Slide1 of55HighthroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)forEDSPHighthroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)forEDSPPresentedbyJohnWambaughOfficeofResearchandDevelopmentNationalCenterforComputationalToxicologyPresentedbyJohnWambaughOfficeofResearchandDevelopmentNationalCenterforComputationalToxicologySlide2 of55PresentationRoadmapIntroductionHighThroughputExposure(HTE)EstimationHighThroughputToxicokinetics(HTTK)andReverseToxicokinetics(RTK)OngoingResearch• RTKforinvitroHTSdata• EvaluationofConfidenceandDomainofApplicabilityFutureApplicationsSlide3 of55ReverseToxicokinetics(RTK)forScreeningandPrioritizationHighThroughputPrioritizationRTKToxCastHTEMethodsThisSAPToxicokineticsprovidesabridgebetweeninvitro,concentration‐basedToxCastdatatoinvivo,dose‐basedexposureestimatesfromExpoCastSlide4 of55IntroductionHTSmethodsexistforidentifyingputativechemical‐inducedchangestohumanbiologyinvitroHTEmethodsexistforpredictinghumanexposureToxicokinetics(TK)providesabridgebetweenHTSandHTEbypredictingtissueconcentrationsduetoexposureReverseTK(RTK)canapproximatelyconvertinvitroHTSresultstodailydosesneededtoproducesimilarlevelsinahumanTheseRTKmethodshavebeenusedbythepharmaceuticalindustrytodeterminerangeofefficaciousdosesandtoprospectivelyevaluatesuccessofplannedclinicaltrialsSlide5 of55GoalsforHighThroughputToxicokineticsThegoalofRTKistoprovideahumandosecontextforinvitroconcentrationsfromHTS•Thisallowsdirectcomparisonswithexposure1.ToaddressthelargeEDSPuniversewecollectinvitro,highthroughputtoxicokinetic(HTTK)datatomakeRTKpredictionsWeevaluateinvitroRTKpredictionstwoways:2.Wecompareinvitropredictionsandinvivomeasurements3.WeperformsimulationstudiestoexaminekeyassumptionsSlide6 of55GoalsforHighThroughputToxicokineticsThegoalofRTKistoprovideahumandosecontextforinvitroconcentrationsfromHTS•Thisallowsdirectcomparisonswithexposure1.ToaddressthelargeEDSPuniversewecollectinvitro,highthroughputtoxicokinetic(HTTK)datatomakeRTKpredictionsWeevaluateinvitroRTKpredictionstwoways:2.Wecompareinvitropredictionsandinvivomeasurements3.WeperformsimulationstudiestoexaminekeyassumptionsSlide7 of55HumanToxicokineticsandExposureChemicalManufactureConsumerBuildingMaterialsProducts,Articles,BuildingMaterialsEnvironmentalReleaseFoodAir,Soil,WaterAir,Dust,SurfacesNear‐FieldDirectNear‐FieldIndirectHumanEcologicalFloraandFaunaDietary Far‐FieldDirectUse(e.g.,lotion)ResidentialUse(e.g.,flooring)MONITORINGDATARECEPTORSMEDIAEXPOSUREPATHWAY(MEDIA+RECEPTOR)BiomarkersofExposureBiomarkersofExposureMediaSamplesEcologicalWasteSlide8 of55Concentration

ResponseInvitroAssayAC50Concentration(M)AssayAC50withUncertaintyHigh-Throughput BioactivityTox21:Examining>10,000chemicalsusing~50assaysintendedtoidentifyinteractionswithbiologicalpathways(Schmidt,2009)ToxCast:Forasubset(>1000)ofTox21chemicalsran>500additionalassays(Judsonetal.,2010)Mostassaysconductedindose‐responseformat(identify50%activityconcentration– AC50– andefficacyifdatadescribedbyaHillfunction)Alldataispublic:http://actor.epa.gov/Slide9 of55InvitroBioactivity,RTKandinVivoToxicDosesComparisonoralequivalentdoses(boxandwhiskerplotsinmg/kg/day)predictedwithRTKandLELandNELvaluesforliverhypertrophyfromanimalstudiesLowestObservedEffectLevelNoObservedEffectLevel(NEL)NEL/100Estimatedchronicexposurelevelsfromfoodresiduesareindicatedbyverticalredlines.Allvaluesareinmg/kg/day.Judsonetal.(2011)Slide10 of55InVitroBioactivity,InVivoToxicity,andExposureAsinEgeghyetal.(2012),thereisapaucityofdataforprovidingcontexttoHTSdata050100150200250300ToxCastPhaseI(Wetmoreetal.2012)ToxCastPhaseII(Wetmoreetal.inpreparation)ChemicalsExaminedChemicalswithTraditionalExposureEstimatesChemicalswithinvivoTKSlide11 of55ToxCastinvitroAC50sOnepointforeachchemical‐invitroassaycombinationwithasystematic(Hillfunction)concentrationresponsecurveWetmoreetal.(2012)Slide12 of55Oraldosein(mg/kg/day)Sumofhepaticandrenalclearance(mg/kg/day) intublintublub*FQ*F*QF*GFRratedoseoralClClCssSteady‐StatePlasmaConcentrationInvitroplasmaproteinbindingandmetabolicclearanceassaysallowapproximatehepaticandrenalclearancestobecalculatedAtsteadystatethisallowsconversionfromconcentrationtoadministereddoseNooralabsorption/bioavailabilityincludedSlide13 of55Addplasma(6donorpoolforhuman)toonewellAddchemicalDetermineconcentrationinbothwells(analyticalchemistry)Double‐wellsconnectedbysemi‐permeablemembraneonaRapidEquilibriumDialysis(RED)PlateIncubateplatestoallowwellswithandwithoutproteintocometoequilibrium...........21,wellwellpubCCF1 2REDMethod:Watersetal.(2008)DataonToxCastchemicalsinitiallycollectedatHamnerInstitutesPublished:Rotroffetal.(2010)‐Pilotstudyusing38PhaseIToxCastChemicalsWetmoreetal.(2012)‐RemainderofeasilyanalyzedPhaseIchemicalsWetmoreetal.(2013)RatPKfor50ToxCast/ToxRefDBcompoundsPlasmaProteinBinding(FractionUnboundinPlasma)Slide14 of55CryopreservedHepatocytes(10donorpoolforhuman)AddChemical(1and10μM)RemoveAliquotsat15,30,60,120minAnalyticalChemistryTherateofdisappearanceofparentcompound(slopeofline)isthehepaticclearance(μL/min/106hepatocytes)Weperformtheassayat1and10μMtocheckforsaturationofmetabolizingenzymes.10μM1μM0 50 100 150Time(min)

Log Conc (μM)Cryopreserved hepatocyteMethod:Shibataetal.(2002)IntrinsicHepaticClearanceDataonToxCastchemicalsinitiallycollectedatHamnerInstitutesSlide15 of55Steady‐StateModelisLinear

Steady-state Concentration (  M)Daily Dose (mg/kg/day)0Prediction intublintublub*FQ*F*QF*GFRratedoseoralClClCssCancalculatepredictedsteady‐stateconcentration(Css)fora1mg/kg/daydoseandmultiplytogetconcentrationsforotherdosesSlope=Cssfor1mg/kg/dayWetmoreetal.(2012)Slide16 of55Steady‐StateInVitro‐InVivoExtrapolation(IVIVE)

Oral Equivalent Daily DoseSteady-state Concentration (M)=in vitroAC500PredictionSwaptheaxesCandividebioactiveconcentrationbyCssforfor a1mg/kg/daydosetogetoralequivalentdoseSlope=mg/kg/dayperCss1mg/kg/dayWetmoreetal.(2012)Slide17 of55ItappearshardertoprioritizeonbioactiveinvitroconcentrationwithoutinvivocontextToxCastinvitroAC50sWetmoreetal.(2012)Slide18 of55Translationfrominvitrotosteady‐stateoralequivalentdosesallowgreaterdiscriminationbetweeneffectivechemicalpotenciesRTKOralEquivalentsWetmoreetal.(2012)Slide19 of55ReverseToxicokinetics(RTK)HighThroughputInVitroBioactiveConcentrationSimulatedHumanInVivoDosesPopulationsthatareMoreSensitiveHTPKinvitrodataMonteCarloSimulationofBiologicalVariabilityCombinationofhigherexposureandsensitivitiesImagesfromThinkstockSlide20 of55VariabilityinSteady‐StateTKModelInvitroclearance(μL/min/106hepatocytes)isscaledtoawholeorganclearanceusingthedensityofhepatocytespergramofliverandthevolumeoftheliver(whichvariesbetweenindividuals)Glomerular filtrationrate(GFR)andbloodflowtotheliver(Ql)bothvaryfromindividualtoindividualFurtherassumethatmeasuredHTTKparametershave20%coefficientofvariation intublintublub*FQ*F*QF*GFRratedoseoralClClCss(Passive)RenalClearanceHepaticClearance(Metabolism)Wetmoreetal.(2012)Slide21 of55MonteCarlo(MC)ApproachtoVariabilitySimCYP (Pharma)Approach intublintublub*FQ*F*QF*GFRratedoseoralClClCss ProbabilitylogLiverFlow(Ql) ProbabilitylogGlomerular FiltrationRate(GFR) ProbabilitylogLiverVolume

ProbabilitylogClintin vitro Probabilitylogfub

ProbabilityCssWetmoreetal.(2012)Slide22 of55Steady‐StateInVitro‐InVivoExtrapolation(IVIVE)

Oral Equivalent Daily DoseSteady-state Concentration (M)=in vitroAC500MedianPredictedCssThehigherthepredictedCss,thelowertheoralequivalentdose,sotheupper95%predictedCssfromtheMChasaloweroralequivalentdoseLower95%PredictedCssUpper95%PredictedCssSlide23 of5521,wellwellpubCCFAddplasmatoonewellAddchemicalDetermineconcentrationinbothwells(analyticalchemistry)Double‐wellsconnectedbysemi‐permeablemembraneonaRapidEquilibriumDialysis(RED)PlateIncubateplatestoallowwellswithandwithoutproteintocometoequilibrium...........1 2Insomecasestheconcentrationinthewellwithoutplasmaisbelowthelimitofdetection(LOD)Pharmaceuticalapplicationmighthaveresourcestoaddressthis– nottruewhenexamining100sofchemicalsAllweknowisthatthefreefractionissmall(<LOD/Cwell2)InWetmoreetal.(2012)0.5%freewasassumedDifferenceBetweenHighThroughputEvaluationandPharmaceuticalApplicationSlide24 of55ModifiedfubDistributionforHTTK intublintublub*FQ*F*QF*GFRratedoseoralClClCss ProbabilitylogLiverFlow(Ql) ProbabilitylogGlomerular FiltrationRate(GFR) ProbabilitylogLiverVolume

ProbabilitylogClintin vitro ProbabilityCss

ProbabilityLogfubFubLimitofDetection

ProbabilityLogfubSlide25 of55Steady‐StateInVitro‐InVivoExtrapolation(IVIVE)

Oral Equivalent Daily DoseSteady-state Concentration (M)=in vitroAC500MedianPredictedCssTakingintoaccountthelimitofdetectionissuesdoesnotchangethemedian(orlower95%Css)butdoeschangetheupperCss,causingloweroralequivalentdosepredictions(greatersensitivity)Upper95%PredictedCssUpper95%PredictedCssWithmodifiedFubdistributionSlide26 of55ChemicalswithHTTKData26Invitroassayslimitedbytimeneededtodevelopchemical‐specificanalyticalchemistrymethod0 100 200 300 400ExistingHumandataExistingRatdataChemicalswithHTTKDataRotroffetal.2010Wetmoreetal.2012Tonnelieretal.2012Wetmoreetal.2013HamnerInstitutesToxCastPharmaceuticalLiteratureSlide27 of55RTKSummaryHTTKmethodsdevelopedforpharmaceuticalshavebeenadaptedtoenvironmentaltestingRTKallowstheEDSPtoinferdailydosesthatproduceplasmaconcentrationsequivalenttothebioactiveconcentrations,but:•Chemical‐specificanalyticalchemistrymethodsmakethisslowerthanbioactivityHTSorHTE•HTTKmethodsdevelopedforpharmaceuticalsmustbeadaptedtoenvironmentaltestingAlthoughweusedMCsimulationtocharacterizesomeaspectsofhumanvariability(e.g.,bodyweightofadults),anykeydeterminantsofvariabilitythatarenotincludedinoursimulationhavenotbeenbeassessedWemustcarefullycharacterizetheuncertaintyinourapproachSlide28 of55GoalsforHighThroughputToxicokineticsThegoalofRTKistoprovideahumandosecontextforinvitroconcentrationsfromHTS•Thisallowsdirectcomparisonswithexposure1.ToaddressthelargeEDSPuniversewecollectinvitro,highthroughputtoxicokinetic(HTTK)datatomakeRTKpredictionsWeevaluateinvitroRTKpredictionstwoways:2.Wecompareinvitropredictionsandinvivomeasurements3.WeperformsimulationstudiestoexaminekeyassumptionsSlide29 of55InvivoPredictiveAbilityandDomainofApplicabilityIndrugdevelopment,HTTKmethodsestimatetherapeuticdosesforclinicalstudies– predictedconcentrationsaretypicallyontheorderofvaluesmeasuredinclinicaltrials(Wang,2010)Forenvironmentalcompounds,therewillbenoclinicaltrialstheiruncertaintymustbewellcharacterizedideallywithrigorousstatisticalmethodologyWewillusedirectcomparisontoinvivodatainordertogetanempiricalestimateofouruncertaintyAnyapproximations,omissions,ormistakesshouldworktoincreasetheestimateduncertaintywhenevaluatedsystematicallyacrosschemicalsSlide30 of55UsinginvivoDatatoEvaluateRTKWhenwecomparetheCsspredictedfrominvitroHTTKwithinvivoCssvaluesdeterminedfromtheliteraturewefindlimitedcorrelation(R2~0.19)Thedashedlineindicatestheidentity(perfectpredictor)line:overestimatefor23underestimatefor65Thewhitelinesindicatethediscrepancybetweenmeasuredandpredictedvalues(theresidual)Slide31 of55PredictingWhenRTKWillWorkWecanusecomputeralgorithmstoanalyzechemicaldescriptorstotrytopredictwhentheresidualwillbesmallFactorsincludedare:•Physico‐chemicalproperties–Log(Kow),molecularweight,acid/baseassociationconstants(pKa),generalpharmaceuticalorperfluorinatedcompoundclassification•InvitroHTTKdata–Plasmaproteinbinding(Fub)andhepaticclearance•Activechemicaltransport–Usequantitativestructureactivityrelationships(QSARs)topredictlikelihoodeachcompoundisasubstratefor17differenttransporters(FromAlexanderSedykh andAlexTropsha(UNC)andSietoBosgra(TNO))Slide32 of55RecursivePartitioningTreeforResidualsThehighertheCss,thelowertheoralequivalentdoseIdeallytheresiduals(differencebetweentheliteraturevalueandthepredicƟon)aresmallorR≡Csslit./Csspred.≈1Ifaresidualislarge,wewouldprefertoover‐predictCsstobeconservative,i.e.R<1R=5Fub=0.5R=1.02Fub=0.06R=10Fub=0.08R=0.5Fub=0.04R=0.9Fub=0.02Fub<0.07R=5Fub=0.5R=10Fub=0.08R=1.02Fub=0.06R=0.5Fub=0.04R=0.9Fub=0.02Yes NoSlide33 of55RecursivePartitioningTreefor(Log)ResidualsIf the(log)ratioofliteraturetopredictionisgreaterthan0,thentheliteratureCssishigherthanthepredictedCssPredictedfrominvitro

Literature in vivoSlide34 of55PredictRTKWillOverestimateCssWhenCssishigherestimatedbioactivedoseislowerRegressiontreepredictsexpectedresidualbasedonphysico‐chemicalproperties,transporterQSARs,andinvitroHTTKdataWecanusethispredictederrorasachemical‐specificestimateoftheaccuracyofHTTKpredictionsRTKPredictionUnderestimatesLiteratureRTKPredictionOverestimatesLiteratureSlide35 of55GoalsforHighThroughputToxicokineticsThegoalofRTKistoprovideahumandosecontextforinvitroconcentrationsfromHTS•Thisallowsdirectcomparisonswithexposure1.ToaddressthelargeEDSPuniversewecollectinvitro,highthroughputtoxicokinetic(HTTK)datatomakeRTKpredictionsWeevaluateinvitroRTKpredictionstwoways:2.Wecompareinvitropredictionsandinvivomeasurements3.WeperformsimulationstudiestoexaminekeyassumptionsSlide36 of55RTKAssumptionsWeWillTestTodate,theTKmodelsusedforenvironmentalchemicalshavebeenrelativelysimple,makingthreekeyassumptions:1)Wholebodyisatthesameconcentration(i.e.,plasma)2)Environmentalexposureisconstantanduniform(i.e.,constantinfusion)3)Enoughtimehaspassedthattheplasmaconcentrationisatsteady‐statewithrespecttotheenvironmentQSARsfortissue‐specificpropertiesaddressthefirstWecantestthesecondtwoassumptionsusingdynamicsimulation(e.g.,morerealistic,sporadicdosing)ofphysiologically‐basedtoxicokinetic(PBTK)modelsSlide37 of55HighThroughputPhysiologically‐basedToxicokineticModels(HTPBTK)PBTKmodelsaremorecomplicated,butcanbuildgeneric,highthroughputPBTK(HTPBTK)modelsparameterizedwith•thesameinvitroHTTKdatausedforRTK,plus•QSARsfortissue‐specificproperties•Assumptionsaboutunknowndynamicprocesses,suchasabsorptionWeusetheseHTPBTKmodelsperformbothsimulationexperimentsandcomparemodelpredictionsfrominvitrodatawithhumanandratinvivomeasurementsSlide38 of55InhaledGasQliverQgutQgutKidneyBloodGutBloodGutLumenQGFRKidneyTissueLiverBloodLiverTissueQrestLungBloodLungTissueQcardiacQmetabBodyBloodRestofBodyQkidney 

# Arterial Blood

Venous BloodPhysiologically‐basedToxicokinetic(PBTK)ModelSometissues(e.g., arterialblood)aresimplecompartments,whileothers(e.g.,kidney)arecompoundcompartmentsconsistingofseparatebloodandtissuesections.Somespecifictissues(lung,kidney,gut,andliver)aremodeledexplicitly,others(e.g.,fat,brain,bones)arelumpedintothe“RestofBody”compartment.Chemicalentersthebodyprimarilythroughoralabsorption,butwedon’tknowabsorptionrateandbioavailability(assume“fast”,i.e.1/hand100%)Theonlywayschemicals“leaves”thebodyarethroughmetabolism(changeintoametabolite)intheliverorexcretionbyglomerular filtrationintotheproximaltubulesofthekidney(whichfilterintothelumenofthekidney).38Slide39 of55PhysiologicalDataUnits Mouse Rat Dog Human RabbitTotalBodyWaterml/kg 725.00 668.00 603.60 600.00 716PlasmaVolumeml/kg 50.00 31.20 51.50 42.86 44CardiacOutputml/min/kg 400.00 296.00 120.00 80.00 212AverageBWkg 0.02 0.25 10.00 70.00 2.5TotalPlasmaProteing/ml 0.06 0.07 0.09 0.07 0.057Plasmaalbuming/ml 0.03 0.03 0.03 0.04 0.0387Plasmaa‐1‐AGPg/ml 0.01 0.02 0.00 0.00 0.0013Hematocritfraction 0.45 0.46 0.42 0.44 0.36Urineml/min/kg 0.035 0.139 0.021 0.014 0.0417Bileml/min/kg 0.069 0.063 0.008 0.003 0.0833GFRml/min/kg 14.0 5.2 6.1 1.8 3.12Volume(L/kg) BloodFlow(ml/min/kg)Tissue Mouse Rat Dog Human Rabbit Mouse Rat Dog Human RabbitAdipose0.07 0.07 0.05 0.21 0.05 10.80 1.60 3.50 3.71 12.80Bone0.05 0.04 0.04 0.07 0.04 23.31 36.11 1.30 3.36 36.11Brain0.02 0.01 0.01 0.02 0.0113.20 5.20 4.50 10.00 5.20Gut0.04 0.03 0.04 0.02 0.05 72.50 39.20 23.00 16.43 44.40Heart0.00 0.00 0.01 0.00 0.0014.00 15.60 5.40 3.43 6.40Kidneys0.02 0.01 0.01 0.00 0.01 65.00 36.80 21.60 17.71 32.00Liver0.05 0.03 0.03 0.02 0.04 90.00 47.20 30.90 20.71 70.80Lung0.01 0.00 0.01 0.01 0.012.00 6.22 10.56 2.00 6.22Muscle0.37 0.39 0.44 0.38 0.54 45.50 30.00 25.00 10.71 62.00Skin0.15 0.17 0.17 0.03 0.04 20.50 23.20 10.00 4.29 23.20Spleen0.00 0.00 0.00 0.00 0.005.50 4.07 1.65 1.10 3.60Rest0.03 0.05 0.00 0.05 0.03 110.19 90.00 5.59 2.97 90.00VolumesandflowsfromSchmitt(2008)+Rabbit(Sipes)OtherparametersfromDaviesandMorris(1993)+Rabbit(Sipes)Slide40 of55Tissue‐specificpartitioningestimated(Schmitt,2008)using:Physicochemicalproperties(logP,pKa)predictedfromstructure(EPISuite)Measuredfractionunboundinplasma(fub)PredictedPartitionCoefficientsPartitioningfigure isfromPeyret (2010),Slide41 of55PredictedTKMetricsCanuseHTPBTKtopredicttraditionalTKmetrics,suchaspeakconcentration(Cmax)andtimeintegratedareaunderthecurve(AUC)forvarioustissuesSlide42 of55EvaluationDataforHTTKFormostnon‐therapeuticcompounds,itisunlikelythattherewilleverbecontrolledhumanTKdataThoughsomeinvivoTKdataexiststoallowstatisticalassessmentofHTTKpredictions,thesedataarepredominantlyforpharmaceuticalcompoundsRatHTTKdatafor53CollectinvivoTKdataforsomeoralloftheratHTTKcompounds:•EvaluationdataforpropertiesthatcanpredictedinvitroandwithQSAR(e.g.,clearanceandvolumeofdistribution)•Measurementofotherkeyprocesses(e.g.,absorptionrates,extrahepaticmetabolism).•Forexample,ifinvivodatacouldbecollectedforbothoralandintravenousdosing,therelativeconcentrationscouldgiveanapproximateassessmentofbioavailabilitythatmightthenbeextrapolatedtohumans.Slide43 of55EvaluatingHTPBTKPredictionsfromInVitroData43HTPBTKpredictionsfortheAUC(timeintegratedplasmaconcentrationorAreaUndertheCurve)invivomeasurementsfromtheliteratureforvarioustreatments(doseandroute)ofrat.Predictionsareenerally conservative–i.e.,predictedAUChigherthanmeasuredOraldoseAUC~3.6xhigherthanintravenousdoseAUC(p‐Value0.021)Slide44 of55EvaluatingHTPBTKPredictionsfromInVitroDataCmaxpredictionsandinvivodataarecorrelated(R2~0.69)44Slide45 of55EvaluatingRTKAssumptionsMostinvivodataisforpharmaceuticalsCanuseHTPBTKsimulationstoevaluateappropriatenessofRTKassumptionsForexample,belowweshowapproachto“steady‐state”duetothreesimulateddailydosesSlide46 of55EvaluationofSteady‐StatePredictionsUsingHTPBTKandassumingthreedailydoses(everyeighthours)Thisallowsustoevaluatetheplausibilityofthesteady‐statedosingassumption.WefindthatthemajorityofchemicalsreachsteadystateinafewweeksAsecondpopulationofchemicalsneverreachsteadystate.Slide47 of55PeakConcentrationvs.CssPeakserumconcentrationsfromtheHTPBTKmodelarecomparedagainstthesteady‐stateconcentrationpredictedbythethreecompartmentmodelforaconstantinfusionexposure(asinWetmoreetal.2012)Thedashed,identity(1:1)lineindicatesthatformostcompoundsthepeakconcentrationsareverysimilartoCssSlide48 of55SummaryofRTKEvaluationWehavecomparedinvitropredictedCsswithinvivomeasuredCssforsomechemicalsandobservedpoor(R2~0.19),butusuallyconservativecorrelationHowever,wecanmodelthedifferencebetweeninvivomeasurementsandHTTKpredictions(i.e.,theresidualsorerrors),achievingamuchgreaterabilitytopredicterrors(R2~0.69)thantomakeoverallpredictionsWeusedsimulationstudiesviaHTPBTKmodelstotestkeyassumptionsofRTK,findingthatformostchemicalsthepeakplasmaconcentrationsareeitherverysimilarorlessthantheCsscalculatedusingmoresimplemodelsSlide49 of550 100 200 300 400 500 600ExistingTKDataAnticipatedDataChemicalswithHTTKDataandInVivoEvalutionDataPharmaceuticals(Obachetal.2008)EnvironmentalChemicals(Wetmoreetal.2012)NationalToxicologyProgramLegacyDataNewExperimentsNewDataforEvaluatingHTTKPredictionsGoalofHTTKmodelingofpharmaceuticalsistodetermineefficaciousdosesforfollow‐onhumanclinicaltrials,thedegreeofconfidenceneededisdifferentForenvironmentalcompounds,theiruncertaintymustbewellcharacterizedideallywithrigorousstatisticalmethodologySlide50 of55

239 Chemicals 88CollectedSummer2014Wetmoreetal.,(2012)IntrinsichepaticclearanceandplasmaproteinbindingdataTobepublishedin2014

181NewDataforHTTKToxCastHTTKtesting:MeasuringmetabolismbyhumanhepatocytesImprovedassaysformeasuringbindingofchemicalstohumanplasmaproteinObtaindataonToxCastchemicalsnotinvestigatedbytheHamnerInstitutestudiesReinvestigatechemicalsthatproveddifficultinpreviouseffortsThisdatawilleventuallyallowdeterminationofhumanoralequivalentdoses(mg/kgBW/day)formostToxCastchemicals.Slide51 of55NERLRocky GoldsmithMike Tornero*Trainees***Elaine HubalCSS Deputy National Program DirectorTina BahadoriCSS National Program DirectorAcknowledgementsEPA Office of Researchand DevelopmentHamner InstitutesNHEERLHisham El-MasriJane Ellen SimmonsMike HughesUniversity of North Carolina,Chapel HillIndiana UniversityNetherlands Organisationfor Applied ScientificResearch (TNO)NCCTRichard JudsonChantel NicolasRobert PearceJames RabinowitzWoody SetzerImran ShahNisha SipesCory StropeRusty ThomasBarbara WetmoreAlexander SedykhAlex TropshaJames SlukaSieto Bosgra**