Slide1 of24AFrameworkforHighThroughputExposure(HTE)EstimationAFrameworkforHighThroughputExposure(HTE)EstimationPresentedbyJohnWambaughOfficeofResearchandDevelopmentNationalCenterforComputationalToxicologyPresentedbyJohnWambaughOfficeofResearchandDevelopmentNationalCenterforComputationalToxicologySlide2 of24PresentationRoadmapIntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearch•Frameworkoverview• HTEForwardDataandModels• HTEReverseDataandModels• SEEMAnalysisandHTEEstimatesFutureApplicationsSlide3 of24IntroductionThetimelycharacterizationofthehumanandecologicalriskposedbythousandsofexistingandemergingcommercialchemicalsisacriticalchallengefacingEPAinitsmissiontoprotectpublichealthandtheenvironmentEnvironmentalFateandTransportConsumerUseandIndoorExposureExpoCastisaninitiativetodevelopthenecessaryapproachesandtoolsforrapidlypredictingexposureforthousandsofchemicals(Cohen‐Hubal,etal.,2010)ProofofConcept(FirstGenerationAnalysis):Usedoff‐the‐shelfhighthroughputexposuremodelsandevaluatedpredictionswithbiomonitoringdatatocharacterizeuncertainty(Wambaughetal.,2013)“Allcasesareunique,andverysimilartoothers.”―T.S.EliotSlide4 of24ExposureScienceinthe21stCentury2012NRCreport:NewtoolsneededforscreeningandprioritizationofchemicalsfortargetedtoxicitytestingNew,focusedexposureassessmentsormonitoringstudiesneededBetterquantificationofpopulationvulnerabilityneededFigure from Egeghy et al. (2012),“The exposure data landscape for manufactured chemicals”Slide5 of24HowtoMakeGoodForecasts1)Thinkprobabilistically(especially,Bayesian):Weuseanapproachthatevaluatesmodelperformancesystematicallyacrossasmanychemicals(andchemistries)aspossible2)Forecastschange:Today’sforecastreflectsthebestavailabledatatodaybutwemustacceptthatnewdataandnewmodelswillcausepredictionstoberevisedOrrinPilkey &OlindaPilkey‐Jarvis(2007)NateSilver(2012)3)Lookforconsensus:Weevaluateasmanymodelsandpredictors/predictionsaspossibleSlide6 of24ExposureForecasting(ExpoCast)DevelopthetoolsanddatanecessarytorapidlyquantifyhumanandecologicalexposurepotentialofchemicalsFocusisdistinctfrommanyexistingexposuretoolsthatsupporteitherscreeninglevelassessmentsonaperchemicalbasisorfullregulatoryriskassessmentInNateSilver’sterminology:apredictionisaspecificstatementaforecastisaprobabilisticstatementWikipedia(statistics):“wheninformationistransferredacrosstime,oftentospecificpointsintime,theprocessisknownasforecasting”Slide7 of24GoalsforHighThroughputExposure•Incorporatemultiplemodelsintoconsensuspredictionsfor1000sofchemicals•Evaluate/calibratepredictionswithavailablemeasurementdataacrossmanychemicalclasses•Empiricallyestimateuncertaintyinpredictions•Analogouseffortsforbothhumanandecologicalexposures,buthumanismoredevelopedtodateSlide8 of24WhyBayesianStatistics?Ifweobservethreeyellowcars,whatdoweinferaboutthecolorofothercars?Slide9 of24WhyBayesianStatistics?Wecouldinferthatallcarsareyellow,BUT:•Thisconflictswithourpriorexperience,and•Isnotthatmuchmorelikelythanotherexplanations(itcouldhavehappenedbychance)Slide10 of24WhyBayesianStatistics?BayesiananalysisisarigorousstatisticalmethodologyAttempttoconsidersalllikelyexplanationsfordata,ratherthanfocusingondeterminingjustthesinglemostlikelyexplanationPropermathematicalcombinationofpriorexpectationsandnewlyobtaineddatainordertoobtainanewdistributionofexpectedvaluesSlide11 of24ExposureSpaceChemicalManufactureConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsEnvironmentalReleaseFoodAir,Soil,WaterAir,Dust,SurfacesHumanEcologicalFloraandFaunaDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)MONITORINGDATARECEPTORSMEDIABiomarkersofExposureBiomarkersofExposureMediaSamplesWasteSlide12 of24ExposurePathwaysChemicalManufactureConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsEnvironmentalReleaseFoodAir,Soil,WaterAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanEcologicalFloraandFaunaDietary Far‐FieldDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)MONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureBiomarkersofExposureMediaSamplesEcologicalWasteSlide13 of24ForwardPredictionofExposureChemicalManufactureConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsEnvironmentalReleaseFoodAir,Soil,WaterAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanEcologicalFloraandFaunaDietary Far‐FieldDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)MONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureBiomarkersofExposureMediaSamplesEcologicalWasteDataandModelsHTEForwardDataandModelsPeterEgeghySlide14 of24ForwardPredictionTreatdifferentmodelslikerelatedhigh‐throughputassays– consensusUSEtoxRAIDARUnitedNationsEnvironmentProgramandSocietyforEnvironmentalToxicologyandChemistrytoxicitymodelVersion1.01Rosenbaumetal.2008RiskAssessmentIDentificationAndRankingmodelVersion2.0Arnotetal.2006Slide15 of24ForwardPredictionDifferentHTEmodelsprovidedifferent,buthopefullyrelated,chemical‐specificpredictionsWelookforconsensussincesomemodelsmayperformbetterfordifferentchemicals(andchemicalsclasses)7834EDSPChemicalsUSEtoxorRAIDARPredictions(kg/kgemitted)Slide15 of24Slide16 of24ReverseInferenceofExposureChemicalManufactureConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsEnvironmentalReleaseFoodAir,Soil,WaterAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanEcologicalFloraandFaunaDietary Far‐FieldDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)MONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureBiomarkersofExposureMediaSamplesEcologicalWaste•Biomarkersreflectallpathways,notjustthoseinforwardmodel– missedpathwayscontributetoestimateduncertaintyDataandModelsHTEReverseDataandModelsWoodrowSetzerSlide17 of24ReverseInferenceDifferentHTEmodelsprovidedifferentchemical‐specificpredictionsSomemodelsmaydoabetterjobthanothersoverall,sowewanttoevaluateperformanceagainstmonitoringdataSeparateevaluationscanbedoneforvariousdemographicsCDC,FourthNationalExposureReport(2011)Slide18 of24SystematicEmpiricalEvaluationofModels(SEEM)TherearefourbasicstepsintheSEEMframework1.Forwardpredictionofexposures,whichinvolvesmodelcurationandparameterization2.Inferenceofexposuresfrommonitoringdata3.Systematicevaluationandcalibrationofthepredictionsagainsttheinferredexposures4.Extrapolationofthecalibratedmodelpredictionsandestimateduncertaintytochemicalswithnomonitoringdata.ToachievetheseaimstheSEEMframeworkusedBayesianformalismandmultivariate,linearregressionfordemonstratingandevaluatingpredictiveabilitySlide19 of24IllustrationoftheSEEMFrameworkEstimateUncertaintyEDSPChemicalsQSARsandHTEDataBiomonitoringData Inferred (Reverse) ExposureModel1Model2...CalibratemodelsApplycalibrationanduncertaintytootherchemicalsEvaluateModelPerformanceandRefineModelsForwardPredictionsExposureInferenceDataset1Dataset2...Slide20 of24BetterModelsandDataShouldReduceUncertaintyImagefromLittleetal.(2012)Uncertainty/VariabilityofNHANESBiomonitoring~10%Farfield(Industrial)Releases~60%Indoor/ConsumerUseIndirectExposureDirectExposure•ConsolidatedHumanActivitiesDatabase(CHAD)•ACToR UseDB,CPCPdb,andCPcatDB•SHEDS‐HTSlide21 of24SEEMEvolution– 1stand2ndGenerationNear‐fieldHumanExposureModelandPredictors•Existingcomplexfateandtransportmodelshavelowcorrelationtomeasuredexposures•Nearfieldfactormostimportant•Simple,readilyavailabledata•Bettercorrelationtomeasuredexposures•Similarpredictionsacrossdemographics

1 st GenCalibration/EvaluationDataSEEMConclusion

2 nd GenUSEtoxRAIDARNearField/FarFieldNHANESUrineDataProductionVolumeUseCategoriesProductionVolumePhys‐Chem PropertiesNHANESUrineData

3 rd Gen•AnalysisyettobedoneSHEDS‐HTLiteratureModelsCPcat DatabaseNHANESUrineDataTowarda3rdGenerationSEEMAnalysis:NewDataandModelsKristinIsaacsSlide22 of24ModelandPredictorsCalibration/EvaluationDataSEEMConclusionUSEtoxRAIDARSHEDS‐HTDownthedrainEPA/USGSwatermonitoringdataandfishconc.dataProductionVolumeSEEMEvolution– Far‐fieldWaterEco(fish)andHumanExposureHTEXAMS‐KABAM‐BASS

Far

‐ field Water•AnalysisyettobedoneHighThroughputEcologicalExposureModelingCraigBarberEvaluationDataandStatisticalFrameworkforEcologicalExposurePredictionsWoodrowSetzerSlide23 of24SummaryHighthroughputcomputationalmodelpredictionsofexposureispossibleSEEMcomparesthepredictionsofrelevantmodelstoexposuresinferredfrommonitoringdatatodevelopacalibratedpredictionandempiricaluncertaintyestimateTheSEEMcalibrationanduncertaintycanthenbeappliedtootherchemicalsWehavecompletedtwogenerationsoftheSEEMhumanexposureanalysisandaredevelopingathirdgenerationhumananalysisandafirstgenerationecologicalexposureanalyais“Asfarasthelawsofmathematicsrefertoreality,theyarenotcertain;andasfarastheyarecertain,theydonotrefertoreality.”AlbertEinstein,quotedinJRNewman,TheWorldofMathematics(1956)Slide24 of24PresentationRoadmapIntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearch•Frameworkoverview• HTEForwardDataandModels• HTEReverseDataandModels• SEEMAnalysisandHTEEstimatesFutureApplications