Slide1 of32SystematicEmpiricalEvaluationofModels(SEEM)AnalysisandHighThroughputExposure(HTE)EstimatesSystematicEmpiricalEvaluationofModels(SEEM)AnalysisandHighThroughputExposure(HTE)EstimatesPresentedbyJohnWambaughOfficeofResearchandDevelopmentNationalCenterforComputationalToxicologyPresentedbyJohnWambaughOfficeofResearchandDevelopmentNationalCenterforComputationalToxicologySlide2 of32PresentationRoadmapIntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearch• Frameworkoverview• HTEForwardDataandModels• HTEReverseDataandModels•SEEMAnalysisandHTEEstimatesFutureApplicationsSlide3 of32GoalsforHighThroughputExposure•Incorporatemultiplemodelsintoconsensuspredictionsfor1000sofchemicals•Evaluate/calibratepredictionswithavailablemeasurementdataacrossmanychemicalclasses•EmpiricallyestimateuncertaintyinpredictionsSlide4 of32ModelEvaluationChemicalManufactureConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsEnvironmentalReleaseFoodAir,Soil,WaterAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanEcologicalFloraandFaunaDietary Far‐FieldDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)MONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureBiomarkersofExposureMediaSamplesEcologicalWasteDataandModelsDataandModelsSlide5 of32IllustrationoftheSEEMFrameworkEstimateUncertaintyEDSPChemicalsQSARsandHTEDataBiomonitoringData Inferred (Reverse) ExposureModel1Model2...CalibratemodelsApplycalibrationanduncertaintytootherchemicalsEvaluateModelPerformanceandRefineModelsForwardPredictionsExposureInferenceDataset1Dataset2...Slide6 of32P1P2P3P4U1U2φ11φ21φ31φ32φ42ܷଵൌ߮ଵଵܲଵ൅ ߮ଶଵܲଶ൅߮ଷଵܲଷܷଶൌ߮ଷଶܲଷ൅ ߮ସଶܲସ߮ଷଵ൅ ߮ଷଶൌ 1ሻߠ ,ߤlogNormalሺParentExposureUrinaryExcretionofAnalytedata1SummarizedNHANESDatadata2߮ଵଵ, ߮ସଶൌ 1φ22߮ଶଵ൅ ߮ଶଶൌ 10	 ൑ 	 ߮௜௝൑ 1InferredExposuresSlide7 of32Multipleregressionmodels:SEEMisaLinearRegressionLog(ParentExposure)=a+m*log(ModelPrediction)+b*NearField+εSlide8 of32Multipleregressionmodels:AveragevalueacrossallchemicalsSEEMisaLinearRegressionLog(ParentExposure)=a+m*log(ModelPrediction)+b*NearField+εSlide9 of32Multipleregressionmodels:SEEMisaLinearRegressionCanhavemultiplecoefficientsm1,m2,...formultiplemodels(andcombinationsofmodels)FirstgenerationwehadmRAIDAR(i.e.,RAIDARperunitkgrelease),mUSEtox,andmProductionVolumeRegressioncoefficient(orweight)fordifferentmodelpredictionsLog(ParentExposure)=a+m*log(ModelPrediction)+b*NearField+εSlide10 of32Multipleregressionmodels:SEEMisaLinearRegressionFirstgenerationSEEMalsousedearlyversionofACToR UseDBtoconstructabinary(1or0)“nearfield”sourceindicatorChemicalswithuseindicatedinconsumerproducts,pharmaceuticals,personalcareproducts,fragrance,andfoodadditiveEffectofNearvs.FarFieldwassinglemostimportantpredictorforfirstgenerationSEEManalysisOutperformedUSEtoxandRAIDARforNHANESchemicals(thosemodelsmaybemoreappropriateforpersistentchemicalsorecologicalendpoints)WeightfordescriptorLog(ParentExposure)=a+m*log(ModelPrediction)+b*NearField+εSlide11 of32Log(ParentExposure)=a+m*log(ModelPrediction)+b*NearField+εMultipleregressionmodels:SEEMisaLinearRegressionε~N(0, 

# 2)Residualerror,unexplainedbytheregressionmodel

Inferred ExposureWeightedHTEModelPredictionsSlide12 of32R2≈0.14FirstGenerationSEEMForwardPredictionsvs.InferredExposure(ES&T,2013)Slide13 of32SecondGenerationSEEMAnalysisIndoor/consumeruseistheprimarydeterminantofNHANESexposureinFirstGenerationAnalysis(ES&T,2013)Thepredictiveabilityofthissimple,near‐fieldexposuretoforecastexposureraisesthequestion:•Whatothersimpleheuristicsmightbeusedtoclassifychemicals?Can’texactlyanswerthat,butwhatwecanaskis:•Giventhevarietyofhighthroughputexposuredescriptors(data),whichfactorsbestexplainexposuresinferredfromtheavailableNHANESbiomonitoringdata?Slide14 of32HighThroughputDescriptorsforNHANESSlide15 of32UsingAkaikeInformationCriterion(AIC)toAvoidOver‐fittingInfirstgenerationanalysiswewereevaluatingexistingmodelswiththeavailable(few)chemicalsNowwearetryingtobuildamodelusingessentiallythesamenumberofchemicals:thereisadangerofover‐fittingAIC(1974)isastatisticalmeasureofmodelPenalizegoodnessoffitwithnumberofparametersneededtoachievethatfitBuildsconfidencethatamodelisappropriateforextrapolationtoothercircumstancesModeldoesnotjustdescribethenoiseinaparticulardatasetNoisydataandofOver‐fittingOver‐fittingLinearfunctionYXSlide16 of32NotAllDescriptorsAreUsefulTheaveragerelativeAIC(smallerisbetter)formodelsmadewithdifferentnumbersofparametersforexplaining1500differentcombinationsofchemicalexposuresThepredictorsinvolvedintheoptimalmodelwithhigherfrequenciesarerepresentedbydarkercircles,andthosewithlowerfrequenciesbylightercircles16Slide17 of32NotAllDescriptorsAreUsefulTheaveragerelativeAIC(smallerisbetter)formodelsmadewithdifferentnumbersofparametersforexplaining1500differentcombinationsofchemicalexposuresThepredictorsinvolvedintheoptimalmodelwithhigherfrequenciesarerepresentedbydarkercircles,andthosewithlowerfrequenciesbylightercirclesAsasanitycheck,tworandomvariablesgeneratedfrombinomialdistributionwithprobability50%and10%ofobtaining1,arenotselectedasoptimaldescriptorsinthefivefactormodel17Slide18 of32CorrelationforSecondGenerationModelR2≈0.5Slide19 of32Number of ChemicalsHeuristicDescriptionInferred NHANESChemical Exposures(106)Full ChemicalLibrary ( 7784)ACToR “Consumer use &Chemical/Industrial Processuse”Chemical substances in consumer products (e.g.,toys, personalcare products, clothes, furniture, and home-care products) thatare also used in industrial manufacturing processes. Does notinclude food or pharmaceuticals.37683ACToR “Chemical/IndustrialProcess use with noConsumer use”Chemical substances and products in industrial manufacturingprocesses that are not used inconsumer products. Does notinclude food or pharmaceuticals14282ACToR UseDB “PesticideInert use”Secondary (i.e., non-active) ingredients in a pesticide whichserve a purpose other than repelling pests. Pesticide use ofthese ingredients is known dueto more stringent reportingstandards for pesticide ingredients, but many of thesechemicals appear to be also used in consumer products16816ACToR “Pesticide Active use”Active ingredients in productsdesigned to prevent, destroy,repel, or reduce pests (e.g., insect repellants, weed killers, anddisinfectants).76877TSCA IUR 2006 TotalProduction VolumeSum total (kg/year) of productionof the chemical from all sitesthat produced the chemicalin quantities of 25,000 pounds ormore per year. If information for a chemical is not available, itis assumed to be produced at <25,000 pounds per year.1067784High‐throughputexposureheuristics19Slide20 of32(ES&T,2013)R2≈0.5R2≈0.14ImprovementofSecondoverFirstGenerationSEEMSlide21 of32CDC,FourthNationalExposureReport(2011)NHANESDataBreaksDownbyDemographicsWilldifferentdemographicshavedifferentheuristics?Slide22 of32PredictorsDoNotVaryBetweenGroupsTheverticallinesindicatethe95%credibleintervalacrossthe1500differentexposurescenariosinferredfromtheNHANESurinedataSHEDS‐HTshouldhelpSlide23 of32Exposure Predictionsfor7968EDSPChemicalsThesearethecurrentcalibratedandevaluatedpredictionsfromtheSEEMframeworkUpper95%PredictionMedianPredictionSlide24 of32Exposure Predictionsfor7968EDSPChemicalsWefocusonthemedianandupper95%predictionsbecausethelower95%isbelowtheNHANESlimitsofdetection(LoD)Dottedlinesindicate25%,median,and75%oftheLoD distributionUpper95%PredictionMedianPredictionNHANESLoDSlide25 of32Exposure Predictionsfor7968EDSPChemicalsChemicalscurrentlymonitoredbyNHANESaredistributedthroughputthepredictionsChemicalswiththefirstandninthhighest95%limitaremonitoredbyNHANESNHANESLoDSlide26 of32Exposure Predictionsfor7968EDSPChemicalsThegreystripesindicatethe4182chemicalswithnouseindicatedbyACToRUseDBforanyofthefourusecategoryheuristicsNHANESLoDSlide27 of32ConclusionsWeusedtheSEEMframeworktocompareHTEfactorstotheNHANESdataforasubsetofchemicalsinordertodetermineanempiricalcalibrationandestimateofuncertaintyThesecondgenerationcalibratedmeta‐modelcanestimaterelativelevelsofchemicalexposuresfor7968EDSPchemicals•Thisincludesthousandsofchemicalswithnootherdataonhumanexposure•SamefactorsarepredictiveacrossdemographicscharacterizedbyNHANESDifferentdemographicshavedifferentmean(overall)exposures:•Therearedemographic‐specificaspectsnotcurrentlydescribedbyavailableHTEfactorsUpcomingthirdgenerationSEEManalysis:•Developingnewdatasourcesforconsumerproducts•DevelopingandevaluatingnewmechanisticHTmodelsforexposurefromconsumeruseandindoorenvironment(e.g.,SHEDS‐HT)•ShouldhelpdecreaseuncertaintiesandincreaseconfidenceinextrapolationSlide28 of32External CollaboratorsJon Arnot (ARC)Deborah Bennett (University of California, Irvine)Alicia Frame (Dow Chemical)Anran Wang (NCSU)Olivier Jolliet (University of Michigan)Jade Mitchell (Michigan State)Barbara Wetmore (Hamner)Kathie DionisioPeter EghehyKristin IsaacsRichard JudsonThomas KnudsenChantel NicolasRobert PearceJames RabinowitzWoody SetzerCory StropeDan ValleroJohn WambaughExpoCast TeamChemical Safety for Sustainability (CSS)CollaboratorsMichael BreenRocky GoldsmithChris GrulkeHaluk OzkaynakJon SobusMark StrynarCecilia Tan*Trainees*****Elaine HubalCSS Deputy National Program DirectorTina BahadoriCSS National Program DirectorAcknowledgements*Slide29 of32PresentationRoadmapIntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearchFutureApplicationsSlide30 of32IntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearch• RTKforinvitroHTSdata• EvaluationofConfidenceandDomainofApplicabilityFutureApplicationsPresentationRoadmap