PRINCIPLES OF BIOMARKER...

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PRINCIPLES OF BIOMARKER DEVELOPMENT

Margery A. Connelly, PhD, MBA

1st International Workshop on NASH Biomarkers

April 29, 2016

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Types of Biomarkers

Pathological Process or

Disease State

EfficacySafety

Pharmacologic response

Mechanism-of-action

Normal Biological Process

DiagnosisRisk Prediction

Prognosis

Causal toversus

Reflective ofDisease

2 Principles of Biomarker Development - 1st International Workshop on NASH Biomarkers - April 2016

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Biomarker Development

Payers and

Healthcare Costs

Physician Use in Patient

Care

Biological Plausibility

Analytical Development

Performance Evaluation

Clinical Validation

Intended Use

Clinical Utility

Regulatory Agency

Biomarker Use in Clinical Trials


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Development of Biomarker Assays

► Biological or pathological association

► Analytical validation (CLSI guidance1) Within-run and within-lab precision

Biological or intra-individual variability

Limit of detection, quantitation

Linearity and reportable range

Reference interval in normal healthy population

Specimen stability (temperature, storage, freeze-thaw)

► Assay interference Endogenous; lipemia or hemolysis

Exogenous; common drug substances

CLSI, Clinical and Laboratory Standards Institute; STARD, Standards for Reporting of Diagnostic Accuracy Studies. 1. www.clsi.org 2. Boursier et al, J Hepatology 2015; 62(4):807-15.


http://www.clsi.org/

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Evaluating Biomarker Performance

► Study population including subjects with and without disease

► Receiver operator characteristic (ROC) curves Determination of sensitivity and specificity Area under the curve (AUC), 95% confidence intervals (CI)

– TP = true positives, subjects correctly identified diseased (+,+)– TN = true negatives, subjects correctly identified healthy (-,-)– FP = false positives, subjects incorrectly identified diseased (+,-)– FN = false negatives, subjects incorrectly identified healthy (-,+)– Predictive values (NPV and PPV)

Xia et al., Metabolomics 2013;9:280-299.

Area of High Sensitivity

= TP/(TP +FN)

Area of High Specificity

= TN/(TN +FP)

True

pos

itive

rate

(TP

R)

False positive rate(FPR)


AUROC curve of ELF test predicting stages 0,1 (none or mild) vs. 2-6 (significant fibrosis) in NAFLD cohort (Ishak classification)

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Evaluating Biomarker Performance

► Determination of cut-points/medical decision limits using ROC curves Point where sensitivity = specificity

Youden index J; maximal vertical distance to diagonal

Choose higher specificity or sensitivity depending on clinical use

– Cost/benefit ratio

– For NASH screening test, high sensitivity and specificity to avoid liver biopsies (FP)

– A test with high NPV to rule out disease; subjects who don’t need liver biopsy (TN)

► Evaluate medical decision limits using clinical data sets Compare outcomes in subjects above and below the cut point

► The smaller the cohort used for “training set” the larger the uncertainty (confidence limits)

► Need to validate findings in multiple independent populations

Pepe et al., Clin Chem 2016;62(5):in press.

Youden J


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Clinical Validation► Clinical validation (STARD checklist1)

Careful choice of study population(s)

– Based on clinical question, intended use

– Age, gender, ethnicity considerations

– Avoid spectrum bias cohort with more of one stage of liver disease missing a stage, e.g. healthy or hepatic steatosis

Standardized, quality-controlled data collection

Assay performance

Liver-FibroSTARD standards2

► FDA guidance for biomarker qualification and clinical trial use► Clinical utility and use in clinical practice

Is biomarker test actionable? Can a physician use it to guide patient care?

Prospective studies addressing intended use

Impact of testing on clinical practice (clinical guidelines, cost effectiveness)

STARD, Standards for Reporting of Diagnostic Accuracy Studies. 1. Boursier J. et al, J Hepatology 2015; 62(4):807-15. 2. http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/ucm284076.htm


http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/ucm284076.htm

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Challenges for Development of NASH Biomarkers

► Outcome of interest is liver biopsy evaluation Procedure-related risk of bleeding and mortality Sampling error; if only a portion of the liver is affected by inflammation,

may be missed by liver biopsy Intra- and inter- observer variability in pathology interpretation Invasive, unpleasant for the patient Relatively high cost

► ~10% progress from hepatic steatosis to NASH

► Asymptomatic transition from simple steatosis to NASH

► Hard to distinguish individuals that will progress quickly vs. slowly

► Biomarkers for hepatic steatosis are good for detecting NASH

Sanyal et al, Hepatology, 2015; 61(4):1392–1405.


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Challenges for Development of NASH Biomarkers

► Spectrum bias may cause the AUROC to look better in one population but not others

► Single biomarkers are not likely to achieve high sensitivity and specificity

► Panels of biomarkers exhibit better performance Reasonable number of biomarkers in the panel Easy to measure or easily obtained data Raise specificity (liver-specific) or sensitivity (detection)

Sanyal et al, Hepatology, 2015; 61(4):1392–1405.

With the development of genomics, proteomics, metabolomics, lipidomics, glycomics, there are many potential biomarkers that may be useful for NASH;further validation needs to occur and tests need to be developed on clinical

instruments to test clinical utility


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FibronectinHyaluronic acid (HA)Type IV collagen S

Procollagen type III N-terminal Peptide (PIIINP)Tissue inhibitor of metalloproteinase 1 (TIMP-1)

Cytokines: TNF-α, IL-1β, IL-6, IL-8, IFNγ, TGFβAdipokines: Adiponectin, Leptin, Resistin

Acute phase reactants: hsCRP, GlycAChemoattractants: MCP-1

Ferritin, Soluble CD14

Biomarkers of Non-Alcoholic Fatty Liver Disease (NAFLD)

Alanine aminotransferase (ALT)Aspartate aminotransferase (AST)

Alkaline phosphatase (ALP)Gamma-glutamyl-transpeptidase (GGT)Acute phase reactants: α1-antitrypsin,

ceruloplasmin, haptoglobin, GlycA, albumin Lipid panel, NMR lipoprotein parameters

Measures of insulin resistance

Malondialdehyde, TBARS, Oxidized LDLCytokeratin-18 (CK-18) fragments

Ferritin

1. Adapted from Fitzpatrick et al., J Gastroenterol 2014;20(31):10851-10863.2. Armutcu et al., Adv in Clin Chem 2013;61:67-125.

Oxidative Stress and Apoptosis

Fibrosis

Inflammation

Hepatocyte Function

Coagulation

Platelet countProthrombin time (PT)

International normalized ratio (INR)


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Single versus Panels of Biomarkers for NASHSingle Biomarkers► Liver function tests: ALT, AST, GGT, albumin

► Measures of insulin resistance (HOMA-IR, adiponectin)

Biomarker Panels► NAS or NASH activity score: steatosis (0–3), hepatocellular ballooning (0–2), lobular

inflammation (0–3) based on liver biopsy; NASH = NAS ≥5 (COMPARATOR)

► NASH diagnostic: cleaved and intact CK-18, adiponectin, resistin (AUROC 0.70-0.85)

► NASH diagnostic panel: gender, BMI, diabetes, triglycerides, cleaved and intact CK-18 (AUROC 0.81)

► ION: Index of NASH >55 (AUROC 0.88)

Limitations ► Need more rigorous clinical validation (e.g. multiple, larger,

more clearly defined study populations without spectrum bias)

1. Buzzetti et al., Int J Endocrinol 2015; 343828. 2. Otgonsuren et al., Hepatol 2014;29:2006-2013.3. Armutcu et al., Adv in Clin Chem 2013;61:67-125. 4. Yilmaz et al., Curr Drug Targets 2013;14:1357-1366.5. Pearce et al., Biomarker Res 2013:1:7-17. 6. Image adapted from Mayo Foundation for Medical Education and Research, www.mayoclinic.org.


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NASH FibroSure Test► Includes 10 biomarkers in combination with age, gender, BMI

SteatoTest: Marker of hepatic steatosis grade S0-S3 (0.00-1.00) Steatosis score >0.5, sensitivity= 71%, specificity= 72% for identification of steatosis 1

NASHTest: Diagnostic assessment of the presence of NASH N0 = No NASH, N1 = Borderline NASH, N2 = NASH per the Kleiner classification2

Prediction of NASH, sensitivity = 88%, specificity = 50% (AUROC 0.69-0.83)3,4

FibroTest: Quantitative surrogate fibrosis marker (0.00-1.00) Corresponds to Metavir F0-F4 fibrosis staging Fibrosis score of >0.3, sensitivity = 83%, specificity = 78% for ≥F3 (AUROC 0.88)4,5

FibroSure ContentAlpha-2-macroglobulin Alanine transaminase (ALT)

Haptoglobin (HA) Aspartate aminotransferase (AST)

Apolipoprotein A1 Total cholesterol

γ-glutamyl transpeptidase (GGT) Triglycerides

Total bilirubin Fasting glucose

Widely used by physicians ~100,000 FibroSure Tests were performed at LabCorp in 2014

1. Poynard et al., Comp Hepatol 2005;4:10. 2. Kleiner et al., Hepatol 2005;41(6):1313-1321. 3. Poynard et al., 41st Annual EASLD Meeting 2006;Abs86. 4. Buzzetti et al., Int J Endocrinol 2015;343828. 5. Ratziu et al., BMC Gastroenterol 2006;6:6.


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Enhanced Liver Fibrosis (ELF) Test

► 3 direct biomarkers of fibrosis combined into one score: Hyaluronic acid (HA) Procollagen III N terminal peptide (PIIINP) Tissue inhibitor of metalloproteinase 1 (TIMP1)

► Good at diagnosing severe fibrosis in patients with chronic hepatitis B, C, HIV ► Not much validation in subjects with NAFLD► Significant overlap with normal ranges, which may confound interpretation of

results in mild to moderate fibrosis range► Used in NASH trials as surrogate marker for fibrosis with encouraging results

1. ELF Test is trademarked by Siemens Healthcare Diagnostics, Inc. Licensed for use outside of US. 2. Yoo et al., Liver Int 2013;33:706-713.

ELF Score Severity of Liver Fibrosis Fibrosis Stage AUROC

< 7.7 None to mild ≥F2 0.82

≥ 7.7 - < 9.8 Moderate ≥F3 0.90

≥ 9.8 Severe ≥F4 0.87

≥ 11.3 Cirrhosis


AUROC curve of ELF predicting stages 0,1 Vs 2-6 in NAFLD cohort (none or mild fibrosis from significant fibrosis Ishak classification)

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NMR LipoProfile® Analysis


► For ease of use and interpretation, 73 subclass signals are grouped into 9 common subclass categories

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1. Frazier-Wood et al., Metab Syndr Relat Disord 2012;10:244-251. 2. Shalaurova et al., Metab Syndr Relat Disord 2014;12(8):422-9. 3. Mackey et al., Diabetes Care 2015;38(4):628-636. 4. Dugani et al., JAMA Cardiol 2016;in press.

LP-IR, lipoprotein insulin resistance.

Confidential

► Characteristic lipoprotein changes occur in patients who become insulin resistant1,2

► The “LP-IR score” is a multivariable algorithm, 6 lipoprotein parameters related to insulin resistance2

► Validated in hyperinsulinemic-euglycemicclamp studies; associated with Si and HOMA-IR in multiple clinical cohorts2

► LP-IR is associated with type 2 diabetes risk3,4

► Simple, inexpensive way to assess insulin resistance in large clinical trials

VLDLparticlenumber(VLDL-P)

Large VLDL

Medium VLDL

Small VLDL

IDL

Medium LDL

Small LDL

Large HDL

Medium HDL

Small HDL

LIPOPROTEIN SUBCLASS PARTICLE NUMBERS

Positive Association Negative Association

LDLparticlenumber(LDL-P)

HDLparticlenumber(HDL-P)

VLDL Size

LDL Size

HDL Size

NMR Measured Insulin Resistance Score, LP-IR


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BIDMC NAFLD Registry (n=128)


Jiang et al., Liver Int 2016; doi: 10.1111/liv.13076.

Age 50.4 ± 12.2 % Female 39.7% BMI 35 ± 7 Hypertension 48.4% Hyperlipidemia 50.8% Diabetes 30.2% Statin use 32.5% ALT (IU/dL) 78 ± 56 AST (IU/dL) 51 ± 35 Conventional lipid panel Total cholesterol (mg/dL) 193 ± 46 Triglyceride (mg/dL) 199 ± 126 LDL-C (mg/dL) 111 ± 40 HDL-C (mg/dL) 45 ± 14 Comprehensive VLDL profiles VLDL particle concentrations (nmol/L) Total VLDL particles 80.2 ± 50.2 VLDL subclasses Large VLDL 10.1 ± 9.5 Medium VLDL 40.7 ± 24.9 Small VLDL 29.3 ± 26.1 Mean VLDL size (nm) 55.3 ± 7.6

( ) PNPLA3 I148M genotype (n, %) CC 46 (36.5%) GC 54 (42.9%) GG 26 (20.6%) NAS (n, %) 0-3 (simple steatosis) 27 (21%) 4-8 (NASH) 100 (89%) Fibrosis Stage (n, %) 0 40 (31.5%) 1 28 (22.1%) 2 38 (29.9%) 3 11 (8.6%) 4 10 (7.9%)

BIDMC, Beth Israel Deaconess Medical Center.

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Mean VLDL Size is Significantly Larger in NASH


Simple steatosis (n = 25)

NASH(n = 103)

P value 1

Background48.5 ± 11.2 50.8 ± 12.3 0.4Age

Gender % Female 36.0% 40.8% 0.7BMI 32.6 ± 4.8 34.9 ± 7.0 0.1%Diabetes 12.0% 34.0% 0.03CK18 (U/L) 283 ± 182 414 ± 415 0.1PNPLA3 I148M 44.0% 67.0% 0.03TM6SF2 E167K 22.6% 19.9% 0.7Conventional lipid panel

199 ± 52 191 ± 45 0.4Total cholesterol (mg/dL)Triglycerides (mg/dL) 178 ± 106 202 ± 131 0.4LDL-C (mg/dL) 118 ± 41 109 ± 39 0.3HDL-C (mg/dL) 46 ± 14 45 ± 14 0.9

Comprehensive lipoprotein profileVLDL particle concentrations (nmol/L)

Total VLDL particlesVLDL subclasses

Large VLDL Medium VLDLSmall VLDL

Mean VLDL size (nm)

84 ± 64

8.5 ± 8.541 ± 3335 ± 31

52.2 ± 8.4

79 ± 47

10.4 ± 9.740 ± 23

28 ± 25.056.1 ± 7.2

0.6

0.40.90.20.02

1P value calculated by two-tailed student T test or Chi-square test


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VLDL size is Associated with NASH

Adjusted β 95% CI P value

Age -0.02 -0.04 - 0.01 0.1Gender (%female) 0.20 -0.27 - 0.67 0.4BMI 0.03 -0.01- 0.06 0.2Diabetes 0.42 -0.15 - 0.99 0.1Statin use 0.31 -0.25 - 0.86 0.3PNPLA3 I148M 0.98 0.51 - 1.45 <0.001TM6SF2 E167K 0.60 0.03 - 1.18 0.04Mean VLDL size (nm) 0.06 0.03 - 0.09 <0.001


1Model adjusted with all variables in table

Steatosis Score (0-4) Lobular inflammation Ballooning degeneration

β Coefficient1 P value Risk ratio2 P value Risk ratio2 P value

Mean VLDL Size (nm) 0.03 (0.02 - 0.05) <0.001 1.05 (1.00 – 1.11) 0.06 1.02 (1.00 – 1.03) 0.02

CK18 (X102 U/L) 0.03 (-0.001 - 0.06) 0.003 1.02 (0.94 – 1.11) 0.6 1.01 (0.98 – 1.04) 0.6

1Adj β coeff. calc for steatosis score (0-4) as cont. dep. variable using VLDL size or CK18. Adj for age, gender, BMI, diabetes, statin use, PNPLA3 I148M2Adj risk ratio for presence of moderate to severe (score 2-3) vs. mild lobular inflammation (score 0-1) using VLDL size or CK183Adj risk ratio for presence of ballooning degeneration vs. no ballooning using VLDL size or CK18

Each nm increase in mean VLDL size was associated with 1.2% increase in the relative risk of NASH

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Small VLDL Particles Reduced in Liver Fibrosis


1P value calculated by two-tailed student T test or Chi-square test

FibrosisStage 0-1(n = 69)

FibrosisStage 2-4(n = 59)

P value 1

Background48.9 ±12.6 52.1 ± 11.6 0.1Age

% Female 42.0% 37.3% 0.6BMI 33.1 ± 5.6 36.1 ± 7.4 0.01%Diabetes 13.0% 49.2% <0.001CK18 (U/L) 299 ± 270 493 ± 465 0.004PNPLA3 I148M 56.8% 69.5% 0.1TM6SF2 E167K 21.2% 19.3% 0.7Conventional Lipid panel

202 ± 40 181 ± 51 0.01Total cholesterol (mg/dL)Triglyceride (mg/dL) 204 ± 131 192 ± 121 0.6LDL-C (mg/dL) 118 ± 37 102 ± 41 0.02HDL-C (mg/dL) 47 ± 15 44 ±14 0.3

Comprehensive lipoprotein profileVLDL particle concentrations (nmol/L)

Total VLDL particlesVLDL subclasses

Large VLDL Medium VLDLSmall VLDL

Mean VLDL size (nm)

86 ± 59

10.5 ± 10.441 ± 4034 ± 31

54.3 ± 8.3

73 ± 38

9.6 ± 8.440 ± 2123 ± 17

56.5 ± 6.6

0.2

0.60.8

0.020.1


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Small VLDL are Associated with Liver Fibrosis


1Risk ratio calculated for significant liver fibrosis (stage 2 or above) compared to minimal fibrosis (stage 0-1) 2Model adjusted with all variables in table

AdjustedRisk Ratio2 95% CI P value

Age 1.00 0.98 - 1.03 0.9

Gender (%female) 0.67 0.36 - 1.26 0.2

BMI 1.02 0.98 - 1.06 0.4

Diabetes 3.49 1.39 - 8.79 0.008

Statin use 1.41 0.66 - 3.03 0.4

PNPLA3 I148M 2.07 1.01 - 4.03 0.03

TM6SF2 E167K 0.58 0.24 - 1.43 0.2

Large VLDL (nmol/L) 1.00 0.96 - 1.03 0.8

Medium VLDL (nmol/L) 1.00 0.98 - 1.02 0.9

Small VLDL (nmol/L) 0.98 0.96 - 1.00 0.03

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Performance Characteristics



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VLDL Profile Changes with NAFLD Progression



VLDL LDL HDL

Concentration Size Concentration Size Concentration Size

PNPLA3 I148M - ↓↓ - ↑↑ - ↑↑

TM6SF2 E167K ↓ - ↓ ↑↑ - ↑↑

Liver fibrosis ↓ - - - ↓ ↑

NASH activity - ↑↑ ↑ ↓↓ - ↓

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Future Panels May Include NMR Biomarkers

► LP-IR Index1,2

► NASH specific analytes3-5

VLDL size positively associated with NASH Small LDL particle number; related to cardiovascular disease risk

► Liver fibrosis specific analytes3,6

Small VLDL particle number inversely associated with liver fibrosis and adds to NFS Branched chain amino acid (BCAA) concentrations affected by reduced liver function

► GlycA, glycoprotein marker of inflammation7-10

Reduced with later stage liver fibrosis and cirrhosis

► NMR parameters, quantified from the same spectrum, may enhance sensitivity and selectivity of existing panels and may aid with staging of NAFLD

1. Shalaurova et al., Met Syn Rel Dis 2014;12(8):422-429. 2. Mackey et al., Diab Care 2015;38(4):628-636. 3. Jiang et al., Liver Int 2016; doi: 10.1111/liv.13076. 4. Sanyal et al., Hepatol 2015;61(4):1392–1405. 5. Garcia et al., PLoS One 2015;10(11);e014676. 6. Cheng et al., PLoS One 2015;10(10);e0138889. 7. Otvos et al., Clin Chem 2015;61(5):714-23. 8. Akinkuolie et al., JAHA 2014;3(5):e001221. 9. Akinkuolie et al., ATVB 2015;35(6):1544-50. 10. Sands et al., Amer J Gastroenterol 2015;110:159-169.

BCAA


NMR spectrum

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Biomarker Development

Payers and

Healthcare Costs

Physician Use in Patient Care

Biological Plausibility

Analytical Development

Performance Evaluation

Clinical Validation

Intended Use

Clinical Utility

Regulatory Agency

Biomarker Use in Clinical Trials


Collaborative efforts between academia, pharma and diagnostic labs, along with regulatory input (FDA) may be the best way to develop new

biomarkers or scores that will substitute for liver biopsy as the gold standard end point for clinical trials and clinical care

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Covance Inc., headquartered in Princeton, NJ, USA, is the drug development business of Laboratory Corporation of America Holdings (LabCorp). COVANCE is a registered trademark and the marketing name for Covance Inc. and its subsidiaries around the world.


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