary profiles were found to be more variable thanfemales, particularly in the urinary excretion of the TCA cycleintermediates (24) and, in general, male rats have a greatermetabolic activity than females. This results in a significantlygreater exposure and hence prolonged pharmacological activ-ity for many drugs in female rats compared with males (25).However, males tend to be more susceptible to toxins and,therefore, are generally preferred for toxicity testing (26).For instance, chloroform produces liver damage in male andfemale mice but renal injury only in males (26).

Figure 2 PC plot of urine samples from 8-week-old (open squares)and 13-week-old (solid circles) rats showing distinct clustering ofsamples from animals differing in age by only a few weeks.

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The toxicity of cadmium and mercury salts in the rat isknown to be more marked in males than in females (27).A striking effect of cadmium treatment in the rat is a suddenand large reduction in the TCA cycle intermediates in theurine. This effect is more pronounced and persistent in malesand has been attributed to inhibition of renal carbonic anhy-drase resulting in tubular acidosis (27). Cadmium toxicity alsoinduces creatinuria in male rats and has been related to testi-cular toxicity (27). Gender-related differences in cocaine toxi-city are well documented in the rat with lower doses andplasma concentrations required to induce toxic signs andsymptoms in male rats than in females (26). There are, how-ever, cases where female rats show a greater response to toxintreatment than males. For instance, female rats are known toexhibit a higher occurrence of d-galactosamine-induced fattyliver than males (28).

In a study of male and female HW control rats, whereurine samples were collected during the light cycle of theday, clear differences were observed in the urinary 1H NMRprofiles between genders (24) (Fig. 3). Female rat urines com-prised higher levels of N-acetyl glycoproteins, dimethylgly-cine, and certain bile acids, whilst male rat urine samplescontained elevated levels of a sulfate-conjugated chlorogenicacid metabolite, meta-hydroxyphenylpropionic acid (m-HPPA) and 2-oxoglutarate. The effect of gender on biochem-ical composition of the urine can be clearly illustrated usingPCA (Fig. 4). In Fig. 4, male and female HW rat urinary spec-tral data are clearly separated in PC5 accounting. Thisseparation of the data was enhanced by partial least squaresdiscriminant analysis (PLSDA) to predict the gender withgreater than 99% accuracy. Gender-related differences in sul-fotransferase enzyme activities have been documented foramines and alcohol substrates in a number of species (29).The higher levels of m-sulfate-conjugate of m-HPPA in maleurine samples compared with those of female rats may be ofimportance in drug metabolism investigations where sulfa-tion is a major route of detoxification mechanism as in thecase of paracetamol (24). The elevated levels of bile acid meta-bolites in female rat urine samples compared with that of


males reflect the increased rate of cholesterol and bile acidsynthesis and metabolism in females (24).

Separation of male and female plasma samples from HWrats has also been achieved using PCA (Fig. 5). This separa-tion was attributed to lower concentrations of plasma lipopro-teins in female rats compared with their male counterparts(Fig. 6). This difference is possibly related to a protective roleof estrogenic hormones (30). It has been postulated that suchsex-linked differences are under the influence of a sex-linkedchromosome, most likely the X-chromosome (31). From clini-cal chemistry measurements, male rats have elevated concen-trations of the enzymes alkaline phosphatase (ALP), alanineaminotransferase (ALT), alpostate aminotransferase (AST)glutamate dehydrogenase (GDH), and plasma triglycerides,whilst females have higher concentrations of total proteins(TPR) and albumin (23,32,33).

Sex-related differences in toxicity have been related todifferences in hepatic drug metabolism. The expression of

Figure 3 The 600MHz 1H NMR spectra (d9.0–0.5) of control urinesamples collected from male (lower spectrum) and female (upperspectrum) Han Wistar rats. KEY: DMG, dimethylglycine; TMAO,trimethylamine-N-oxide; HoD, deuterated water; m-HPPA, meta-hydroxyphenylpropionic acid; PAG, phenylacetylglycine; NACs, N-acetyl-glycoprotiens; u1, unknown.

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Figure 4 Scores maps showing (a) PC1=PC5 scores and (b)t[1]=t[2] PLS-DA scores derived from the 1H NMR spectra of controlurine samples showing separation of samples based upon gender ofHan Wistar rats.


sex-specific cytochrome P450s in rats and mice is regulated bygrowth hormone, thyroid hormone, and sex hormones (34).For instance, the sex-specific cytochrome P450s CYP2C11,CYP2C13, and CYP3A2 are expressed in males whereasCYP2C12 is expressed in females (35). Male rats secretegrowth hormone in a rhytmic manner, whereas, growth hor-mone secretion in the female rat is ‘‘continuous’’ and ismimicked by the expression of certain cytochrome P450enzymes. However, gender-related variation in drug metabo-lism between the sexes is exaggerated in the rat comparedwith other species such as the mouse (36).

2.4. Species Differences

Not surprisingly control urine samples from different speciescan be separated by metabonomic analysis, enabling evalua-tion of biomarkers across species. For instance, the excretionof glycine conjugates in the urine has been shown to be

Figure 5 PC1=PC2 scores maps derived from the single-pulse 1HNMR spectra of plasma samples showing gender-related separation.

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species-dependent (37). The glycine conjugation of benzoicacid to hippuric acid and its subsequent urinary excretionoccurs in primates, rodents, and rabbits, however benzoic acidis excreted unchanged or as the glucuronide conjugate byinsects, birds, and reptiles (37). Similarly, the excretion ofphenylacetic acid as the parent compound or as the gluta-mine, glycine, or taurine conjugate is species-dependent, forexample, it is excreted as phenylacetylglutamine in humans(38) and phenylacetylglycine in rats (39). Figure 7 shows aPCA plot of urinary data from humans, rats, rabbits, and miceillustrating the inter-species differences in urinary profiles,where each species occupies a discrete area on the scoresmap (19). So far, the majority of metabonomic publicationshave been concerned with toxicology and physiological

Figure 6 The 600MHz single-pulse 1H NMR spectra d5.5–0.5 ofblood plasma samples collected at 48hr postdose from a controlmale rat (lower spectrum) and a control female rat (upper spec-trum). Abbreviations: 3HB, 3-d-hydroxybutyrate; HDL, high den-sity lipid; HOD, residual water resonance; LDL, low density lipid;NAC, N-acetylglycoprotein; OAC, O-acetylglycoprotein, VLD, verylow density lipid


variation in the rat, however, this is likely to change as thetechnology increases in popularity.

Distinct species variations in the metabolic adaptation ofsmall animals according to diet and growth strategy are knownto affect the metabolic composition of urine (40). Themetabolicprofiles of three wild mammals (bank vole, shrew, and woodmouse) with different natural diets have previously been stu-died using 1HNMRand statistical PR, and comparedwith thatof the SD laboratory rat (40). The four species were clearlyseparated by their urinary metabolite profiles. For instancethe bank vole contained higher amounts of aromatic aminoacids in its urine compared with the laboratory rat, whilst ratscontained higher levels of hippuric acid (Fig. 8), which isknown to be effected by diet, age, and gut microflora (13,18).Rat urine was also more homogenous in composition comparedwith the wild animals, containing less amino acids and TCAcycle intermediates and appeared to have less inter-groupvariation. This is not surprising as the laboratory strains ofrat are in-bred to increase physiological homogeneity. The

Figure 7 PC plot of combined NMR spectra from untreated ani-mals and humans showing clear separation of all species.

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lower concentrations of amino acids found in the rat urinemayindicate that wild animals are not able to metabolize fully thehigh protein content of laboratory chow andmay express lowertransaminase activity than rats (40). In addition, all three wildanimals had higher concentrations of plasma triglyceridescompared to the laboratory rat. This may have toxicologicalimplications associated with increased half-life of lipophilicxenobiotics in wild animals compared with the rat (40).

The pharmaceutical industry is continually looking formodel species or strains that best mimic the human for toxico-logical screening or disease evaluation. Studies into inter-spe-cies differences in response to toxins between laboratory

Figure 8 The 600MHz spectra from bank vole (a), wood mouse (b),and rat (c). Bank vole urine contained a variety of aromatic com-pounds whilst rat urine could be distinguished by a relatively highconcentration of hippuric acid. Key: A, lactate; B, alanine; C, acetate;D, glutamate; E, succinate; F, citrate; G, creatinine; H, creatine; I,TMAO; J, glucose and sugar containing region; K, urea; L, tyrosine;M, tryptophan; N, hippurate; O, urocanate; P, phenylalanine.


animals have been carried out by Holmes et al. (41,42) by com-paring the SD and F344 rat and the Multimammate desertMouse (Mastomys natalensis). The multimammate desertmouse has a large proportion of long looped nephrons relativeto other strains ofmouse and therefore, likemost desert speciesis more efficient at concentrating urine (43). Analysis of the 1HNMR spectra of the urine from Mastomys and F344 ratsrevealed that there were higher levels of creatine, succinate,N-acetylglycoprotein, pyruvate, betaine, glycine, and otheramino acids in control Mastomys urines compared with controlF344 rat urine samples. The latter excreted greater amounts of2-oxoglutarate and trimethylamine-N-oxide than the Mast-omys. The relatively high levels of organic and amino acidsin the urine of Mastomys may be a result of their greater capa-city to conserve water by concentrating the urine (42).

Species differences between the Mastomys and the F344rats following treatment with the nephrotoxins HgCl2 (41),2-BEA (44) and propyleneimine (42) were also observed, withthe former proving to be more resistant to nephrotoxicity thanthe F344 rat strain. This was postulated to be related to renalmedullary:cortical ratios and the elevated concentrations ofthe renal osmolytes, which have been shown to protect againstosmotic stress in the kidney (41). Similarly, the Syrian ham-ster, which like the Mastomys also has a greater capacity forurine concentration than the rat, is documented to be less sus-ceptible to chemically induced renal papillary necrosis (45).One of the major differences in the response of the Mastomysto BEA and propyleneimine treatment compared with theF344 rat was the induction of taurinuria (42,44), Taurine isknown to protect renal medullary cells from osmotic stress(46) and, therefore, may accumulate in the inner medulla ofthe Mastomys, protecting it from nephrotoxins (42).

A metabonomic approach employing multivariate statis-tical or PR analysis of 1H NMR urine spectra was applied toinvestigate the between species differential biochemicalresponses of rats and mice to hydrazine exposure (8). Severalmetabolic responses to hydrazine were common to boththe rat and mouse, including elevated levels of urinary2-aminoadipate and creatine and depletion of the TCA cycle

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intermediates. Metabolic trajectories were mapped in PCspace using the mean spectral data at each time point to pro-vide a way of monitoring the progression of and recovery fromthe toxic lesion. The difference in direction and shape of thetrajectories between the rat and mouse reflected the distinctpattern of toxin induced changes in the metabolic profile,which may prove to be an indication of variation betweenthe exact mechanisms of toxicity between the two species.The differences in response of the rat and mouse to hydrazinetreatment were supported by histopathology (8).

2.5. Strain Differences

Differences in response to xenobiotics between strains of com-monly used experimental animals have been well documen-ted. Strain differences in rats are known to effectmetabolism and variations in enzyme activities in the liverand kidney have been well documented. For instance, varia-tions in tryptophan metabolite excretion and enzyme activ-ities have been found between two strains of albino miceand also between three strains of rat (Wistar, Gunn, andSD) (47,48). The susceptibility of male mice to chloroforminduced nephrotoxicity is known to vary between the differentstrains (49). The urinary profiles of SD and F344 rats aresimilar except that the SD rat has slightly higher urinary con-centrations of glucose and amino acids. The sensitivity of theF344 rat to nephrotoxin exposure is well documented (50–52)and hence this particular strain of rat is commonly used inmechanistic nephrotoxicity studies (52,53). In contrast, theSD rat is not considered to be particularly sensitive to nephro-toxic insult (54) and, as such, consideration of such differ-ences in sensitivity between strains is, vital prior tocarrying out toxicity studies.

Metabonomics has previously been utilized successfullyin determining the difference between HW and SD rats(1,10,41,42,44,55–58). The two strains are very similar meta-bolically and genetically, and are both routinely used for toxi-city screening. However, using PCA, NMR urinary profiles ofthe two strains could be partially separated in metabolic


space (Fig. 9). Despite the degree of overlap, the strain of ratcould be predicted correctly 86% of the time using the super-vised classification method SIMCA (Soft Independent Model-ing Classification Anology). From visual inspection of the 1HNMR spectra of urine samples from the two strains, HW ratswere determined to have higher levels of acetate, lactate, andtaurine, whilst SD rats had elevated levels of hippuric acid(Fig. 10). Further work by Holmes et al. (56) observed distinctdifferences between the metabolic profiles of control urinefrom SD and HW laboratory rats using probabilistic neuralnetworks (PNN). Strain-related differences in metabolicresponse to a number of liver and kidney toxins could alsobe characterized using this approach.

Metabonomic methodology has successfully beenapplied to differentiate morphologically indistinguishable

Figure 9 The PCA plot of PC1 vs. PC2 from mean-centered data-reduced 1H NMR spectra of 900 control urine samples from HW (� )and SD (& ) rats.

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but genetically different species of earthworm from analysisof tissue extracts and celomic fluid using 1H NMR spectro-scopy and multivariate statistics (59). Similarly, thisapproach has been utilized successfully for the determinationof the metabolic differences between two strains of laboratorymice, the AlpK:ApfCD (white) and C57BL107 (black) mice.By PCA, it was possible to separate the two strains and topredict the strain of the mouse in 98% of cases using PLS-DA(Fig. 11). From comparison of the 1H NMR spectra of their

Figure 10 The 600MHz 1H NMR spectra of whole rat urine (A)Han-Wistar and (B) Sprague–Dawley animals. Abbreviations:DMG, dimethylglycine; HOD, residual water; m-HPPA, m-(hydroxyphenylpropionic acid); NAGs, N-acetylglycoproteins; 2-OG, 2-oxoglutarate; TMAO, trimethylamine-N-oxide.


urine, the white mice had noticeably higher elevatedlevels of 2-oxoglutarate, citrate, trimethylamine-N-oxide, andguanidinoacetic acid whilst black mice had higher levels oftaurine, creatinine, dimethylamine, and trimethylamine(Fig. 12). Perturbations in renal osmolytes were postulatedto be the result of strain differences in enzyme activity.The ability to predict phenotype or genotype has obviousapplications to the study of genetic polymorphism andgenetic modulation (transgenics).

2.6. Transgenic Models

The number of different species with sequenced genomes is onthe increase. Using NMR spectroscopy together with patternrecognition analysis, it is possible to derive metabolic profilesor metabotype of the biofluid, tissue, extract, or intact tissuefrom a genetically modified animal (60). Using this approach,themdxmouse, a model of Duchenne muscular dystrophy has

Figure 11 The PC scores plot derived from the 1H NMR spectra ofurine samples obtained from Alpk:ApfCD and C57BL10J mice.

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been investigated (61,62). Samples of cardiac and brain tissuefrom the mdx mouse were shown to have distinct metabolicprofiles compared with tissue from control mice. By calcula-tion of the metabolite ratios in the two tissue types, theseparation between mdx and control mice observed by PCAwas attributed to elevation of the taurine levels relative toboth creatine and phosphocholine. Taurine has previouslybeen reported as a biomarker for dystrophic tissue in skeletalmuscle (63,64) and is thought to be an adaptive response to aloss of dystrophin (61). Duchenne muscular dystrophy hasalso been investigated in intact tissue using 1D and 2D highresolution magic-angle-spinning (MAS) NMR coupled withPR (63). Changes in the intensity and shape of lipid reso-nances together with increases in lactate and threonine wereobserved. The variation in lipid composition in mdx skeletalmuscle has previously been identified in vivo and in intactbiopsy samples (65,66).

Figure 12 The 500MHz 1H NMR spectra of typical urine samplesobtained from (a) Alpk:ApfCD mouse and (b) C57BL10J mouse.Key: 2OG: 2-oxeglutrate, TNA trimethylamine-N-oxide.


2.7. Hormonal Effects

Female rats tend to be less widely used for toxicity studiespartly because of variations in hormonal status related tothe estrus cycle. Female mice housed together will stopcycling if no males are present but will resume their estruscycle on exposure to male mouse pheromones (67). This effectis known as the Whitten effect and can be used to synchronizefemale estrus cycles or for timing mating (67). The Whitteneffect is less marked in female rats than in mice. Grouphoused female mice tend to stop cycling and display eitherpseudopregnancy (Lee-Boot effect) or anestrus (67).

Sex hormones directly influence morphological and func-tional aspects of the kidney proximal tubule (68), for instancein the rat, pregnancy is associated with an increased glucosefiltration rate and a decreased urine flow rate (69). However,hormonal effects on urinary composition are subtle comparedwith differences observed due to inter-animal or straindifferences (2). Hormones are also known to effect the compo-sition of plasma, for instance serum levels of calcium are inde-pendent of food consumption and are instead regulated by thehormonal glands (75).

Estrus cycle-related perturbations in metabolic urinaryprofiles have been investigated using metabonomic technol-ogy. A control population of female SD rats was sampled twicedaily (am and pm) for 10 days (2). The stage of the estrus cycleat each urine collection time point was determined by vaginalcytology. The rats progressed through at least one completeestrus cycle every 3–4 days. This cycle comprises of four dis-tinct stages of, namely: proestrus, estrus, metestrus and dies-trus. The partial separation of the different stages of the cycleobserved by PCA of these data (Fig. 13) was attributed mainlyto the levels of citrate, trimethylamine-N-oxide, creatine,creatinine, taurine, glucose, and N-acetyl glycoprotein reso-nances at each of the four stages (Fig. 14). Sex-related differ-ences in the elimination of citrate may be linked to estrogenlevels (9). Menstruation is known to effect the N-oxidationof trimethylamine in females resulting in a fall in the urinarytrimethylamine-N or trimethylamine ratio (70). In addition,

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steroid hormones have previously been shown to influenceflavin-containing monogenase activities in rodents and man(72). Food intake in the mature female is lower during estrusthan during diestrus, sometimes by as much as 6 g=day,

Figure 13 The PC1 vs. PC2 scores plots of mean-centered datafrom NMR spectra of urine from a rat sampled during the light per-iod of the day, for 10 days, over two estrus cycles. (a) A locus of datapoints with time. (b) Separation of each stage of the estrus cycle.�¼ estrus, `¼diestrus, &¼metestrus.


Figure 14 The 600MHz 1H NMR spectra (d4.5–0.5) of urine sam-ples collected from a female rat during three stages of the estruscycle (a) diestrus, (b) estrus, and (c) metestrus. Key: DMA, dimethy-lamine; DMG, dimethylglycine; 2-OG, 2-oxoglutarate; TMAO, tri-methylamine-N-oxide.

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which may explain the differences in glucose levels betweenthe different stages of the estrus cycle (72).

2.8. Diurnal Effects

Rats are generally nocturnal, feeding, drinking, and matingat night. Therefore, the difference in their activity levels fallsinto a diurnal pattern and this is reflected in their urinaryprofiles both in terms of biochemical composition and thevolume of urine excreted. This effect needs to be consideredwhere split collections are taken in the laboratory for metabo-nomic analysis or clinical chemistry measurements, forinstance, 0–8 and 8–24hr postdose, however, is negated ifsamples are collected continually over 24hr.

Time-dependent variations in pharmacological activitieshave previously been reported. For instance, a time-depen-dent variation in the diuretic effect of furosemide accordingto light–dark cycle and time of food intake has been observed(73). Diurnal effects on the metabolic composition of controlurine have been investigated in the SD rat using a metabo-nomic approach. Light and dark cycle samples could be sepa-rated using a simple PC model (Fig. 15). Urine samplescollected during the day were found to have lower levels oftaurine, hippuric acid, and creatinine together with elevatedlevels of glucose, succinate, dimethylglycine, glycine, crea-tine, and betaine (Fig. 16). The effects of diurnal variationon control rat urinary profiles using 1H NMR spectroscopyand statistical pattern recognition have also been documentedin the HW rat (74). Diurnal changes related to hormonalaction may also affect serum endogenous metabolite levelsin the rat. For instance, plasma concentrations of phosphateand calcium are inversely related with calcium levels beinggreater during the light-cycle (75).

Gavaghan et al. (3) have recently investigated the diur-nal variation in metabolism between two different phenotypesof mice, AlpK:ApfCD (white) and C57BL107 (black) mice.Samples collected am contained higher levels of creatine, hip-puric acid, trimethylamine, succinate, citrate, and 2-oxogluta-rate, and lower levels of trimethylamine-N-oxide, taurine,


spermine, and 3-hydroxy-iso-valerate relative to pm samples.The increase in urinary excretion of the TCA cycle intermedi-ates in pm urine samples may reflect the increased metabolicactivity of mice during the night due to their nocturnal habits.

2.9. Water Deprivation

The effects of restricting the water supply to male rats havepreviously been studied by Clausing and Gottschalk (76).Rats were either given water ad libitum or 10ml restrictedwater supply. Water restricted animals had decreased urinevolume, food consumption, body weight development andorgan weights, together with impaired renal function. There-fore, reduced water intake may be a confounding factor in stu-dies into the effects of nephrotoxins on rats. In recent studies,where SD rats were deprived of water for 48hr, separation of

Figure 15 The PC3 vs. PC5 scores plot of mean-centered datafrom NMR spectra of female rat urine sampled during severalestrus cycles, illustrating the separation of day from night-timeurine samples. &¼night, `¼day.

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48hr urine samples from predose and controls was observedfrom PCA of urinary NMR data (unpublished data). Thiswas attributed to elevated levels of creatinine and depletedlevels of taurine, 2-oxoglutarate, succinate, citrate, and hip-puric acid. The decrease in the levels of 2-oxoglutarate,citrate, and succinate may be due to the inhibition of mito-chondrial respiration. Alterations were also observed fromclinical chemistry measurements, including increased urin-ary albumin, sodium, osmolality, and glucose, and decreasesin potassium and urine volume.

2.10. Fasting

Moderate food or caloric restriction has historically beenlinked to increased longevity and decreased disease incidence(77). In a study carried out by Levin et al. (77), whereby ratswere fed ad libitum or given 75%, 50%, or 25% of the amountof feed consumed by controls, the severely restricted group

Figure 16 The 600MHz 1H NMR spectra (d4.5–0.5) of urine sam-ples collected from a female rat during (a) the night and (b) the dayKey: DMG, dimethylglycine; 2-OG, 2-oxoglutarate; TMAO, tri-methylamine-N-oxide.


developed bone marrow necrosis, thymic atrophy, and mildtesticular degeneration. In the mildly and moderatelyrestricted groups changes were considered adaptive and non-detrimental. Food restriction during the first few years of lifein the rat decreases proteinuria, increases para-aminohippu-ric acid transport and reduces the incidence and severity ofrenal lesions, therefore delaying age-related detrimentalchanges in kidney function (78,79). In a recent study, groupsof SD male rats were food restricted to 25% and 50% of thead libitum fed control group, for 2weeks or were 100%restricted for 1 day only (unpublished data). All restricted ani-mals showed depleted levels of urinary TCA cycle intermedi-ates and relatively small increases in creatine, due to musclebreakdown, along with decreases in creatinine and taurine.In the 100% restricted group, a decrease in trans-aconitateconcomitant with an increase in phenylacetylglycine wasobserved in the 0–48hr urine samples, with animals recover-ing by 72hr. The 100% food restricted group of animalsappeared to recover fully after 72hr. In work carried out byRikimaru et al. (80), the excretion of creatinine in the urineof rats, per unit of skeletal muscle mass, was promoted by fooddeprivation. Previous work has been carried out to identifyserum profiles that reflect changes in food intake in both maleand female rats where animals received food ad libitum orwere food restricted by 35% (81). Both hierarchical clusteranalysis (HCA) and PCA distinguished the dietary groups oforigin for male rats to greater than 85% accuracy using 56known metabolites and females to 94% and 100% accuracy,respectively, for 63 identified metabolites.

2.11. Dietary and Gut Microfloral Influences

In animals, volatile fatty acids produced from microbialfermentation in the gut contribute directly to metabolism.In the ruminant and rat, most of the propionic acid inintermediary metabolism originates from microbial fermen-tation in the gut (82). Phipps et al. (12,13) have shown thatchanges to a rat’s diet can alter the ratio of hippuric acidand chlorogenic acid metabolites (such as meta-(hydroxyphe-

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nyl)-propionic acid (m-HPPA) and 3-hydroxy-cinnamica (3-HCA)), excreted in the urine (13,83). These changes wereattributed to variations in the composition of the diet andredistribution of gut microflora responsible for the metabo-lism of plant phenolics and aromatic amino acids. Further sta-tistical analysis of a large homologous population of rats fedon a standard rat diet where all batches were within the man-ufacturers specification, identified two subpopulations of ratin the first two PCs (Fig. 17). These two subpopulations wereattributable to the presence of either chlorogenic acid metabo-lites or hippuric acid (Fig. 18). In previous studies, a steadyincrease in chlorogenic acid metabolite excretion is accompa-nied by a concomitant decrease in hippuric acid excretion andvice versa, reiterating the possibility of a redistribution of thegut microflora (13,83).

In the work carried out by Nicholls et al. (84), the urinarymetabolite profiles of germ free rats were compared beforeand after exposure to a ‘normal’ environment (Fig. 19). Anumber of changes were observed over the first 17 daysincluding an increase in glucose, decrease in the TCA cycleintermediates and increases in trimethylamine-N-O, hippuricacid, phenylacetylglycine, and m-HPPA. At 21 days, the urin-ary profiles resembled that of control animals. The metabolicchanges observed are indicative of the colonization and redis-tribution of gut microflora and the varying health of the ani-mal. In earlier work carried out by Goodwin et al. (85),increases in the urinary excretion of benzoic acid, phenylace-tic acid, and m-HPPA and p-HPPA were observed after fecalinoculation of germ free rats. The gut microbial compositionhas a significant effect on the urinary metabolite profile andhence should be taken into consideration when interpretingthe effects of orally dosed drugs. Some of the changesobserved in the urine after drug treatment may be a resultof metabolism by organisms in the gastrointestinal tract.

2.12. Temperature Effects

Exposure to cold triggers a number of mechanisms, whichreduce heat loss, such as vasoconstriction of blood vessels


under the skin. To increase heat production, output from thethyroid gland is increased, which in turn speeds up metabo-lism. Mice are known to be more sensitive to temperaturechanges than rats. The effect of a drop in temperature onendogenous metabolism was inadvertently observed in asmall number of mice. These animals were found to have ele-vated levels of several endogenous metabolites including glu-cose and some amino and organic acids (Fig. 20). Increasingcarbohydrate and fat metabolism has previously beenobserved by Panin et al. (86) on exposure of HW rats to cold(þ5�). Heat stress can be caused by hot-boxing of rats at 40�C,prior to blood sampling from the tail vein and has severalmetabolic consequences including elevation of blood lactate,with concomitant decreases in plasma triglyceride andglucose (87).

Figure 17 Principal components scores plot (PC1 vs. PC2) ofmean-centered data derived from 1H NMR spectra of control wholerat urine showing two subpopulations of urine samples.

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Figure 18 The 600MHz 1H NMR spectra of whole rat urine repre-sentative of (a) samples from subpopulation B containing high levels ofhippuric acid and (b) samples from subpopulation A containing highlevels of 3-HPPA and other plant phenolic metabolites.


Figure 19 Stack plot of 500MHz 1H NMR spectra (9.5–5.5) ofwhole rat urine at selected time points after removal of a germ freeenvironment.

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Figure 20 1H NMR spectra of urine from a control mouse suffer-ing from hypothermia.


2.13. Sleep Deprivation

The effects of sleep deprivation have been well documented inthe rat (88,89). In rats subjected to total sleep deprivation, allanimals died within 11–21days (88). No anatomical cause ofdeath was determined although animals showed signs oflesions to the tail and paws and weight loss despite increasedfood consumption. The weight loss was attributed to increasedenergy expenditure. In later studies carried out by Rechtschaf-fen et al. (89), total sleep deprivation caused similar symptomstogether with decreased body temperature and perturbationsin the plasma hormones, norepinephrine, and thyroxine.Changes suggested that sleep may be necessary for thermore-gulation. Studies into the effect of sleep deprivation and recov-ery sleep on plasma corticosterone in the rat concluded thatstress is not a major contributor to this condition (90).

2.14. Stress and Acclimatization

Stress induces both biochemical and physiological responsesin laboratory animals. Physiological stress occurs within nor-mal physiological limits whereas overstress or distress mayoccur which is detrimental to biological processes andrequires some form of biochemical and biological response.All animals respond in some way to the presence of humansand within any study stress effects can be caused by restraint,temperature, noise, food deprivation, and procedural inducedfear or distress, for example, when dosing. Rapid removal oflarge volumes of blood can lead to hypovolemic shock, hencethe tight guidelines over the number of times blood can besampled over the course of a study.

Acclimatization is the term given to describe the ability ofa particular organism to adapt to a particular environmentalstress, by prolonged exposure to that stress, without enhance-ment by genetic modification (91). Within toxicology studies,acclimatization generally refers to the adaptation of each ani-mal to the novel environment of ametabolism cage, where theyare housed individually. As rats are generally sociable ani-mals, solitary confinement and environmental constrictions(such as no bedding to avoid contamination of urine samples)

430 Bollard et al.

may cause stress effects in these animals (67). In a study car-ried out by Stanley et al. (23), male HW rats (n¼ 18) weredivided into two groups. One group (n¼ 10) experienced accli-matization to metabolism cages for three separate 7-hr periodsprior to continual housing for 72hr, whereas the remaininggroup (n¼ 8) experienced no acclimatization period. The ani-mals experienced regular light–dark cycles of 12hr light fol-lowed by 12hr dark and urine samples were collected at 6hrintervals across the study. The 1H NMR urinalysis coupledwith PR showed that 50% of the rats in the unacclimatizedgroup showed elevation of urinary glucose at 54 and 60hr com-pared with the normal control rats (Fig. 21). Glucose levelsreturned to within the acclimatized control range by 72hr, asshown by the mean scores metabolic trajectories from PCA ofthese data (Fig. 22). The unacclimatized rats also showed a sig-

Figure 21 The 600MHz single-pulse 1H NMR spectra (d4.5–1.5)of urine samples collected at 54hr from (A) acclimatised maleHan-Wistar rats and (B) unacclimatized male Han-Wistar rats.Key: 2-OG, 2-oxoglutarate; CAMS, chlorogenic acid metabolites;TMAO, trimethylamine-N-oxide; DMG, dimethylglycine; NAC,N-acetyl glycoproteins.


nificant increase in water consumption compared with theacclimatized animals at 54hr (Dunnett’s test, p< 0.05). Pre-vious investigations have shown that rodents exposed to envir-onmental constraint often exhibit polydipsia and glycosuria asa precursor to the development of diabetes (92).

3. PHYSIOLOGICAL VARIATION IN HUMANS

Metabonomic studies involving human subjects pose morecomplex problems due to the greater influence of intrinsic fac-tors including genetics, ageing, gender, and menopausal sta-tus. Extrinsic factors are also more numerous in humansubjects compared with laboratory animals and include awider range of dietary variation, socio-economic status, artifi-cial hormones, smoking, stress, exercise, and fitness levels. Aparticular problem we have found with human studies isthe high degree of non-compliance including alcohol consum-ption and taking of over-the-counter medication such asparacetamol, ibuprofen, or aspirin. All these substances have

Figure 22 Comparison of the mean metabolic PC1=PC2 trajec-tories derived from 1H NMR spectra of urine samples collected fromacclimatized and unacclimatized male rats.

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characteristic metabolite excretion patterns (93,94), whichoften mask resonances from the endogenous metabolites weare interested in. Nevertheless, the use of metabonomics inclinical investigations is becoming popular due to the rapid,multicomponent and relatively inexpensive analysis thatcan be performed on each sample. Much success has beenhad with diagnosis of the severity of atherosclerosis fromplasma metabolite profiles using metabonomic technology(95). In the proceeding sections, we will introduce some ofthe physiological factors that we consider when performinghuman clinical investigations.

3.1. Inter-Subject Variation

In general, the composition of human urine in healthy indivi-duals may be considered as an expression of an individual’smetabolism. In a study carried out by Zuppi et al. (9), urinefrom 50 normal subjects were analyzed by 1H NMR spectro-scopy. From quantification of peak heights expressed asmmol=mol creatinine, the mean values calculated for a seriesof urine samples from the same individual showed low stan-dard deviations. In contrast, when the urines from all 50 indi-viduals were compared quantitatively, high standarddeviations were found as a result of inter-person variability.The greatest metabolite variability between subjects wasfound in the concentration of hippuric acid, as was the casefor intra-individual and inter-day variability. This is mostlikely due to differences in gut microflora between individualsand even within the same individual over time. Other urinarymetabolites, which varied between individuals, included acet-ate, citrate, lactate, and glycine (a precursor of hippuric acid).The concentrations of trimethylamine and trimethylamine-oxide in the urine are also known to vary between individualsas a result of dietary influences (see Sec. 3.5), differences ingut microflora (96), enzyme activity, and gender (10,97).

3.2. Gender

Sex hormones are known to have control over the morphologyof the kidney and hence to effect urinary metabolite profiles


(28,68), for instance in humans, glycosuria is common in preg-nant females (98) and an increase in citrate concentration infemale urine may be related to estrogen levels. In a study car-ried out by Hodgkinson (22) on 29 normal male and femalesubjects, women were found to excrete more citrate thanmen. However, it is possible that this may originate fromblood and epithelial cells, which have been found in femaleurine. In a recent study carried out in healthy male andfemale subjects where urine samples were collected everymorning over 2weeks, PCA of the spectral data showedseparation in PC1 (Fig. 23). This was attributed to elevatedlevels of citrate and glycine in female urine samples. Glycineis a precursor to hippuric acid, therefore, its variation in urinemay be related to the gut microflora. Male samples containedhigher creatinine levels, possibly related to the higher musclecontent of the male body.

3.3. Water Deprivation and Water Loading

Previous studies in human subjects have been carried out intothe effect of fluid deprivation, with urine sample collection

Figure 23 The PC1 vs. PC2 scores plot of NMR spectroscopicurinary data from healthy male and female subjects.

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occurring 2hr after fluid deprivation, 1 hr after fluid restora-tion and 1hr after fluid loading in each subject. Using theunsupervised learning method, non-linear mapping (NLM),it was possible to clearly distinguish between samples col-lected from water-deprived subjects and those collected dur-ing the water-restored state. In addition, samples collectedafter water loading could also be partially separated fromurines obtained after water-deprivation. Separation of theseclasses of data was attributed to alterations in the levels ofcitrate, hippuric acid, trimethylamine-N-oxide, and 3-D-hydroxybutyrate.

3.4. Fasting

In a study carried out by Bales et al. (99), urine sampleswere collected from healthy male subjects after an overnightfast and then at regular intervals during and after furtherfasting for a total of 48hr. From 1H NMR analysis of urinesamples, an increase in the excretion rates of acetylcarnitine,acetoacetate, 3-D-hydroxybutyrate, acetone, creatinine, andsarcosine was observed 24hr after the commencement offasting, together with depletion in urinary hippuric acid(Fig. 24). Within 2hr of food consumption, there was a dra-matic fall in the rate of excretion of acetoacetate, acetateand acetylcarnitine and several hours later only smallamounts of these metabolites were excreted. The rate ofexcretion of 3-d-hydroxybutyrate, however, did not returnto control levels until 10hr after fasting stopped. In earlierstudies carried out by Hoppel and Genuth (100,101), carni-tine, acetylcarnitine, and 3-d-hydroxybutyrate were detectedin both urine and plasma samples after fasting. Using 1HNMR spectroscopy, studies into the effect of fasting on plasmametabolite levels detected a depletion in the mobile pool offatty acids as well as a gradual decrease in glucose andincrease in the ketone bodies (102).

3.5. Diet

A diet rich in fish is known to result in the excretion of highconcentrations of trimethylamine and trimethylamine-


N-oxide in the urine (97). In a study of normal subjects givena meal of trimethylamine-N-oxide containing fish, trimethyla-mine-N-oxide appeared rapidly in the plasma and urine, sug-gesting that trimethylamine-N-oxide is efficiently cleared bythe healthy kidney (18). Consumption of a fish meal will cause

Figure 24 The 400MHz 1H NMR spectra of urine samples col-lected after 12, 35 and 48hr of fasting from a healthy male subject.The rise in intensity of acetoacetate, 3-D-hydroxybutyrate, acetone,and acetylcarnitine can be observed.

436 Bollard et al.

such samples to become outliers when performing multivari-ate data analysis methods (unpublished data) due to the highintensity of the trimethylamine-N-oxide resonance (Fig. 25).This can be overcome by removing the region correspondingto trimethylamine-N-oxide or exclusion of the sample fromthe model. Other sources of trimethylamine and trimethyla-mine-N-oxide in the body may be the degradation andrecycling of biliary phospholipids as well as bacterial metabo-lism (97,103).

Diets rich in carbohydrates, such as an Italian diet,cause an increase in excretion of citrate, lactate, alanine,and glycine (104). Work carried out by Zuppi et al. (104) intothe influence of diet on urinary endogenous metabolite pro-files used a group of 25 normals from Rome and 25 normals

Figure 25 The PC1 vs. PC2 scores plot of NMR spectroscopicurinary data from healthy male and female where one subject isan outlier due to consuming a fish meal.


from Svaldbard (Norway). The subjects were studied duringthe same seasonal period to negate the effect of differentlight–dark cycles. Those subjects from Rome were submittedto a high carbohydrate diet whilst the Svaldbard group atea diet higher in lipids but lower in carbohydrates andincluded a large amount of preserved food. The lower concen-tration of alanine, lactate, and citrate in the subjects fromSvaldbard may be related to the lower carbohydrate diet inthis group. In addition, these individuals also had higherlevels of hippuric acid and lower levels of glycine in the urine.This was postulated to be due to the high levels of benzoicacid, a precursor of hippuric acid, found in preserved food.

In a study carried out in healthy male and female sub-jects, where morning urine samples were collected each dayover two weeks, from PCA of the NMR spectroscopic data,separation of meat eaters from vegetarians was observed inPC1 (Fig. 26). This was attributed to increased levels of hip-puric acid, lactate, and citrate in vegetarians and elevatedcreatine in meat eaters (Fig. 27). The dietary contribution tocreatinine excretion in human urine has been estimated tobe approximately 240mg=day (103) and is known to increasewith increased meat consumption (105).

3.6. Exercise

Holmes et al. (10), have previously carried out studies into dif-ferent forms of mild physiological stress. Urine samples werecollected from subjects 1 hr after exercise or from at an equva-lent time subjects. Spectra of urine samples collected afterexercise showed a higher concentration of lactate than thosefrom controls most likely as a result of anaerobic respirationin these individuals. Zuppi et al. (9), have also observed ele-vated lactate in human urine samples after exercise.

3.7. Stress

Stress describes the way in which the body copes with variousstressors from a wide range of sources including physio-logical=oxidative stress causing real tissue and=or cellular

438 Bollard et al.

damage such as that observed due to ageing, exercise, dis-ease, and pain (106). Psychological stress influences includefear, distress, and sleep deprivation. Acute stress such as thatobserved with factors such as public speaking (107) are shortlived and the body should recover fully but if this stressbecomes sustained or chronic this can have detrimentaleffects on the psychological and physiological well-being ofthe subject. This can result in depression, hypertension, car-diovascular disease, gastrointestinal problems irritablebowel, dyslipidemia, insulin resistance, and diabetes.

All stressors induce the ‘‘fight or flight’’ response by trig-gering release of catecholamines such as adrenaline from theadrenal medulla and stimulation of glucocorticoid (cortisol)hormone production. The latter also exhibit circadian rhythm

Figure 26 The PC1 vs. PC2 scores plot of NMR spectroscopicurinary data from healthy male and females separated accordingto diet.


and are at the maximum at the start of light=wakefulness.This leads to rapid mobilization of glycogen and triacyglycer-ols from stores, increased oxygen delivery to the brain, heart,and skeletal muscle as well as increased metabolic rate. Thebiochemical consequences of stress are the stimulation of glu-coneogenesis leading to increased blood glucose and decreasedamino acids and effects on lactate and pyruvate metabolism.Increases in the production of ketone bodies such as 3-d-hydro-xybutyrate and acetone reflect breakdown of glycerol fromfats. Many of these metabolites can be routinely detected byNMR of biofluids making metabonomics an ideal platformfor monitoring stress responses especially those associatedwith chronic stress which forms part of the etiology of manydiseases. Work is currently being carried out in the metabo-nomic field to develop a further understanding of the biochem-ical mechanism of chronic stress in animal models and invarious disease=illness states in man.

Figure 27 The 600MHz 1H NMR spectra (d0.4–4.6) of urine froma meat-eating and vegetarian female subject.

440 Bollard et al.

REFERENCES

1. Holmes E, Nicholls AW, Lindon JC, Ramos S, Spraul M,Neidig P, Connor SC, Connelly J, Nicholson JK. Developmentof a model for classification toxic-induced lesions using 1HNMR spectroscopy of urine combined with pattern recogno-tion. NMR Biomed 1998; 11:235–244.

2. Bollard ME, Holmes E, Lindon JC, Mitchell SC, BranstetterD, Zhang W, Nicholson JK. Investigations into biochemicalchanges due to diurnal variation and estrus cycle in femalerats using high resolution 1H NMR spectroscopy of urineand pattern recognition. Analy Biochem 2001; 295:194–202.

3. Gavaghan CL, Wilson ID, Nicholson JK. Physiological varia-tion in metabolic phenotyping and functional genomic stu-dies: use of orthogonal signal correction and PLSDA. FEBSLett 2002; 530:191–196.

4. Gavaghan CL, Holmes E, Lenz E, Wilson ID, Nicholson JK.An NMR-based metabonomic approach to investigate the bio-chemical consequences of genetic strain differences: applica-tion to the C57BL10J and Alpk:ApfCD mouse. FEBS Lett2000; 484(3):169–174.

5. Tate AR, Damment AJP, Lindon J. Investigation of the meta-bolic variation in control urine using 1H NMR spectrocopy.Anal Biochem 2001; 291:17–260.

6. Holmes E, Antti H. Chemometric contributions to the evolu-tion of metabonomics: mathematical solutions to characteris-ing and interpreting complex biological NMR spectra. Analyst2002; 127(12):1549–1557.

7. Vomel T, Platt D. Age-dependent phagocytosis of engthro-cytes by the isolated perfuse rat liver after golactos hepatitisalpha naphthyl isothiacyanate cholestasis. Arch GerontolGerontol 1986; 5:351–359.

8. Beckwith Hall BM. NMR Spectroscopic Studies on Hepato-toxic Processes. Ph.D. thesis, 1998.

9. Bollard ME, Keun H, Ebbels T, Beckonert O, Antti H, LindonJC, Holmes E, Nicholson JK. Comparative metabonomics ofdifferential species toxicity of hydrazine in the rat and mouse.Toxicol Appl Pharmacol accepted 2004.


10. Zuppi C, Messana I, Forni F, Rossi C, Pennacchietti L,Ferrari F, Giardina B. 1H NMR spectra of normal urines,reference ranges of the major metabolic. Clinica ChimicaAct 1997; 265:85–97.

11. Holmes E, Foxall PJ, Nicholson JK, Neild GH, Brown SM,Beddell CR, Sweatman BC, Rahr E, Lindon JC, Spraul M.Automatic data reduction and pattern recognition methodsfor analysis of 1H nuclear magnetic resonance spectra ofhuman urine from normal and pathological states. Anal Bio-chem 1994; 220(2):284–296.

12. Temellini A, Mogavero S, Giulianotti PC, Pietrabissa A.Mosca F, Pacifici GM. Conjugation of benzoic acid with gly-cine in human liver and kidney: a study on individual varabil-ity. Xenobiotica 1993; 23:1427–1433.

13. Phipps AN, Wright B, Stewart J, Wilson ID. Use of protonNMR for determining changes in metabolic excretion profilesinduced by dietry changes in the rat. Pharm Sci 1997; 3:143–146.

14. Phipps AN, Stewart J, Wright B, Wilson ID. Effect of diet onthe urinary of hippuinc acid and other dietry derived aro-matics in rats. A complex interaction between diet, gut micro-flor substate specificite. Xenobiotica 1998; 28:527–537.

15. Guneral F, Bachmann C. Age-related reference values fororganic acids in a healthy Turkish pediatric population. ClinChem 1994; 40(6):862–868.

16. Nordmann J, Nordmann R. Organic acid in bio+urine AdvClin Chem 1961; 4:53–120.

17. Gibson GG, Skett P. Introduction to Drug Metabolism.London, UK: Chapman and Hall, 1986.

18. Moser VC, Padilla S. Age- and gender-related differences inthe time course of behavioral and biochemical effectsproduced by oral chlorpyrifos in rats. Toxicol Appl Pharmacol1998; 149(1):107–119.

19. Bell JD, Lee JA, Lee HA, Sadler PJ, Wilkie DR, WoodhamRH. Nuclear magnetic resonance studies of blood plasmaand urine from subjects with chronic renal failure: identifica-

442 Bollard et al.

tion of trimethylamine-N-oxide. Biochim Biophys Acta 1991;1096(2):101–107.

20. Robertson DG, Reily MD, Lindon JC, Holmes E, NicholsonJK. Metabonomic Technology as a tool for rapid throughputin vivo toxicity screening. Comprehensive Toxicol 2002;14:583–610.

21. Fujita S, Chiba M, Ohta M, Kitani K, Suzuki T. Alteration ofplasma sex hormone levels associated with old age and itseffect on hepatic drug metabolism in rats. J Pharmacol ExpTher 1990; 253(1):369–374.

22. Carlson SE, Mitchell AD, Carter ML, Goldfarb S. Evidencethat physiological levels of circulating estrogens and neonatalsex-imprinting modify postpubertal hepatic microsomal3-hydroxy-3-methylglutaryl coenzyme A reductase activity.Biochim Biophys Acta 1980; 633:154–161.

23. Hodgkinson A. Citric acid excrction in normal adults and inpatients with renal calculus Clin Sci 1962; 23:203–212.

24. Stanley EG. 1H NMR Spectroscopic and Chemometric Stu-dies on Endogenous Physiological Variation in Rats, Ph.D.thesis, 2002.

25. Bonate PL. Gender-related differences in zenobiotic metabo-lism. J Clin Pharmacol 1991; 31:684–690.

26. Morishima HO, Abe Y, Matsuo M, Akiba K, Masaoka T,Cooper TB. Cierder-related differences in cocaire toxicity inthe rat. J Lab Clin Med. 1993; 122(2):157–163.

27. Nicholson JK, Higham DP, Timbrell JA, Sadler PJ. Quantita-tive high resolution 1H NMR urinalysis studies on the bio-chemical effects of cadmium in the rat. Mol Pharmacol 1989Sep; 36(3):398–404.

28. Kattermann R, Sirowej H. Liver injury and lipid metabolsim:sex differences in the fatty liver induced by D-galactosamine.Acta Hepatogastroenterol(Stuttg) 1979 ; 26:112–121.

29. Shiraga T, Iwasaki K, Takeshita K, Matsuda H, Niwa T,Tozuka Z, Hata T, Guengerich FP. Species- and gender-related differences in amine, alcohol and phenol sulphoconju-gations. Xenobiotica 1995; 25(10):1063–1071.


30. Mendelsohn ME. Protective effects of estrogen on the cardio-vascular system. Am J Cardiol 2002; 89:12E–18E.

31. Joossen JV. Mechanisms of hypercholesterolemia and athero-sclerosis. Acta Cardiol suppl 1988; 29:63–83.

32. Sakemi T, Ohtsuka N, Tomiyoshi Y, Moritio F. Sex-differences in progression of adriamycin-induced nephropa-thy in rats. Am J Nephrol 1996; 16:540–547.

33. Emmelot P, Bos CJ. Studies on plasma membranes XV, a sexdifference in alkaline phosphatase activities of plasma mem-branes isolated from rat liver. Biochim Biophys Acta 1971;249:293–300.

34. Kato R, Yamazoe Y. Sex-specific cytochrome P450 as a causeof sex and species related differences in drug toxicity. ToxicolLett. 1992; 64–65 Spec No:661–665.

35. Czerniak R. Gender-based differences in pharmacokinetics inlaboratory animal models. Int J Toxicol 2001; 20(3):161–163.

36. Shapiro BH, Agrawal AK, Pampori NA. Gender differences indrug metabolism regulated by growth hormone. Int J Bio-chem Cell Biol 1995; 27(1):9–20.

37. Jones AR. Some observations on the urinary excretion of gly-cine conjugates by laboratory animals. Xenobiotica 1982;12:387–395.

38. Shockcor JP, Unger SE, Wilson ID, Foxall PJD, NicholsonJK, Lindon JC. Combined HPLC, NMR spectroscopy, andion-trap mass spectrometry with application to the detectionand characterization of xenobiotic and endogenous metabo-lites in human urine. Anal Chem 1996; 68:4431–4435.

39. Nicholls AW, Nicholson JK, Haselden JN, Waterfield CJ. Ametabonomic approach to the investigation of druginducedphospholipidosis: an NMR spectroscopy and pattern recogni-tion study. Biomarkers 2000; 5(6):410–423.

40. Griffin JL, Walker LA, Garrod S, Holmes E, Shore RF,Nicholson JK. NMR spectroscopy based metabonomic studieson the comparative biochemistry of the kidney and urine ofthe bank vole (Clethrionomys glareolus), wood mouse (Apode-mus sylvaticus), white toothed shrew (Crocidura suaveolens)

444 Bollard et al.

and the laboratory rat. Comp Biochem Physiol B BiochemMol Biol 2000; 127(3):357–367.

41. Holmes E, Bonner FW, Nicholson JK. Comparative biochem-ical effects of low doses of mercury II chloride in the F344 ratthe Multimammate mouse (Mastomys natalensis). Comp Bio-chem Physiol 1996; 114(1):7–15.

42. Holmes E, Bonner FW, Nicholson JK. 1H NMR spectroscopicand histopathological studies on propyleneimine-inducedrenal papillary necrosis in the rat and the multimammatedesert mouse (Mastomys natalensis). Comp Biochem PhysiolC Pharmacol Toxicol Endocrinol 1997; 116(2):125–134.

43. Weinberg JM, Harding PG, Humes HD. Mitochondrial bioe-nergetics during the initiation of mercuric chloride inducedrenal injury. J Biol Chem 1982; 257:60–67.

44. Holmes E, Bonner FW, Nicholson JK. Comparative studies onthe nephrotoxicity of 2-bromoethanamine hydrobromide inthe Fischer 344 rat and the multimammate desert mouse(Mastomys natalensis). Arch Toxicol 1995; 70(2):89–95.

45. Carlton WW, Engelhardt JA. Experimental renal papilliarynecrosis in the Syrian Hamster. Food Chem Toxicol 1989;27(5):331–340.

46. Kinne RKH, Boese SH, Kinne-Saffran E, Ruhfus B, Tinel H,Wehner F. Osmoregulation in the renal papilla: membranes,messengers and molecules. Kidney Int 1996; 49:1686–1689.

47. Costa C, DeAntoni A, Baccichetti F, Vanzan S, Appodia M,Allegri G. Strain differences in the tryptophan metaboliteexcretion and enzyme activities along the kynurenine path-way in rats. Ital J Biochem 1982; 31(6):412–418.

48. Costa C, DeAntoni A, Baccichetti F, Biasiolo M, Allegri G.Metabolites and enzyme activities involved in tryptophanmetabolism in two strains of mouse. Ital J Biochem 1984;33(5):319–324.

49. Ahmadizadeh M, Echt R, Kuo CH, Hook JB. Sex and straindifferences in mouse kidney: Bowman’s capsule morphologyand susceptibility to chloroform. Toxicol Lett 1984; 20(2):161–171.


50. Gartland KP, Bonner FW, Nicholson JK. Investigations intothe biochemical effects of region-specific nephrotoxins. MolPharmacol 1989; 35(2):242–250.

51. Gartland KP, Bonner FW, Timbrell JA, Nicholson JK. Bio-chemical characterisation of para-aminophenol-inducednephrotoxic lesions in the F344 rat. Arch Toxicol 1989;63(2):97–106.

52. Gartland KP, Eason CT, Bonner FW, Nicholson JK. Effects ofbiliary cannulation and buthionine sulphoximine pretreat-ment on the nephrotoxicity of para-aminophenol in theFischer 344 rat. Arch Toxicol 1990; 64(1):14–25.

53. Elfarra AA, Jakobson I, Anders MW. Mechanism of S-(1,2-dichlorovinyl)glutathione-induced nephrotoxicity. BiochemPharmacol 1986; 35(2):283–288.

54. Newton JF, Yoshimoto M, Bernstein J, Rush GF, Hook JB.Acetaminophen nephrotoxicity in the rat.II. Strain differ-ences in nephrotoxicity and metabolism of p-aminophenol, ametabolite of acetaminophen. Toxicol Appl Pharmacol 1983;69(2):307–318.

55. Holmes E, Nicholls AW, Lindon JC, Connor SC, Connelly JC,Haselden JN, Damment SJ, Spraul M, Neidig P, NicholsonJK. Chemometric models for toxicity classification based onNMR spectra of biofluids. Chem Res Toxicol 2000;13(6):471–478.

56. Holmes E, Nicholson JK, Tranter G. Metabonomic character-isation of genetic variation in toxicological and metabolicresponses using probabilistic neural networks. Chem ResTox 2001; 24:182–191.

57. Holmes E, Bonner FW, Sweatmen BC, Lindon JC,Beddell CR, Rahr E, Nicholson JK. NMR spectroscopy andpattern recognition analysis of the biochemical processesassociated urine with the progression of and recovery fromnephotoxic lesions in the rat induced by mercury(11) chlordoand 2-bromoetharamine. Mol Pharmacol 1992; 42(5):922–930.

58. Holmes E, Nicholls AW, Lindon JC, Connor SC, Connelly JC,Haselden JN, Damment SJP, Spraul M, Neidig P, Nicholson,

446 Bollard et al.

JK. Chemometric models for toxicity classificationn based onNMR spectra of biopl. Chem Res Toxicol 2000; 13:471–478.

59. Bundy JG, Spurgeon DJ, Svendsen C, Hankard PK, OsbornD, Lindon JC, Nicholson JK. Earthworm species of the genusEisenia can be phenotypically differentiated by metabolicprofiling. FEBS Lett 2002; 521(1–3):115–120.

60. Griffin JL. Metabolic profiles to define the genome: Can wehear the silent phenotypes. Philos Trans R coc Lond BiolSci 2004; 359(1446):857–871.

61. Griffin JL, Williams HJ, Sang E, Clarke K, Rae C, NicholsonJK. Metabolic profiling of genetic disorders: a multitissue 1Hnuclear magnetic resonance spectroscopic and pattern recog-nition study into dystrophic tissue. Anal Biochem 2001;293(1):16–21.

62. Griffin JL, Williams HJ, Sang E, Nicholson JK. Abnormallipid profile of dystrophic cardiac tissue as demonstrated byone- and two-dimensional magic-angle spinning (1) H NMRspectroscopy. Magn Reson Med 2001; 46(2):249–255.

63. McIntosh LM, Garrett KL, Megeney L, Rudnicki MA,Anderson JE. Regeneration and myogenic cell proliferationcorrelate with taurine levels in dystrophin- and MyoD-defi-cient muscles. Anat Rec 1998; 252(2):311–324.

64. McIntosh L, Granberg KE, Briere KM, Anderson JE. Nuclearmagnetic resonance spectroscopy study of muscle growth,mdx dystrophy and glucocorticoid treatments: correlationwith repair. NMR Biomed 1998; 11(1):1–10.

65. Gillet B, Doan BT, Verre-Serrie C, Barbere B, Berenger G,Morin S, Koenig J, Peres M, Sebille A, Beloeil JC. In vivo2D 1H NMR of mdx mouse muscle and myoblast cells duringfusion: evidence for a characteristic signal of long chain fattyacids. Neuromuscul Disord 1993; 3(5–6):433–438.

66. Singer S, Sivaraja M, Souza K, Millis K, Corson JM. 1H-NMRdetectable fatty acyl chain unsaturation in excised leiomyo-sarcoma correlate with grade and mitotic activity. J ClinInvest 1996; 98(2):244–250.

67. Wolfensohn S, LloydM.Handbook of Laboratory AnimalMan-agement and Welfare. 2nd ed. Blackwell Sciences Ltd, 1998.


68. Schiebler TH, Danner KG. The effect of sex hormones on theproximal tubules in the rat kidney. Cell Tissue Res 1978 Sep26; 192(3):527–549.

69. Bishop JH, Green R. Effects of pregnancy on glucose handlingby rat kidneys. J Physiol 1980; 307:491–502.

70. Zhang AQ, Mitchell SC, Smith RL. Exacerbation of symptomsof fish odour syndrome during menstruction The Lancet.1996; 348:1740–1741.

71. Ayesh R, Mitchell SC, Smith RL. Dysfunctional N-oxidationof trimethylamine and the influence of testosterone treat-ment in man. Pharmacogenetics 1995; 5:244–246.

72. Baker HJ, Lindsey JR, Weisbroth SH. The Laboratory Rat:Vol. 1, Biology and Diseases. New York: Academic Press,1979.

73. Fujimura A, Ohashi K, Ebihara A. Chronopharmacologicalstudy of furosemide; (VIII) influence of feeding restriction.Life Sci 1991; 49(24):1829–1834.

74. Tate AR, Damment AJP, Lindon JC. Investigation of themetabolic variation in control rat urine using 1H NMR spec-troscopy. Anal Biochem 2001; 291:17–26.

75. Kishikawa T, Takahashi H, Shimazawa E, Ogata E. Diurnalchanges in calcium and phosphate metabolism in rats. HormMetab Res 1980; 12:545–551.

76. Clausing P, Gottschalk M. Effects of drinking water acidifica-tion, restriction of water supply and individual caging onparameters of toxicological studies in rats. Z Versuchstierkd1989; 32(3):129–134.

77. Levin S, Semler D, Ruben Z. Effects of two weeks of feedrestriction on some common toxicologic parameters inSprague–Dawley rats. Toxicol Pathol 1993; 21:1–14.

78. Tucker SM, Mason RL, Beauchene RE. Influence of diet andfeed restriction on kideny function of aging male rats. JGerontol 1976; 31:264–270.

79. Ricketts WG, Birchenall-Sparks MC, Hardwick JP,Richardson A. Effect of age and dietary restriction on protein

448 Bollard et al.

synthesis by isolated kidney cells. J Cell Physiol 1985;125:492–498.

80. Rikimaru T, Oozeki T, Ichikawa M, Ebisawa H, Fujita Y.Comparisons of urinary creatinine skeletal muscle massindices of muscle protein catabolism in rats fed ad libitumwith restricted food intake deprived of food. J Nutr Sci Vita-minol 1989; 35(3):190–209.

81. Honglian S, Vigneau-Callahan KE, Shestopalov AI, MilburyPE, Matson WR, Kristal BS. Characterisation of diet-dependent metabolic serotypes: primary validation of maleand female serotypes in independent cohorts of rats. J Nutr2002; 132:1039–1046.

82. Bain MD, Jones M, Borriello SP, Reed PJ, Tracey BM,Chalmers RA, Stacey TE. Contribution of gut bacterial meta-bolism to human metabolic disease. Lancet 1988;1(8594):1078–1079.

83. Gavaghan CL, Nicholson JK, Connor SC, Wilson ID, WrightB, Holmes E. Directly coupled high-performance liquid chro-matography and nuclear magnetic resonance spectroscopicwith chemometric studies on metabolic variation inSprague–Dawley rats. Anal Biochem 2001; 291(2):245–252.

84. Nicholls AW, Mortishire-Smith RJ, Nicholson JK. NMR spec-troscopic and metabonomic studies of urinary metabolitevariation in acclimatising germ free rats. Chem Res Toxicol.2003; 16(11):1395–1404.

85. Goodwin BL, Ruthven CRJ, Sandler M. Gut flora and the ori-gin of some urinary aromatic phenolic compounds. Biochem-ical Pharmacology 1994; 47:2294–2297.

86. Panin LE, Starova TIu, Tret’iakova TA, Kolpakov AR,Kolosova IE, Solov’ev VN. Changes in glycolysis and glyco-genolysis in tissues and level of certain hormones in bloodand urine from rats with varying lengths of cold adaptation.Vopr Med Khim 1995; 41(4):14–18.

87. Harrison MH. Effects of acute heat stress on tissue carbohy-drate in fasted rats. Aviat, Space and Environ Med 1976;47:165–167.


88. EversonCA, BergmannBM,RechtschaffenA. Sleep deprivationin the rat: III. Total sleep deprivation. Sleep 1989; 12(1):13–21.

89. Rechtschaffen A, Bergmann BM, Everson CA, Kushida CA,Gilliland MA. Sleep deprivation in the rat: X. Integrationand discussion of the findings. 1989 Sleep 2002; 25(1):68–87.

90. Tobler I, Murison R, Ursin R, Ursin H, Borbely AA. The effectof sleep deprivation and recovery sleep on plasma corticoster-one in the rat. Neurosci Lett 1983; 35(3):297–300.

91. Vander AJ, Sherman JH, Luciano DS. Human Physiology:The Mechanisms of Body Function. 6th ed. New York, USA:McGraw-Hill, 1994:515–560.

92. Schoenecker B, Heeler KE, Freimanis T. Development ofstereotypes and polydipsia in wild caught bank voles (Clethi-onomys glareolus) and their laboratory-bred offspring. Ispolydipsia a symptom of diabetes mellitus? Appl Anim BehavSci 2000; 68:349–357.

93. Nicholson JK, Wilson ID. High resolution nuclear magneticresonance spectroscopy of biological samples as an aid to drugdevelopment. Prog Drug Res 1987; 31:427–479.

94. Spraul M, Hofmann M, Dvortsak P, Nicholson JK, Wilson ID.Liquid chromatography coupled with high-field proton NMRfor profiling human urine for endogenous compounds anddrug metabolites. J Pharm Biomed Anal 1992; 10(8):601–605.

95. Ankeny RA. Sequencing the genome from nematode tohuman: changing methods, changing science. Endeavour2003; 27(2):87–92.

96. Zhang AQ, Mitchell SC, Smith RL. Dietary precursors of tri-methylamine in man: a pilot study. Food Chem Toxicol 1999;37(5):515–520.

97. Wang Y, Tang H, Nicholson JK, Hylands PJ, Sampson J, Hor-meo E. An NMR - based metabonomic strategy for the detec-tion of the metablic effects of chamomile (Matricana recutitaL) ingestion. J Agricultural and food chemistry. Acceted 2004.

98. Davison JM, Hytten FE. The effect of pregnancy on therenal handling of glucose. Br J Obstet Gynaecol 1975; 82(5):374–381.

450 Bollard et al.

99. Bales JR, Bell JD, Nicholson JK, Sadler PJ. 1H NMR studiesof urine during fasting: excretion of ketone bodies and acetyl-carnitine. Magn Reson Med 1986; 3(6):849–856.

100. Hoppel CL, Genuth SM. Carnitine metabolism in normal-weight and obese human subjects during fasting. Am J Phy-siol 1980; 238(5):E409–E415.

101. Hoppel CL, Genuth SM. Urinary excretion of acetylcarnitineduring human diabetic and fasting ketosis. Am J Physiol1982; 243(2):E168–E172.

102. Nicholson JK, O’Flynn MP, Sadler PJ, Macleod AF, Juul SM,Sonksen PH. Proton-nuclear-magnetic-resonance studies ofserum, plasma and urine from fasting normal and diabeticsubjects. Biochem J 1984; 217(2):365–375.

103. Mitchell SC, Zhang AQ. Methylamine in human urine. ClinChim Acta 2001; 312(1–2):107–114.

104. Zuppi C, Messana I, Forni F, Ferrari F, Rossi C, Giardina B.Clinica Chimica Acta Influence of feeding on metaboliteexcretion evidenced by urine 1H NMR spectral profiles: acomparsion between subjects living in Rome and subjects liv-ing at arctic latitude (Svaldbard) 1998; 278:75–79.

105. Walser M. Creatinine excretion as a measure of protein nutri-tion in adults of varying age. JPEN J Parenter Enteral Nutr1987; 11(5 suppl):73S–78S.

106. Gangemi S, Luciotti G, D0 Urbano E, Mallanace A, Santoro D,Bellinghieri G, Davi G, Romano M. Physical exerciseincreases urinary excretion of lipoxin A(4) and related com-pounds. J Appl physiol 2003; 94(6):2237–2240.

107. Yamaguchi T, Shioji I, Sugimoto A, Yamaoka M. Psychologi-cal stress increases bilirubin metabolites in human urine. .Biochem Biophys Res Commun 2002:293:517–520.


11

Environmental Applications ofMetabonomic Profiling

JACOB G. BUNDY

Biochemistry Department, Universityof Cambridge, Cambridge, U.K.

The current major challenges for environmental toxicologistsand ecotoxicologists include the need to develop a mechanisticunderstanding of the toxic action of pollutants at a molecularlevel, and to understand how molecular and cellular eventsaffect higher order (population and ecosystem) functioning(1). Postgenomic technologies will be vital in order to addressthese questions, and metabonomics could play a major role inhelping do so. This chapter will summarize some of the advan-tages and disadvantages of NMR-based metabonomic profil-ing as applied to ecotoxicology, and review the progress thathas been made to date.

Recent reviews on the use of NMR in plant scienceare available elsewhere (2), as well as the topic of plant

453

metabolomics (3). There are fewer published studies onmicrobes, and these tend to concern standard laboratoryorganisms such as Saccharomyces cerevisiae and Escherichiacoli (4–8). Hence, the subjects of metabonomics in plants andin microbes will not be covered.

1. DIFFERENCES TO CLINICAL STUDIES

The clinical applications of metabonomics are addressed else-where in this book. What is sufficiently different about theapplication of metabonomic techniques to environmental pro-blems that warrants a separate chapter? The basicapproach—generation of metabolic profiles via high-resolu-tion NMR spectroscopy, and multivariate pattern recognition(PR) to help interpret the multiple changes occurring in thesespectra—is identical. However, there are a number of differ-ences that will affect, e.g., study design, usefulness of theresults obtained, etc., and it is important to mention thesehere.

1.1. Goals

One of the major differences between clinical and environ-mental metabonomic studies is caused by the endpoints ofinterest—human medicine is individual based, whereas eco-toxicology is concerned with populations, and hence assessingthe risk to sensitive species and ecosystems. The additionalfactors of exposure, bioavailability, and food-chain transferbecome important. A simple adoption of a clinical studydesign to an environmental setting might well be regardedas of low value by some ecotoxicologists, however, wellexecuted the metabonomic aspects of the study.

Fortunately, these problems need not be solved in isola-tion. The use of biomarkers in ecotoxicology and pollutionmonitoring is an active research field, and metabonomics fitswell into this area—it can be thought of as providing a seriesof small-molecule biomarkers of toxic stress. Thus, current

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thinking in biomarker research can be used to help interpretmetabonomic studies with maximum value to real environ-mental issues.

The use of biomarkers (here considered as biochemicalmarker molecules only) has a number of exceptional advan-tages for ecotoxicological assessment. The foremost advantageis that the biomarker provides an integrated measure of theactual response of an organism to a pollutant (9–11). This isof importance firstly in taking account of actual exposure:total environmental concentrations are not likely to be repre-sentative of the amounts bioavailable to an organism. Pollu-tants may also be released in pulses, and chemical samplingmay miss episodes or hotspots of contamination (9). Secondly,biomarkers may be useful in taking into account an organ-ism’s actual state of health: for instance, there may be a bio-logical response to a toxic insult that results in eitherresistance or tolerance, either at an individual or populationlevel. Clearly, the effects of a certain level of a pollutantmay be very different, depending on whether the animalsare from a na€��ve or previously exposed population, and rely-ing on simple chemical concentrations may well be mislead-ing. Thus, if biomarkers could be used to give an indicationof biological effect, i.e., fingerprinting a particular stressor,this could be of great value (11,12). Morgan et al. (13) definebiomarker strategies as falling into three groups: (i) applica-tion of biomarkers at different levels of complexity as partof a rapid toxicity screening program; (ii) development ofmechanistic links between molecular biomarkers and higherorder (functional) biological levels; and (iii) use of multivari-ate-profiling techniques to define characteristic fingerprintsor profiles of biological stress=harm that are more specificthan single biomarker responses. Handy et al. (9) state that,given the current state of the art in biomarker research, themost potentially useful approach is the application of suitesof biomarkers at the molecular, cellular, and physiologicallevel. Metabonomics clearly falls into this third group,although it is easy to imagine potential applications in, say,the rapid profiling of chemicals for regulatory test purposesagainst required test organisms.

Environmental Applications 455

1.2. Sample Size and Time Course

Metabonomics is described as the ‘‘time-resolved’’ measure-ment of response to a toxic insult (14), and many studies showthe high value of being able to follow the course of biologicalresponses to a toxin through time (15). This can easily be donewhen repeated samples may be taken from the same indivi-dual (for example, urine samples). More often than not, how-ever, it is impossible to obtain repeat samples from anorganism of environmental relevance, and destructive sam-pling is the only possible method. Often, test species are sosmall that it would be essential to combine many individualssimply to obtain a single spectrum, for example, with the clas-sic aquatic test organism Daphnia magna, or with any of theisopod species frequently used for soil toxicity testing (16). Itis possible to obtain a reasonable 1H spectrum based on a tis-sue extract of as little as 5mg tissue dry weight, using a mod-ern high-field spectrometer and a standard 5mm probe.(Sensitivity could also be increased and hence sample require-ment reduced by using more sensitive, higher field instru-ments, or by cryogenically cooled probes; microvolumeprobes and magic-angle-spinning probes also require lesssample.) However, it is preferable to work with larger quanti-ties of tissue, e.g., 20mg dry weight or more, if available.

Clearly, therewill be somesituationswhere repeated sam-ples can be taken—for example, urine from wild mammals orblood plasma from fish large enough to allow such sam-pling—but this is likely to be an exception. It is possible to fol-low a time course by sacrificing different individuals atdifferent timepoints, and in fact this ismore ‘‘realistic’’ in termsof modeling toxic response at the population level, althoughperhaps less easy to interpret for mechanistic information.However, the numbers of animals required for destructive-sampling studies has meant that environmental metabonomicstudies have tended not to cover many time points.

1.3. Variation and Statistical Models

Clinical drug toxicity studies are typically carried out onrelatively homogeneous rat or mouse populations, which are

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kept under ideal, reproducible conditions. This minimizesthe obscuring of actual treatment effects by other confoundingfactors—diet, environmental effects such as temperature,different populations and differential survival, genetic inho-mogeneities, age, life-cycle stage, etc. Environmental studiesare, in general, less well controlled. One approach to thisproblem is to mimic clinical studies by focusing solely onlaboratory experiments, which are deliberately as ‘‘unna-tural’’ as possible, and diet, surroundings, and interactionsbetween individuals are all specified. Alternatively, more‘‘realistic’’ studies may use either microcosm=mesocosm expo-sures to mimic environmental effects; expose laboratory-sourced organisms in situ to be recovered at a later date; orcollect autochthones from regions of concern, e.g., contami-nated sites or along a known gradient of environmental con-tamination.

These more ecologically relevant studies will also requiretesting in parallel laboratory experiments, in order to demon-strate that environmental biomarkers are indeed directly pro-duced by specific toxins or stressors. There is also the addedcomplicating factor that observed metabonomic effects maybe caused either by changes at the individual level—by affect-ing regulation of a suite of genes related to stress, metabo-lism, etc.—as is observed in laboratory studies, or bychanges at the population level, i.e., actual adaptation orselection for differential populations (17,18).

Thus, increased baseline variability is characteristic ofenvironmental metabonomic studies, and additional environ-mental factors are likely to complicate interpretation of fielddata. Even biomarkers that are often thought of as having‘‘known’’ mechanistic interpretations turn out to be remark-ably difficult to interpret in a field context—for example; theresponse of metallothionein (MT) levels to cadmium (Cd) isa classic, highly studied example. Metallothionein levels arealso affected by other heavy metals, other xenobiotics, season-ality, and are known to vary in fundamental biochemical pro-cesses, e.g., at different points in the cell cycle (19–21). Thus,it is surprisingly difficult to make any kind of reliable predic-tion of the presence=absence of environmental toxicants, even


when laboratory studies show clear dose–response behavior ofbiomarkers=metabonomic profiles.

In ecotoxicology, an increase in the overall variability ofa biochemical=biological trait has been proposed as an indica-tor of stress, even if there is no significant increase=decreasein the mean of that trait (e.g., Ref. 22). Recently, it has alsobeen pointed out that the variability of certain parametersmay also significantly decrease as a consequence of exposureto toxic stressors (23). These approaches have not yet beentaken into account for environmental metabonomic studies,but might well be useful for future work.

1.4. Life-cycle Sensitivities

Laboratory acute toxicity tests may test only exposure duringa specific life-cycle phase. Rapid acute tests often use adultorganisms, because of the relative experimental ease inobtaining animals and running the tests. However, adulthoodis often the least sensitive life-cycle stage; reproduction anddevelopment may often be far more sensitive to chemicalexposure. Fortunately, it is often possible to perform labora-tory exposures which test sensitivity during reproductionand development, and standard toxicity test protocols havebeen devised. There is, therefore, clear potential for metabo-nomic studies which assess biochemical responses during sen-sitive reproduction=development phases. Such studies areonly just beginning to be carried out (24).

1.5. Need for Field Validation

The above sections have introduced some of the additional com-plications entailed by applying metabonomic techniques toenvironmental problems and ecologically relevant test organ-isms. Because of these complications, it is usually regardedas essential in ecotoxicology that field sampling of authenti-cally exposed organisms is carried out. Field sampling of indi-genous populations means that sensitivity at different life-cycle stages is, to an extent, taken into account—even if onlyadult organisms are collected, they would have been exposedat all stages during development. An equally important issue

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is that extraneous environmental factors are automaticallytaken into account. Hence, the likely high baseline variabilityof metabonomic profiles of control (unexposed) organisms willnot be so problematic if it can be shown that exposed=stressedstressed profiles are clearly separable in metabolic space.

2. CHARACTERIZATION OF BASELINE DATABY NMR SPECTROSCOPY

An obvious prerequisite for metabonomic studies on environ-mental species is the ability to take and profile a suitable bio-logical sample. As discussed above (Sec. 1.2), small sample sizemay often be limiting, and in this case whole-body extractsmay be the only way to obtain enough tissue to allow NMRanalysis. This is clearly not ideal, and if it is possible to takemore specific samples, e.g., specific organs=tissue types or bio-fluids, this option should certainly be tested. Metabonomicanalysis can be carried out by treating the spectrum purelyas a digitized vector, and in this case, there is in theory no needfor any prior knowledge of the metabolites to be found in thesamples. But in practice, it is useful to first assign at leastthe major metabolites that can be found in a particular sampletype; assignment of the minor resonances of a spectrum canthen be directed by chemometric identification of resonancesas belonging to biomarker compounds (25). A number of differ-ent organisms have been studied, falling into a range of taxo-nomic groups. The different studies are reviewed brieflybelow; for convenience, a list of the different species and rele-vant references is given in Table 1. In some cases, individualresonances have been assigned from the NMR spectra, and acompilation of all of the observed metabolites is presented inTable 2. It should be noted that the absence of an assignedmetabolite does not imply that the metabolite was not present,as the list is based solely on published data.

Other magnetic resonance approaches have been used foranalysis of environmental samples—in particular, there aremany examples of the use of 31PNMRspectroscopy in compara-tive physiology and biochemistry studies (26), including


Table

1Summary

ofSom

eDifferentSpeciesof

Environmen

talRelev

ance

thathavebeenProfiledbyNMR

forMetabon

omic

Studies

Taxon

omic

group

Species

Tox

icants=other

stressors

tested

Setting

Referen

ce

Terrestrial

invertebrates:

earthworms

Eisen

iaandrei,

Lumbricu

sru

bellus

Cop

per;zincandother

hea

vymetals

resu

lting

from

pollution

from

smelter

Laboratory:filter

paper

exposuresandsoil

microcosm

s.Sem

ifield:mesocosms.

31,36,63

Field

scale:indigen

ous

(LR)andalien

(EA)

culturesex

posed

insitu

Eisen

iaven

eta(tissu

eex

tracts,

coelom

icfluid)

3-Trifluorom

ethylaniline;

12fluorinated

mon

oaromaticanilines

andphen

ols

Laboratory:filter

paper

exposures

andsoilmicrocosm

s.Sem

ifield:mesocosms

33,34

E.fetidaþE.andreiþ

E.ven

eta

(coe

lomic

fluid)

Bothlaboratory

and

wildpop

ulation

ssa

mpled

25,39

E.ven

etaþL.terrestris

Starvation

over

seven

dayperiod

Laboratory:trea

tedon

filter

paper

32

Aporrectod

eaca

liginosa,

A.longa,

A.icterica

,Den

drodrilusru

bidus,

Den

drobaen

aoctaed

ra

Freezetrea

tmen

tLaboratory,using

wild-collected

pop

ulation

s

35

Sch

istocercagregaria

(desertlocu

st;

hem

olymph)

Laboratory

45

460 Bundy

Terrestrial

invertebrates:

other

Arion

subfuscus

(slug),

Oniscu

sasellus

(isopod

;woodlouse),Porcellio

scaber

(isopod

;woo

dlouse),

Glomeris

marginata(m

illiped

e)

Collected

from

field

36

Insecta

Manduca

sexta(tob

acco

hornworm;hem

olymph)

Laboratory

43

Culexpipiens

Laboratory

46

Marine

invertebrates

Haliotis

rufescen

s(red

abalone;

foot

muscle,hep

atopancrea

s,digestivegland)

Witheringsyndrome

(causedbybacterial

infection)

Collected

from

field

47,53

Sycionia

ingen

tis(ridgeb

ack

prawn;hep

atopancrea

s);

Stron

gylocen

truspurp

ura

tus

(purp

leseaurchin;eg

gs);

Laboratory

47

Vertebrates:

mammals

Clethrion

omys

glariolus(bank

vole;

kidney

biopsy,urine)

Cadmium

Laboratory:ex

posure

via

food

61

Apod

emussylvaticu

s(w

oodmou

se;kidney

biopsy

andurine),

Crocidura

suaveolens

(white-toothed

shrew;

kidney

biopsy)

Laboratory

59

Mastom

ysnatalien

sis

(multim

ammate

desertrat;

urine).

2-bromoethanamine

Laboratory:

exposure

byoralroute

62

Vertebrates:

fish

Parophrysvetulus(E

nglish

sole;liver

andgill)

Laboratory

47

Oryziaslatipes

(Japanese

med

aka)

Trich

loroethylene

Laboratory:ex

posure

by

directcontact

24,58


Table

2Listof

Metabolites

thathavebeenAssigned

inNMR-basedStudiesof

Environ

men

talOrganisms.

Presence

ofAssigned

MetaboliteIn

dicatedby‘‘x’’(A

bsence

of‘‘x’’maynot

Necessa

rily

Indicate

Absence

ofMetabolite)

Vertebratesa

Marine

invertebratesb

Terrestrial

invertebrates—

earthwormsc

Terrestrial

invertebrates—

other

d

CSCG

CG

PV

PVI

SP

HR

SI

LRLTEA

EV

EV

DO

DR

AIACAL

AS

OA

PS

GM

MS

SG

uk

gcf

Alanine

xx

xx

xx

xx

xx

xx

xx

xx

xx

xx

xx

Arginine

xx

xx

Asp

aragine

xx

xx

xx

xAsp

artate

xx

xx

xx

xx

xx

xGlutamate

xx

xx

xx

xx

xx

xx

xx

xx

xx

xx

Glutamine

xx

xx

xx

xx

xx

xx

xx

Glycine

xx

xx

xx

xx

xx

xx

xx

Histidine

xx

xx

xx

xx

xx

xx

Methylhistidine

xx

xx

xx

xIsoleu

cine

xx

xx

xx

xx

xx

xx

xx

xx

xx

xx

Leu

cine

xx

xx

xx

xx

xx

xx

xx

xx

xx

Lysine

xx

xx

xx

xx

xx

xx

xx

xx

xx

xMethionine

xx

xx

xx

xx

Orn

ithine

xx

xx

Phen

ylalanine

xx

xx

xx

xx

xx

xx

xx

xx

Proline

xx

xx

xx

Hydroxyproline

xSerine

xx

xx

x

462 Bundy

Threon

ine

xx

xx

xx

xx

xx

xx

Tryptophan

xx

xx

xx

xx

xx

xx

xTyrosine

xx

xx

xx

xx

xx

xx

xx

xx

xx

xx

Valine

xx

xx

xx

xx

xx

xx

xx

xx

xx

x

Acetate

xx

xx

xx

xx

xx

Acetoacetate

xx

xAscorbate

xbutyrate

xa-Hydroxybutyrate

xb-Hydroxybutyrate

xx

Citrate

xx

xx

xx

xFormate

xx

xx

xFumarate

xx

xx

xx

xx

xx

xx

xx

xx

GABA

xx

Hippurate

x4-A

minoh

ippurate

xa-Ketog

lutarate

xx

xLactate

xx

xx

xx

xx

xx

xx

xx

xx

xx

xx

xx

xx

Malate

xx

xx

Malonate

xx

xx

xPyru

vate

xx

xx

xUrocanate

xx

Succinate

xx

xx

xx

xx

xx

xx

xx

Betaine

xx

xx

xx

xx

xx

xx

xCarn

itine

xCholine

xx

xx

xx

x

(Con

tinued

)


Table

2(C

ontinued

)

Vertebratesa

Marine

invertebratesb

Terrestrial

invertebrates—

earthwormsc

Terrestrial

invertebrates—

other

d

CSCG

CG

PVPVI

SP

HR

SI

LR

LTEA

EV

EVDO

DR

AIAC

AL

AS

OA

PS

GM

MS

SG

uk

gcf

Creatine

xx

Creatinine

xDim

ethylamine

xx

xGPC

xHom

arine

xTaurine

xx

xx

xHypotaurine

xN-m

ethyltaurine

xTMAO

xx

Aden

ine

xx

xx

xAden

osine

xx

xx

xx

ADP

xx

ATP

xx

Inosine

xx

xx

xx

xIM

Px

NMN

xx

N-m

ethylnicotinamide

xUridine

xx

xx

xUMP

Fucose

x

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Glucose

xx

xx

xx

xx

xx

xx

xx

myo-Inositol

xMaltose

xx

xx

xx

xSucrose

Trehalose

xx

xx

Glycerol

xGlycerol-3-phosphate

xHEFS

xx

xx

xx

xx

xLom

bricine

xPutrescine

xSarcosine

xUrea

xPhosphatidylcholine

xLipid

triglycerides

x

aCS—Crocidura

suaveolens,

blood

.CG

u—Clethrion

omys

glareolusurine.

CG

k—C.glareoluskidney

,byMAS.PVg—Parophrysvetulus

gill.PV

l—P.vetulusliver.

bSP—Stron

gylocen

truspurp

ura

tus,

eggs.

HR—Haliotis

rufescen

s,muscle.Sycionia

ingen

tis,

hep

atopancrea

s.c L

R—Lumbricu

sru

bellus.

LT—L.terrestris.EA—Eisen

iaandrei.EV—E..ven

eta.EV

cf—E.ven

etacoelom

icfluid.DO—Den

drobaen

aoctaed

ra.DR—Den

drodrilusru

bidus.

AI—

Aporrectod

eaicterica

.AC—A.ca

liginosa.AL—A.longa.

dAS—Arion

subfuscus.

OA—Oniscu

sasellus.

PS—Porcellio

scaber.GM—Glomerulusmarginata.MS—

Manduca

sexta,hem

olymph.

SG—Sch

istocercagregaria,hem

olymph.


two-dimensional (2D) experiments for the identification of agreater number of phosphorylated metabolites than areusually observed by 1D spectroscopy (27). However, because31P NMR reports on a much smaller proportion of free metabo-lites than 1H NMR, only 1H NMR-based studies are describedbelow.

2.1. Terrestrial Invertebrates—Earthworms

Earthworms are an important ecological group of soil animals.They usually form the major portion of animal biomass in soilsthat are suitable for the support of earthworms, and play amajor role in soil functionality by increasing organic carbonturnover rates through mixing of soils and comminution ofvegetable matter (28–30). They contribute to soil fertility andhelp to support populations of soil microorganisms, and areregarded as a key ecological group. Consequently, there is along history of ecotoxicological testing using earthworms, andmore earthworm species have been characterized by 1H NMRspectroscopy than any other taxonomic grouping. Gibb et al.(31) analyzed aqueous tissue extracts of Eisenia andrei andLumbricus rubellus using one-dimensional (1D) and (2D)1H–1H Correlated Spectroscopy (COSY) and homonuclear J-resolved (JRES) spectroscopy. The COSY experiment providesincreased structural information—1D resonances appearalong the diagonal, and off-diagonal cross-peaks are seenbetween resonances which exhibit through-bond J-coupling,i.e., usually between 1H nuclei which are separated by nomorethan two or three bonds. The JRES experiment simplifies thecrowded1Dproton spectrumbypresenting the splitting causedby J-coupling in a second dimension, i.e., effectively rotatingthe multiplet into a second dimension and thus greatly redu-cing NMR signal overlap. A series of small metabolites wereidentified, including sugars, organic acids and bases, osmo-lytes, nucleosides, and amino acids (Table 2). Some resonancesof the compound 2-hexyl-5-ethyl-3-furansulphonate (HEFS)weremistakenly assigned as ethanol; based on our later experi-ments with earthworms, it is also likely that the resonancesassigned to sucrose were in fact from maltose.

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Warne et al. (32,33) increased the number of earthwormspecies that have been characterized to four, adding E.veneta and L. terrestris. These species were also studiedusing aqueous tissue extractions. The metabolites observedwere very similar to those observed by Gibb et al. (31).Bundy et al. (34) identified HEFS in E. veneta tissueextracts; this metabolite is present in all worm species thathave yet been tested (11 species—Table 1, and E. nordens-kioldi, unpublished data). Bundy et al. (35) characterizedanother five species—three Aporrectodea species, A. icterica,A. caliginosa, and A. longa; Dendrodrilus rubidus; and Den-drobaena octaedra. These were studied using an initial acet-onitrile=water tissue extraction, followed by lyophilizationand reconstitution in 2H2O. The acetonitrile=water stepwas included with the intention of precipitating macromole-cules and halting postextraction metabolism. The mostobvious differences that this made to the metabolite profileswere, firstly, that HEFS was present in much higher propor-tion—frequently HEFS was the most abundant metabolitepresent; and secondly, that adenosine, but not inosine, wasobserved in control worms. Recently, we have acquired spec-tra of perchloric acid extracts of L. rubellus tissue; extractionin ice-cold perchloric acid is a standard technique for simul-taneously extracting small-molecule metabolites and haltingenzymatic activity. These give extremely different profiles tothose obtained by aqueous extractions (31,36). The major dif-ferences include much smaller relative concentrations of freeamino acids and free sugars; greatly increased relativeconcentration of HEFS; observation of resonances froma new compound tentatively assigned as lombrici-ne=phosphoryllombricine (resonances at d4.26, d4.00, andd3.48). This assignment is made on the basis of similarityof the resonances to those of l-serine ethanolamine phospho-diester (37), which is a component of lombricine (except thatlombricine contains a d-serine residue, not l-serine (38)), andtheir very rapid (within 10 sec) alteration following extrac-tion if PCA is not used to halt metabolism (unpublishedresults). This very rapid metabolic change would be expectedof phosphagens.


It is also possible to extract coelomic fluid (CF) fromearthworms. This can be collected by invasive puncture, butfor the Eisenia genus, much better results are given by a mildelectrical stimulus to the worm (39). We have found that col-lection by an invasive puncture gives spectra with many con-taminating signals from free amino acids. The CF contains anentirely different set of metabolites to those observed inwhole-body tissue extracts; an initial characterization hasbeen made of the CF of E. veneta (39). In contrast to the tissueextracts, the CF spectra are dominated by signals fromorganic acids (Table 2). In addition, the fluid appears to con-tain several aromatic metabolites in relatively high concen-tration: E. veneta CF contains nicotinamide mononucleotide.E. andrei and E. fetida CF contain many resonances fromseveral so far unknown aromatic compounds (25).

An alternative way of presenting overall species similar-ity in terms of spectroscopic profiles is by direct analysis of thebinned spectral data. Figure 1 shows a comparison of five

Figure 1 Hierarchical cluster analysis (using Ward’s method oflinkages and Euclidean metric) of 600MHz 1H NMR spectral dataof tissue extracts, showing overall earthworm species similarity:Aporrectodea species form a separate cluster.

468 Bundy

earthworm species’ spectral profiles by hierarchical clusteranalysis [the maximum number of species available studiedunder identical experimental conditions (35)]. The three Apor-rectodea species form a cluster, as expected. Species identifica-tion is often tricky for soil invertebrates, and may cause someproblems in earthworm ecotoxicology, where some ‘‘species’’are in fact species complexes (40). Metabonomic toxicity analy-sis offers potential additional benefits in being able to compareindividual similarity and similarity between strains=species.Clearly, it would not be possible to perform chemotaxonomicidentification based purely on a na€��ve use of metabolite pro-files—the observed metabolites will change depending on thephysiological state of the organism, and overall profiles mightwell be affected more by ecotype than by genetic similarity—but it would be an interesting area for future study.

2.1.1. Terrestrial Invertebrates—OtherSoil-dwelling Species

Gibb et al. (36) also profiled four other soil invertebrates thatare either used or could potentially be used for ecotoxicitytesting—a slug, a millipede, and two crustaceans (woodlicespecies). It is surprising how superficially similar the metabo-lite profiles are from these different species in terms of com-pounds present (Table 2). In fact, this is only an indicationof the fact that a few primary metabolites are found acrossthese different species, rather than a true species comparison.Gibb et al. (36) also used hierarchical cluster analysis todemonstrate that more realistic physiological relationshipscould be established (although in this case certain specieswere represented only by a single individual).

2.1.2. Terrestrial Invertebrates – InsectHemolymph

Insects of sufficient size provide an opportunity to sample spe-cific biological compartments. Manduca sexta is a seriousagricultural pest (41), and has been widely studied, withmuch known about its biochemistry. It is therefore an obvious


choice for further metabonomic investigations that may wellbe complementary to existing knowledge. Thompson (42)identified putrescine and trehalose in the 1H NMR spectraof hemolymph of the 5th instar larvae of M. sexta, inaddition to investigating energetic metabolism by 31P NMR.Phalaraksh et al. (43) confirmed the assignment of thesetwo compounds, and assigned a further 17 metabolites visiblein 1D and 2D spectra (Table 2). A presumed unassigned sugarwas also detected, with an observed doublet (J¼ 7.5Hz, dH5.14ppm, and dC 103ppm) typical of sugar anomeric protons(44).

Lenz et al. (45) have similarly characterized the hemo-lymph of final-instar nymphs of Schistocerca gregaria. (Apoint of methodological interest is that because the samplesobtained were small in volume, 20–100mL, a microprobewas used to maintain sensitivity. The adoption of microp-robes, and in addition cryoprobes, will remove some of theproblems associated with the analysis of small volumes thatare typical of environmental samples.) They also observedintriguing differences in spectra depending on whether thenymphs had been raised in solitary or social conditions:solitary nymphs had decreased ethanol and acetate, andincreased putrescine and trehalose concentrations. It is inter-esting that 1H NMR spectroscopic profiling has also shownthat final-instar larvae of the mosquito Culex pipiens pallenshave increased tissue extract concentrations of metabolitesthat are end products of fermentative metabolism, includingethanol and acetate (46). Possibly, the differences in the twogroups are related to completeness of developmental stage.

2.2. Marine Invertebrates

Fan et al. (47) used a combination of both GC–MS and 1HNMR spectroscopy for identification and absolute quantita-tion of 41 different metabolites. Spin-lattice (T1) relaxationtimes were measured for these metabolites to permit calcu-lated corrections for differences arising from incompleterelaxation of the magnetization. Three marine invertebratespecies and extracts of tissues were studied (Table 2): foot

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muscle tissue from Haliotis rufescens (red abalone), hepato-pancreas from Sycionia ingentis (ridgeback prawn), and eggsfrom Strongylocentrus purpuratus (purple sea urchin). (A ver-tebrate species was also profiled, 2.3, as well as five plant spe-cies, the results of which will not be discussed here.) Thestudy was specifically designed to profile osmolytes. Total cor-relation spectroscopy (TOCSY) and 1D nuclear Overhausereffect (NOE) difference spectra were used to help confirmNMR assignments. The NOE acts through space and notthrough bonds, unlike the COSY and TOCSY experiments,and NOE experiments can therefore be valuable in helpingassign metabolites which possess isolated singlet resonances,e.g., from N-methyl protons as found in some osmolyte meta-bolites. Osmolytes were indeed found in high concentrations:betaine was the highest-concentration metabolite in both S.ingentis hepatopancreas and H. rufescens muscle, althoughit was not detected in S. purpuratus; taurine was abundantin all the three invertebrate species, especially in S. ingentisand H. rufescens; alanine, glutamate, aspartate, proline,and arginine were also found in abundance in S. ingentishepatopancreas; and arginine was high in concentration inH. rufescens muscle. The most abundant metabolite in S. pur-puratus eggs was glycine (47). These compounds are allknown to be present in marine invertebrates as osmolytesin order to cope with salinity stress (e.g., 48–51). However,arginine=phosphorylarginine is the major phosphagen forthe majority of invertebrate species, equivalent to creatinein vertebrates (52), and thus it is likely that its high concen-tration in S. ingentis hepatopancreas and H. rufescens muscleis because of its requirement as an energy store.

A more recent metabonomic study has focussed on H.rufescens, confirming that the spectra are dominated by gly-cine and betaine (53). Muscle tissue extracts were analyzedin addition to hemolymph and digestive gland tissue extracts.A number of new metabolites were assigned with includingthe osmolytes N-methyltaurine and dimethylglycine, andthe unusual zwitterionic aromatic compound homarine(1-methyl-2-pyridine carboxylic acid). Homarine is alsobelieved to function as an osmolyte (54).


2.2.1. Vertebrates—Fish

The Japanese medaka (Oryzias latipes) is a well-character-ized model species for developmental biology; 34 separatestages during embryogenesis can readily be discerned by lightmicroscopy, as the developing embryos are transparent. Theadvantages of having an easily manipulable system in whichembryogenesis can be observed means that there are severaldevelopmental environmental toxicology studies which haveused O. latipes (e.g., 55–57). The sensitivity to pollutants isat a maximum during development. Thus, animal models ofdevelopment that can be used in toxicity tests are valuable.Viant (24) and Viant et al. (58) have profiled changes inO. latipes eggs both during normal development and afterexposure to trichloroethylene. At the time of writing, theseexperiments were still ongoing and a full assignment ofobserved peaks was not yet made, and hence this species isnot included in Table 2. However, a brief discussion of toxi-city-influenced changes on spectral profiles is included inSec. 3.2.2.

2.2.2. Vertebrates—Mammals

Griffin et al. (59) compared metabolite profiles from three dif-ferent wild mammals, Clethrionomys glareolus (bank vole),Apodemus sylvaticus (wood mouse), and white-toothed shrew(Crocidura suaveolens), chosen because of their different eco-physiological niches (respectively, herbivorous, gramnivor-ous, and insectivorous). Laboratory rats (Sprague–Dawley)were also included to permit comparison to a typicallaboratory organism. A particular feature of this study is thatbiofluid (urine and blood plasma) spectra were complementedby magic-angle-spinning (MAS) spectra of intact renal cortexkidney tissue samples. Metabolite profiling by MAS 1H NMRspectroscopy means that animal tissue samples can be ana-lyzed directly with no need for any extraction methods—thuspotentially minimizing artifacts introduced by the selectionprocess, whether of selective extraction or chemical conver-sion of metabolites (60). It has also been suggested thatMAS may be useful insofar as MAS-visible metabolites still

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remain in tissue residues even following conventional liquidextraction techniques (61).

Clethrionomys glareolus urine had high concentrationsof aromatic compounds (relative to creatinine) compared toA. sylvaticus and the laboratory rat, including hippurateand the aromatic amino acids phenylalanine, tryptophan,and tyrosine. Rat urine was also lower in TCA cycle compo-nents. Principal components analysis of the autoscaled urinedata (after binning of the spectra into 0.04 ppm regions) com-pletely separated the rat samples from the other two rodentspecies along PC 1; it is worth noting that the rodents wereall fed the same diet (laboratory chow), so the observed differ-ences were not caused by the trivial reason of different diet-ary intake. Higher concentrations of the aromatic aminoacids were also reported in C. glareolus blood plasma as com-pared to A. sylvaticus. Even clearer differences between thelaboratory rat and the wild mammals were observed inMAS renal cortex spectra: in particular, lipid triglyceride sig-nals dominated the wild mammal spectra compared to thelaboratory rat samples. It is tempting to speculate that thismay be because there is a greater selection pressure for thewild animals to build up fat reserves when provided withample food, as they might have need of energy reserves intimes of food scarcity. In conclusion, there were clear differ-ences observed between the biochemistry of the laboratoryrat and the three wild mammal species. The rat spectrawere also observed to have less individual-to-individualvariation than the wild mammal spectra, which is a naturalconsequence of the greater heterogeneity expected of wildpopulations.

The baseline data collected in this study have been usedin further toxicological studies on the effects of cadmium (Cd)and arsenic (As) on C. glareolus (cf. Sec. 3.4.1).

Holmes et al. (62) studied the multimammate desertmouse (Mastomys natalensis): the responses of urinary spec-troscopic profiles to the model kidney toxin 2-bromoethana-mine were compared to those of the laboratory rat.Mastomys natalensis was selected because, as a desert ani-mal, it has a specialized kidney structure to reduce water


losses. A complete assignment of the urinary spectra was notattempted, but specific biomarker compounds were identifiedin the urine. The spectra were not analyzed using multivari-ate methods, but by visual identification of changes in reso-nances. The results are discussed briefly in Sec. 3.2.1.

3. TOXICOLOGICAL AND RELATED STUDIES

In this section, we review those studies that have used meta-bonomic methods—nonselective profiling of toxicity-inducedalterations in metabolic status by multivariate analysis of1H NMR spectra—applied to environmental organisms andenvironmental problems.

3.1.1. Worms—Metal Exposures

Several studies have been carried out with earthworms. Gibbet al. (31) used 1H NMR spectroscopic metabolic profiling oftwo species (L. rubellus and E. andrei) to detect metabolitechanges caused by mesocosm exposure to soil spiked withcopper at up to 160mgkg�1. Eisenia andrei is a small,rapidly-reproducing epigeic earthworm usually found in com-post heaps that is widely used in regulatory toxicity testingbecause of ease of maintaining cultures, despite its lack ofrelevance to soil exposures. Lumbricus rubellus is a more eco-logically relevant soil organism, typically found in soil litterlayers. Histidine (integrated separately and expressed as aninternal ratio relative to tryptophan) was found to becopper-responsive in L. rubellus but not in E. andrei. Adose–responsive increase in histidine=tryptophan ratios wasobserved. The conjecture was made that this might be adirect physiological response by L. rubellus, and that intracel-lular free histidine levels might be upregulated in order toreduce cytotoxicity by chelation of copper ions. A subsequentfield study (63) has been carried out at a site contaminatedwith mixed heavy metals from smelting works, where theprincipal contaminant of concern (based upon comparison offield concentrations with laboratory-derived toxicity data) is

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known to be zinc. Laboratory cultures of E. andrei exposedon-site in buried nylon mesh bags were compared to auto-chthonous earthworms (L. rubellus and L. terrestris). Unfor-tunately, practical difficulties in covering the full gradientof contamination reduced the potential value of the results.Laboratory-reared E. andrei were exposed for a three-weekperiod at three sites; there were only two survivors fromthe site closest to the contamination source, reducing the sta-tistical power of any analysis. There were some interestingresults from the indigenous earthworms that were collected.Lumbricus rubellus was not found at the site closest to thepollution source; L. terrestris was found at this site, butwas not found at the control site furthest from the smelter.Thus, neither indigenous species could be used to cover theentire contamination gradient, again reducing the value ofthe results. However, histidine was again observed to beaffected in both Lumbricus species: histidine concentrationswere increased by a small but significant level in L. rubelluspopulations taken at sites of intermediate pollution, but weredramatically decreased in L. terrestris populations taken atthe most polluted site. Because neither species was foundover the entire contamination gradient, it was difficult to con-clude whether these differences represented a true species-level biochemical difference in response to metals. But theresults do confirm that histidine—previously identified as acopper-responsive biomarker in L. rubellus—is implicated inearthworms’ biochemical responses to metals. Some of the dif-ferences observed in the field study may be because the pri-mary pollutant was zinc, not copper; future additionalmultispecies and multicontaminant microcosm exposureswould be valuable in settling these possibilities. One pointto be borne in mind is that some of the free amino acid con-centrations were probably produced by protein hydrolysisunder the sample extraction conditions used (cf. Sec. 2.1).Thus the histidine response may either be due to a result ofchanges in intracellular free histidine concentrations, orof changes in the total amino acid pool—for example,histidine-rich copper-binding proteins may have beenupregulated (64).


3.1.2. Earthworms—Organics Exposures,Laboratory Tests

A parallel effort has also been made to delineate the changesin biochemical space induced by organic pollutants in earth-worms, using E. veneta as a model (32–34,39). Initial studieswere carried out with single compounds and a modified filter-paper contact test (OECD). The contact test involves insertinga piece of filter paper into a glass vial such that it surroundsthe entire interior of the vial, adding 1mL of an aqueous solu-tion of the test compound, and then introducing a worm (inthe original test, an individual of E. andrei would be exposedfor a 48hr period). This contact test is severely limited as anecological test system; for example, it gives no idea of how acompound’s toxicity will be affected by its bioavailability insoil. It is, though, an easily controllable test system appropri-ate for carrying out preliminary studies, particularly usefulwhen one wishes to ensure exposure of the earthworm to acompound (65).

Warne et al. (33) reported the effects of a model aromaticpollutant, 3-trifluoromethylaniline, on spectroscopic profiles ofaqueous extracts of E. veneta. The compound was dosed usingthe filter-paper contact test using range-finding concentra-tions (1000, 100, 10, 1 and 0.1 mg cm�2). The 1000 and100mg cm�2 level caused complete mortality, placing the com-pound into the ‘‘very toxic’’ category (66). Principal componentanalysis using autoscaled data was used to interpret overalltoxic effects of the data, whereas individual biomarkers foreach dose level were identified by calculating the Pearson’s cor-relation coefficient for each binned variable with a class vari-able for treatment. The only observed biomarker compoundat sub-lethal levels was HEFS (although not assigned in thepaper), which was increased in worms dosed at 10 mg cm�2.Of particular interest was that worms exposed at sub-lethalconcentrations were all significantly separated from controlworms, even at the lowest dose levels of 1 and 0.1 mg cm�2,showing that the overall spectral patterns were sufficient todistinguish the effects of 3-trifluoromethylaniline even thoughthere were no classic single-molecule biomarkers identified.

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A second study with E. veneta exposed to organic pollu-tants via the filter paper contact test used three compounds:4-fluoroaniline, 2-fluoro-4-methylaniline, and 3,5-difluoroani-line (34). As for Ref. 33, aqueous extracts were analyzed by 1D1H NMR spectroscopy, and PCA used as a pattern recognition(PR) technique on the data-reduced spectra. Two distinctlydifferent biochemical effects were observed for two groups ofcompounds: 4-fluoroaniline caused an almost total reductionin maltose concentrations, whereas the other two compounds,2-fluoro-4-methylaniline and 3,5-difluoroaniline, were asso-ciated with a reduction in HEFS concentrations. A multivari-ate technique such as metabonomics might be especiallyuseful in environmental monitoring in helping to assignsources of environmental stress. The unexpected and largebiochemical differences between the toxic effects of similarcompounds (all being monoaromatic fluorinated anilines)demonstrate the value of a non-selective biomarker monitor-ing approach.

The use of coelomic fluid as an alternative source formetabonomic experiments has also been demonstrated (39).Coelomic fluid of E. veneta presents an entirely different pro-file, unsurprisingly, to that of whole-body extracts. The spec-tra are dominated by peaks from organic acids. The compound3-fluoro-4-nitrophenol was shown to affect the metabolite pro-files. Principal components analysis of mean-centered datashowed separation of dosed from control worms along thethird principal component: malonate and acetate were nega-tive biomarkers, and succinate and a resonance assigned totrimethylamine-N-oxide were positive biomarkers of exposureto 3-fluoro-4-nitrophenol. The reasons for the change in thesespecific metabolites are not clear; however, 3-fluoro-4-nitro-phenol is likely to act as an uncoupler of oxidative phosphor-ylation [on the grounds of structural and presumed chemicalsimilarity to the known uncoupler 3-trifluoromethyl-4-nitro-phenol (67,68)], and thus fluctuations in the concentrationsof Krebs cycle intermediates and other organic acids are notsurprising. Part of the rationale for collection and analysisof coelomic fluid was that it receives waste metabolites enroute to excretion in urine via the nephridiopores (30), and


thus it was expected that effects of a toxic chemical distur-bance would be clearer in the coelomic fluid than in whole-body extracts. In fact, the changes in the spectral profileswere small compared to the dramatic changes often observedin mammalian urine spectra (69–71). Further experimentshave confirmed that toxin-induced changes in coelomic fluidare very small, even at lethal levels (unpublished data).Hence, it appears that earthworms’ homeostatic control overthe coelomic fluid at the small-molecule level may be greaterthan previously believed.

3.1.3. Earthworms—Organics Exposures, SoilTests

It is vital to include exposures in realistic environmentalmatrices in ecotoxicity testing. We have exposed E. venetato two model organic compounds (3-fluorophenol and 3-tri-fluoromethylaniline) in soil microcosms at concentrations upto 100mgkg�1 soil dry weight. The compounds were mixedwith the soils as aqueous solutions, and worms added to themicrocosms 24hr after spiking of the soils. The microcosmswere destructively sampled at seven and 28 days, and coelo-mic fluid samples were taken from the worms by electrical sti-mulus. The worms were then snap-frozen and extracted in anacetonitrile=water mixture. No effects of any kind could bedetermined for the compound 3-fluorophenol, even thoughthis was the most toxic of the two (100% mortality at100mg kg�1, and 5% mortality at 75mgkg�1, whereas 3-tri-fluoromethylaniline caused no mortality at any dose level).Neither PCA nor partial-least-squares regression showedany significant grouping of affected animals (unpublishedresults). Life-cycle parameters (cocoon production and cocoonhatching) were monitored as a sensitive environmental end-point, but unfortunately cocoon production was absent orlow even in control microcosms, possibly because of a seasonaleffect. This lack of NMR-detectable differences is an exampleof how even a highly controlled laboratory exposure, far sim-pler than an actual field experiment, may not reproduce bio-marker effects seen in a simpler system, and illustrates the

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need for controlled experiments at all levels of complexity(e.g., laboratory, microcosm, field). Care must be taken notto over-interpret the results—for instance, if a (hypothetical)field study of an organic contaminant had shown metabolitedifferences, this might be caused by indirect effects on foodsupply or environmental physical conditions. For the currentexample, there are several reasons why metabonomic effectsmay not have been observed: the interaction of organic pollu-tants with soils is extremely complex (e.g., 72,73); and it ispossible that the contaminants in the soil had been degradedor rendered unavailable by microbial action, as it has beenshown that similar monoaromatics can be rapidly degradedby soil microbial communities (74).

3.2. Other Invertebrates

Metabonomic pathophysiological assessment has also beenextended to marine invertebrates. Red abalone (Haliotisrufescens) are subjected to a withering disease, most probablycaused by a bacterial infection. Haliotis rufescens is an impor-tant commercial species, so it is of considerable interest toinvestigate the occurrence and effects of this disease.Healthy, withered, and stunted (i.e., suffering from an inter-mediate form of withering syndrome) abalone had hemo-lymph, digestive fluid, and HClO4 tissue extracts of footmuscle analyzed by 1D 1H NMR spectroscopy (53). The spec-tra were integrated into 0.005ppm bins, log-transformed, andanalyzed by PCA. There was a complete separation of thesamples within principal component space for all the threesample types (hemolymph, digestive fluid, and muscleextract): healthy abalone were separated from withered aba-lone along PC1, and from stunted abalone along PC2. Theobserved metabolite changes included decreases of free aminoacids and adenylates in the stunted=withered abalone. Thiswas suggested to be most probably because withering syn-drome in abalone involves starvation. The large pools of oxi-dizable amino acids in marine molluscs are known to beused as a cellular energy source (75), and starvation has beenshown to decrease free amino acid concentrations (76). It was


also observed that homarine was greatly increased in dis-eased animals; the interesting suggestion made was that thisincrease served to maintain overall osmolyte concentrations,which would otherwise be diminished by the decrease in freeamino acids.

3.2.1. Vertebrates—Mammals

Themultimammate desert mouse (M. natalensis) was selectedfor a study of the kidney toxin 2-bromoethanamine hydrobro-mide (BEA), and compared to the Fischer 344 rat (62). Therationale was that M. natalensis, like other desert organisms,has a highly specialized kidney structure in order to maintainwater relations in a desert environment, and might thereforeprove to be a sensitive model for studying BEA, which causesrenal papillary necrosis. Nuclear Magnetic resonance wasused as a profiling tool; PR methods were not applied directlyto the spectral data. Urinary collections were made up to96hr postdose to enable to follow the progress of the effectsof the toxic insult through time. Overall, the response ofM. natalensis was similar to that of the laboratory rat: urin-ary succinate and a-ketoglutarate levels were decreased inthe 0–24hr time period, followed by increased a-ketoglutaratefor the duration of the experiment. Glutarate and adipatewere also increased by BEA treatment. However, there weresome NMR-observable differences in the species response toBEA; M. natalensis exhibited a sustained increase in taurineconcentrations, which was not observed in the rat. Ethanolwas also observed in postdose M. natalensis urine, but notin rat urine. The NMR-profiling results were also comparedto urinary enzyme activity assays (lactate dehydrogenase,g-glutamyltransferase, and alkaline phosphatase). Theresponse in both species was similar, except that alkalinephosphatase activity was elevated in M. natalensis but notin the rat for 48hr following BEA treatment. Clear differ-ences were shown by histopathological analysis: the labora-tory rat was much more sensitive to BEA, with muchhigher levels of cellular damage caused at the same doselevel.

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Effects of both Cd (as CdCl2) and As (as As2O3) on thebank vole C. glareolus have been studied by MAS profilingand metabonomic analysis (61,77). Male voles were exposedto approximately 6 mgCd g�1 body weight for 14 days, result-ing in a final mean Cd concentration in kidney tissue of8.4 mg g�1 dw (77). Renal cortex samples were taken forMAS analysis, using T2-edited (CPMG) sequences. Spectrawere data reduced into 0.04 ppm bins, and analyzed by auto-scaled PCA. Controls were completely separated from Cd-exposed samples (n¼ 5 in each case) along PC 2; Cd exposurecaused decreases in the levels of leucine=isoleucine, gluta-mate, glycine, and taurine levels, and increases in lipid levels(variables assigned to lipid allyl protons and –COCH2– moi-eties). Reported literature values of renal Cd concentrationsthat cause harm to bank voles following dietary Cd exposureare broadly congruent with the results reported by this study:38 mg g�1 dw was reported to cause ‘‘severe testicular andrenal injuries’’ (78). Histopathological changes (focal degen-eration of proximal tubular cells) were caused by values of25–40 mg g�1 wet wt but not 12–16mg g�1 wet wt (79). If an80% tissue water content is assumed, this approximates tochanges at a 5–8mg g�1 dw but not 2.4–3.2mg g�1 level. Thus,metabonomic analysis detected biochemical changes atapproximately the lowest tissue concentrations that havebeen shown to cause cellular damage in C. glareolus (79).Dietary exposure to Cd causes a decrease in tissue Fe levelsin C. glareolus, and supplementation with dietary Fe reducesthe apparent toxicity of Cd (79,80). It has, therefore, been sug-gested that Cd toxicity is indirectly manifested in bank voles,by depression of tissue Fe and Fe-associated oxidative pro-cesses, including probably mitochondrial processes (80). It isnot yet clear how this may be related to the metabonomicresults. Cadmium has also been shown to cause an unex-pected decrease in lipid peroxidation in C. glareolus (81),and thus certainly has the potential to affect lipid metabo-lism, which may be related to the changes observed in lipidresonances.

The effects of dietary exposure to As (28mg g�1 in food fora 14 day period) on C. glareolus kidney tissue were also


studied by MAS 1H NMR metabolite profiling, and comparedto effects on the wood mouse A. sylvaticus (61). Pattern recog-nition techniques were not applied for data analysis. Theresults in this case were less clear cut than for Cd exposure;the main effect observed was broadening of the resonance atd 1.30 in C. glareolus. This resonance was assigned to bethe methylene protons of lipid triglycerides. No discernibleeffects were seen for A. sylvaticus. Some further intriguingeffects were discerned by diffusion-weighted MAS NMR spec-troscopy of the tissue samples: the apparent diffusion coeffi-cient of the slowest-diffusing water pool—assigned tointracellular water—in C. glareolus was increased by Asexposure. It was suggested that this might have been causedby rupture of the smallest cell fraction, indicating renaldamage. It is unlikely that calculation of apparent diffusioncoefficients by NMR spectroscopy will ever be used for envir-onmental site assessment, but it is an example of the latentinformation potentially obtainable by using NMR as an analy-tical technique.

3.2.2. Vertebrates—Fish

The Japanese medaka (O. latipes) provides a frequently usedsystem for studying developmental environmental toxicants.Trichloroethylene (TCE) is a very common environmental pol-lutant and a known disruptor of development (82,83), and wastherefore chosen as a model compound. Developing embryoswere exposed to TCE throughout development, and then ana-lyzed by NMR spectroscopy immediately before hatch. Eggswere exposed in a static nonrenewal system in sealed jarsfor a seven-day period postfertilization (stages 12–34, thepenultimate stage prior to hatch). One hundred eggs werecombined to provide enough sample for NMR, and extractedwith 6% HClO4. One-dimensional spectra taken at 500MHzwere binned into 0.005ppm regions, and log-transformedmean-centered data analyzed by PCA (58). Initial resultsshow a very clear effect of TCE on the embryo spectral pro-files: there is a clear relationship between TCE concentrationand an axis based on PCs 1 and 2 (Fig. 2). The controls are

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completely separated from the dosed embryos for all doselevels, even at the lowest nominal dose of 1mg l�1, and thehighest doses are clearly shown to cause greater changes thanthe lower doses. It is of particular interest that metabonomicchanges were observed at these low concentrations, whichwere considerably lower than the doses which caused a delayin hatching (a common indicator of developmental effects),i.e., the metabonomic biomarkers appeared to be highly sensi-tive even at levels well below the observed gross phenotypiceffects.

Future work will examine the effects of TCE throughoutthe course of development. Metabonomic analysis has beenused to provide a ‘‘developmental trajectory’’ for O. latipes,i.e., the changes in metabolite profiles occurring during nor-mal embryogenesis can be plotted on a PCA scores plot (24).Metabolism during development, as shown by these metabo-nomic changes, is strictly controlled and highly reproducible

Figure 2 Effect of trichloroethylene on biochemical profiles indeveloping medaka embryos: plot of nominal trichloroethylene con-centrations against data from principal components analysis of500MHz 1H NMR spectra, 2.5Hz bin width. Error bars¼SD(n¼ 4), dashed lines indicate 95% confidence limits.


between individual replicates. This developmental trajectorywill provide a baseline for the examination of future develop-mental toxicants: for example, it should be possible to distin-guish at what developmental point metabolic disturbancesare induced.

4. CONCLUSIONS AND FUTUREIMPLICATIONS

4.1. Unusual Metabolism

Some problems occur in the metabolic profiling of nonmodelorganisms. One of these is the appearance of unusual andunassigned metabolites. It is impossible to state how manynew or unusual and hence unassigned metabolites may bedetected in a metabolic-profiling study of a ‘‘new’’ organism.There are not enough previous examples to be able to predictwith any degree of confidence whatsoever what proportion ofmetabolites in a sample from a new organism may be genu-inely novel. For example, in the course of work within ourown group, we have discovered that every earthworm speciesthat we have yet tested (11 different species to date from fivedifferent genera) contains the aromatic compound HEFS.This compound was previously identified in earthworms,although the incorrect structural isomer was given (84).However, this previous discovery was not known to us whenwe were attempting to assign the observed resonances inthe NMR spectra, and hence the situation was the same asif the compound had never been characterized. Thus, 1Dand 2D 1H and 13C NMR spectroscopic data and high-resolu-tion mass spectrometry were needed to determine the struc-ture (34). This compound bears no clear relation to anyclass of biochemicals and its biogenesis is not obvious. Itsfunction in earthworms is unknown, although the extremelyhigh concentration of up to 0.1% total wet weight (84) mayindicate that it has some kind of structural or other physiolo-gical role. We conjecture that it may protect against dehydra-tion—a regular peril faced by earthworms—possibly bystabilizing membranes. Strongly amphiphilic metabolites

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have been shown to help maintain water relations in otherspecies (85,86). 2-hexyl-5-ethyl-3-foransolfonate is a goodexample of a completely new metabolite being (re)discov-ered consequent to observation of unassigned 1H resonances.Figure 3 shows the 1H NMR spectrum of an HClO4 extract ofL. rubellus, and shows that the spectrum is dominated by

Figure 3 500MHz 1H NMR spectrum of tissue extract (wholeorganism) of the earthworm L. rubellus, showing large number ofunassigned and nonstandard metabolites. Metabolites are labeleddirectly on the spectrum. A: high-frequency region of spectrum; ver-tical scale is expanded tenfold relative to B. Resonances from HEFSand formate are not represented to their full height. B: low-fre-quency region of spectrum. Gly: glycine. Glu: glutamate. Gln: gluta-mine. MeHis: methylhistidine. Ala: alanine. Val: valine. Tyr:tyrosine. U1: unknown compound, but possibly related to lombrici-ne=phosphoryllombricine. Selected other unassigned resonancesare marked by ‘‘?’’.


resonances from compounds that are not found in similarspectra from vertebrates. Similarly, the aromatic compoundhomarine found in abalone was initially assigned on the basisof the structural information implicit in the NMR spectra(53), and it was only subsequently realized that this com-pound had already been identified in marine invertebrates(54). These examples show the value of 1H NMR spectroscopyas an analytical technique for nonmodel organisms: the non-selective nature of the technique means that even previouslyunknown metabolites are detected, and the high level ofstructural information given by the spectra means thatunknown and=or unassigned metabolites can be quicklyrecognized as such, together with many clues as to theirpossible chemical class and structure.

4.2. Baseline Variability

This is a key issue for genuine environmental studies (i.e.,involving field and=or mesocosm studies), and may well benoticeable even in laboratory studies on nonmodel organisms,which are likely to be genetically and hence metabolicallymore heterogeneous than typical laboratory rat and mousestrains. Even well-characterized univariate biochemical mar-kers such as metallothionein or heat-shock protein levelshave been found to be highly variable in field tests. Metabo-nomic profiles will be affected by a wide range of physicaland environmental factors, including temperature and waterstress, salinity, seasonality, nutrient status, and life-cyclestage (87). Any realistic use of metabonomics for assessmentof field samples will require a consideration of these factors.Identification of suitable controls is important in those situa-tions where there are clear and known sources of contamina-tion; alternatively, in situ exposure of test animals willaddress many of these problems, but tells us nothing aboutthe actual health of the indigenous organisms and ecosys-tems. It is possible that the multivariate response of meta-bolite profiles could, in the long run, actually proveadvantageous: if enough background data were acquired,the NMR spectra could be used to fingerprint not

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only exposure to pollutants, but also the degree of otherbiotic=abiotic stresses.

4.3. Prior Knowledge

Proteomics and transcriptomics both require prior knowledgeof sequence data, and are thus currently limited to a rela-tively small set of species. Nuclear magnetic resonance-basedmetabonomic profiling has a unique advantage in having norequirement for prior knowledge—any sample can be ana-lyzed with equal facility. Novel biofluids may offer specificproblems, e.g., may have a high macromolecule content orbe difficult to collect, but tissue extracts can be readily pre-pared (assuming sufficient biomass is available) and goodspectra can very easily be obtained. This is certainly one ofthe major advantages of metabonomics over other ‘‘omic’’techniques, e.g., transcriptomics=proteomics—the use of amodel organism is not essential. This is particularly usefulfor environmental applications, as there are many existingbioassays using many different species. It also opens up thepossibility of using metabonomic profiling for systematics, inthe broader sense of comparison of biological functions acrossspecies=strains=populations rather than the restricted senseof identifying and categorizing species. An example of sucha biological function might be sensitivity to a specific toxinor pollutant.

If the 1H NMR spectra are treated solely as fingerprints,then there is genuinely no need for prior knowledge. How-ever, it is usually of interest to know what metabolites arecontributing to the resonances that vary significantlybetween samples or treatments. Thus, every new sample type(e.g., different species, different tissue, or biofluid) mayrequire an initial assignment of its 1H NMR-observable reso-nances, which might seem a major task. In practice, the situa-tion is likely to be somewhere between these two extremes:new samples will generally benefit from initial spectralassignment, but this may not be too onerous given that theprimary metabolites are the same even across extremelyvaried phyletic groups (Table 2). It is certainly true that


‘‘unusual’’ species may well contain unexpected and unas-signed metabolites, which then require a large amount of timeand effort for characterization (e.g., Ref. 34). But even if theobserved resonances cannot all be assigned, it is still possibleto gather valuable biochemical information (6), e.g., by com-paring profiles between different organisms, or by comparingeffects of chemicals to those of pollutants of known mechan-isms of toxic action. Thus, we have been able to show that sib-ling earthworm species, apparently occupying the sameecological niche, are in fact highly differentiated at thebiochemical metabolite level, even though we have not yetidentified the aromatic metabolites that differ between thespecies (25).

4.4. Lack of Success

The application of metabonomic methods to environmentalproblems is, in reality, still at the potential stage. There areno examples of completely new or unexpected discoveries orinsights into biochemical function or mechanism that havebeen produced by metabonomic methods. There are certainlyno examples of actual application of these methods to riskassessment or environmental monitoring; use in a regulatoryor other setting is hampered by the lack of knowledge of base-line data variability for suitable species. Metabonomics maybe thought of as multivariate profiling of a suite of small-molecule biomarkers; for a biomarker or suite of biomarkersto be useful and accepted as a monitoring tool, it is essentialthat there is a clear mechanistic understanding of how thebiomarker response is related to the chemical stressor (9).To date, there is no completely reliable predictive model thatcan be used to generate falsifiable hypotheses about changesin metabolism that could be tested by metabonomic methods,even for the simplest and best-understood model organisms,although attempts are being made to construct such models(6,88,89). Consequently, it is not surprising that there arecurrently no examples of metabonomic studies using environ-mentally relevant organisms where there are unequivocalmechanistic links between metabolite-level changes and

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specific chemical effects. This may be unsurprising, but is stillproblematic for the adoption of metabonomic techniques forecotoxicity assessment.

Nonetheless, the techniques have been tested and vali-dated in a number of different organisms, including verte-brates and invertebrates, and in settings ranging fromartificial exposure in laboratory systems to collection of auto-chthonous populations from contaminated field sites. Earth-worms have been tested with both metals and organics, inlaboratory and field tests, and furthermore the physiologicaleffects on metabonomic profiles (starvation, freezing) havealso been investigated. Analysis of earthworm tissue extra-cts=coelomic fluid could quite plausibly be used for soil eco-toxicity assessment. Specific sites that are known to becontaminated with hotspots, or point sources of toxins, couldbe assessed by comparison with appropriate controls.

In conclusion, some of the most important problemsfacing ecotoxicologists include:

a. Assessing the effects of mixture toxicity, in particu-lar, interactions between different chemical stres-sors.

b. Determining the degree of ‘‘health’’ of the organismsfound in an ecosystem—are pollutants that are pre-sent actually stressing the organisms?

c. Determining subtle sublethal endpoints, in particu-lar, effects on reproduction and development.

All of these are difficult problems, and addressing themwill require much effort and input from different disciplines.Modern postgenomic technologies may well transform theability and speed with which these scientific questions canbe answered. Metabonomics has not yet been widely adoptedfor ecotoxicological assessment, and there are many problemswith doing so, as discussed above. But it has certain crucialadvantages—such as applicability to any species, withoutrequiring prior sequence information; potential for rapid andhigh-throughput testing; and direct relationship to organ-ism-level biological functionality—that could render ametabo-nomic approach highly informative and of immense value.


REFERENCES

1. Moore MN. Biocomplexity: the post-genome challenge in eco-toxicology. Aquat Toxicol 2002; 59:1–15.

2. Ratcliffe RG, Shachar-Hill Y. Probing plant metabolism withNMR. Annu Rev Physiol Plant Mol Biol 2001; 52:499–526.

3. Sumner LW, Mendes P, Dixon RA. Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phyto-chemistry 2003; 62:815–1017.

4. Tweeddale H, Notley-McRobb L, Ferenci T. Effect of slowgrowth on Escherichia coli, as revealed by global metabolitepool (‘‘metabolome’’) analysis. J Bacteriol 1998; 180:5109–5116.

5. Tweeddale H, Notley-McRobb L, Ferenci T. Assessing theeffects of reactive oxygen species on Escherichia coli using ametabolome approach. Redox Rep 1999; 4:237–241.

6. Raamsdonk LM, Teusink B, Broadhurst D, Zhang N, Hayes A,Walsh MC, Berden JA, Brindle KM, Kell DB, Rowland JJ,Westerhoff HV, van Dam K, Oliver SG. A functional genomicsstrategy that uses metabolome data to reveal the phenotype ofsilent mutations. Nat Biotechnol 2001; 19:45–50.

7. Buchholz A, Takors R, Wandrey C. Quantification of intracel-lular metabolites in Escherichia coli K12 using liquid chroma-tographic-electrospray ionization tandem mass spectrometrictechniques. Anal Biochem 2001; 295:129–137.

8. Castrillo JI, Hayes A, Mohammed S, Gaskell SJ, Oliver S. Anoptimized protocol for metabolome analysis in yeast usingdirect infusion electrospray mass spectrometry. Phytochemis-try 2003; 62:929–937.

9. Handy RD, Galloway TS, Depledge MH. A proposal for the useof biomarkers for the assessment of chronic pollution and inregulatory toxicology. Ecotoxicology 2003; 12:331–343.

10. Peakall DB, Walker CH. The use of biomarkers in environ-mental assessment. 3. Vertebrates. Ecotoxicology 1994; 3:173–179.

11. Kammenga JE, Dallinger R, Donker MH, Kohler HR,Simonsen V, Triebskorn R, Weeks JM. Biomarkers in

490 Bundy

terrestrial invertebrates for ecotoxicological soil risk assess-ment. Rev Environ Contam Toxicol 2000; 164:93–147.

12. Adams SM. Biomarker=bioindicator response profiles of organ-isms can help differentiate between sources of anthropogenicstressors in aquatic ecosystems. Biomarkers 2001; 6:33–44.

13. Morgan AJ, Sturzenbaum SR, Kille P. A short overview ofmolecular biomarker strategies with particular regard torecent developments in earthworms. Pedobiologia 1999;43:574–584.

14. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: under-standing the metabolic responses of living systems to patho-physiological stimuli via multivariate statistical analysis ofbiological NMR spectroscopic data. Xenobiotica 2000;29:1181–1189.

15. Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabo-nomics: a platform for studying drug toxicity and gene func-tion. Nat Rev Drug Discov 2002; 1:153–162.

16. Cortet J, Gomot-De Vauflery A, Poinsot-Balaguer N, Gomot L,Texier C, Cluzeau D. The use of invertebrate soil faunain monitoring pollutant effects. Eur J Soil Biol 1999; 35:115–134.

17. Belfiore NM, Anderson SL. Genetic patterns as a tool for mon-itoring and assessment of environmental impacts: the exampleof genetic ecotoxicology. Environ Monit Assess 1998; 51:465–479.

18. Salminen J, van Gestel CAM, Oksanen J. Pollution-inducedcommunity tolerance and functional redundancy in a decom-poser food web in metal-stressed soil. Environ Toxicol Chem2001; 20:2287–2295.

19. Mouneyrac C, Amiard-Triquet C, Amiard JC, Rainbow PS.Comparison of metallothionein concentrations and tissuedistribution of trace metals in crabs (Pachygrapsus marmora-tus) from a metal-rich estuary, in and out of the reproductiveseason. Comp Biochem Physiol 2001; 129B:193–209.

20. Roesijadi G. The basis for increased metallothionein in anatural population of Crassostrea virginica. Biomarkers1999; 4:467–472.


21. Kagi JHR. Overview of metallothionein. Methods Enzymol1991; 205:613–626.

22. Holloway GH, Crocker HJ, Callaghan A. The effects of noveland stressful environments on trait distribution. Funct Ecol1997; 11:579–584.

23. Orlando EF, Guillette LJ. A re-examination of variation asso-ciated with environmentally stressed organisms. APMIS 2001;109:S178–S185.

24. Viant MR. Improved methods for the acquisition and interpre-tation of NMR metabolomic data. Biochem Biophys Res Comm2003; 310:943–948.

25. Bundy JG, Spurgeon DJ, Svendsen C, Hankard PK, Osborn D,Lindon JC, Nicholson JK. Earthworm species of the genusEisenia can be phenotypically differentiated by metabolic pro-filing. FEBS Lett 2001; 521:115–120.

26. Van den Thillart G, van Waarde A. Nuclear magnetic reso-nance spectroscopy of living systems: applications in compara-tive physiology. Physiol Rev 1996; 76:799–837.

27. Gradwell MJ, Fan TW-M, Lane AN. Analysis of phosphory-lated metabolites in crayfish extracts by two-dimensional1H-31P NMR heteronuclear total correlation spectroscopy (het-eroTOCSY). Anal Biochem 1998; 263:139–149.

28. Darwin C. On the formation of vegetable mould. Trans GeolSoc Lond 1840; 5:505–509.

29. Darwin C. The Formation of Vegetable Mould, Throughthe Actions of Worms, With Observations on Their Habits.London: Murray, 1881.

30. Edwards CA, Bohlen PJ. Biology and Ecology of Earthworms.3rd ed. London: Chapman and Hall, 1996.

31. Gibb JOT, Svendsen C, Weeks JM, Nicholson JK. 1H NMRspectroscopic investigations of tissue metabolite biomarkerresponse to Cu(II) exposure in terrestrial invertebrates: iden-tification of free histidine as a novel biomarker of exposureto copper in earthworms. Biomarkers 1997; 2:295–302.

32. Warne MA, Lenz EM, Osborn D, Weeks JM, Nicholson JK.Comparative biochemistry and short-term starvation effects

492 Bundy

on the earthworms Eisenia veneta and Lumbricus terrestrisstudied by 1H NMR spectroscopy and pattern recognition. SoilBiol Biochem 2001; 33:1171–1180.

33. Warne MA, Lenz EM, Osborn D, Weeks JM, Nicholson JK. AnNMR-based metabonomic investigation of the toxic effects of3-trifluoromethyl-aniline on the earthworm Eisenia veneta.Biomarkers 1999; 5:56–72.

34. Bundy JG, Lenz EM, Bailey NJ, Gavaghan CL, Svendsen C,Spurgeon D, Hankard PK, Osborn D, Weeks JM, TraugerSA, Speir P, Sanders I, Lindon JC, Nicholson JK, Tang H.Metabonomic assessment of toxicity of 4-fluoroaniline, 3,5-difluoroaniline and 2-fluoro-4-methylaniline to the earthwormEisenia veneta (Rosa): identification of new endogenousbiomarkers. Environ Toxicol Chem 2002; 21:1966–1972.

35. Bundy JG, Ramløv H, Holmstrup M. Multivariate metabolicprofiling using 1H nuclear magnetic resonance spectroscopyof freeze-tolerant and freeze-intolerant earthworms exposedto frost. CryoLetters 2003; 24:347–358.

36. Gibb JOT, Holmes E, Nicholson JK, Weeks JM. Proton NMRspectroscopic studies on tissue extracts of invertebrate specieswith pollution indicator potential. Comp Biochem Physiol B1997; 118:587–598.

37. Quirk PG, King GF, Campbell ID, Boyd CAR. Quantitation ofmetabolites of isolated chicken enterocytes using NMR spec-troscopy. Am J Physiol 1989; 256:G423–G429.

38. Beatty IM, Magrath DI, Ennor AH. Occurrence of d-serine inlombricine. Nature 1959; 183:591.

39. Bundy JG, Osborn D, Weeks JM, Lindon JC, Nicholson JK. AnNMR-based metabonomic approach to the investigation of coe-lomic fluid biochemistry of earthworms under toxic stress.FEBS Lett 2001; 500:31–35.

40. Bouche MB. Earthworm species and ecotoxicological studies.In: Greig-Smith PW, Becker H, Edwards P, Heimbach F,eds. Ecotoxicology of Earthworms. Andover: Intercept,1992:20–35.

41. Herzog GA, McPherson RM, Jones DC, Ottens RJ. Baselinesusceptibility of tobacco hornworms (Lepidoptera: Sphingidae)


to acephate, methomyl and spinosad in Georgia. J Entomol Sci2002; 37:94–100.

42. Thompson SN. NMR spectroscopy: its basis, biological applica-tion and use in studies of insect metabolism. Insect Biochem1990; 20:223–237.

43. Phalaraksh C, Lenz EM, Lindon JC, Nicholson JK, FarrantRD, Reynolds SE, Wilson ID, Osborn D, Weeks JM. NMR spec-troscopic studies on the haemolymph of the tobacco hornworm,Manduca sexta: assignment of 1H and 13C NMR spectra.Insect Biochem Mol Biol 1999; 29:795–805.

44. Fan TW-M. Metabolite profiling by one- and two-dimensionalNMR analysis of complex mixtures. Prog NMR Spectrosc1996; 28:161–219.

45. Lenz EM, Hagele BF, Wilson ID, Simpson SJ. High resolution1H NMR spectroscopic studies of the composition of the haemo-lymph of crowd- and solitary-reared nymphs of the desertlocust, Schistocerca gregaria. Insect Biochem Mol Biol 2001;32:51–56.

46. Takahashi M, Shudo C, Matsushita K, Umeda M, Nishina M,Hori E, Kato K, Takashita M, Ohsaka A. Qualitative andquantitative changes of metabolites in developmental stagesof Culex pipiens pallens as demonstrated by proton NMR.Jap J Zool 1989; 40:127–131.

47. Fan TW-M, Colmer TD, Lane AN, Higashi RM. Determinationof metabolites by 1H NMR and GC: analysis for organic osmo-lytes in crude tissue extracts. Anal Biochem 1993; 214:260–271.

48. Yancey PH, BlakeWR, Conley, J. Unusual organic osmolytes indeep-sea animals: adaptations to hydrostatic pressure andother perturbants. CompBiochemPhysiol A2002; 133:667–676.

49. Yin M, Palmer HR, Fyfe-Johnson AL, Bedford JJ, Smith RAJ,Yancey PH. Hypotaurine, N-methyltaurine, taurine, and gly-cine betaine as dominant osmolytes of vestimentiferan tube-worms from hydrothermal vents and cold seeps. PhysiolBiochem Zool 2000; 73:629–637.

50. Peluso C, Barbarisi A, Savica V, Reda E, Nicolai R, Benatti P,Calvani M. Carnitine: an osmolyte that plays a metabolic role.J Cell Biochem 2000; 80:1–10.

494 Bundy

51. Dietz TH, Wilcox SJ, Byrne RA, Lynn JW, Silverman H.Osmotic and ionic regulation of North American zebra mussels(Dreissena polymorpha). Am Zool 1996; 36:364–372.

52. Ennor AH, Morrison JF. Biochemistry of the phosphagens andrelated guanidines. Physiol Rev 1958; 38:631–674.

53. Viant MR, Rosenblum ES, Tjeerdema RS. NMR-based metabo-lomics: a powerful approach for characterizing the effects ofenvironmental stressors on organism health. Environ SciTechnol 2003; 37:4982–4989.

54. Netherton JC, Gurin S. Biosynthesis and physiological roleof homarine in marine shrimp. J Biol Chem 1982; 257:11971–11975.

55. Newman JW, Denton DL, Morisseau C, Koger CS, WheelockCE, Hinton DE, Hammock BD. Evaluation of fish models ofsoluble epoxide hydrolase inhibition. Environ Health Perspect2001; 109:61–66.

56. Wester PW, Vos JG. Toxicological pathology in laboratoryfish—an evaluation with two species and various environmen-tal contaminants. Ecotoxicology 1994; 3:21–44.

57. Arcand-Hoy LD, Benson WH. Fish reproduction: an ecologi-cally relevant indicator of endocrine disruption. Environ Tox-icol Chem 1998; 17:49–57.

58. Viant MR, Bundy JG, Pincetich CA, Tjeerdema RS. A novelmetabolomic approach for investigating developmental toxicityin medaka (Oryzias latipes). Metabolic Profiling: Application toToxicology and Risk Reduction. NIEHS, May 14–15, 2003.

59. Griffin JL, Walker LA, Garrod S, Holmes E, Shore RF,Nicholson JK. NMR spectroscopy based metabonomic studieson the comparative biochemistry of the kidney and urine ofthe bank vole (Clethrionomys glareolus), wood mouse (Apode-mus sylvaticus), white toothed shrew (Crocidura suaveolens)and the laboratory rat. Comp Biochem Physiol B 2000;127:357–367.

60. Cheng LL, Ma MJ, Becerra L, Hale T, Tracey I, Lackner A,Gonzalez RG. Quantitative neuropathology by high resolutionmagic angle spinning proton magnetic resonance spectroscopy.Proc Natl Acad Sci USA 1997; 94:6408–6413.


61. Griffin JL, Walker L, Shore RF, Nicholson JK. High-resolutionmagic angle spinning 1H NMR spectroscopy studies on therenal biochemistry in the bank vole (Clethrionomys glareolus)and the effects of arsenic (As3þ) toxicity. Xenobiotica 2001;31:377–385.

62. Holmes E, Bonner FW, Nicholson JK. Comparative studies onthe nephrotoxicity of 2-bromoethanamine hydrochloride in theFischer 344 rat and the multimammate desert mouse (Mast-omys natalensis). Arch Toxicol 1995; 70:89–95.

63. Bundy JG, Spurgeon DJ, Svendsen C, Hankard PK, WeeksJM, Osborn D, Lindon JC, Nicholson JK. Environmental meta-bonomics: applying combination biomarker analysis in earth-worms at a metal contaminated site. Ecotoxicology. In press.

64. Kurahashi T, Miyazaki A, Suwan S, Isobe M. Extensive inves-tigations on oxidized amino acid residues in H2O2-treatedCu,Zn-SOD protein with LC-ESI-Q-TOF-MS,MS-MS for thedetermination of the copper-binding site. J Am Chem Soc2001; 123:9268–9278.

65. Edwards CA, Bater JE. The use of earthworms in environmen-tal management. Soil Biol Biochem 1992; 24:1683–1689.

66. Roberts DL, Dorough HW. Relative toxicities of chemicals tothe earthworm Eisenia fetida. Environ Toxicol Chem 1984;3:67–78.

67. Niblett PD, Ballantyne JS. Uncoupling of oxidative phosphor-ylation in rat liver mitochondria by the lamprey larvicide TFM(3-trifluoromethyl-4-nitrophenol). Pest Biochem Physiol 1976;6:363.

68. Viant MR, Walton JH, Tjeerdema RS. Comparative sublethalactions of 3-trifluoromethyl-4-nitrophenol in marine molluscsas measured by 31P NMR. Pest Biochem Physiol 2001;71:40–47.

69. Coen M, Lenz EM, Nicholson JK, Wilson ID, Pognan F, LindonJC. An integrated metabonomic investigation of acetamino-phen toxicity in the mouse using NMR spectroscopy. ChemRes Toxicol 2003; 16:295–303.

70. Nicholls AW, Holmes E, Lindon JC, Shockcor JP, Farrant RD,Haselden JN, Damment SJP, Waterfield CJ, Nicholson JK.

496 Bundy

Metabonomic investigations into hydrazine toxicity in the rat.Chem Res Toxicol 2001; 14:975–987.

71. Holmes E, Nicholls AW, Lindon JC, Connor SC, Connelly JC,Haselden JN, Damment SJP, Spraul M, Neidig P, NicholsonJK. Chemometric models for toxicity classification based onNMR Spectra of biofluids Chem Res Toxical 2000; 13:471–478.

72. Luthy RG, Aiken GR, Brusseau ML, Cunningham SD,Gschwend PM, Pignatello JJ, Reinhard M, Traina SJ, WeberWJ, Westall JC. Sequestration of hydrophobic organic con-taminants by geosolids. Environ Sci Technol 1997; 31:3341–3347.

73. Chung N, Alexander M. Differences in sequestration and bioa-vailability of organic compounds aged in dissimilar soils.Environ Sci Technol 1998; 32:855–860.

74. Damborsky J, Schultz TW. Comparison of the QSAR modelsfor toxicity and biodegradability of phenols and anilines.Chemosphere 1997; 34:429–446.

75. Moyes CD, Suarez RK, Hochachka PW, Ballantyne JS. Acomparison of fuel preferences of mitochondria fromvertebrates and invertebrates. Can J Zool 1990; 68:1337–1349.

76. Watanabe H, Yamanaka H, Yamakawa H. Changes in the con-tent of extractive components in disk abalone fed with marinealgae and starved. Nippon Suisan Gakk 1993; 59:2031–2036.


78. Swiergosz R, Zakrzewska M, Sawicka-Kapusta K, Bacia K,Janowska I. Accumulation of cadmium in and its effect onbank vole tissues after chronic exposure. Ecotoxicol EnvironSafety 1998; 41:130–136.

79. Włostowski T, Krasowska A, Łaszkiewicz-Tiszczenko B. Diet-ary cadmium induces histopathological changes despite asufficient metallothionein level in the liver and kidneys ofthe bank vole (Clethrionomys glareolus). Comp BiochemPhysiol C 2000; 126:21–28.

80. Włostowski T, Krasowska A, Bonda E. An iron-rich diet pro-tects the liver and kidneys against cadmium-induced injury


in the bank vole (Clethrionomys glareolus). Ecotoxicol EnvironSafety 2003; 54:194–198.

81. Włostowski T, Krasowska A, Godlewska-Zylkiewicz B. Dietarycadmium decreases lipid peroxidation in the liver and kidneysof the bank vole (Clethrionomys glareolus). J Trace Elem MedBiol 2000; 142:76–80.

82. Williams P, Benton L, Warmerdam J, Sheehan P. Compara-tive risk analysis of six volatile organic compounds in Califor-nia drinking water. Environ Sci Technol 2002; 36:4721–4728.

83. Williams-Johnson MM, Ashizawa AE, De Rosa CT. Trichlor-oethylene in the environment: public health concerns. HumEcol Risk Assess 2001; 7:737–753.

84. Yoneda T, Soeda M, Suzuki Y, Moriyama S, Hiroyuki S,Mihara H. Isolation of 2-ethyl-5-hexylfuran-3-sulfonic acidFrom Earthworms as a Pharmaceutical. Japanese PatentsJP63005088 and JP07023366. Tokyo, Japan: Japanese PatentOffice, 1988.

85. Hoekstra FA, Golovina EA. The role of amphiphiles. CompBiochem Physiol A 2002; 131:527–533.

86. Oliver AE, Hincha DK, Crowe JH. Looking beyond sugars: therole of amphiphilic solutes in preventing adventitious reac-tions in anhydrobiotes at low water contents. Comp BiochemPhysiol A 2002; 131:515–525.

87. Heugens AHW, Hendriks AJ, Dekker T, van Straalen NM,Admiraal W. A review of the effects of multiple stressors onaquatic organisms and analysis of uncertainty factors for usein risk assessment. Crit Rev Toxicol 2001; 31:247–284.

88. Tomita M. Whole-cell simulation: a grand challenge of the 21stcentury. Trends Biotech 2001; 19:205–210.

89. Tomita M, Hashimoto K, Takahashi K, Shimizu TS,Matsuzaki Y, Miyoshi F, Saito K, Tanida S, Yugi K, VenterJC, Hutchison CA. E-CELL: Software enviroment for whole-cell stimulation. Bioinformatics 1999; 15:72–84.

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12

Current Challenges and FutureDevelopments in Metabonomics

Technology

DONALD G. ROBERTSON

Departments of Worldwide Safety Sciences,Pfizer Global Research and Development,


1. PERSPECTIVE

In 2002, I attended a conference at which metabonomics wasthe subject of an all-afternoon session. The format was in fourpresentations with an extended period of interactive Q&Aafter each session. One of the most heated discussions arosebetween an avid supporter of the technology (not myself)and a member of the regulatory community over whethermetabonomics was an ‘‘old dog with new tricks’’ or a ‘‘newdog’’. The regulatory representative took the ‘‘old dog’’ view

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with the metabonomics advocate expressing indignation as tohow this cutting edge technology could be considered as an olddog of any sort. It was a highly entertaining and amusinghalf-hour debate. As I reflect back on that debate, it becomesquite apparent to me that both discussants were ‘‘right’’ atleast in their own perception—it was simply a matter of per-spective. Clearly metabonomics technology will allow us to dosome things we currently do, but much more simply, faster orcheaper. In vivo toxicity screening and target organ identifi-cation are routinely conducted today, but metabonomicsmay enable us to do these much more rapidly and cost effec-tively. This example might be considered a new trick for anold dog—a faster and simpler way to do what we have tradi-tionally done in in vivo studies with classical histologicaland clinical pathology assessment. However, the ability tonon-invasively, yet repeatedly assess toxicity, in some casesprior to any traditional manifestation of toxicity, certainly issomething more than a ‘‘new trick.’’ It opens up a wholenew avenue of scientific thought (some might say a can ofworms) about what toxicity is and how we should properlyassess it for a meaningful evaluation of risk.

2. THE POWER OF THE METABONOMICAPPROACH IN TOXICOLOGY

Metabonomics (as well as many other ‘‘omic’’ sciences) makesreal the possibility of true systems biology assessment. Thequestion is—are we ready for such an assessment? The pre-ceding chapters should make it abundantly clear that weare pushing back the boundaries of when we can identify a‘‘response’’ be it toxic or otherwise to an extremely sensitivelevel. In many cases, this leads us to some of the most funda-mental questions in the field of toxicology. When does aneffect become ‘‘toxicity’’? Are there thresholds of systemicresponse that are indicative of entering a toxic state vs. ‘‘nor-mal’’ adaptation? For that matter when does normal adaptiveresponse become abnormal (i.e., toxicity)? Complicating mat-ters even further is the fact that some dose–response curves

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may be ‘‘U’’ shaped. It is well recognized that the lack of cer-tain vitamins or trace elements is just as harmful (read thattoxic) as too much of the very same molecule. However, themanifestations of the toxicities at either end of the dose–response curve are very different. This concept has beenextended to non-nutritional substances by the concept ofhormesis (1), a premise well recognized in radiation biology.Chemical hormesis suggests that certain compounds, tradi-tionally thought of as toxic, may actually have beneficialeffects at low doses.

The traditional approach for managing such thornyquestions is to avoid dealing with them all together. We mightnot know how to define toxicity but we will know it when wesee it. We simply monitor for classical signs of toxicity (e.g.,abnormal clinical pathology and=or histopathology) and weknow the dose is too high when these manifestations becomeevident. Figure 1 provides a simple diagrammatic representa-tion of the course of a candidate therapeutic agent throughthe body of an organism. When drugs behave the way we likethem to, the compound enters the body undergoing disposi-tion and transformation, produces a biochemical change,which may lead to a physiologic change and in some cases amorphologic change. Depending on the target, any one or allof these responses may be part of the desired effect. However,each level of response can lead to an untoward response aswell. For example, inhibition of a particular kinase may pro-duce the anti-inflammatory result we desire in our target tis-sue of the joint, but the same inhibition in a non-target tissuecan result in toxicity. Lowering cholesterol may be a admir-able goal, except lowering cholesterol too much may producea plethora of pathologies (2). The same is true of physiologicalresponse such as blood pressure. This concept, frequentlycalled exaggerated pharmacology, is well understood and inmany instances can be anticipated. If anticipated, these unto-ward responses can be appropriately monitored to establish areasonable therapeutic index. However, secondary or indirectresponses to compounds (or their metabolites), which are fre-quently unknown, are much more problematic. To addressthese potential toxicities, we have to essentially survey the

Metabonomics Technology—Challenges and Developments 501

entire animal for inappropriate physiologic or morphologicresponse to a compound. The trick of course, is to ensure thatthe unknown target is actually obtained at necropsy and theappropriate biochemical or physiologic assay is in place toassess the potential toxicity. Obviously, we can do neitherwith any certainty so we employ range-finding studies to

Figure 1 Simplified diagrammatic representation of the course ofa candidate therapeutic agent through the body of an organism. Theright side indicating positive and desired outcomes the right sidethe negative possible outcomes. What is evident is that there maybe many common responses between efficacious and a toxicoutcome.

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narrow down the dose ranges for definitive studies and iden-tify target organs. We can then subsequently make sure theappropriate assays are in place—assuming any are available.

What does all this have to do with metabonomics? Meta-bonomics enables the potential of gaining significantly moreinformation from fewer animals about a broader range of end-points without any a priori knowledge of target organ(s).Metabonomics can be done non-invasively, the time courseof effect from pretest baseline, through efficacious responseto initiation of toxic response and reversal to baseline canall be obtained from a single animal. Moreover, complicatedresponses, involving different targets and different timecourses, can be readily identified (though not so readily decon-voluted—see below). Looking at Fig. 1, we can see that meta-bonomics, along with other ‘‘omic’’ technologies, allows us toassess events from the onset of the initial biomolecularresponse to the death of the animals. The toxicologist nowhas to figure out what to do with that information. In otherwords, we will need to address the ‘‘thorny’’ questions.

The pressing needs for dealing with these questions areexemplified by some of the issues currently facing toxicoge-nomics advocates as they interact with regulatory bodies.Gene changes such as increased expression of proto-oncogenes in large format transcriptomic or otherwise unex-plainable findings have lead to a great deal of soul-searchingwithin the industry as to how best to use this kind of data forassessing safety (3). With regard to metabonomics, whatwould regulatory bodies do with studies in which traditionalno-effect doses (no histologic or clinical pathologies) havemetabonomic profiles consistent with hepatotoxicity evidentat higher doses? Will that mean the loss of a no-effect doseand perhaps a reduced or eliminated therapeutic index? Thiswould be a clear disincentive for any metabonomics applica-tion with clinical relevance. Will we have to have a mechanis-tic understanding of all metabonomic data prior to its use atregulatory agencies? While clearly this would be desirablefor all involved, in reality it will be a long time coming. Theseand other key questions will need resolution before the fullpotential of metabonomics technology can be realized.


3. METABONOMICS AS AN ‘‘OMIC’’TECHNOLOGY

3.1. ‘‘Omics’’ as a Tool of Systems Biology

John Lindon et al. described the origin and definition of meta-bonomics as a word (this volume, Chapter 1). An even broadernet finds metabonomics in the family of ‘‘omics’’ technologies,which include toxicogenomics (or transcripotomics) and pro-teomics as well as a host of other derivations of these technol-ogies, many of which have been given their own names. Whatis common about these approaches is that they are more sys-tem than analyte oriented. The role of metabonomics as a sys-tems biology tool has been recently reviewed (4) and itsinclusion as a sister science to toxicogenomics and proteomicshas been well recognized (5–7). A systems approach has sig-nificant advantages to the toxicologist, but these advantagesdo come with a price. The advantages include the possibilityof systemic toxicity evaluation from a single sample. In onesense, biological and medical sciences have come full circlewith regard to diagnosis of disease and=or toxicity. Beforethe advent of modern medicine, the only useful informationphysicians frequently had to diagnose ailments was, overteffects or clinical signs (fever, inappetence, rash, malaise,etc.) or the most crude ‘‘biochemical’’ assessments (urine smellor taste). The postulated cause of these effects was as eitherunknown or highly imaginative (misalignment of ‘‘humors’’,etc.). Even though the proposed etiology may have been even-tually disproved, the methodology of associating a pattern ofclinical changes with a specific ailment has survived to today.Physicians frequently make tentative diagnoses on presenta-tion of a series of clinical findings alone, without necessarilyhaving to understand the mechanistic link of each clinicalfinding to the suspected disease. As most clinical signs aresystemic manifestations of what may be a very focused dis-ease or toxicity, they are very much akin to ‘‘omic’’ data inthat they can reveal the response of an organism to a toxicor disease insult in toto. However, omic data have the signifi-cant advantages of being objectively measured, sensitivelyquantified (though measurement may be relative) and most

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importantly they can provide mechanistic insights. This isparticularly important for the preclinical toxicologist whocannot ask their subjects to tell us how they feel. We cannow assess an animal in distress via a peripheral sample(regardless of the target), frequently before any clinical man-ifestations become evident and in many cases before morpho-logical changes are evident. In some cases, this could be moresensitively and precisely accomplished by incorporation of anappropriate biochemical measurement in the study protocol,but that would presume the toxicologist knew what to lookfor before the study started. This is most often not the case.

The forgoing discussion should make apparent a signifi-cant drawback that has plagued the toxicologists’ use of‘‘omic’’ data. The problem with systems biology is that it issystems biology. This tautology emphasizes the fact thatwhen using omic data, one must consider the entire systemicresponse of an animal to the toxin, not just how the omic datareflect (or not) changes relevant to the target of interest. Thiswas brought home to us in some early metabonomic evalua-tion work where we noted that 13-week old rats had quite adistinct metabonomic profile compared to 8-week old rats(8). While fascinating, it was certainly not what we wereinterested in for evaluating the utility of the technology forassessing renal and hepatic toxicants. Though age differencesare easily handled by proper study design, indirect effects orsecondary target organ effects of a toxin within an individualanimal cannot be so easily compensated for. For example, ifan animal loses weight in response to a hepatotoxic insult,the weight loss itself will have profound systemic manifesta-tions at the gene, protein and intermediate metabolite levelindependent of and confounding to the effects induced bythe target organ pathology. How do you separate those effectsfrom one another? It can be done, but it is not a trivial taskand clearly represents one of the biggest challenges to the tox-icologist attempting to use omic data for safety evaluation.However, the assessment of safety involves the organism asa whole. We are not only interested in renal safety or liversafety or the effect on any individual target organ. Oureventual goal is to extrapolate our preclinical findings to poten-


tial effects on the quantity and quality of life of the humanpopulation who may be exposed to the drug. If the goal of thetoxicologist is to assess a therapeutic index for a candidate noveltherapeutic, a simple, but comprehensive descriptor of toxicity(and potentially efficacy as well) that takes into account allpotential targets, could be extremely powerful. The converseto this argument is that it will be difficult to deconvolute a spe-cific target biomarker from a set of data that reflects the entiresystemic response to a compound. As pointed out for other omictechnologies—metabonomics data present a significant threatfor serious misinterpretation (9). Metabonomics does not excusethe toxicologist from the conduct of high quality science and thecritical examination of the generated data.

3.2. The ‘‘Panomics’’ Approach

One of the plethora of omics related terms to arise in the pastfew years is the term ‘‘panomics.’’ Other terms have beenused, to mean the same thing, but panomics can be consideredas an omics of omics. An obvious panomics approach is utiliz-ing transcriptomics, proteomics, and metabonomics on thesame study enabling pursuit of toxic effects from the gen-e=transcript level through protein expression to phenotypicbiomolecular expression. This approach engenders an inher-ent synergy of these technologies by following the logical bio-logic progression from initial biochemical response to a toxicstimulus to overt toxicity, taking into account any cascadingbiochemical or physiological responses along the way. Tothose experienced with proteomic and transcriptomic data,this may seem a bit of a stretch as the temporal and quantita-tive discordance between gene expression and protein expres-sion has been well recognized (10). However, as depicted inFig. 2, metabonomics may allow for normalization of whatcould otherwise be uninterruptible results. Three theoreticaland highly stylized data sets from a ‘‘panomics’’ experimentare represented. Results a–c are theoretical transcripts,results d–f represent theoretical proteins, and results g–irepresent theoretical metabolites. Panel A represents theideal situation where a response to a chemical insult induces

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Figure 2 Highly stylized experimental outcomes from hypotheti-cal panomics experiments. "¼ Increased expression compared tobaseline, #¼decreased expression compared to baseline, �¼nochange from baseline. See text for further explanation.


an upregulation of transcript a, no change in transcript b anda decrease in transcript c, 4 hr after exposure. This is followedin temporal sequence by subsequent and similar changes inproteins at 8 hr and metabolite expression at 12 hr. Panel B,though still highly stylized, represents a more realistic setof data in which four animals are sampled at each time pointgiving what appears to be varying results in transcript, pro-tein, and metabolite expression. In actuality, the expressionprofiles are identical to panel A if individual animal temporalvariation is understood as demonstrated in panel C. The onlydifference between panels C and B is that the results havebeen normalized by response and not by time. This is simplya practical extension of observations put forward byNicholson et al. (11). This ability to understand individualanimal variation is a powerful advantage metabonomics canbring to panomics analyses. There are, of course, a numberof caveats. Metabonomics is not a real time analysis (at leastnot yet), so rapid changes would be difficult to normalize.Additionally, if serum is the biofluid being investigated, thelimitation of sample number over time would not be anydifferent than serum proteomic analysis. If urine is being used,the limitation becomes the timely availability of a volume suffi-cient for analysis. Despite these limitations, the ability to under-stand toxicity in the context of individual animal response, notartificially imposed sampling times, represents one of thegreatest advantages metabonomics brings to the toxicologist.

4. SHORT TERM NEEDS FOR METABONOMICSAS A SCIENCE

While advances in NMR and MS instrumentation have beendiscussed in earlier chapters, there still remain needs forthe technology outside analytical concerns. Though metabo-nomics in concept (12,13) has been around longer than meta-bonomics as a term (14), it is still a relative technologicalnewcomer in the armamentarium of the toxicologist. Oneof the most pressing needs for this technology within thetoxicology community is wider utilization that will bring

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greater acceptance. The initial slowness in uptake of metabo-nomics was largely due to the steep capital investmentrequired to conduct NMR-based metabonomics. Frequently,the slowness in up take was not due to unavailability ofNMR facilities, but in the unfamiliarity of toxicologists withNMR spectroscopists and vice versa. Nuclear magnetic reso-nance spectroscopists and toxicologists, though usuallyemployed by most medium to large-size pharmaceutical oracademic concerns, typically travel in different circles, dis-couraging collaborative efforts. As metabonomics gainsawareness in the greater scientific community, the advan-tages of collaboration of these two groups have become self-evident, leading to an ever-increasing pace of expansion ofthe technology. The consortium for metabonomic toxicology(COMET) served as a model of cross discipline collaborationfor the purpose of evaluating metabonomics as a tool to eval-uate preclinical toxicology within the pharmaceutical indus-try (15). The consortium for metabonomic toxicologycertainly demonstrated that toxicologists and NMR spectro-scopists could get along, but provided synergistic energy withminimal turf-guarding concerns. Another development hasbeen the recent expansion of MS-based metabonomics(16,17). Mass spectrometry as a technique is widely availableand is certainly more familiar to toxicologists. As theseapproaches gain acceptance, the pool of metabonomics practi-tioners will grow substantially.

A byproduct of wider use of the technology will be defini-tive case studies of metabonomics derived biomarkers andmechanistic work. Although some initial pharmaceuticallyrelevant metabonomics derived biomarker work related tomarkers for phospholipidosis has been published (18,19),many more examples will be required before skeptics will beconvinced of the utility of the technology for this application.An even greater body of work exists describing the utility ofmetabonomics in mechanistic studies (20–23). However, thework has been conducted by relatively a few laboratories.Wider utilization will lead to wider acceptance of this power-ful tool for mechanistic work. A corollary to panomics studiesis the use of integrated metabonomics, metabonomics of var-


ious biofluids and tissues, which brings many advantages tothe toxicologist interested in both mechanistic and biomarkerapplications (21,24). A significant limitation to widespreadutilization of this technology is the relative paucity of MAScapable instrumentation (see Chapter 5). As the benefits ofthe approach become evident, wider utilization of MASas part of an integrated metabonomics assessment withintoxicity studies will gain wider use.

Beyond the needs indicated above, one of the most press-ing requirements for the technology, and for all omic technol-ogies for that matter, are appropriate informatics tools. Omictechnologies generate data at a faster rate than any non-silicon based life form can assimilate. While data visualiza-tion tools and multivariate statistical packages are now avail-able (see Chapter 8), there is yet no established informaticstools that will take metabolic findings from pattern to path-way with the ability to link in panomics data. Although theprinciples of such a system have been described (25), andinitial reports of combined toxicogenomic proteomic investiga-tion are appearing in the literature (26,27), there is still apaucity of truly ‘‘panomic’’ toxicity studies. Despite thisabsence, many groups are working towards this goal, and itcan be envisioned that true panomic studies will soon beappearing.

5. CAUTIONARY NOTE

The previous section specified to one of the greatest needs ofmetabonomics as the need for expanded use of the technology.However, this advice should be heeded. One of the currentproblems with other omic sciences is the proliferation of plat-forms, vendors, and junk science. One need only go to anymajor scientific meeting to be deluged with pitches for the lat-est platforms, CROs and software packages that will solve allomic related problems. One is frequently amazed as to whatpasses for ‘‘validation’’ for people selling these approaches.For some, anything producing the desired and expected resultin one study can be considered validated. For many toxicolo-

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gists conducting experiments in industrial and academiclaboratories, experimental data frequently leave us trying tounderstand why things did not work or what the meaning isof data that were generated. However, when reviewing ‘‘vali-dation studies’’ in sales pitches from vendors trying to selltheir latest omic wares, we are more often left asking thequestion of why their experiments did work. It is difficult tounderstand why animals given carbon tetrachloride, forexample, have gene responses only along the expected pathsof hepatic toxicity and oxidative stress related effects. Is thistruly all that happened? Should not the fact that the animalslost weight or that several had frank renal lesions play a partin the response. If not why not? While this is a hypotheticalillustration, there have been too many examples of suchwok being pitched to the scientific community. We are beingtold what we want to hear—modern day toxicological snakeoil. This has lead to a backlash of sort, particularly in therealm of toxicogenomics in the industrial sector, such thatany ‘‘predictive’’ toxicogenomic screens are now looked at witha rather jaundiced eye. This is tragic, because there is realpotential for such work, but too many people doing shoddywork oversold it too quickly. What makes this particularlyworrisome for those of us in the pharmaceutical business, isthat the regulatory community has taken notice of develop-ments within the omics sciences (6,28) and a major black-eye now would hurt all those trying to push the omic sciencesforward within the arena of drug safety evaluation.

The same fate could befall metabonomics technology ifwe are not careful. It will be the responsibility of metabo-nomics practitioners to thoroughly evaluate the shortcom-ings of the technology as well as its advantages, makingthe scientific community as aware of the former as well asthe latter. If the science is truly as good as we think, it willeasily withstand the crucible of rigorous peer review. As weplay our roles as reviewers of manuscripts, advisors oncommittees, and purchasers of services, we will need tomake clear what is good science and what is self-servingnon-sense.


6. CONCLUSION

Metabonomics has clearly come of age as a science. Thisvolume only touched on major applications of the technologyincluding environmental (29) and clinical (30–32) applicationsthat could (and probably will be) the subject of future volumesall by themselves. However, the focus of this volume was tox-icological applications, and even within that narrowed scope,metabonomics has gone beyond the ‘‘emerging technology’’stage and is entering the realm of routine practice. Not allapplications of the technology are at the same stage, but thegrowth rate in publications is expanding at a rapid pace(Fig. 3) clearly suggesting the science is gaining growingacceptance. Although toxicological applications are certainlyexpanding with the science, there is still a lot of roomfor growth. The potential for rapid screening technology

Figure 3: Cumulative publication rate of papers containing meta-bonomics or metabolomics as a key word. Data obtained from Med-Line search covering the years 1996–2003.

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development, biomarker discovery, and the use of the technol-ogy as a powerful tool for understating basic mechanisms oftoxicity will all serve to make metabonomics an indispensabletechnique for toxicologists in the 21st century.

REFERENCES

1. Calabrese EJ, Baldwin LA. Chemical hormesis: its historicalfoundations as a biological hypothesis. Toxicol Pathol 1999;27:195–216.

2. Robertson DG, Breider MA, Milad MA. Preclinical safety eva-luation of avasimibe in beagle dogs: an ACAT inhibitor withminimal adrenal effects. Toxicol Sci 2001; 59:324–334.

3. Castle AL, Carver MP, Mendrick DL. Toxicogenomics: a newrevolution in drug safety. Drug Discov Today 2002; 7:728–736.

4. Nicholson JK, Wilson ID. Understanding ‘global’ systems biol-ogy: metabonomics and the continuum of metabolism. Nat RevDrug Discov 2003; 2:668–676.

5. Fiehn O. Combining genomics, metabolome analysis, and bio-chemical modeling to understand metabolic networks. CompFunct Genom 2001; 2:155–168.

6. Aardema MJ, MacGregor JT. Toxicology and genetic toxicologyin the new era of ‘‘toxicogenomics’’: Impact of ‘‘-omics’’ technol-ogies. Mut Res 2002; 499:13–25.

7. Reo NV. NMR-based metabolomics. Drug Chem Toxicol 2002;4:375–382.

8. Robertson DG, Reily MD, Sigler RE, Wells DF, Paterson DA,Braden TK. Metabonomics: evaluation of nuclear magneticresonance (NMR) and pattern recognition technology for rapidin vivo screening of liver and kidney toxicants. Toxicol Sci2000; 57:326–337.

9. Lewis LL. Key challenges for toxicologists in the 21st century.Trends Pharmacol Sci 2001; 22:281–285.

10. Gygi SO, Rochon Y, Franza BR, Aebersold R. Correlationbetween protein and mRNA abundance in yeast. Mol Cell Biol1999; 19:1720–1730.


11. Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabo-nomics: a platform for studying drug toxicity and gene func-tion. Nat Rev Drug Discov 2002; 1:153–162.

12. Nicholson JK, Higham D, Timbrall JA, Sadler PJ. Quantita-tive 1H NMR urinalysis studies on the biochemical effects ofacute cadmium exposure in the rat. Mol Pharmacol 1989;36:398–404.

13. Gartland KP, Anthony ML, Beddell CR, Lindon JC, NicholsonJK. Proton NMR studies on the effects of uranyl nitrate on thebiochemical composition of rat urine and plasma. J PharmBiomed Anal 1990; 8(8–12):951–954.

14. Nicholson JK, Lindon JC, Holmes E. ‘‘Metabonomics’’: under-standing the metabolic responses of living systems to patho-physiological stimuli via multivariate statistical analysis ofbiological NMR spectroscopic data. Xenobiotica 1999;29:1181–1189.

15. Lindon JC, Nicholson JK, Holmes E, Antii H, Bollard ME,Keun H, Beckonert O, Ebbels TM, Reily MD, Robertson DG,Stevens GJ, Luke P, Breau AP, Cantor GH, Bible RH,Niederhauser U, Senn H, Schlotterbeck G, Sidelmann UG,Laursen SM, Tymiak A, Car BD, Lehman-McKeeman L,Colet J, Loukaci A, Thomas C. Contemporary Issues in Toxicol-ogy: the role of metabonomics in toxicology and its evaluation bythe COMET project. Toxicol Appl Pharmacol 2003; 187:137–146.

16. Plumb RS, Stumpf CL, Gorenstein MV, Castro-Perez JM, DearGJ, Anthony M, Sweatman BC, Connor SC, Haselden JN.Metabonomics: the use of electrospray mass spectrometrycoupled to reversed-phase liquid chromatography showspotential for the screening of rat urine in drug development.Rapid Comm Mass Spectrom 2002; 16:1991–1996.

17. Pham-Tuan H, Kaskavelis L, Daykin CA, Janssen HG.Method development in high-performance liquid chromatogra-phy for high-throughput profiling and metabonomic studies ofbiofluid samples. J Chromatogr 2003; 789:283–301.

18. Nicholls AW, Nicholson JK, Haselden JN, Waterfield CJ. Ametabonomic approach to the investigation of drug-inducedphospholipidosis: an NMR spectroscopy and pattern recogni-tion study. Biomarkers 2000; 5:410–423.

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19. Espina JR, Shockcor JP, Herron WJ, Car BD, Contel NR,Ciaccio PJ, Lindon JC, Holmes E, Nicholson JK. Detection ofin vivo biomarkers of phospholipidosis using NMR-based meta-bonomics approaches. Magn Reson Chem 2001; 39:559–565.

20. Beckwith-Hall BM, Nicholson JK, Nicholls AW, Foxall PJD,Lindon JC, Connor SC, Abdi M, Connelly J, Holmes E. Nuclearmagnetic resonance spectroscopic and principal componentsanalysis investigations into biochemical effects of three modelhepatotoxins. Chem Res Toxicol 1998; 11:260–272.

21. Waters NJ, Holmes E, Williams A, Waterfield CJ, Farrant RD,Nicholson JK. NMR and pattern recognition studies on thetime-related metabolic effects of a-naphthylisothiocyanate onliver, urine, and plasma in the rat: an integrative metabo-nomic approach. Chem Res Toxicol 2001; 14:1401–1412.

22. Slim RM, Robertson DG, Albassam M, Reily MD, Robosky L,Dethloff LA. Effect of dexamethasone on the metabonomicsprofile associated with phosphodiesterase inhibitor-inducedmesenteric vascular lesions in rats. Toxicol Appl Pharmacol2002; 183:108–116.

23. Mortishire-Smith RJ, Skiles GL, Lawrence JW, Spence S,Nicholls AW, Johynson BA, Nicholson JK. Use of metabo-nomics to identify impaired fatty acid metabolism as themechanism of a drug-induced toxicity. Chem Res Toxicol2004; 17:165–173.

24. Coen M, Lenz EM, Nicholson JK, Wilson ID, Pognan F, LindonJC. An integrated metabonomic investigation of acetamino-phen toxicity in the mouse using NMR spectroscopy. ChemRes Toxicol 2003; 16:295–303.

25. Lu B, Lawton MP. Data integration in the new era of toxicoge-nomics. Toxicol Sci 2003; 72(S1):98.

26. Ruepp SU, Tonge RP, Shaw J, Wallis N, Pognan F. Genomicsand proteomics analysis of acetaminophen toxicity in mouseliver. Toxicol Sci 2002; 65:135–150.

27. Heijne WHM, Stierum RH, Slijper M, van Bladeren PJ, vanOmmen B. Toxicogenomics of bromobenzene hepatotoxicity:a combined transcriptomics and proteomics approach.Biochem, Pharmacol 2003; 65:857–875.


28. MacGregor JT. The future of regulatory toxicology: impact ofthe biotechnology revolution. Toxicol Sci 2003; 75:236–248.

29. Bundy JG, Spugeon DJ, Svendsen C, Hankard PK, Osborn D,Lindon JC, Nicholson JK. Earthworm species of the genusEisenia can be phenotypically differentiated by metabolic pro-filing. FEBS Lett 2002; 521:115–120.

30. Brindle JT, Antti H, Homes E, Tranter G, Nicholson JK,Bethell HW, Clarke S, Schofield PM, McKilligin E, MosedaleDE, Grainger DJ. Rapid and noninvasive diagnosis of the pre-sence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med 2002; 8:1439–1444.

31. Brindle JT, Nicholson JK, Schofield PM, Grainger DJ, HolmesE. Application of chemometrics to 1H NMR spectroscopic datato investigate a relationship between human serum metabolicprofiles and hypertension. Analyst 2003; 18:32–36.

32. Moolenaar SH, Engelke UFH, Wevers RA. Proton nuclearmagnetic resonance spectroscopy of body fluids in the fieldof inborn errors of metabolism. Ann Clin Biochem 2003; 49:16–24.

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Index

2-Bromoethanamine, 3532-Bromoethylamine, 211

Renal papillary pathology, 211NMR urinalysis, 211

2-oxoglutarate, 405

Accelerator mass spectrometry, 37Acetaminophen, 357Acute renal failure, 138

Administration offurosemide, 138

Acylcarnitine excretion, 136Intracellular carnitine

insufficiency, 136Adaptive response, 49

Induction of cytochrome P450isozymes, 49

Age-related Differences, 402Alpha-CH proton, 235Analgesic, 366

Analog-to-digital signal, 239Anestrus, 418Arsenic-induced hemorrhage, 182Diffusion-weighted 1H MAS

NMR spectroscopy, 182Aselective biomarkers, 4032P-postlabeling assay, 40

Automated phasing, 202

Base-line distortion correctionalgorithms, 202

Baseline variability, 457b-Blocker practolol, 248b-lactam ring, 9Bile formation, 146Bile, 146, 357Bile salts, 146Biliary cirrhosis, 357Hepatobiliary diseases, 357

Bile-aciduria and glycosuria, 343

517

Biliary toxicants, 211Bioinformatics, 55Biomarker of lipid

peroxidation, 40Malondialdehyde, 40

Biomarkers of exposure, 31Biomarkers of effective dose, 32Biomarkers of internal dose, 31

Biomarkers, 12, 27, 454Biomarkers of exposure, 28Biomarkers of response, 28Biomarkers of susceptibility, 28

Biomolecular NMR, 77Block-scaling, 321Blood plasma levels, 138Renal papillary damage, 138

Blood plasma lipoproteincontent, 124

Malignancy, 124

Cadmium toxicity, 405Capillary electrochromatography,

255Capillary electrophoresis, 255Carbon tetrachloride, 213Cardiovascular diseases, 8Catabolism, 343Cell culture supernatants, 2Cell lysis, 156Centrilobular hepatocellular

necrosis, 214Hepatocellular mitosis, 214Hepatocellular vacuolation, 214

Centrilobular hepatotoxicity, 213Centrilobular mononuclear

infiltrates, 214Cephalosporins, 364Chemical noise, 107Chemometrics analyses, 5Chlorpyrifos, 403Cholestasis, 343Choline containing

metabolites, 187Duchenne muscular

dystrophy, 187

[Choline containingmetabolites]

Multiple sclerosis, 187Chromophores, 4Consortium for metabonomic

toxicology, 509Consortium on metabonomic

toxicology, 20Coomans plot, 305Correlation spectroscopy, 88Cortical nephrotoxicity, 316Cortical tubular epithelial

necrosis, 214Creatinuria, 373, 405Cross-validation, 278

Data, 6Degree of renal failure, 126Degree of shielding, 80Detoxification mechanism, 405Deuterium isotope shift, 123Dexamethasone, 385Diabetes, 125

Administration of insulin, 125Diabetic mouse model, 209Diet, 435Diffusion-ordered NMR

spectroscopy, 87Dipolar couplings, 16Direct infusion mass

spectrometry, 103Diurnal Effects, 421Diurnal rhythm, 204Diurnal variation, 399DNA adducts, 35DNA repair, 56Drug discovery-screening

paradigm, 218Drug safety assessment, 337Duchenne muscular

dystrophy, 416

Ecotoxicology, 454Electronic spectroscopy, 4

518 Index

Endogenous analytes, 201Endogenous low mass

metabolites, 225Endogenous metabolites, 3Enzymuria, 360Estrus cycle, 204, 418

Diestrus, 418Estrus, 418Metestrus, 418Proestrus, 418

Exaggerated pharmacology, 501Exercise, 438

Fast acetylators, 61Fasting, 424Fibrosis, 359Flavin-containing monogenase

activities, 419Flow-probes, 240Food restriction, 424

Para-aminohippuric acidtransport, 424

Proteinuria, 424Renal lesions, 424

Fourier transform, 4Fourier transformation, 85Free induction decay, 85Free induction decays, 202

Exponential decay function, 202Fast Fourier transformation,

202Frequency domain spectra, 202

Functional NMR-basedmetabonomicslaboratory, 75

Gas chromatography, 4Gel-electrophoresis, 12Gender Differences, 404Gender, 433Genomics, 2Glomerulonephritis, 357Glutathione conjugates, 34Glycine conjugation, 409

Glycogenolysis and glycolysis, 342Glycosuria, 341Gut microfloral communities, 8

1H magic-angle spinning, 161H-NMR spectroscopy, 196Han-Wistar Zucker rats, 236Heat shock=stress proteins, 49Hemoglobin adducts, 36Hepatotoxicity, 316Hepatotoxicity, 343Hydrazine, 346

Hepatotoxin carbontetrachloride, 207

Hepatotoxin, 213Heteronuclear single quantum

coherence, 91Hierarchical cluster analysis

(HCA), 265, 424Hierarchical clustering, 400High resolution magic-angle-

spinning (MAS) NMR, 417High-resolution NMR

spectroscopy, 454High-throughput screening

efforts, 195Histopathologic assessment,

208, 338Homeostasis, 3Homonuclear Hartmann-Hahn

experiment, 88Hormonal effects, 918HPRT mutation frequency, 51Hyperplasia, 343Hypertrophy, 360

In vivo toxicologic studies, 208Influence vectors, 10Infrared spectroscopy, 103Instrument noise, 107Inter-individual variability, 401Internal variability, 205Inter-Subject Variation, 433Invasive procedure, 197Invasive, 30

Index 519

J-resolved experiment, 87

Karplus equation, 83Ketonuria, 380Kidney proximal tubule, 418Kidney toxicity, 217Krebs cycle, 215, 347Kupffer cells, 344

Latent information, 107Lee-Boot effect, 418Leverage, 279Light–dark cycle, 399Linear relationship, 78Magnetic field strength, 78Nuclear magnetic moment, 78Observation frequency, 78

Lipophilic xenobiotics, 411Lipoprotein, 12Atherosclerosis, 12

Liquid chromatography, 4Liquid-liquid extractions, 238Liver and kidney, 338Idiosyncratic, 339Necrosis, 338Steatosis, 338

Liver toxicants, 211Loadings, 278Logical blocking or QUILT

analysis, 400Lung damage, 46CCl6 protein, 46

Lung disease, 51Breath analysis, 51

Magic angle spinning, 79, 350Magic-angle-spinning (MAS)

spectra, 472Magnetic resonance imaging, 76Magnetic resonance

spectroscopy, 76Mapping space, 18Markers of liver dysfunction, 45

[Markers of liver dysfunction]Levels of certain bile acids, 45

Mass spectral data, 231Mass spectrometer, 245Mass spectrometry, 4, 173Mean-Centering, 271Measurement of enzyme

activity, 47Megavariate data analysis, 267Metabolic axes, 8Metabolic control analysis, 7Metabolic disturbances, 126

Renal function, 126Metabolic profiling, 77Metabolomics, 3Metabonomic literature, 230, 338

Vasculopathies, 338Metabonomic technology, 196, 499Metabonomic-based techniques,

180Metabonomics, 2, 110, 173, 453

NMR-based metabonomicprofiling, 453

Plant metabolomics, 454Meta-hydroxyphenylpropionic

acid, 405Methylamine metabolism

intermediates, 209Molecular mobility, 84Molecular self-diffusion

coefficients, 79MS, HPLC, GC=MS, 264MS instrumentation, 508Multidimensional

fingerprint, 398Multifocal tubular basophilia, 214Multimammate Mouse, 412Multivariate data analysis, 264Multivariate pattern recognition

(PR), 454Multivariate projection

methods, 264Partial least squares

projections, 264Principal component

analysis, 264

520 Index

Nalgene cages, 200Negative electrospray

ionization, 231Neural network analysis, 400Neuronal ceroid lipofuscinosis, 209Neurotoxicant, 348NMR spectroscopy, 30NMR spectrum, 78NMR, 508NMR-spectroscopy, 264Non-invasive evaluation, 196Noninvasive, 30Nuclear hyperplasia, 214Nuclear magnetic resonance

(NMR) spectroscopy, 75Nuclear magnetic resonance, 4Nuclear Overhauser effect, 90

Omic technologies, 503Proteomics, 504Toxicogenomics, 504

Orthogonal signal correction, 322Osmolarity, 200Oxidations and reductions, 232Oxidative DNA damage and lipid

peroxidation, 408-hydroxy-2-guanosine, 40

Panomics, 506Paraaminophenol (PAP), 206Pareto scaling, 271Partial least squares discriminant

analysis, 405Partial least squares model, 185Patent information, 108Pattern recognition method, 203

Multivariate analysis, 203Principal component

analysis, 203Pattern recognition, 14, 196, 264Pattern separations, 380PCA plot, 204, 409Peak parking, 242Peak picking, 242

Peak toxicity, 205Phospholipidosis, 297, 349Amphiophilic drugs, 349

Plasmaglucose, 12Diabetes, 12

Plasma, 116Point-swarms, 285Principal component analysis, 400Principal components, 203Probabilistic neural networks, 414Projection approach, 265Protein–ligand interactions, 79Proteome, 3Proteomics, 2Proton–proton NOE, 90Pseudopregnancy, 418

Quantitative signal distortion, 131

Radiochromatogram, 248Raman spectroscopy, 103Real time analysis, 508Red blood cell NMR

spectroscopy, 128Refrigerated metabolism

racks, 200Renal carbonic anhydrase, 405Renal functional patency, 213Renal inner medulla, 135Renal medullary:cortical

ratios, 412Renal toxicity, 359Nephrotoxicity, 359Proximal Tubular Toxicity, 360Renal Glomerular Toxicity, 369Proteinuria, 369

Renal Medullary Toxicity, 365Vascular Toxicity, 374Vasculitis, 375

Sample preparationrequirements, 200

Scaling, 270

Index 521

Score plot, 276Scores, 276Screening paradigm, 197Screening study design, 208Seminal fluid, 142Seminal vesicle fluid, 142Serum, 116Signal-to-noise ratio, 249Sleep deprivation, 430Slow acetylators, 60Solid phase extraction

chromatography, 15Solid phase extraction, 233Species Difference, 408Spin–spin coupling, 249Sprague–Dawley rats, 207Sprague–Dawley (SD) control

rat, 401Standard normal variate

correction, 322Standard tox endpoint, 206Steatosis, 47Strain Differences, 413Stress and acclimatization, 430Stress, 438Superconducting solenoid, 94Supervised classification method

SIMCA, 414Sym-endogenous metabolites, 9

Tautology, 505TCA cycle intermediates, 404Temperature effects, 425Tensor interactions, 175Tetramethylsilane, 81Therapeutic index, 501Time slicing, 241TOtal Correlated SpectroscopY,

228Toxicology, 500Dose–response curve, 501Toxicity, 500

Transcriptomic, 6Transcriptomic data, 6Transcriptomics, 2

Gene-chip technologies, 2Transgenic models, 416Tricarboxylic acid cycle

intermediates, 208Triple-quantum filtered COSY, 88Trypan Blue exclusion assay, 179Tubular acidosis, 405Tubular nephrotoxin, 206Tumor suppressor genes, 63

UDP-glucuronosyl transferase, 9Ultraviolet spectroscopy, 4Unit-Variance-Scaling, 270Urinary biomarkers, 216Urinary NMR profile, 208Urinary profiling, 219UV chromatogram, 243UV chromophores, 76

Vaginal cytology, 418Variability, 204Variable ionization efficiency, 4Vasculitis, 220

Water deprivation, 422Water Loading, 434Water resonance, 133

Water resonance frequency, 133Wavelet analysis, 325Weighting, 270Whitten effect, 418Wister rats, 208

Xenobiotic metabolites, 8, 232Xenobiotic toxicity, 140Xenobiotics, 219, 226, 398

522 Index

Metabonomics in Toxicity Assessment

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