Towarda3rdGenerationSEEMAnalysis:NewDataandModelsTowarda3rdGenerationSEEMAnalysis:NewDataandModelsPresentedbyKristinIsaacs,Ph.D.NationalExposureResearchLaboratoryOfficeofResearchandDevelopmentPresentedbyKristinIsaacs,Ph.D.NationalExposureResearchLaboratoryOfficeofResearchandDevelopmentSlide2 of34PresentationRoadmapIntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearch• ApplyingSEEMtoEcologicalExposure•ThirdGenerationSEEMAnalysisforHumanExposureFutureApplicationsSlide3 of34NewDataandModelsShouldLeadtoImprovedHumanExposurePredictionsSecondgenerationSEEManalysiswasbasedonheuristicsdeterminedfromalgorithmicallygeneratedusecategories•Explained~50%ofthechemicaltochemicalvarianceinexposuresindicatedbyNHANESbutdidnotvarysignificantlybetweendemographicsNewdatasourcesshouldallowricherchemicaldescriptors•Mayexplainmoreofexposures•MaycharacterizedemographicdifferencesNewmechanisticmodelsshouldbebettersuitedtoextrapolationifpredictiveabilitycanbedemonstratedPreparingtoperformthirdgenerationSEEManalysisSlide4 of34BetterModelsandDataShouldReduceUncertaintyImagefromLittleetal.(2012),seealsoNazaroffetal.(2012),Bennettetal.(2012),WengerandJolliet(2012)Uncertainty/VariabilityofNHANESBiomonitoring~10%Farfield(Industrial)Releases~60%Indoor/ConsumerUseIndirectExposureDirectExposure•ConsolidatedHumanActivitiesDatabase(CHAD)•ACToR UseDB,CPCPdb,andCPcatDB•SHEDS‐HTSlide5 of34IllustrationoftheSEEMFrameworkEstimateUncertaintyEDSPChemicalsQSARsandHTEDataBiomonitoringData Inferred (Reverse) ExposureModel1Model2...CalibratemodelsApplycalibrationanduncertaintytootherchemicalsEvaluateModelPerformanceandRefineModelsForwardPredictionsExposureInferenceDataset1Dataset2...Slide6 of34IllustrationoftheSEEMFrameworkEstimateUncertaintyEDSPChemicalsQSARsandHTEDataBiomonitoringData Inferred ExposureModel1Model2...CalibratemodelsApplycalibrationanduncertaintytootherchemicalsWeightedHTEModelPredictionsExposureInferenceDataset1Dataset2...Slide7 of34IntroductionNewdataforhigh‐throughputforwardpredictionofhumanexposure•Consumerproductcharacterization•ChemicaluseinformationNewmodelforhigh‐throughputestimationofnear‐fieldexposure•StochasticHumanExposureandDoseSimulationModel‐HighThroughputUseofnewdataandmodelsina3rdGenerationSEEManalysisSlide8 of34ConsumerProductCharacterizationConsumerProductChemicalProfileDatabase(CPCPdb)•Goldsmithetal., FoodandChemicalToxicology,2014•Curatedfromretailer‐providedmaterialdatasafetysheets(MSDS)usingbothautomatedandmanualmethods•Chemicalingredientsbyproduct,including%compositionwhereavailable•Mappedtoconsumerproductretailcategory,productnameavailableforadditionalcategorizations•Totals:1797chemicalsin8921productsHouseholdProductDatabase(HPDB)‐NationalLibraryofMedicine•MSDS‐based•Totals:3864chemicals/substancesin12073products•TieredproductcategorizationSlide9 of34MappingChemicalIngredientstoProductCategoriesGoldsmithetal.,2014Slide10 of34ProductCategoriesofNHANESChemicalsGoldsmithetal.,2014Slide11 of34CPCatDB:ChemicalandProductCategoryDatabaseThereisafurtherneedtocataloghowchemicalsareusedOnemajorinputtoexposuremodelingInformationexistsbutwaswidelydispersedACToR canhelpbringthisdatatogetherCreateCPCatdatabase•Chemicalusecategories•Productusecategories•MappingfromchemicalstoproductsACToRUseDB andCPCPdb integratedintoCPCatDBSlide12 of34GeneralClassesofChemicalUseCategoriesinCPCatDBGeneraluseassociatedcategories•e.g.,usedinlipsticks,asapesticideFunctional‐usecategories•e.g.,asolventProduct‐usecategories•e.g.,chemicalsusedinbathtubtoysTherapeutic‐usecategories•e.g.,antibioticsIndustrialsector‐usecategories•e.g.,miningSlide13 of34UseCategoryHarmonizationinCPCatDBEachdatasourcehasitsowncategoryhierarchyCategoriesfromdatasourcesweremanuallymappedtooneormoreharmonizedcategoriescalledCPCat“cassettes”EachCPCatcassetteismadeupofoneormoretermsdescribingtheproductorusageCassettesandsourcecategoriescanprovideadditionalheuristics/modelsforevaluationwithintheSEEMframeworkSlide14 of34CPCat datasources(>40,000uniquechemicalsincluded)OriginaldatasourceClassofcategoriesOriginalcategoriesCPCatcassettesChemicalsACToRDataSetsandLists General‐use 131 173 35,838ACToRUseDB General‐use 15 15 31,622CDR2012:Consumer General‐use 34 36 3,321IndustrialFunction Functional‐use 34 27 5,023IndustrialSector Industrialsector‐use 42 43 5,226DfE Functional‐use 11 9 444Dow Functional‐use 19 18 104DrugBank Therapeutic‐use 582 460 1,7542006IUR General‐use 19 24 1,152KemI Functional‐use 61 31 876NICNAS General‐use 17 17 177RetailProductCategories Product‐use 359 191 2,778SPIN:detpcat General‐use 781 284 6,491IndustrialSector Industrialsector‐use 580 221 4,603NACE Industrialsector‐use 57 52 7,745UC62 General‐use 61 59 9,059Toxome Functional‐use 16 16 442Slide15 of34SHEDS‐HT:OverviewStochasticHumanExposureandDoseSimulationModel(SHEDS)– HighThroughput(HT)DevelopedfromORD’shigher‐tierSHEDS‐MultimediaexposuremodelProducespopulation‐leveldistributionsofexposurestochemicalswithnear‐fieldsourcesCalculatesaggregateexposurestochemicalsviamultiplepathways,scenarios,androutesSlide16 of34SHEDSApproachOutput0.010.11101001000100000 20 40 60 80 100Percentile

Dose•Population Exposure•Population Dose0.010.11101000 20 40 60 80 100Percentile

ExposureAlgorithms•Calculate IndividualDaily Exposures/Doses:Input Databases•Census• Human Activity•Food Consumption•Usage Data•Exposure Factor Distributions•Chemicals in ConsumerProductsDermalInhalationNon‐DietaryIngestionDietaryIngestionTotalIntakeSlide17 of34BackgroundandHistory:SHEDS‐Multimedia199920142001SHEDS‐Multimediaversion2(SASandC++)2002‐2005Develop/applySHEDS‐WoodforCCA2FIFRASAPs2004‐2006DevelopSHEDS‐Multimediav.38/2007FIFRASAPreviewofSHEDS‐Multimediaversion32007‐2009:MultimediaV.4,Includingdietarymodule2011‐2013SHEDS‐LiteDevelopment/parameterization(SAS/R)2014SHEDS‐HTInitialcasestudyapplicationto2500+chemicals2010:FIFRASAPreviewofSHEDS‐MMversion4MethodsApplicationsXueetal.Hand‐to‐mouthdataXueetal.Object‐to‐mouthdataMokhtari etal.SensitivitymethodsÖzkaynak etal.;GlenandSmithPrototypemodeldevelopmentandcomparisontoSHEDS‐MMXueetal.LongitudinalmethodsGlenetal.LongitudinalmethodsIsaacsetal.Source‐to‐concentration(fugacity)methods(inrevision)Isaacsetal.SHEDS‐HT(inreview)Hore etal.ChlorpyrifosStoutandMasonChlorpyrifosBucketal.ChlorpyrifosZartarianetal.ChlorpyrifosZartarianetal.CCAXueetal.CCAZartarianetal.CCAStoutetal.DiazinonGeorgopoulos etal.ArsenicXueetal.ArsenicTulveetal.PyrethroidsZartarianetal.PermethrinÖzkaynak etal.SoilanddustXueetal.MeHgGlenandIsaacsSensitivityanalysismethods1999‐2000SHEDS‐Pesticidesversion1Slide18 of34SHEDS‐HT:MotivationandDesiredFeaturesBroadapplicability,yetminimallyparameterizedRetentionofcriticalknowledgeandalgorithmsofSHEDS‐MMFlexibleinputdatastructure•Allowforvarioustiersofinputs(noconstraintondata‐richchemicals;nounnecessaryassumptionsfordata‐poorchemicals)•AllowforstraightforwardupdateofpredictionsasmoredatabecomeavailableExtensible/Scalable•Addnewexposurescenariosorroutestoexpanddomainofapplicabilitywithoutsignificantrecoding•AddnewchemicalseasilySlide19 of34SHEDS‐MMtoSHEDS‐HT•High‐tier•Minute‐leveltimeresolution•Bestforsingle/fewchemicals•SAS‐based•Separatedietary/residentialmodules•40+interfacescreens•~200inputs/distributions•Longitudinal•PotentiallylongruntimesforlargepopulationsSHEDS‐HTSHEDS‐MM•Reducedtemporalresolution(daily)•Variancedecompositionbasedsensitivity:eliminationofminorinputs•Additionalexposurescenarios(directexposurestoconsumerproducts;emissionfromarticles)•Reducedversionofindooremissionsfugacitymodule•Low‐to‐midtier•Appropriatefor1000sofchemicals•Retainspopulationandlife‐stageinformation•R‐based(freelyavailable)•Fastexecution(1000person‐daysperchemical~10seconds)•Combineddietary/residential:aggregatepredictions•ExtendabletoadditionalscenariosSlide20 of34ForwardPredictionofExposureChemicalManufactureEnvironmentalReleaseFoodAir,Soil,WaterEcologicalFloraandFaunaDietary Far‐FieldMONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureMediaSamplesEcologicalWasteDataandModelsConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanDietaryDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)BiomarkersofExposureSlide21 of34ForwardPredictionofExposureSHEDS‐HTPathwaysChemicalManufactureEnvironmentalReleaseFoodAir,Soil,WaterEcologicalFloraandFaunaDietary Far‐FieldMONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureMediaSamplesEcologicalWasteDataandModelsConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanDietaryDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)BiomarkersofExposureSlide22 of34ExposureFields,Scenarios,Routes,andDataStreamsIncludedinSHEDS‐HTPathwayScenarios Routes Pathway‐SpecificDataStreamsNear‐fielddirectDermalexposureduringhouseholduseofconsumerproductsInhalationofvaporduringuseofconsumerproductsInhalationofaerosolmassduringuseofconsumerproductsDermalInhalationIngestion(directingestionplusincidentalnon‐dietaryingestionviahand‐to‐mouthtransfer)ConsumerproductchemicalcompositionConsumerproductusepatternsChemicalpropertiesTime‐activitydataVariousexposurefactordistributionsNear‐fieldindirectApplicationofconsumerproductstohouseholdsurfaces,air,orpetsEmissionofchemicalsfromconsumerarticlesorbuildingproductsDermalInhalationIngestion(objecttomouthtransferplusincidentalnon‐dietaryingestionviahand‐to‐mouthtransfer)ConsumerproductchemicalcompositionChemicalemissionratesfromarticlesorbuildingmaterialsConsumerproductusepatternsConsumerproductarticleorbuildingmaterialusepatternsChemicalfateandtransportinformationTime‐activitydataandvideographyinformationVariousexposurefactordistributionsDietaryConsumptionofcontaminatedfoodordrinkingwater(fromagriculturalchemicaluseorleachingfromfoodpackaging)DietaryingestionChemicalconcentrationsinfoodsanddrinkingwaterPopulationfoodandwaterintakesSlide23 of34SHEDS‐HT:DataandModulesSlide24 of34SHEDS‐HTModulesDirectPathway•Dermal,inhalation(vaporandaerosols),andingestionroutes•Formulatedsimilarlytootheravailablemodels;probabilistic•Dependentoncategory‐specificfrequencyofconsumerproductuse,populationprevalence,massofproductused,andproductchemicalcompositionDietaryPathway•ConsistentwithSHEDS‐MMdietarymethodsreviewedduringthe2010FIFRASAP•Consumptiondata:NHANES‐WhatWeEatInAmerica(1999‐2006)•CombinedwithfoodandwaterresiduedistributionstoproducedailyintakeexposuresSlide25 of34SHEDS‐HTExposureModulesFateandTransport(Source‐to‐Concentration)•Startingpoint:fugacity‐basedindoorfateandtransportmodeldevelopedforusewithSHEDS‐Multimedia(basedonBennettandFurtaw,ES&T,2004)•Reducedfugacitymodelusingvariancedecomposition‐basedsensitivityanalyses(GlenandIsaacs,Env ModelandSoftware,2012).Criticalparametersretained:Vaporpressure,decayratesonsurfaces,airexchangerates,Kow,solubilityIndirectPathway•Fugacitymoduleisusedtomodelthefateandtransportofchemicalwithinahomebasedonprevalence(PR)andfrequencyofconsumerproductuse•Individualsbreatheindoorairortouchcontaminatedsurfacespost‐use,resultinginexposureviainhalation,dermal,object‐to‐mouth,andhand‐to‐mouthroutesAirSurfacesParticlesAirExchangeDeposition/ResuspensionCleaningSlide26 of34InitialCaseStudy:ChemicalsinConsumerProductsandPesticidesinFoodsandDrinkingWaterCPCPdb andHPDB•Thiscasestudyfocusedon“consumable”products•Usableingredientdatawereextractedfor2177consumerproductchemicalsProductsmappedto~250harmonizedconsumerproductcategoriesappropriateforassigningexposurescenariosanduse•Direct/indirectscenarios•Productusedataweregeneratedbycategory;bestguessassumptionsmadewhennodataavailableDistributionsofresiduesinfoodsanddrinkingwaterwereobtainedfor330pesticidesfromtheUnitedStatesDepartmentofAgriculture’sPesticideDataProgram(PDP)databases(1997‐2011)Slide27 of34ModelParameterizationCAS#XXX‐XX‐XARTSANDCRAFTSPAINTBODYPAINTPENSANDMARKERSDETAILINGAUTOFLUIDSANDADDITIVESSURFACECLEANERLIMEREMOVERAIRFRESHENERADHESIVEDEGREASERBLEACHLAUNDRYDETERGENTBUBBLESOLUTIONEXTERIORCLEANERINTERIORINSECTICIDESHAVINGCREAMSHAMPOOSELFTANNERHANDSOAPHANDLOTIONFOOTCAREHAIRSTYLINGBARSOAPBODYWASHBATHOIL/SALTSBODYLOTIONCONDITIONERDEPILATORYDEODORANTEYEMAKEUPFACECREAMFACEMAKEUPFACEWASHDIRECTINHALATIONOFAEROSOLORVAPORDIRECTDERMALINDIRECTVIAINHALATION,DERMALANDINCIDENTALINGESTIONPREVALENCEFREQUENCYMASSCHEMICAL‐SPECIFICPROPERTIESCONSUMERPRODUCTCATEGORIESEXPOSURESCENARIOSPRODUCTCATEGORYAND/ORSCENARIO‐SPECIFICEXPOSUREFACTORS/PARAMETERSPERCENTONSKINPERCENTRETAINEDONSKINDURATIONOFUSECHEMICALAIRBORNEFRACTIONDIETARYCONCENTRATIONSINFOODSCONCENTRATIONINWATERDIRECTINGESTIONPERCENTINGESTEDSlide28 of34InitialCaseStudy:SummaryofChemicalExposureScenariosScenarioNumberofChemicalsNumberofConsumerProductCategoriesNumberofProductsDietary330‐ ‐DirectDermal2203 229 18847DirectIngestion303 6 566DirectInhalationofAerosol1099 62 6100DirectInhalationofVapor1979 164 13743Indirect945 77 6684TOTAL2507 246 20187Slide29 of34InitialCaseStudyResults:SHEDS‐HTResultsfor2507ChemicalswithNear‐fieldSourceExposurePathwaysPercentilesoftotalintakedoseforallagesMeantotalintakedosebyage/gendercohort,sortedbythemeanvaluesforchildrenages0‐5yearsSlide30 of34InitialComparisonswithDataSHEDS‐HTpredictedchemicalintakedosescomparedtooralequivalentintakedosesinferredfromNHANESbiomarkerdatafor39chemicals(Wambaughetal.,ES&T,2013).Left:SHEDSmedianintakedoseversusbiomonitoring‐basedpredictedmedianintakes.Right:SHEDS5th,50th,95thpercentilepredictionscomparedtomedianinferredpredictionsusingNHANESwithuncertainties.Slide31 of34PlannedNextStepsFocusonevaluationtoinformdevelopmentpriorities•Evaluationofindividualcomponentswithchemicaldatafromnewandongoingfieldstudies•FullevaluationwithSEEMframework(3rdgenerationanalysis)Expansionofchemicaldomain•Additionalscenarios– Emissionofchemicalsfromarticles,foodpackaging,others•AdditionalconsumerproductingredientdataIdentifyingandincorporatingotheravailablesourcesofconsumerproductusedataRobustroute‐andchemical‐specificdoseforwardpredictions:absorption,distribution,metabolism,excretion(ADME)Slide32 of34ThirdGenerationAnalysisLittleetal.model(2012)RAIDARandUSEtox near‐fieldexposuresOthernewliteraturemodelsChemicalInformationfromCPCPdb/CPCatDB(updated/newheuristics)SystematicEvaluationAllowsModelEvaluation/RefinementandExtrapolationtoUpdatedConsensusPredictionsMultipleExposurePathwaysPredictedbySHEDS‐HTOtherNear‐FieldModelPredictionsSlide33 of34Newdatadescribingchemicalconsumerproductcompositions,consumerproductuse,andchemicalusehaverecentlybeenidentifiedandcuratedThesedatahavebeenusedtosupportthedevelopmentofnewhigh‐throughputmechanistic,multipathwaymodelsofhumanexposureThesenewmodelsanddatawillbeusedtobuildnewconsensusmodelswithintheSEEMframeworkthatpotentiallybetterdescribethevarianceamongchemicals inhumanexposure,andreducetheuncertaintyinconsensusmodelpredictionsSummarySlide 34 of 34Kathie Dionisio, ORDPeter Eghehy, ORDKristin Isaacs, ORDRichard Judson, ORDThomas Knudsen, ORDChantel Nicolas, ORISERobert Pearce, SSCJames Rabinowitz, ORDWoody Setzer, ORDCory Strope, ORISEDan Vallero, ORDJohn Wambaugh, ORDExpoCast Team***Elaine HubalCSS Deputy NationalProgram DirectorTina BahadoriCSS National ProgramDirectorAcknowledgementsHalûk Özkaynak , ORDPeter Egeghy, ORDRocky Goldsmith, ORDGraham Glen, AlionChris Grulke, ORDRaina Brooks, SSCDaniel Vallero, ORDLuther Smith, AlionSHEDS-HTDevelopment Team*Jianping Xue, ORDValerie Zartarian, ORDHalûk Özkaynak, ORDSHEDS-MMDevelopment Team**CPcat TeamKathie Dionisio, ORDAlicia M. Frame, DowRocky Goldsmith, ORDAlan Liddell, NCSUTommy Cathey,Lockheed MartinDoris Smith, SEEJames Vail, SEEAlexi Ernstoff, U DenmarkPeter Fantke, U DenmarkOlivier Jolliet, U MichiganRichard Judson, ORDJohn Wambaugh, ORD****Trainees