Prioritisation of Polybrominated Diphenyl Ethers (PBDEs ... · Water SOLUBILITY at 25°C Sol25...
Transcript of Prioritisation of Polybrominated Diphenyl Ethers (PBDEs ... · Water SOLUBILITY at 25°C Sol25...
Introduction
The estimation of the risk of chemical compoundsis an important part of the EuropeanRegistration, Evaluation, Authorisation andRestriction of Chemicals (REACH) system. Therisk presented by chemical compounds dependson their toxicity and emission scenario, as well ason their physicochemical properties, which gov-ern their distribution, metabolism and degrada-tion in the environment. In general, theevaluation of risk is a very complex problem,since chemical compounds, depending on theirphysicochemical properties, can potentiallyimpact the environment in very different ways.For example, some compounds can be very per-sistent and can cause their effects over tens andeven thousands of years. Examples of such chem-icals are perfluorinated compounds, some ofwhich can have half-lives for atmospheric degra-dation of thousands of years (1). Other chemicalsare easily degraded or have an extremely low sol-ubility, and will therefore not be of concern.
Several multimedia models, such as theMultimedia Environmental Pollutant AssessmentSystem (MEPAS; 2), the Multimedia ContaminantFate, Transport, and Exposure Model (MMSOILS)
provided by the US Environmental ProtectionAgency (http://www.epa.gov/ceampubl/mmedia),and the SimpleBox tool (3) developed by the DutchNational Institute of Public Health and theEnvironment (RIVM), have been used to provide ameans of simulating the accumulation and degra-dation of chemicals in different media, based on anumber of explicit mathematical models for thetransfer and degradation of molecules.
In this contribution to the field, as part of the EUCADASTER project, the estimation of thePredicted Environmental Concentration (PEC)and the Predicted No-Effect Concentration (PNEC)were performed by using SimpleBox (3) for the for-mer, and the Species Sensitivity Distribution(SSD) approach (4, 5) for the latter. Both methodsrequire the input of various physicochemical prop-erties of the chemicals, as well as data on their eco-toxicity. Since such data are usually not readilyavailable, risk assessors frequently face thedilemma of having to find experimental values forthese properties, or accepting their predicted val-ues based on the properties of similar molecules.
QSAR (quantitative structure–activity relation-ship) methods permit learning from available exper-imental data. They can be used to substituteexperimental measurements, thus decreasing cost,
Prioritisation of Polybrominated Diphenyl Ethers (PBDEs)by Using the QSPR-THESAURUS Web Tool
Igor V. Tetko,1,2 Pantelis Sopasakis,1 Prakash Kunwar,1 Stefan Brandmaier,1 SergiiNovoratskyi,2 Larysa Charochkina,3 Volodymyr Prokopenko3 and Willie J.G.M. Peijnenburg4,5
1Institute of Structural Biology, Helmholtz-Zentrum München — German Research Centre forEnvironmental Health (GmbH), Munich, Germany; 2eADMET GmbH, Neuherberg, Munich, Germany;3Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyir,Ukraine; 4National Institute of Public Health and the Environment (RIVM), Laboratory for Ecological RiskAssessment, Bilthoven, The Netherlands; 5Leiden University, Institute of Environmental Sciences (CML),Department of Conservation Biology, Leiden, The Netherlands
Summary — The prioritisation of chemical compounds is important for the identification of those chemi-cals that represent the highest threat to the environment. As part of the CADASTER project(http://www.cadaster.eu), we developed an online web tool that allows the calculation of the environ-mental risk of chemical compounds from a web interface. The environmental fate of compounds in theaquatic compartment is assessed by using the SimpleBox model, while adverse effects on the aquatic com-partment are assessed by the Species Sensitivity Distribution approach. The main purpose of this web toolis to exemplify the use of quantitative structure–activity relationships (QSARs) to support risk assessment.A case study of QSAR integrated risk assessment of 209 polybrominated diphenyl ethers (PBDEs) demon-strates the treatment and influence of uncertainty in the predicted physicochemical and toxicity parame-ters in probabilistic risk assessment.
Key words: fate assessment, hazard assessment, multimedia models, online tools, risk assessment.
Address for correspondence: Igor Tetko, Institute of Structural Biology, Helmholtz-Zentrum München —German Research Centre for Environmental Health (GmbH), Neuherberg D-85764, Munich, Germany.E-mail: [email protected]
ATLA 41, 127–135, 2013 127
time and, when in vivo toxicological studies are con-ducted to generate effect data, also animal use. Likeexperimental measurements, QSAR predictionsalso contain some uncertainty. In previous work (6),the fate analysis of five polybrominated diphenylethers (PBDEs) was performed, based on QSAR-predicted uncertainties. Compounds of this classhave received a lot of attention, due to their persist-ence and toxicity. Therefore, the commercial pro-duction of some of these chemicals is restrictedaccording to the Stockholm Convention onPersistent Organic Pollutants. The purpose of thiswork was to demonstrate how one can perform riskestimation and prioritisation of chemical com-pounds by using QSAR predictions, and how theuncertainty of the QSAR predictions employed canbe included into the environmental risk assessment.
Methods
The use of the SimpleBox model required a num-ber of physicochemical properties of molecules,which are listed in Table 1.
When experimental or predicted values are notavailable, the user can employ pre-defined defaultvalues, which are usually selected to provide a con-servative estimation of the fate assessment. Some ofthese parameters, such as “Standard mass fractionorganic carbon in soil/sediment”, “Gas phase diffu-sion coefficient”, “Water phase diffusion coefficient”,and “Mineral density sediment and soil”, are likelyto be the same for different compounds. Severalother parameters can be calculated by using pre-defined formulae (3) from other physicochemicalparameters — for example, the “Gas/water partition
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Table 1: List of physicochemical and biological properties required for risk assessment inSimpleBox
Property name Abbreviation Units QSAR model
Gas phase DIFFUSION coefficient DIFFgas [m2.s–1]
Water phase DIFFUSION coefficient DIFFwater [m2.s–1]MOLECULAR WEIGHT Molweight [g.mol–1]
Soil Organic Carbon/Water Partition Coefficient Koc [–] Y
Solid/water PARTITION COEFFICIENT for standard solids Kp [–]Octanol/water PARTITION COEFFICIENT Kow [–] YStandard mass FRACTION organic carbon in soil/sediment CORG [–]Mineral DENSITY sediment and soil RHOsolid [kg.m–3]
Gas/water PARTITION COEFFICIENT at 25°C Kh [–]VAPOUR PRESSURE at 25°C Pvap25 [Pa] YENTHALPY of vaporisation H0vap [kJ.mol–1]Water SOLUBILITY at 25°C Sol25 [mg.L–1] YENTHALPY of dissolution H0sol [kJ.mol–1]Junge’s constant JungeCons [Pa.m]Melting point Tm [oC] Y
Gas phase degradation RATE CONSTANT at 25°C kdeg.air [s–1] Ya
OH radical CONCENTRATION C.OHrad [cm–3]FREQUENCY FACTOR OH radical reaction k0.OHrad [cm3.s–1] YACTIVATION ENERGY OH radical reaction Ea.OHrad [kJ.mol–1]
Dissolved phase degradation RATE CONSTANT at 25°C kdeg.water [s–1] YBiodegradability test result biodeg [r/r–/i/p]CONCENTRATION BACTERIA in test water BACT.test [CFU.ml–1]RATE INCREASE factor per 10°C Q.10 [–]
Bulk degradation RATE CONSTANT standard sediment at 25°C kdeg.sed [s–1] Yb
Bulk degradation RATE CONSTANT standard soil at 25°C kdeg.soil [s–1] Yb
akdeg.air was considered to be a sum of photolytic and OH degradation. bThe degradation in soil and sediments wereconsidered to be two and nine times higher than in water, as considered by the US Environmental Protection Agency(http://www.epa.gov/pbt/tools/toolbox.htm) and also in the previous study (6).
coefficient at 25°C” is approximated, based on thesolubility of chemical compounds in water and theirvapour pressure. Of course, the user can also provideexperimental values for all parameters and importmore-confident estimations. However, in theabsence of both experimental and predicted values,the use of the default, or calculated, values is theonly feasible solution.
In the previous study (6), QSAR models for sevenphysicochemical parameters were employed (seeTable 1). In addition to these properties, we also con-sidered the QSAR model for the octanol/water parti-tion coefficient, which was developed by theCADASTER project participants (7). All of the mod-els from the previous study (6) were considered forimplementation in the web tool. The models formelting point (Tm, °C; 7), octanol/water partitioncoefficient (log Kow; 7), vapour pressure (Vp, log[1/Pa]; 7), photolysis lifetime in air (log [tphoto_degrada-tion], hour; 8) were developed exclusively with 2-Ddescriptors, and thus were easily reproduced. Themodel for water solubility (log S, log [mol/L]) thatwas used in the previous study was based only on n= 12 molecules (7), and it was not validated. Insteadof this model, we evaluated solubility with theALOGPS program, which has been demonstrated tohave a very good accuracy in several studies (9, 10).We advanced this program to version 3.01c byextending the training set to 8102 compounds. TheALOGPS 3.01c predictive error for the twelve PBDEmolecules was a root mean squared error (RMSE) =0.48, which is lower than its RMSE = 0.69 for thewhole training set. ALOGPS 3.01c thus providedgood accuracy for the PBDE compounds and wasused to predict this property.
Several other models used in the previous study(6) were based on 3-D descriptors, which were cal-culated by using DRAGON, as well as MOPAC pro-grams. The chemical structures were optimised byusing the AM1 semi-empirical method in theHYPERCHEM program (Version 7.03 for Windows,2002), and descriptors were calculated by using*.hin files. An attempt to re-implement themwithin the QSPR-THESAURUS (http://qspr-the-saurus.eu) did not succeed, presumably due to dif-ferences in the structure optimisation protocols.Indeed, QSPR-THESAURUS uses CORINA (11)for the generation of 3-D structures that are fur-ther processed by using the AM1 method providedby MOPAC 7.1, developed by Stewart (12). There -fore, these models were recalculated by using thestandard protocol available at the QSPR-THESAURUS website. We used the neural net-work method, which was applied to two sets ofdescriptors: a) E-State indices (13) and predictedsolubility; and b) octanol/water partition coeffi-cients predicted with the ALOGPS 2.1 program(10) for all the properties analysed. The perform-ances of the models developed were evaluated byusing the standard five-fold cross-validation proto-
col. The newly-developed models had similar per-formances in comparison to the models used in theprevious study (6).
The biodegradation half-life in water was esti-mated in the previous study (6) by using a modeldeveloped by Aronson et al. (14). Aronson et al.mapped Biowin3 category predictions (from EPISuite) to a set of experimental half-lives, whichwere observed for each category. The categorieswere formed according to the Biowin3 quantitativepredictions (Biowin3 “primary” predictions). Theaccuracy of this model was not very high. Wetested it for the prediction of n = 249 moleculesused in a Molcode model Q8-10-30-265 available atthe JRC website (http://qsardb.jrc.it). The Aronsonet al. model (14) performance of RMSE = 0.76 wassignificantly lower than the RMSE = 0.42 log unitscalculated by the Molcode model. Unfortunately,the Molcode model was developed by using 3-Ddescriptors calculated with the CODESSA pro-gram (15), which were absent from the QSPR-THESAURUS database. Therefore, this modelcould not be reproduced. The use of our standardneural network-based approach, yielded a modelwith RMSE = 0.40, by using a five-fold cross-vali-dation protocol for the same set of molecules. Theuse of the same training set protocol as in theMolcode model provided a calculated coefficient ofdetermination of Q2 = 0.86 for the testing set (n =83) compounds. This coefficient was higher thanthe Q2 = 0.82 reported for the same compounds bythe authors of the Molcode model. Thus, the newlyimplemented model provided the predictions of thehighest accuracy compared to the previous studies,and it was used to estimate the degradation of thechemical compounds in water.
All of the models developed and the data used, aswell as the statistical parameters of the models,are accessible on the QSPR-THESAURUS data-base webpage (http://qspr-thesaurus.eu/).
Emission scenario
As in the previous work (6), the compound airemission scenario was considered. The parametersanalysed in the previous work (persistency andlong-range transport potential) do not depend onthe factual emission volume. In the current proj-ect, an emission rate of 10 tonnes/year to the airwith an uncertainty of 10% was used. These num-bers are of the same order of magnitude as the esti-mation of 22–31 tonnes peak emission of PBDE-47in 1997 (16).
Species Sensitivity Distribution (SSD)
The SSD approach was used to estimate the prob-ability distribution of PNEC values. The theoreti-
Prioritisation of PBDEs by using the QSPR-THESAURUS web tool 129
cal background of SSD has been explained else-where (4, 5). For the purpose of this analysis, weused the 5% percentile at the 50% confidence valueas the estimate of PNEC.
Aquatic toxicity
The toxicity models for Daphnia and two fishspecies were used to predict the toxicity of chemi-cal compounds to aquatic species. Unfortunately,there are only limited aqueous toxicity data forPBDEs, which are not sufficient to build any mod-els for this class of compounds. Therefore, in addi-tion to PBDEs, we also used diphenyl ethers andaryl bromides. This allowed us to create acceptabletraining sets for three species, which were used tomodel the data. Nonetheless, the models developedshould be considered as more suitable for demon-stration purposes rather than being state-of-the-art models. The newly-developed models, includingthe underlying data, are publicly available athttp://qspr-thesaurus.eu/article/11457.
Uncertainty in input parameters
A Gaussian distribution was assumed to best fitthe uncertainties in the predictions. Whileresearchers often use the Student t-distribution forlinear models, its basic assumptions about identi-cal, independent, and normally distributed modelerrors are frequently not valid. In addition, the cor-relations of input variables and, moreover, theprocess of variable selection cannot be easilyaccounted for, and could result in an erroneousestimation of standard errors of predictions(SEPs). The standard error of the predictions isestimated as:
[SEP(Yp)]2 = s2 N1 + XTp(XT X)–1XpO = s2 (1 + h)[Equation 1]
where s2 is the model error, X is the matrix of theexplanatory variables (descriptors) for the trainingmolecules, Xp is the same matrix for the p test mol-ecules and h is the vector of calculated leveragevalues for the latter molecules. As it was shown inthe previous benchmarking study, the leverage(17) provided one of the lowest performances forthe estimation of the prediction errors for mole-cules. Moreover, in practice, h has values that areusually less than one and, thus, the predictionerrors are dominated by the average errors of themodel. Therefore, we assumed that predictionerrors for linear models followed a normal distri-bution, whose standard deviation is estimated by s.
For neural network models, we estimated theprediction errors based on the standard deviationof ensemble predictions. This approach provided
the best estimation in benchmarking studies bothfor regression and classification models (17).
Risk estimation
After calculating the distribution of the predictedvalues for fate (PEC, as provided by SimpleBox)and effect (PNEC, as provided by SSD analysis)assessment, one could easily estimate the risk bythe numerical integration of the overlap of bothdistributions. However, since the number of stepsin Monte Carlo iterations was limited to N =10,000, the estimation of risks with p < 1/N wasnot possible. In order to do this, we also calculatedmedian values for both distributions, and usedtheir difference to rank molecules and rule out theones that exhibit a high risk of toxicity.
Results and Discussion
The software that was developed in this project isavailable at http://qspr-thesaurus.eu/risk. It can beaccessed through the menu item “Risk Assess -ment” of the web tool. The QSPR-THESAURUSwebsite itself was developed as an extension of theOnline CHEmical Modeling environment(OCHEM; 18). Any modern web browser that sup-ports JavaScript can be used to run it.
Figure 1 demonstrates the graphical interfacethat allows users to specify data for the fate assess-ments. The user can either select experimentalvalues, which are available in the QSPR database,use model predictions, or provide their values forsome of the parameters based on expert judge-ment. All of the intermediate results from theMonte Carlo simulation runs (both for fate andeffect assessments) can be downloaded and usedfor offline analysis.
The online supplementary information (TableS1) contains the risk estimations for all 209 com-pounds from the PBDE data set. A graphical rep-resentation of the calculated results is shown inFigure 2. It is clear that compounds with largernumbers of Br atoms are more likely to pose a sig-nificant risk to the environment. Indeed, all PBDEcongeners with more than five bromine atoms werepredicted to yield a non-zero risk and could be toxicto aquatic species under the emission scenario con-sidered. An example of a risk assessment forPBDE-177 is given in Figure 3.
The web tool developed also allows a visualinspection of the impact of individual physico-chemical parameters on the fate assessment ofchemical compounds via sensitivity analysis. Thesensitivity is evaluated by using the Spearman’srank correlations between each individual param-eter and the calculated PEC values, provided bySimpleBox. The use of Monte Carlo simulations
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therefore enables us to identify which parametershave the highest impact on the resulting concen-tration of the chemicals in the environment.
Figure 4 indicates that, for the fate assessmentof PBDE-177 (shown in Figure 3), the highestimpact is provided by the model predicting degra-dation in air via photolysis. If this mechanism isnot considered, the degradation in air due tohydroxyl radical oxidation, the rate of which isabout five-fold smaller than the rate of photolysis,does not have a major effect on the accumulation ofchemicals in water. In the latter case, the domi-nating effect is due to the vapour pressure. Theabsence of photolytic degradation in the air alsoincreases the environmental risk of the compound.In itself, this is obvious, as in the absence of degra-dation in air, the predicted concentration in theenvironment increases, thus contributing to ahigher estimated risk.
Conclusions
We have developed a web tool for the exemplifica-tion of the use of QSAR models for the fate, effectand risk assessments of chemical compounds. Theweb tool developed allows an easy and interactiveanalysis of the fate and effect assessments of chem-ical compounds by using the SimpleBox and SSDapproaches, respectively. The PEC and PNEC val-ues calculated with both of these approaches can
be used to provide a risk assessment of chemicalsunder the described emission scenario. We havealso demonstrated its use for the risk assessmentof 209 PBDEs. The use of the tool developed is lim-ited by the availability of experimental values orQSAR models for the classes of moleculesanalysed. The QSPR-THESAURUS database con-tains a number of such models described in multi-ple publications (6, 7, 19, 20), which weredeveloped during the CADASTER project for thefour classes of compounds. However, in case ofexperimental values and/or compound classes notyet being available in the database, the users canprovide the required properties according to theirown expert knowledge. Thus, the approach devel-oped can be easily used for the estimation of risk ofnew classes of molecules beyond those analysed inthe CADASTER project.
Online Supplementary Information
Table S1 contains the predicted risk values for 209PBDEs and is available at www.frame.org.uk.
Acknowledgements
This study was partly supported by the EUthrough the CADASTER project (FP7-ENV-2007-212668) and the FP7 MC ITN project Environ -
Figure 2: Environmental risk for PBDEs as function of the number of Br atoms in themolecule
The environmental risk was calculated as a numerical integration of the overlap of PEC and PNEC distributions (seeFigure 3).
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mental Chemoinformatics (ECO), grant agreementNo. 238701, and the GO-Bio 1B BMBF projectiPRIOR, grant agreement No. 315647. The authorsthank Dr Ullrika Sahlin (Linnaeus University) forproviding the R-code used in this publication, andProfessor Mark Huijbregts (Radboud University)and Mr Sarfraz Iqbal (Linnaeus University) fortheir helpful remarks.
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Prioritisation of PBDEs by using the QSPR-THESAURUS web tool 135