Slide1 of25Inference(ReversePrediction)ofHumanExposureInference(ReversePrediction)ofHumanExposurePresentedbyR.WoodrowSetzerNationalCenterforComputationalToxicologyOfficeofResearchandDevelopmentPresentedbyR.WoodrowSetzerNationalCenterforComputationalToxicologyOfficeofResearchandDevelopmentSlide2 of25PresentationRoadmapIntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearch• Frameworkoverview• HTEForwardDataandModels•HTEReverseDataandModels• SEEMAnalysisandHTEEstimatesFutureApplicationsSlide3 of25ChemicalManufactureConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsEnvironmentalReleaseFoodAir,Soil,WaterAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanEcologicalFloraandFaunaDietary Far‐FieldDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)MONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureBiomarkersofExposureMediaSamplesEcologicalWaste•Biomarkersreflectallpathways,notjustthoseinforwardmodel– missedpathwayscontributetoestimateduncertaintyDataandModelsReverseInferenceofExposureSlide4 of25GoalTodevelophumanexposureestimatesatapopulationlevelfrombiomonitoringdata,andcharacterizetheuncertaintyofthoseestimates.Slide5 of25ChemicalsthatCouldbeModeled(1936)Production/ReleaseDataIUR(6759 compoundswithproductionof>25,000lbsayear)CPRI(242pesticideswithtotallbsapplied)NHANESChemicalsforFirstGeneration(2127)5131DataAvailabilityforEvaluatingModelPredictionsGround‐truthwithCDCNHANESurinedataManychemicalshadmedianconc.belowthelimitofdetection(LoD)Mostchemicals>LoDnothighproductionvolume82chemicalsinferredforWambaughetal.(2013)– FirstgenerationAddingmorechemicals(106currently),dozensmoreexpectedwithserummodel– SecondgenerationSlide6 of25NationalHealthAndNutritionExaminationSurvey•NATIONALaveragesfor“descriptivehealthandnutritionalstatisticsforsex,raceandHispanicorigin,andagesubdomains ofthepopulation”.•Annualsurvey,datareleasedon2‐yearcycle.•Oversampling:•Age12‐19,60+•Mexican‐American,blackpopulation•In2000‐2006,pregnantwomenandeveryone<130%ofpovertylevel.•Bloodandurinesamplesanalyzed(nomorethan1/person)foralargelistofenvironmentalandrelatedchemicals,thelistvariesoversampleyear,ages6+Slide7 of25PopulationSubgroupsAnalyzedTotalpopulationSex•Females•Femalesofchild‐bearingage(16– 49y.o.)•MalesAge•6‐11yearolds•12‐19yearolds•20‐65yearolds•66+yearsoldObesity•BMI≤30•BMI>30Slide8 of25UrineConcentrationAbsorbedDoseExposureAbsorptionBioavailabilityMetabolismEliminationDistributionUrineProductionProductComposition/ChemicalUse/OccurrenceProductionVolume/ReleaseBehaviorPatternsFateandTransportFactorsEmissivity,etc.FactorsContributingtoUrineConcentrationofEnvironmentalChemicalsandMetabolitesSlide9 of25UrineConcentrationAbsorbedDoseExposureAbsorptionBioavailabilityMetabolismEliminationDistributionUrineProductionProductComposition/ChemicalUse/OccurrenceProductionVolume/ReleaseBehaviorPatternsFateandTransportFactorsEmissivity,etc.FactorsContributingtoUrineConcentrationofEnvironmentalChemicalsandMetabolitesDomainofReverseInferenceSlide10 of25FromNHANESSampletoEstimatesofExposureacomplex,multistagesample:individualsinsamplerepresentdifferentnumbersofpeopleinthepopulation•Statisticalanalysesmustaccommodatesamplingweightsandstrata.•~2000urinesamplesevaluatedforenvironmentalchemicals–rarehighvalues(e.g.fromextremeoccupationalexposures)arelikelytobemissed.Slide11 of25EstimateExposureinTwoStagesStage1:UsestatisticalmethodsappropriatetoNHANESdesigntoestimatepopulationsummarieslikequantilesorlog(geometricmean)forurinaryanalytesStage2:SummariesfromStage1aredatatoestimateexposuretoparentchemicals.Slide12 of25ChallengesandAssumptionsManyobservations<LOD•Assumelognormalpopulationdistributionandtreat<LODobservationsascensoredMappingfromparentchemicaltourineanalytenot1‐1•Assumeallmassobserved,andfractionofparenttoanalyteunknown.Populationvariabilityincludesacomponentduetodiscreteexposureeventsandclearance•Needtoassumesteady‐stateexposureatindividuallevelUrinevoidsvaryinvolume•Scaleanalytes tocreatinineconcentration,anduseage,sex,ethnicity– adjustedvaluefordailycreatinineexcretion.Slide13 of25LimitofDetectionObservations<analyticdetectionlimits:modeledasleftcensoredobservationsfromlognormalpopulationdistributionFraction<LOD•Data:#<LOD,totalsamplesize•Parametersfordistribution:ln(geometricmean(GM)),SD•SD~otherchemicalswithmorecompletedata•Generally,theseestimateshavegreateruncertaintySlide14 of25MappingofNHANESParentstoAnalytes14ParentchemicalChemicalmeasuredinurineSlide15 of25  iiiiMWmg/kg/dayMWmg/kg/day000 

# ParentProducts1234 dayg*gmgkg701mg/kg/daycreatinecreatineiiLaKind andNaiman (2008)Steady‐stateassumptionLinkingNHANESUrineDataandExposureSlide16 of25P1P2P3P4U1U2GeometricMean:ExposuretoParentCompoundGeometricMean:UrinaryExcretionofAnalytedata1DatafromFirstStagedata2StageTwoModelSlide17 of25P1P2P3P4U1U2φ11φ21φ31φ32φ42ܷଵൌ߮ଵଵܲଵ൅ ߮ଶଵܲଶ൅߮ଷଵܲଷܷଶൌ߮ଷଶܲଷ൅ ߮ସଶܲସ߮ଷଵ൅ ߮ଷଶൌ 1GeometricMean:ExposuretoParentCompoundGeometricMean:UrinaryExcretionofAnalytedata1DatafromFirstStagedata2߮ଵଵ, ߮ସଶൌ 1φ22߮ଶଵ൅ ߮ଶଶൌ 10	 ൑ 	 ߮௜௝൑ 1StageTwoModelSlide18 of25P1P2P3P4U1U2φ11φ21φ31φ32φ42ܷଵൌ߮ଵଵܲଵ൅ ߮ଶଵܲଶ൅߮ଷଵܲଷܷଶൌ߮ଷଶܲଷ൅ ߮ସଶܲସ߮ଷଵ൅ ߮ଷଶൌ 1ሻߠ ,ߤlogNormalሺGeometricMean:ExposuretoParentCompoundGeometricMean:UrinaryExcretionofAnalytedata1DatafromFirstStagedata2߮ଵଵ, ߮ସଶൌ 1φ22߮ଶଵ൅ ߮ଶଶൌ 10	 ൑ 	 ߮௜௝൑ 1StageTwoModelSlide19 of25Bayesianvs.ClassicalStatisticsWhyuseBayesianStatistics?•Bayesianstatisticsemphasizestheuncertaintyoftheestimates•ItiscomputationallysimplerinthiscomplicatedmodelSecondGenerationestimatesusetheRpackagerstan(StanDevelopmentTeam,2013)tocarryoutHamiltonianMonteCarloSamplingfromthemodelposterior.Slide20 of25EvaluatingModelGoodness‐of‐FitSlide21 of25ModelFitstoUrineAnalyteDataSlide22 of25EstimatesofExposureSlide23 of25ContributionstoUncertainty•Geometryofrelationshipsbetweenparentchemicalandanalytes•NumberofsamplesbelowLODImpactofUncertaintyonEstimatesSlide24 of25EstimatesofPopulationGMAbsorbedDoseofParentChemicals:TotalPopulationand6‐11YearOlds.Slide25 of25SummaryWehaveuseddataonurineanalytes fromNHANEStoinferUSpopulationgeometricmeanexposurestoarangechemicalsThemodelallowsquantificationoftheuncertaintyoftheexposureinferences.Slide26 of25ReserveSlidesSlide27 of25P1P2P3P4U1U2φ11φ21φ31φ32φ42ܷଵൌ߮ଵଵܲଵ൅ ߮ଶଵܲଶ൅߮ଷଵܲଷܷଶൌ߮ଷଶܲଷ൅ ߮ସଶܲସ߮ଷଵ൅ ߮ଷଶൌ 1ܲ௜ሻߠ ,ߤlogNormalሺ

	~GeometricMeanExposureRateParentCompoundGeometricMeanRateofUrinaryExcretionofMetabolitedata1DatafromFirstStageIf‘good’estimatey:ݕ௝logNormalሺln

~ܷ௝݁ݏ ,௝ሻIfmanyobservationsare<lod:Dataarecountsofobservations<lod(Ngtlod)andsamplesize(SS):1) SDjissampledfromthesmootheddistributionofestimatesofpopulationsd (ofln(y)).2) ComputePr{observation>lod|Uj,SDj}:3) Thenumberofobservations>lodisbinomialwithn=samplesizeandp=Pralodj:



# Pralod௝ൌන݈ܰ݊ ,ݔܷ௝ܦܵ ,௝ݔ݀ஶ୪୬	ሺ௟௢ௗೕሻ 

# Ngtlo d௝~ Binomialሺܵܵ௝, 

# Pralo d௝ሻDataareestimatesofgeometricmeans,yj,andestimatedstandarderrors,sej:data2߮ଵଵ, ߮ସଶൌ 1φ22߮ଶଵ൅ ߮ଶଶൌ 10	 ൑ 	 ߮௜௝൑ 1