ADME-ENABLING TECHNOLOGIES IN DRUG DESIGN AND DEVELOPMENT

ADME-ENABLING TECHNOLOGIES IN DRUG DESIGN AND DEVELOPMENT

EDITED BY

DONGLU ZHANGSEKHAR SURAPANENI

A JOHN WILEY & SONS, INC., PUBLICATION

Copyright © 2012 by John Wiley & Sons, Inc. All rights reserved.

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Library of Congress Cataloging-in-Publication Data:ADME-enabling technologies in drug design and development / edited by Donglu Zhang, Sekhar Surapaneni. p. ; cm. Includes bibliographical references and index. ISBN 978-0-470-54278-1 (cloth) I. Zhang, Donglu. II. Surapaneni, Sekhar. [DNLM: 1. Drug Design. 2. Drug Evaluation, Preclinical. 3. Pharmaceutical Preparations–metabolism. 4. Pharmacokinetics. 5. Technology, Pharmaceutical–methods. QV 744] LC-classification not assigned 615.1'9–dc23 2011030352

Printed in the United States of America.

ISBN: 9780470542781

10 9 8 7 6 5 4 3 2 1

v

CONTENTS

FOREWORD xxiLisa A. Shipley

PREFACE xxvDonglu Zhang and Sekhar Surapaneni

CONTRIBUTORS xxvii

PARTA ADME:OVERVIEWANDCURRENTTOPICS 1

1 RegulatoryDrugDispositionandNDAPackageIncludingMIST 3Sekhar Surapaneni

1.1 Introduction 31.2 NonclinicalOverview 51.3 PK 51.4 Absorption 51.5 Distribution 6

1.5.1 PlasmaProteinBinding 61.5.2 TissueDistribution 61.5.3 LactealandPlacentalDistributionStudies 7

1.6 Metabolism 71.6.1 In vitroMetabolismStudies 71.6.2 Drug–DrugInteractionStudies 81.6.3 In vivoMetabolism(ADME)Studies 10

1.7 Excretion 111.8 ImpactofMetabolismInformationonLabeling 111.9 Conclusions 12References 12

2 OptimalADMEPropertiesforClinicalCandidateandInvestigationalNewDrug(IND)Package 15Rajinder Bhardwaj and Gamini Chandrasena

2.1 Introduction 152.2 NCEandInvestigationalNewDrug(IND)Package 16

vi CONTENTS

2.3 ADMEOptimization 172.3.1 Absorption 182.3.2 Metabolism 202.3.3 PK 22

2.4 ADMEOptimizationforCNSDrugs 232.5 Summary 24References 25

3 DrugTransportersinDrugInteractionsandDisposition 29Imad Hanna and Ryan M. Pelis

3.1 Introduction 293.2 ABCTransporters 31

3.2.1 Pgp(MDR1,ABCB1) 313.2.2 BCRP(ABCG2) 323.2.3 MRP2(ABCC2) 32

3.3 SLCTransporters 333.3.1 OCT1(SLC22A1)andOCT2(SLC22A2) 343.3.2 MATE1(SLC47A1)andMATE2K(SLC47A2) 353.3.3 OAT1(SLC22A6)andOAT3(SLC22A8) 363.3.4 OATP1B1(SLCO1B1,SLC21A6),OATP1B3(SLCO1B3,

SLC21A8),andOATP2B1(SLCO2B1,SLC21A9) 373.4 In vitroAssaysinDrugDevelopment 39

3.4.1 ConsiderationsforAssessingCandidateDrugsasInhibitors 39

3.4.2 ConsiderationsforAssessingCandidateDrugsasSubstrates 39

3.4.3 AssaySystems 403.5 ConclusionsandPerspectives 45References 46

4 PharmacologicalandToxicologicalActivityofDrugMetabolites 55W. Griffith Humphreys

4.1 Introduction 554.2 AssessmentofPotentialforActiveMetabolites 56

4.2.1 DetectionofActiveMetabolitesduringDrugDiscovery 584.2.2 MethodsforAssessingandEvaluatingtheBiological

ActivityofMetaboliteMixtures 584.2.3 MethodsforGenerationofMetabolites 59

4.3 AssessmentofthePotentialToxicologyofMetabolites 594.3.1 MethodstoStudytheFormationofReactiveMetabolites 604.3.2 ReactiveMetaboliteStudies:In vitro 614.3.3 ReactiveMetaboliteStudies:In vivo 614.3.4 ReactiveMetaboliteDataInterpretation 614.3.5 MetaboliteContributiontoOff-TargetToxicities 62

4.4 SafetyTestingofDrugMetabolites 624.5 Summary 63References 63

5 ImprovingthePharmaceuticalPropertiesofBiologicsinDrugDiscovery:UniqueChallengesandEnablingSolutions 67Jiwen Chen and Ashok Dongre

5.1 Introduction 675.2 Pharmacokinetics 685.3 MetabolismandDisposition 70

CONTENTS vii

5.4 Immunogenicity 715.5 ToxicityandPreclinicalAssessment 745.6 Comparability 745.7 Conclusions 75References 75

6 ClinicalDoseEstimationUsingPharmacokinetic/PharmacodynamicModelingandSimulation 79Lingling Guan

6.1 Introduction 796.2 BiomarkersinPKandPD 80

6.2.1 PK 806.2.2 PD 816.2.3 Biomarkers 81

6.3 Model-BasedClinicalDrugDevelopment 836.3.1 Modeling 836.3.2 Simulation 846.3.3 PopulationModeling 856.3.4 QuantitativePharmacology(QP)andPharmacometrics 85

6.4 First-in-HumanDose 866.4.1 DrugClassificationSystemsasToolsforDevelopment 866.4.2 InterspeciesandAllometricScaling 876.4.3 AnimalSpecies,PlasmaProteinBinding,and

in vivo–in vitroCorrelation 886.5 Examples 89

6.5.1 First-in-HumanDose 896.5.2 PediatricDose 90

6.6 DiscussionandConclusion 90References 93

7 PharmacogenomicsandIndividualizedMedicine 95Anthony Y.H. Lu and Qiang Ma

7.1 Introduction 957.2 IndividualVariabilityinDrugTherapy 957.3 WeAreAllHumanVariants 967.4 OriginsofIndividualVariabilityinDrugTherapy 967.5 GeneticPolymorphismofDrugTargets 977.6 GeneticPolymorphismofCytochromeP450s 987.7 GeneticPolymorphismofOtherDrugMetabolizingEnzymes 1007.8 GeneticPolymorphismofTransporters 1007.9 PharmacogenomicsandDrugSafety 1017.10 WarfarinPharmacogenomics:AModelfor

IndividualizedMedicine 1027.11 CanIndividualizedDrugTherapyBeAchieved? 1047.12 Conclusions 104Disclaimer 105ContactInformation 105References 105

8 OverviewofDrugMetabolismandPharmacokineticswithApplicationsinDrugDiscoveryandDevelopmentinChina 109Chang-Xiao Liu

8.1 Introduction 109

viii CONTENTS

8.2 PK–PDTranslationResearchinNewDrugResearchandDevelopment 109

8.3 Absorption,Distribution,Metabolism,Excretion,andToxicity(ADME/T)StudiesinDrugDiscoveryandEarlyStageofDevelopment 110

8.4 DrugTransportersinNewDrugResearchandDevelopment 1118.5 DrugMetabolismandPKStudiesforNewDrugResearch

andDevelopment 1138.5.1 TechnicalGuidelinesforPKStudiesinChina 1138.5.2 StudiesonNewMolecularEntity(NME)Drugs 1148.5.3 PKCalculationProgram 117

8.6 StudiesonthePKofBiotechnologicalProducts 1178.7 StudiesonthePKofTCMS 118

8.7.1 TheChallengeinPKResearchofTCMs 1188.7.2 NewConceptonPKMarkers 1208.7.3 IdentificationofNontargetComponentsfrom

HerbalPreparations 1228.8 PKandBioavailabilityofNanomaterials 123

8.8.1 ResearchandDevelopmentofNanopharmaceuticals 1238.8.2 BiopharmaceuticsandTherapeuticPotentialof

EngineeredNanomaterials 1238.8.3 BiodistributionandBiodegradation 1238.8.4 DoxorubicinPolyethylene

Glycol-Phosphatidylethnolamine(PEG-PE)Nanoparticles 124

8.8.5 Micelle-EncapsulatedAlprostadil(M-Alp) 1248.8.6 PaclitaxelMagnetoliposomes 125

References 125

PARTB ADMESYSTEMSANDMETHODS 129

9 TechnicalChallengesandRecentAdvancesofImplementingComprehensiveADMETToolsinDrugDiscovery 131Jianling Wang and Leslie Bell

9.1 Introduction 1319.2 “A”IstheFirstPhysiologicalBarrierThataDrugFaces 131

9.2.1 SolubilityandDissolution 1319.2.2 GIPermeabilityandTransporters 136

9.3 “M”IsFrequentlyConsideredPriortoDistributionDuetothe“First-Pass”Effect 1399.3.1 HepaticMetabolism 1399.3.2 CYPsandDrugMetabolism 140

9.4 “D”IsCriticalforCorrectlyInterpretingPKData 1429.4.1 Blood/PlasmaImpactonDrugDistribution 1429.4.2 PlasmaStability 1439.4.3 PPB 1449.4.4 Blood/PlasmaPartitioning 144

9.5 “E”:TheEliminationofDrugsShouldNotBeIgnored 1459.6 Metabolism-orTransporter-RelatedSafetyConcerns 1469.7 ReversibleCYPInhibition 147

9.7.1 In vitroCYPInhibition 147

CONTENTS ix

9.7.2 HumanLiverMicrosomes(HLM)+PrototypicalProbeSubstrateswithQuantificationbyLC-MS 147

9.7.3 ImplementationStrategy 1499.8 Mechanism-Based(Time-Dependent)CYPInhibition 149

9.8.1 CharacteristicsofCYP3ATDI 1509.8.2 In vitroScreeningforCYP3ATDI 1509.8.3 InactivationRate(kobs) 1509.8.4 IC50-Shift 1519.8.5 ImplementationStrategy 152

9.9 CYPInduction 1529.10 ReactiveMetabolites 153

9.10.1 Qualitativein vitroAssays 1539.10.2 Quantitativein vitroAssay 154

9.11 ConclusionandOutlook 154Acknowledgments 155References 155

10 PermeabilityandTransporterModelsinDrugDiscoveryandDevelopment 161Praveen V. Balimane, Yong-Hae Han, and Saeho Chong

10.1 Introduction 16110.2 PermeabilityModels 162

10.2.1 PAMPA 16210.2.2 CellModels(Caco-2Cells) 16210.2.3 P-glycoprotein(Pgp)Models 162

10.3 TransporterModels 16310.3.1 IntactCells 16410.3.2 TransfectedCells 16510.3.3 XenopusOocyte 16510.3.4 MembraneVesicles 16510.3.5 TransgenicAnimalModels 166

10.4 IntegratedPermeability–TransporterScreeningStrategy 166References 167

11 MethodsforAssessingBlood–BrainBarrierPenetrationinDrugDiscovery 169Li Di and Edward H. Kerns

11.1 Introduction 16911.2 CommonMethodsforAssessingBBBPenetration 17011.3 MethodsforDeterminationofFreeDrugConcentration

intheBrain 17011.3.1 In vivoBrainPKinCombinationwithin vitroBrain

HomogenateBindingStudies 17111.3.2 UseofCSFDrugConcentrationasaSurrogatefor

FreeDrugConcentrationintheBrain 17111.4 MethodsforBBBPermeability 172

11.4.1 In situBrainPerfusionAssay 17211.4.2 High-throughputPAMPA-BBB 17311.4.3 Lipophilicity(LogD7.4) 173

11.5 MethodsforPgpEffluxTransport 17311.6 Conclusions 174References 174

x CONTENTS

12 TechniquesforDeterminingProteinBindinginDrugDiscoveryandDevelopment 177Tom Lloyd

12.1 Introduction 17712.2 Overview 17812.3 EquilibriumDialysis 17912.4 Ultracentrifugation 18012.5 Ultrafiltration 18112.6 Microdialysis 18212.7 Spectroscopy 18212.8 ChromatographicMethods 18312.9 SummaryDiscussion 183Acknowledgment 185References 185

13 ReactionPhenotyping 189Chun Li and Nataraj Kalyanaraman

13.1 Introduction 18913.2 InitialConsiderations 190

13.2.1 ClearanceMechanism 19013.2.2 SelectingtheAppropriatein vitroSystem 19113.2.3 SubstrateConcentration 19113.2.4 EffectofIncubationTimeandProteinConcentration 19213.2.5 DeterminationofKineticConstantKmandVmax 19213.2.6 DevelopmentofAnalyticalMethods 192

13.3 CYPReactionPhenotyping 19313.3.1 SpecificChemicalInhibitors 19413.3.2 InhibitoryCYPAntibodies 19513.3.3 RecombinantCYPEnzymes 19613.3.4 CorrelationAnalysisforCYPReactionPhenotyping 19813.3.5 CYPReactionPhenotypinginDrugDiscoveryversus

Development 19813.4 Non-P450ReactionPhenotyping 199

13.4.1 FMOs 19913.4.2 MAOs 20013.4.3 AO 200

13.5 UGTConjugationReactionPhenotyping 20113.5.1 InitialConsiderationsinUGTReactionPhenotyping 20213.5.2 ExperimentalApproachesforUGT

ReactionPhenotyping 20213.5.3 UseofChemicalInhibitorsforUGTs 20313.5.4 CorrelationAnalysisforUGTReactionPhenotyping 204

13.6 ReactionPhenotypingforOtherConjugationReactions 20413.7 IntegrationofReactionPhenotypingandPredictionofDDI 20513.8 Conclusion 205References 206

14 FastandReliableCYPInhibitionAssays 213Ming Yao, Hong Cai, and Mingshe Zhu

14.1 Introduction 21314.2 CYPInhibitionAssaysinDrugDiscoveryandDevelopment 215

CONTENTS xi

14.3 HLMReversibleCYPInhibitionAssayUsingIndividualSubstrates 21714.3.1 ChoiceofSubstrateandSpecificInhibitors 21714.3.2 OptimizationofIncubationConditions 21714.3.3 IncubationProcedures 21714.3.4 LC-MS/MSAnalysis 22114.3.5 DataCalculation 221

14.4 HLMRIAssayUsingMultipleSubstrates(CocktailAssays) 22214.4.1 ChoiceofSubstrateandSpecificInhibitors 22214.4.2 OptimizationofIncubations 22314.4.3 IncubationProcedures 22314.4.4 LC-MS/MSAnalysis 22414.4.5 DataCalculation 224

14.5 Time-DependentCYPInhibitionAssay 22614.5.1 IC50ShiftAssay 22614.5.2 KIandKinactMeasurements 22714.5.3 DataCalculation 228

14.6 SummaryandFutureDirections 228References 230

15 ToolsandStrategiesfortheAssessmentofEnzymeInductioninDrugDiscoveryandDevelopment 233Adrian J. Fretland, Anshul Gupta, Peijuan Zhu, and Catherine L. Booth-Genthe

15.1 Introduction 23315.2 UnderstandingInductionattheGeneRegulationLevel 23315.3 In silicoApproaches 234

15.3.1 Model-BasedDrugDesign 23415.3.2 ComputationalModels 234

15.4 In vitroApproaches 23515.4.1 LigandBindingAssays 23515.4.2 ReporterGeneAssays 236

15.5 In vitroHepatocyteandHepatocyte-LikeModels 23815.5.1 HepatocyteCell-BasedAssays 23815.5.2 Hepatocyte-LikeCell-BasedAssays 239

15.6 ExperimentalTechniquesfortheAssessmentofInductioninCell-BasedAssays 23915.6.1 mRNAQuantification 24015.6.2 ProteinQuantification 24115.6.3 AssessmentofEnzymeActivity 244

15.7 ModelingandSimulationandAssessmentofRisk 24415.8 AnalysisofInductioninPreclinicalSpecies 24515.9 AdditionalConsiderations 24515.10 Conclusion 246References 246

16 AnimalModelsforStudyingDrugMetabolizingEnzymesandTransporters 253Kevin L. Salyers and Yang Xu

16.1 Introduction 25316.2 AnimalModelsofDMEs 253

16.2.1 SectionObjectives 25316.2.2 In vivoModelstoStudytheRolesofDMEsin

DeterminingOralBioavailability 254

xii CONTENTS

16.2.3 In vivoModelstoPredictHumanDrugMetabolismandToxicity 257

16.2.4 In vivoModelstoStudytheRegulationofDMEs 25916.2.5 In vivoModelstoPredictInduction-BasedDDIs

inHumans 26016.2.6 In vivoModelstoPredictInhibition-BasedDDIs

inHumans 26116.2.7 In vivoModelstoStudytheFunctionofDMEsin

PhysiologicalHomeostasisandHumanDiseases 26216.2.8 Summary 263

16.3 AnimalModelsofDrugTransporters 26316.3.1 SectionObjectives 26316.3.2 In vivoModelstoCharacterizeTransportersinDrug

Absorption 26416.3.3 In vivo ModelsUsedtoStudyTransporters

inBrainPenetration 26616.3.4 In vivo ModelstoAssessHepaticandRenalTransporters 26816.3.5 Summary 270

16.4 ConclusionsandthePathForward 270Acknowledgments 271References 271

17 MilkExcretionandPlacentalTransferStudies 277Matthew Hoffmann and Adam Shilling

17.1 Introduction 27717.2 CompoundCharacteristicsThatAffectPlacentalTransfer

andLactealExcretion 27717.2.1 PassiveDiffusion 27817.2.2 DrugTransporters 27917.2.3 Metabolism 280

17.3 StudyDesign 28117.3.1 PlacentalTransferStudies 28117.3.2 LactealExcretionStudies 285

17.4 Conclusions 289References 289

18 HumanBileCollectionforADMEStudies 291Suresh K. Balani, Lisa J. Christopher, and Donglu Zhang

18.1 Introduction 29118.2 Physiology 29118.3 UtilityoftheBiliaryData 29218.4 BileCollectionTechniques 293

18.4.1 InvasiveMethods 29318.4.2 NoninvasiveMethods 293

18.5 FutureScope 297Acknowledgment 297References 297

PARTC ANALYTICALTECHNOLOGIES 299

19 CurrentTechnologyandLimitationofLC-MS 301Cornelis E.C.A. Hop

19.1 Introduction 301

CONTENTS xiii

19.2 SamplePreparation 30219.3 ChromatographySeparation 30219.4 MassSpectrometricAnalysis 30419.5 Ionization 30419.6 MSModeversusMS/MSorMSnMode 30519.7 MassSpectrometers:SingleandTripleQuadrupoleMass

Spectrometers 30619.8 MassSpectrometers:Three-DimensionalandLinearIonTraps 30819.9 MassSpectrometers:Time-of-FlightMassSpectrometers 30819.10 MassSpectrometers:FourierTransformandOrbitrapMass

Spectrometers 30919.11 RoleofLC-MSinQuantitativein vitroADMEStudies 30919.12 Quantitativein vivoADMEStudies 31119.13 MetaboliteIdentification 31219.14 TissueImagingbyMS 31319.15 ConclusionsandFutureDirections 313References 314

20 ApplicationofAccurateMassSpectrometryforMetaboliteIdentification 317Zhoupeng Zhang and Kaushik Mitra

20.1 Introduction 31720.2 High-Resolution/AccurateMassSpectrometers 317

20.2.1 LinearTrapQuadrupole-Orbitrap(LTQ-Orbitrap)MassSpectrometer 318

20.2.2 Q-tofandTripleTime-of-Flight(TOF) 31820.2.3 HybridIonTrapTime-of-FlightMass

Spectrometer(IT-tof) 31820.3 PostacquisitionDataProcessing 318

20.3.1 MDF 31920.3.2 BackgroundSubtractionSoftware 319

20.4 UtilitiesofHigh-Resolution/AccurateMassSpectrometry(HRMS)inMetaboliteIdentification 32020.4.1 FastMetaboliteIdentificationofMetabolically

UnstableCompounds 32020.4.2 IdentificationofUnusualMetabolites 32220.4.3 IdentificationofTrappedAdductsof

ReactiveMetabolites 32520.4.4 AnalysisofMajorCirculatingMetabolitesof

ClinicalSamplesofUnlabeledCompounds 32720.4.5 ApplicationsinMetabolomics 328

20.5 Conclusion 328References 329

21 ApplicationsofAcceleratorMassSpectrometry(AMS) 331Xiaomin Wang, Voon Ong, and Mark Seymour

21.1 Introduction 33121.2 BioanalyticalMethodology 332

21.2.1 SamplePreparation 33221.2.2 AMSInstrumentation 33221.2.3 AMSAnalysis 333

21.3 AMSApplicationsinMassBalance/MetaboliteProfiling 334

xiv CONTENTS

21.4 AMSApplicationsinPharmacokinetics 33521.5 Conclusion 337References 337

22 RadioactivityProfiling 339Wing Wah Lam, Jose Silva, and Heng-Keang Lim

22.1 Introduction 33922.2 RadioactivityDetectionMethods 340

22.2.1 ConventionalTechnologies 34022.2.2 RecentTechnologies 341

22.3 AMS 34622.4 IntracavityOptogalvanicSpectroscopy 34922.5 Summary 349Acknowledgments 349References 349

23 ARobustMethodologyforRapidStructureDeterminationofMicrogram-LevelDrugMetabolitesbyNMRSpectroscopy 353Kim A. Johnson, Stella Huang, and Yue-Zhong Shu

23.1 Introduction 35323.2 Methods 354

23.2.1 LiverMicrosomeIncubationsofTrazodone 35423.2.2 HPLCandMetabolitePurification 35423.2.3 HPLC-MS/MS 35523.2.4 NMR 355

23.3 TrazodoneandItsMetabolism 35523.4 TrazodoneMetaboliteGenerationandNMRSample

Preparation 35623.5 MetaboliteCharacterization 35623.6 ComparisonwithFlowProbeandLC-NMRMethods 36123.7 MetaboliteQuantificationbyNMR 36123.8 Conclusion 361References 362

24 SupercriticalFluidChromatography 363Jun Dai, Yingru Zhang, David B. Wang-Iverson, and Adrienne A. Tymiak

24.1 Introduction 36324.2 Background 36324.3 SFCInstrumentationandGeneralConsiderations 364

24.3.1 DetectorsUsedinSFC 36524.3.2 MobilePhasesUsedinSFC 36624.3.3 StationaryPhasesUsedinSFC 36724.3.4 ComparisonofSFCwithOther

ChromatographicTechniques 36724.3.5 SelectivityinSFC 368

24.4 SFCinDrugDiscoveryandDevelopment 36924.4.1 SFCApplicationsforPharmaceuticalsand

Biomolecules 37024.4.2 SFCChiralSeparations 37224.4.3 SFCApplicationsforHigh-ThroughputAnalysis 37424.4.4 PreparativeSeparations 375

24.5 FuturePerspective 375References 376

CONTENTS xv

25 ChromatographicSeparationMethods 381Wenying Jian, Richard W. Edom, Zhongping (John) Lin, and Naidong Weng

25.1 Introduction 38125.1.1 AHistoricalPerspective 38125.1.2 TheNeedforSeparationinADMEStudies 38125.1.3 ChallengesforCurrentChromatographicTechniquesin

SupportofADMEStudies 38225.2 LCSeparationTechniques 383

25.2.1 BasicPracticalPrinciplesofLCSeparationRelevanttoADMEStudies 383

25.2.2 MajorModesofLCFrequentlyUsedforADMEStudies 385

25.2.3 ChiralLC 38725.3 SamplePreparationTechniques 388

25.3.1 Off-LineSamplePreparation 38825.3.2 OnlineSamplePreparation 38925.3.3 DriedBloodSpots(DBS) 390

25.4 High-SpeedLC-MSAnalysis 39025.4.1 UHPLC 39025.4.2 MonolithicColumns 39125.4.3 Fused-CoreSilicaColumns 39225.4.4 FastSeparationUsingHILIC 393

25.5 OrthogonalSeparation 39425.5.1 OrthogonalSamplePreparationandChromatography 39425.5.2 2D-LC 395

25.6 ConclusionsandPerspectives 395References 396

26 MassSpectrometricImagingforDrugDistributioninTissues 401Daniel P. Magparangalan, Timothy J. Garrett, Dieter M. Drexler, and Richard A. Yost

26.1 Introduction 40126.1.1 ImagingTechniquesforADMETStudies 40126.1.2 MassSpectrometricImaging(MSI)Background 401

26.2 MSIInstrumentation 40326.2.1 MicroprobeIonizationSources 40326.2.2 MassAnalyzers 404

26.3 MSIWorkflow 40626.3.1 PostdissectionTissue/OrganPreparationandStorage 40626.3.2 TissueSectioningandMounting 40626.3.3 TissueSectionPreparation,MALDIMatrixSelection,

andDeposition 40726.3.4 SpatialResolution:Relationshipbetween

LaserSpotSizeandRasterStepSize 40726.4 ApplicationsofMSIforin situADMETTissueStudies 408

26.4.1 DeterminationofDrugDistributionandSiteofAction 40826.4.2 AnalysisofWhole-BodyTissueSectionsUtilizingMSI 40926.4.3 IncreasingAnalyteSpecificityfor

MassSpectrometricImages 41126.4.4 DESIApplicationsforMSI 412

26.5 Conclusions 413References 414

xvi CONTENTS

27 ApplicationsofQuantitativeWhole-BodyAutoradiography(QWBA)inDrugDiscoveryandDevelopment 419Lifei Wang, Haizheng Hong, and Donglu Zhang

27.1 Introduction 41927.2 EquipmentandMaterials 41927.3 StudyDesigns 420

27.3.1 ChoiceofRadiolabel 42027.3.2 ChoiceofAnimals 42027.3.3 DoseSelection,Formulation,andAdministration 420

27.4 QWBAExperimentalProcedures 42027.4.1 Embedding 42027.4.2 Whole-BodySectioning 42127.4.3 Whole-BodyImaging 42127.4.4 QuantificationofRadioactivityConcentration 421

27.5 ApplicationsofQWBA 42127.5.1 CaseStudy1:DrugDeliverytoPharmacologyTargets 42127.5.2 CaseStudy2:TissueDistributionandMetabolite

Profiling 42227.5.3 CaseStudy3:TissueDistributionandProteinCovalent

Binding 42427.5.4 CaseStudy4:RatTissueDistributionandHuman

DosimetryCalculation 42527.5.5 CaseStudy5:PlacentaTransferandTissue

DistributioninPregnantRats 43027.6 LimitationsofQWBA 432References 433

PARTD NEWANDRELATEDTECHNOLOGIES 435

28 GeneticallyModifiedMouseModelsinADMEStudies 437Xi-Ling Jiang and Ai-Ming Yu

28.1 Introduction 43728.2 DrugMetabolizingEnzymeGeneticallyModified

MouseModels 43828.2.1 CYP1A1/CYP1A2 43828.2.2 CYP2A6/Cyp2a5 43828.2.3 CYP2C19 43928.2.4 CYP2D6 43928.2.5 CYP2E1 44028.2.6 CYP3A4 44028.2.7 CytochromeP450Reductase(CPR) 44128.2.8 GlutathioneS-Transferasepi(GSTP) 44128.2.9 Sulfotransferase1E1(SULT1E1) 44228.2.10 Uridine5′-Diphospho-Glucuronosyltransferase1(UGT1) 442

28.3 DrugTransporterGeneticallyModifiedMouseModels 44228.3.1 P-Glycoprotein(Pgp/MDR1/ABCB1) 44228.3.2 MultidrugResistance-AssociatedProteins(MRP/ABCC) 44228.3.3 BreastCancerResistanceProtein

(BCRP/ABCG2) 44428.3.4 BileSaltExportPump(BSEP/ABCB11) 44428.3.5 PeptideTransporter2(PEPT2/SLC15A2) 44428.3.6 OrganicCationTransporters(OCT/SLC22A) 445

CONTENTS xvii

28.3.7 MultidrugandToxinExtrusion1(MATE1/SLC47A1) 44528.3.8 OrganicAnionTransporters(OAT/SLC22A) 44528.3.9 OrganicAnionTransportingPolypeptides(OATP/SLCO) 44528.3.10 OrganicSoluteTransporterα(OSTα) 446

28.4 XenobioticReceptorGeneticallyModifiedMouseModels 44628.4.1 ArylHydrocarbonReceptor(AHR) 44628.4.2 PregnaneXReceptor(PXR/NR1I2) 44628.4.3 ConstitutiveAndrostaneReceptor(CAR/NR1I3) 44628.4.4 PeroxisomeProliferator-ActivatedReceptorα

(PPARα/NR1C1) 44728.4.5 RetinoidXReceptorα(RXRα/NR2B1) 447

28.5 Conclusions 448References 448

29 PluripotentStemCellModelsinHumanDrugDevelopment 455David C. Hay

29.1 Introduction 45529.2 HumanDrugMetabolismandCompoundAttrition 45529.3 HumanHepatocyteSupply 45629.4 hESCS 45629.5 hESCHLCDifferentiation 45629.6 iPSCS 45629.7 CYPP450ExpressioninStemCell-DerivedHLCs 45729.8 TissueCultureMicroenvironment 45729.9 CultureDefinitionforDerivingHLCSfromStemCells 45729.10 Conclusion 457References 458

30 RadiosynthesisforADMEStudies 461Brad D. Maxwell and Charles S. Elmore

30.1 BackgroundandGeneralRequirements 46130.1.1 FoodandDrugAdministration(FDA)Guidance 46130.1.2 ThirdClinicalStudyafterSingleAscendingDose

(SAD)andMultipleAscendingDose(MAD)Studies 46230.1.3 FormationoftheADMETeam 46230.1.4 HumanDosimetryProjection 46230.1.5 cGMPSynthesisConditions 46230.1.6 FormationofOneCovalentBond 462

30.2 RadiosynthesisStrategiesandGoals 46330.2.1 DeterminationoftheMostSuitableRadioisotope

fortheHumanADMEStudy 46330.2.2 SynthesizetheAPIwiththeRadiolabelintheMost

MetabolicallyStablePosition 46330.2.3 IncorporatetheRadiolabelasLateintheSynthesisas

Possible 46530.2.4 UsetheRadiolabeledReagentastheLimitingReagent 46530.2.5 ConsiderAlternativeLabeledReagentsandStrategies 46630.2.6 DevelopOne-PotReactionsandMinimizethe

NumberofPurificationSteps 46730.2.7 SafetyConsiderations 467

30.3 PreparationandSynthesis 46730.3.1 DesignatedcGMP-LikeArea 46730.3.2 Cleaning 467

xviii CONTENTS

30.3.3 Glassware 46830.3.4 EquipmentandCalibrationofAnalyticalInstruments 46830.3.5 ReagentsandSubstrates 46830.3.6 PracticeReactions 46830.3.7 ActualRadiolabelSynthesis 468

30.4 AnalysisandProductRelease 46930.4.1 ValidatedHPLCAnalysis 46930.4.2 OrthogonalHPLCMethod 46930.4.3 LiquidChromatography-MassSpectrometry(LC-MS)

Analysis 46930.4.4 ProtonandCarbon-13NMR 46930.4.5 DeterminationoftheSAoftheHighSpecific

ActivityAPI 46930.4.6 MixingoftheHighSpecificActivityAPIwith

UnlabeledClinical-GradeAPI 47030.4.7 DeterminationoftheSAoftheLowSpecific

ActivityAPI 47030.4.8 OtherPotentialAnalyses 47030.4.9 EstablishmentofUseDateandUseDateExtensions 47030.4.10 AnalysisandReleaseoftheRadiolabeledDrugProduct 471

30.5 Documentation 47130.5.1 QAOversight 47130.5.2 TSEandBSEAssessment 471

30.6 Summary 471References 471

31 FormulationDevelopmentforPreclinicalinvivoStudies 473Yuan-Hon Kiang, Darren L. Reid, and Janan Jona

31.1 Introduction 47331.2 FormulationConsiderationfortheIntravenousRoute 47331.3 FormulationConsiderationfortheOral,Subcutaneous,and

IntraperitonealRoutes 47431.4 SpecialConsiderationfortheIntraperitonealRoute 47531.5 SolubilityEnhancement 47531.6 pHManipulation 47631.7 CosolventsUtilization 47731.8 Complexation 47931.9 AmorphousFormApproach 47931.10 ImprovingtheDissolutionRate 47931.11 FormulationforToxicologyStudies 47931.12 TimingandAssessmentofPhysicochemicalProperties 48031.13 CriticalIssueswithSolubilityandStability 481

31.13.1 Solubility 48131.13.2 ChemicalStabilityAssessment 48131.13.3 MonitoringofthePhysicalandChemicalStability 482

31.14 GeneralandQuickApproachforFormulationIdentificationattheEarlyDiscoveryStages 482

References 482

32 InvitroTestingofProarrhythmicToxicity 485Haoyu Zeng and Jiesheng Kang

32.1 Objectives,Rationale,andRegulatoryCompliance 48532.2 StudySystemandDesign 486

CONTENTS xix

32.2.1 TheGoldStandardManualPatchClampSystem 48632.2.2 SemiautomatedSystem 48732.2.3 AutomatedSystem 48732.2.4 ComparisonbetweenIsolatedCardiomyocytesand

StablyTransfectedCellLines 48932.3 GoodLaboratoryPractice(GLP)-hERGStudy 48932.4 Medium-ThroughputAssaysUsingPatchXpressasaCaseStudy 49032.5 NonfunctionalandFunctionalAssaysforhERGTrafficking 49132.6 ConclusionsandthePathForward 491References 492

33 TargetEngagementforPK/PDModelingandTranslationalImagingBiomarkers 493Vanessa N. Barth, Elizabeth M. Joshi, and Matthew D. Silva

33.1 Introduction 49333.2 ApplicationofLC-MS/MStoAssessTargetEngagement 494

33.2.1 AdvantagesandDisadvantagesofTechnologyandStudyDesigns 494

33.3 LC-MS/MS-BasedROStudyDesignsandTheirCalculations 49433.3.1 SampleAnalysis 49633.3.2 ComparisonandValidationversus

TraditionalApproaches 49733.4 LeveragingTargetEngagementDataforDrugDiscoveryfroman

Absorption,Distribution,Metabolism,andExcretion(ADME)Perspective 49733.4.1 DrugExposureMeasurement 49733.4.2 ProteinBindingandUnboundConcentrations 49833.4.3 MetabolismandActiveMetabolites 500

33.5 ApplicationofLC-MS/MStoDiscoveryNovelTracers 50233.5.1 CharacterizationoftheDopamineD2PETTracer

RaclopridebyLC-MS/MS 50233.5.2 DiscoveryofNovelTracers 503

33.6 NoninvasiveTranslationalImaging 50333.7 ConclusionsandthePathForward 507References 508

34 ApplicationsofiRNATechnologiesinDrugTransportersandDrugMetabolizingEnzymes 513Mingxiang Liao and Cindy Q. Xia

34.1 Introduction 51334.2 ExperimentalDesigns 514

34.2.1 siRNADesign 51434.2.2 MethodsforsiRNAProduction 51534.2.3 ControlsandDeliveryMethodsSelection 51734.2.4 GeneSilencingEffectsDetection 52034.2.5 ChallengesinsiRNA 524

34.3 ApplicationsofRNAiinDrugMetabolizingEnzymesandTransporters 52734.3.1 ApplicationsofSilencingDrugTransporters 52734.3.2 ApplicationsofSilencingDrugMetabolizingEnzymes 53434.3.3 ApplicationsofSilencingNuclearReceptors(NRs) 53434.3.4 Applicationsinin vivo 535

xx CONTENTS

34.4 Conclusions 538Acknowledgment 539References 539

Appendix DrugMetabolizingEnzymesandBiotransformationReactions 545Natalia Penner, Caroline Woodward, and Chandra Prakash

A.1 Introduction 545A.2 OxidativeEnzymes 547

A.2.1 P450 547A.2.2 FMOs 548A.2.3 MAOs 549A.2.4 MolybdenumHydroxylases(AOandXO) 549A.2.5 ADHs 550A.2.6 ALDHs 550

A.3 ReductiveEnzymes 550A.3.1 AKRs 550A.3.2 AZRsandNTRs 551A.3.3 QRs 551A.3.4 ADH,P450,andNADPH-P450Reductase 551

A.4 HydrolyticEnzymes 551A.4.1 EpoxideHydrolases(EHs) 551A.4.2 EsterasesandAmidases 552

A.5 Conjugative(PhaseII)DMEs 553A.5.1 UGTs 553A.5.2 SULTs 553A.5.3 Methyltransferases(MTs) 553A.5.4 NATs 554A.5.5 GSTs 554A.5.6 AminoAcidConjugation 555

A.6 FactorsAffectingDMEActivities 555A.6.1 SpeciesandGender 556A.6.2 PolymorphismofDMEs 556A.6.3 ComedicationandDiet 556

A.7 BiotransformationReactions 557A.7.1 Oxidation 557A.7.2 Reduction 560A.7.3 ConjugationReactions 561

A.8 Summary 561Acknowledgment 562References 562

INDEX 567

xxi

FOREWORD

The discovery, design, and development of drugs is a complex endeavor of optimizing on three axes: efficacy, safety, and druggability or drug-likeness. Each of these axes is a potential cause of attrition as a new molecular entity progresses through the many phases of drug development. Out of the 5000–10,000 compounds evalu-ated in discovery efforts, only 250 enter preclinical testing, 5 enter clinical trials, and only 1 is granted approval by the Food and Drug Administration at a cost that is estimated between US$1.3–1.6 billion [1]. Efforts to increase innovation, decrease attrition, and lower the cost of drug development are the focus of the pharma-ceutical industry and regulatory agencies alike. Advances have been made in some disciplines such as drug metab-olism and pharmacokinetics (PK), particularly in the area of absorption, distribution, metabolism, and excre-tion (ADME) studies. For example, a root cause analy-sis of clinical attrition [2] showed that unacceptable PK or bioavailability accounted for 40% of clinical attrition in the 1990s but within a decade had been reduced to less than 10%, in large part by the identification and mitigation of risks associated with ADME/PK proper-ties earlier in the drug discovery process. This was enabled by the introduction of automated high- and medium-throughput screening of lead optimization can-didates in the discovery space. While impressive, this improvement alone is not sufficient to reverse the rising costs and long development cycle times. It is, however, a step in the right direction. As the pharmaceutical industry has evolved, the focus of ADME studies has shifted from studies conducted primarily in support of regulatory submissions to playing a significant role in the earliest stages of the discovery phase of drug devel-opment. The engagement of ADME scientists in the

discovery space has allowed drug candidates to progress in the development pipeline to the next milestone with greater probability of success because desirable charac-teristics, such as good aqueous solubility for absorption, high bioavailability, and balanced clearance, have been engineered into the molecules, and liabilities such as high first-pass metabolism and unacceptable drug–drug interactions potential have been engineered out.

The history of the discipline of drug metabolism and PK and ADME studies, with its roots in organic chem-istry and pharmacology, has been well chronicled [3–8]. The rapid advancement of the discipline over the past 50 years is clearly linked to the development of ever-increasingly sophisticated analytical tools and the growth of the pharmaceutical industry. The vast number of tools at the disposal of drug metabolism scientists has transformed the study of xenobiotics from descriptive to quantitative, in vivo to the molecular levels, and from simply characterizing to predicting ADME properties.

It would be beyond the scope of this introduction to provide a historical accounting of the numerous advances of technology that have shaped the field. There are, however, three noteworthy milestones in the evolu-tion of the discipline that merit mention: the use of radioisotopes in metabolism and distribution studies; the discovery of the superfamily of drug metabolizing enzymes, the cytochrome P450s; and the revolutionizing impact of mass spectrometry as both a qualitative and quantitative tool.

With the discovery of a new radioisotope of carbon, 14C, by Martin and Ruben [9], this powerful analytical tool enabled the first radiolabeled studies that eluci-dated the metabolic pathways and the disposition of xenobiotics in rats [10, 11]. The use of radiotracers went

xxii FOREWORD

on to become an indispensable tool in biochemical pathway elucidation and in drug disposition studies. While 14C-labeled compounds are predominantly used in in vivo studies to fulfill regulatory requirement, the development of new reagents and techniques in tritium labeling now have allowed stereo- and site-selective synthesis with high specific activity, making these labeled molecule readily available for use in the earliest phases of drug discovery [12, 13].

The discovery of the cytochrome P450s and their role in the metabolism of endo- and xenobiotics opened a field of science that continues to grow and have a tre-mendous impact on the development of drugs and the practice of medicine. The pioneering research in this field has been well documented by Estabrook, a key contributor to our current understanding of this super-family of enzymes [14]. The magnitude of research on the cytochrome P450s has exploded since 2003 (from greater than 2000 literature references to over 67,000 citations, as reflected by searching the PubMed database in 2011) The expanding knowledge of the cytochrome P450s has impacted early discovery efforts via assays for metabolic stability, species comparison in the selection of the most relevant species for toxicology studies, iden-tification of the primary enzymes involved in the metab-olism of a candidate drug, and potential polymorphic or drug–drug interaction liabilities of a candidate drug. The influence of the research on the cytochrome P450s also reaches into the clinical realm of drug development in the need for and design of clinical drug–drug interaction trials as well as in the regulatory guidance on drug inter-actions [15, 16].

No single analytical technique has had a more power-ful effect on drug development than mass spectrometry, with an impact on multiple disciplines, such as chemis-try, biology, and ADME [17]. An excellent review of mass spectrometry and its applications in drug metabo-lism and PK has recently been published [18] Mass spec-trometry moved from the being a specialized tool largely used in structure identification to a “routine,” but albeit powerful, analytical technology used across the pharma-ceutical industry and academia alike. The selectivity, sensitivity, and speed of mass spectrometry enabled much of the success seen with high-throughput screen-ing and advances in bioanalytical analysis in a multitude of biological matrices in both PK and biotransforma-tion studies.

The ADME scientist of today is fortunate to have an arsenal of tools at his or her disposal, many of which will be expanded upon in this book. The advances in technologies often have implications in adjacent tech-nologies that further the discipline of drug metabolism and PK and allow an integrated approach to solving

problems and advancing drug candidates through the phases of drug development.

REFERENCES

1. Burrill & Company. Analysis for Pharmaceutical Research and Manufacturers of America; and Pharmaceutical Research and Manufacturers of America, PhRMA Annual Member Survey (Washington, DC: PhRMA, 2010). Citations at http://www.phrma.org/research/infographics, 2010.

2. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nature Reviews. Drug Discovery 3:711–715.

3. Conti A, Bickel MH (1977) History of drug metabolism: Discoveries of the major pathways in the 19th century. Drug Metabolism Reviews 6(1):1–50.

4. Bachmann C, Bickel MH (1985) History of drug metabo-lism: The first half of the 20th century. Drug Metabolism Reviews 16(3):185–253.

5. Murphy PJ (2001) Xenobiotic metabolism: A look from the past to the future. Drug Metabolism and Disposition 29:779–780.

6. Murphy PJ (2008) The development of drug metabolism research as expressed in the publications of ASPET: Part 1, 1909–1958. Drug Metabolism and Disposition 36:1–5.

7. Murphy PJ (2008) The development of drug metabolism research as expressed in the publications of ASPET: Part 2, 1959–1983. Drug Metabolism and Disposition 36:981–985.

8. Murphy PJ (2008) The development of drug metabolism research as expressed in the publications of ASPET: Part 3, 1984–2008. Drug Metabolism and Disposition 36:1977–1982.

9. Ruben S, Kamen MD (1941) Long-lived radioactive carbon: C14. Physical Review 59:349–354.

10. Elliott HW, Chang FNH, Abdou IA, Anderson HH (1949) The distribution of radioactivity in rats after administra-tion of C14 labeled methadone. The Journal of Pharma-cology and Experimental Therapeutics 95:494–501.

11. Morris HP, Weisburger JH, Weisburger EK (1950) The distribution of radioactivity following the feeding of carbon 14-labeled 2-acetylaminofluorene in rats. Cancer Research 10:620–634.

12. Saljoughian M (2002) Synthetic tritium labeling: Reagents and methodologies. Synthesis 13:1781–1801.

13. Voges R, Heys JR, Moenius T Preparation of Compounds Labeled with Tritium and Carbon -14, Chichester, U.K.: John Wiley and Sons, 2009.

14. Estabrook RW (2003) A passion for P450’s (remem-brances of the early history of research on cytochrome P450). Drug Metabolism and Disposition 31:1461–1473.

Lisa A. Shipley

FOREWORD xxiii

15. Guideline on the Investigation of Drug Interactions (EMA/CHMP/EWP/125211/2010). (2010) http://www.ema.europa.eu/ema/pages/includes/document/open_document.jsp?webContentId=WC500090112.

16. Guidance for Industry: In Vivo Drug Metabolism/Drug Interaction Studies-Study Design, Data Analysis, and Rec-ommendations for Dosing and Labeling. (1999) http://www.fda.gov/cder/guidance/index.htm.

17. Ackermann BL, Berna MJ, Eckstein JA, Ott LW, Chad-hary AK (2008) Current applications of liquid chromatog-raphy/mass spectrometry in pharmaceutical discovery after a decade of innovation. Annual Review Of Analytical Chemistry 1:357–396.

18. Ramanathan R, ed. Mass Spectrometry in Drug Metabo-lism and Pharmacokinetics. Hoboken, NJ: John Wiley and Sons, 2009.

xxv

PREFACE

Understanding and characterizing absorption, metabo­lism, distribution, and excretion (ADME) properties of new chemical entities and drug candidates is an integral part of drug design and development. ADME is the discipline that is involved in the entire process of drug development, right from discovery, lead optimization, and clinical drug candidate selection through drug development and regulatory process. The complexity of ADME studies in drug discovery and development requires a drug metabolism scientist to know all avail­able technologies in order to choose the right exp­erimental approach and technology for solving the problems in a timely manner. During the last decade, tremendous progress has been made in wide array of technologies including mass spectrometry and molecu­lar biology tools, and these enabling technologies are widely employed by ADME scientists. The generation of ADME data to support discovery and development teams is a gated process and timely generation of data to make right decisions is of paramount importance. Given the complexity of the drug discovery and devel­opment process, right techniques and tools should be used to generate timely data that is useful for decision making and regulatory filing. This requires an under­standing of not only the breadth and depth of ADME technologies but also their limitation and pitfalls so sci­entists can make appropriate choices in employing these tools. A book on integrated enabling technologies will not only be useful to drug metabolism scientists but also could be a very helpful reference for scientists from the fields of pharmacology, medicinal chemistry, phar­maceutics, toxicology, and bioanalytical sciences in aca­demia and industry.

This book is divided into four main sections. Part A provides the reader with an overview of ADME con­

cepts and current topics including ADME and trans­porter studies in drug discovery and development, active and toxic metabolites, modeling and simulation, and developing biologics and individual medicines. Part B describes the ADME systems and methods; these include ADME screening technologies, permeability and transporter studies, distribution across specialized barriers such as blood–brain barrier (BBB) or placenta, cytochrome P450 (CYP) inhibition, induction, pheno­typing, animal models for studying metabolism and transporters, and bile collection. Part C of the book discusses analytical tools including liquid chromatogra­phy­mass spectrometry (LC­MS) technologies for quantitation, metabolite identification and profiling, accelerator mass spectrometry (AMS) and radioprofil­ing, nuclear magnetic resonance (NMR), supercritical fluid chromatography (SFC) and other separation tech­niques, mass spectrometric imaging, and quantitative whole­body autoradiography (QWBA) tissue distribu­tion techniques. Part D presents new and evolving tech­nologies such as stem cells, genetically modified animal models, and siRNA techniques in ADME studies. Other techniques included in this section are target imaging technologies, radiosynthesis, formulation, and testing of cardiovascular toxicity potential.

We would like to thank our colleagues who are the experts and leading practitioners of the techniques described in the book for their contributions. We hope that this book is useful and serves as a quick reference to all drug hunters and to all those who are new to the discipline of ADME.

Donglu ZhangSekhar Surapaneni

xxvii

CONTRIBUTORS

Suresh K. Balani,  DMPK/NCDS,  Millennium: The  Takeda  Oncology  Company,  Cambridge,  MA, USA

Praveen V. Balimane,  Bristol-Myers Squibb, Princeton, NJ, USA

Vanessa N. Barth,  Translational Sciences, Eli Lilly and Company, Indianapolis, IN, USA

Leslie Bell,  Novartis Institutes for BioMedical Research, Cambridge, MA, USA

Rajinder Bhardwaj,  DMPK,  Chemical  Sciences  and Pharmacokinetics,  Lundbeck  Research  USA, Paramus, NJ, USA

Catherine L. Booth-Genthe,  Respiratory  Therapeutic Area  Unit,  GlaxoSmithKline,  King  of  Prussia,  PA, USA

Hong Cai,  Bristol-Myers Squibb, Pennington, NJ, USA

Gamini Chandrasena,  DMPK,  Chemical  Sciences  and Pharmacokinetics,  Lundbeck  Research  USA, Paramus, NJ, USA

Jiwen Chen,  Bristol-Myers  Squibb,  Pennington, NJ, USA

Saeho Chong,  College  of  Pharmacy,  Seoul  National University, Seoul, Korea

Lisa J. Christopher,  Bristol-Myers  Squibb,  Princeton, NJ, USA

Jun Dai,  Bristol-Myers Squibb, Princeton, NJ, USA

Li Di,  Pfizer  Global  Research  and  Development, Groton, CT, USA

Ashok Dongre,  Bristol-Myers  Squibb,  Pennington, NJ, USA

Dieter M. Drexler,  Bristol-Myers Squibb, Wallingford, CT, USA

Richard W. Edom,  Janssen Pharmaceutical Companies of Johnson & Johnson, Raritan, NJ, USA

Charles S. Elmore,  Radiochemistry,  AstraZeneca, Mölndal, Sweden

Adrian J. Fretland,  Nonclinical  Safety,  Early  ADME Department, Roche, Nutley, NJ, USA

Timothy J. Garrett,  Clinical and Translational Science Institute, University of Florida, Gainesville, FL, USA

Lingling Guan,  Ricerca  Biosciences,  Concord,  OH, USA

Anshul Gupta,  Drug Metabolism and Pharmacokinet-ics, AstraZeneca, Waltham, MA, USA

Yong-Hae Han,  Bristol-Myers  Squibb,  Princeton, NJ, USA

Imad Hanna,  Drug Metabolism and Pharmacokinetics, Novartis  Institutes  for  BioMedical  Research,  East Hanover, NJ, USA

David C. Hay,  MRC  Centre  for  Regenerative  Medi-cine, Edinburgh, UK

Haizheng Hong,  College  of  Oceanography  and  Envi-ronmental Sciences, Xiamen University, Fujian, China

Cornelis E.C.A. Hop,  Department of Drug Metabolism and Pharmacokinetics, Genentech, South San Fran-cisco, CA, USA

xxviii    CONTRIBUTORS

Matthew Hoffmann,  Celgene  Corporation,  Summit, NJ, USA

Stella Huang,  Bristol-Myers  Squibb,  Wallingford,  CT, USA

W. Griffith Humphreys,  Bristol-Myers  Squibb,  Prince-ton, NJ, USA

Wenying Jian,  Johnson  &  Johnson  Pharmaceutical Research & Development, Raritan, NJ, USA

Xi-Ling Jiang,  Department of Pharmaceutical Sciences, School  of  Pharmacy  and  Pharmaceutical  Sciences, University  at  Buffalo, The  State  University  of  New York, Buffalo, NY, USA

Kim A. Johnson,  Bristol-Myers  Squibb,  Wallingford, CT, USA

Janan Jona,  Small  Molecule  Process  and  Product Development/Preformulation,  Amgen  Inc.,  Thou-sand Oaks, CA, USA

Elizabeth M. Joshi,  Department  of  Drug  Disposition, Lilly Research Laboratories, Indianapolis, IN, USA

Nataraj Kalyanaraman,  Pharmacokinetics  and  Drug Metabolism, Amgen Inc., Thousand Oaks, CA, USA

Jiesheng Kang,  Sanofi-Aventis  U.S.  Inc.,  Bridgewater, NJ, USA

Edward H. Kerns,  Therapeutics for Rare and Neglected Diseases, NIH Center for Translational Therapeutics, Rockville, MD, USA

Yuan-Hon Kiang,  Small Molecular Process and Product Development/Preformulation,  Amgen  Inc.,  Thou-sand Oaks, CA, USA

Wing Wah Lam,  Janssen Pharmaceutical Companies of Johnson & Johnson, Raritan, NJ, USA

Chun Li,  Metabolism and Pharmacokinetics, Genomics Institute  of  the  Novartis  Research  Foundation,  San Diego, CA, USA

Mingxiang Liao,  DMPK/NCDS,  Millennium:  The Takeda Oncology Company, Cambridge, MA, USA

Heng-Keang Lim,  Janssen  Pharmaceutical  Companies of Johnson & Johnson, Raritan, NJ, USA

Zhongping (John) Lin,  Frontage  Laboratories,  Inc. Malvern, PA, USA

Chang-Xiao Liu,  State Key Laboratory of Drug Tech-nology  and  Pharmacokinetics,  Tianjin  Institute  of Pharmaceutical Research, Tianjin, China

Tom Lloyd,  Worldwide  Clinical Trials  Drug  Develop-ment  Solutions  Bioanalytical  Sciences,  Austin,  TX, USA

Anthony Y.H. Lu,  Department  of  Chemical  Biology, Ernest Mario School of Pharmacy, Rutgers Univer-sity, Piscataway, NJ, USA

Qiang Ma,  Receptor  Biology  Laboratory,  Toxicology and Molecular Biology Branch, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Morgantown, WV, USA

Daniel P. Magparangalan,  Covidien,  St.  Louis,  MO, USA

Brad D. Maxwell,  Bristol-Myers  Squibb,  Princeton, NJ, USA

Kaushik Mitra,  Merck & Co. Inc., Rahway, NJ, USA

Voon Ong,  San Diego, CA, USA

Ryan M. Pelis,  Department of Pharmacology, Dalhou-sie University, Halifax, Nova Scotia, Canada

Natalia Penner,  Department of Drug Metabolism and Pharmacokinetics,  Biogen  Idec,  Cambridge,  MA, USA

Chandra Prakash,  Department  of  Drug  Metabolism and Pharmacokinetics, Biogen Idec, Cambridge, MA, USA

Darren L. Reid,  Small Molecular Process and Product Development/Preformulation,  Amgen  Inc.,  Thou-sand Oaks, CA, USA

Kevin L. Salyers,  Pharmacokinetics and Drug Metabo-lism, Amgen Inc., Thousand Oaks, CA, USA

Mark Seymour,  Xceleron, Heslington, York, UK

Adam Shilling,  Incyte Corp, Wilmington, DE, USA

Lisa A. Shipley,  Drug Metabolism and Pharmacokinet-ics, Merck & Co., Inc., West Point, PA, USA

Yue-Zhong Shu,  Bristol-Myers  Squibb,  Princeton, NJ, USA

Jose Silva,  Janssen  Pharmaceutical  Companies  of Johnson & Johnson, Raritan, NJ, USA

Matthew D. Silva,  Amgen  Inc.,  Thousand  Oaks, CA, USA

Sekhar Surapaneni,  Drug  Metabolism  and  Pharmaco-kinetics, Celgene Corporation, Summit, NJ, USA

Adrienne A. Tymiak,  Bristol-Myers Squibb, Princeton, NJ, USA

Jianling Wang,  Novartis  Institutes  for  BioMedical Research, Cambridge, MA, USA

Lifei Wang,  Bristol-Myers Squibb, Princeton, NJ, USA

Xiaomin Wang,  Celgene Corporation, Summit, NJ, USA