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.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002.
Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com.
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