Quantitative pharmacology and individualized therapy strategies in development of therapeutic proteins for immune-mediated inflammatory diseases

512 Pages·2019·14.731 MB·English

by  Mould, Diane Renee, Zhou, Honghui

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QuantitativePharmacologyandIndividualizedTherapyStrategies
inDevelopmentofTherapeuticProteinsforImmune-Mediated
InflammatoryDiseases
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Quantitative Pharmacology
and Individualized Therapy Strategies
in Development of Therapeutic Proteins
for Immune-Mediated Inflammatory
Diseases
Editedby
HonghuiZhou
JanssenResearchandDevelopment,LLC
SpringHouse,PA
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DianeR.Mould
ProjectionsResearch,Inc.
Phoenixville,PA
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Thiseditionfirstpublished2019
©2019JohnWiley&SonsInc.
Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,or
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LibraryofCongressCataloging-in-PublicationData
Names:Zhou,Honghui,editor.|Mould,DianeR.(DianeRenee),1960-editor.
Title:Quantitativepharmacologyandindividualizedtherapystrategiesindevelopmentoftherapeutic
proteinsforimmune-mediatedinflammatorydiseases/editedbyHonghuiZhou,DianeR.Mould.
Description:Firstedition.|Hoboken,NJ:Wiley,[2019]|Includes
bibliographicalreferencesandindex.|
Identifiers:LCCN2018051418(print)|LCCN2018053075(ebook)|ISBN9781119289210
(AdobePDF)|ISBN9781119289227(ePub)|ISBN9781119289197(hardback)
Subjects:|MESH:AutoimmuneDiseases–drugtherapy|Proteins–therapeuticuse|
Proteins–pharmacokinetics|ProteinEngineering–methods|Models,Theoretical
Classification:LCCRC600(ebook)|LCCRC600(print)|NLMWD305|DDC
616.97/806–dc23
LCrecordavailableathttps://lccn.loc.gov/2018051418
CoverDesign:Wiley
CoverImages:(Left)©molekuul_be/Shutterstock,(Right)CourtesyofDianeR.Mould,
(Background)©almagami/iStock.com
Setin10/12ptWarnockProbySPiGlobal,Chennai,India
PrintedintheUnitedStatesofAmerica
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v
Contents
ListofContributors xvii
AbouttheEditors xxi
Foreword xxiii
Preface xxvii
1 DiseaseInterceptioninAutoimmuneDiseases:Froma
ConceptualFrameworktoPracticalImplementation 1
AnishSuri
(cid:2) 1.1 IntroductiontoDiseaseInterception 1 (cid:2)
1.1.1 WhatisDiseaseInterceptionandHowDoesThisImpactOur
ProspectiveThinkingTowardNovelSolutionsforPatientsSuffering
fromAutoimmuneDiseases? 2
1.2 DiseaseInterceptioninAutoimmuneDiseases 3
1.3 ProgressinModulationoftheAdaptiveImmuneResponsein
AutoimmuneInflammatoryDiseases 5
1.4 TheComplexInterplaybetweentheSpecificityofthePathogenic
ImmuneRepertoireandItsSculptingbythe
Environment–ImplicationsforDiseaseInterception 8
1.5 ClinicalApplicationandConcludingRemarks 14
Acknowledgments 15
References 15
2 TheRoleofBiomarkersinTreatmentAlgorithmsfor
UlcerativeColitis(UC) 25
ReenaKhannaandBrianG.Feagan
2.1 Background 25
2.1.1 SerumBiomarkers 26
2.1.2 SerumHematologicMarkers 28
2.1.3 FecalMarkers 29
2.1.3.1 FecalCalprotectin 29
2.1.3.2 AdditionalFecalBiomarkers 31
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vi Contents
2.1.4 UrineBiomarkers 31
2.1.5 EndoscopicOutcomes 31
2.2 Histology 32
2.2.1 TissueMarkers 33
2.3 TreatmentAlgorithms 34
2.3.1 DifferentiatingInflammatoryandNoninflammatoryDisease 34
2.4 AssessingResponsetoTherapy 35
2.5 PredictingRelapse 35
2.6 Summary 35
References 35
3 MechanismandPhysiologicallyBasedPK/PDModelin
AssistingTranslationfromPreclinicaltoClinical:
UnderstandingPK/PDofTherapeuticProteinsat
Site-of-Action 43
XiChenandWeirongWang
3.1 Introduction 43
3.2 BiologicDistributiontoTissueSiteofAction 44
3.2.1 Overview 44
3.2.2 BioanalyticalMethodsforBiologicsatTissueSites 45
(cid:2) 3.2.3 FullPBPKModelandMinimalPBPK(mPBPK)Model 46 (cid:2)
3.2.4 ApplicationofPBPKandmPBPKModelstoFacilitate
UnderstandingofBiologicTissueDistribution 49
3.3 TargetEngagementofBiologicsatSiteofAction 50
3.3.1 Overview 50
3.3.2 BioanalyticalMethodstoUnderstandTargetEngagementby
Biologics 51
3.3.3 MechanisticPBPK/PDModelingtoFacilitateUnderstandingof
TargetEngagementatSiteofAction 52
3.4 TranslationalApplicationofMechanisticPBPKModeling 54
3.5 Conclusion 59
References 59
4 ApplicationofMinimalAnticipatedBiologicalEffectLevel
(MABEL)inHumanStartingDoseSelectionfor
ImmunomodulatoryProteinTherapeutics–Principlesand
CaseStudies 65
HaiqingWang,ZhengYang,andRongShi
4.1 Introduction 65
4.2 SafetyandImmune-RelatedToxicitiesofImmunomodulatory
ProteinTherapeutics 66
4.3 UncertaintiesofToxicologyApproachinFIHSafeStartingDose
SelectionforImmunomodulatoryProteinTherapeutics 68
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Contents vii
4.3.1 HEDCalculationforImmunomodulatoryProteinTherapeutics 68
4.3.2 DeterminationofSafetyFactorforImmunomodulatoryProtein
Therapeutics 69
4.3.3 TGN1412IncidentandMinimalAnticipatedBiologicalEffect
Level 70
4.4 IncorporatingMabelApproachinFIHStartingDoseSelectionfor
High-RiskImmunomodulatoryProteinTherapeutics 71
4.4.1 InvitroCytokineReleaseAssayandOtherInvitroAssaysasToxicity
AssessmentforImmunomodulatoryProteinTherapeutics 73
4.4.2 IntegrateInvitroPharmacologyDatatoEstimateMABELfor
High-riskImmunomodulatoryProteinTherapeutics 74
4.5 CaseStudiesofMabelCalculation 75
4.5.1 CaseStudyI:MABELDeterminationforAnti-CD28Antagonist
DomainAntibodyBMS-931699 75
4.5.2 CaseStudyII:MABELDeterminationforAnti-CD40LReceptor
AntagonistBMS-986004 78
4.5.3 CaseStudyIII:MABELDeterminationforMOXR0916,anAgonistic
AntibodyTargetingOX40 82
4.5.4 CaseStudyIV:MABELDeterminationforBispecific
ImmunomodulatoryP-cadherinLP-DART(PF-06671008)in
(cid:2) Immune-oncology 83 (cid:2)
4.6 DiscussionandConclusion 85
References 87
5 Model-BasedMeta-AnalysisUseintheDevelopment
ofTherapeuticProteins 93
TimothyJ.Taylor,BillFrame,andAngelaD.Taylor
5.1 Introduction 93
5.2 TypesofMBMAandDatabaseConsiderations 94
5.3 DataAnalyticModelsUsefulforMBMA 96
5.4 Example1:MBMAinInflammatoryBowelDisease 97
5.4.1 OverviewofInflammatoryBowelDiseaseandClinical
Endpoints 98
5.4.2 MBMAforInflammatoryBowelDiseaseTreatedwithBiologics 99
5.5 MBMALiteratureSearch 99
5.6 Kinetic-PharmacodynamicModels 100
5.6.1 K-PDModelsResults 104
5.6.1.1 CDAIK-PDModelResults 104
5.6.1.2 CR100K-PDModel 112
5.6.1.3 C-ReactiveProteinK-PDModel 112
5.6.1.4 ImmunogenicityK-PDModel 112
5.7 MBMAImplicationsforInflammatoryBowelDisease 116
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5.8 Example2:MBMAinRheumatoidArthritis 117
5.9 Conclusion 119
References 120
6 UtilityofJointPopulationExposure–ResponseModeling
ApproachtoAssessMultipleContinuousandCategorical
EndpointsinImmunologyDrugDevelopment 125
ChuanpuHuandHonghuiZhou
6.1 Introduction 125
6.2 LatentVariableIndirectResponseModels 126
6.3 ResidualCorrelationModelingBetweenaContinuousanda
CategoricalEndpoint 128
6.3.1 ApplicationExample:UstekinumabinPsoriaticArthritis(PsA) 129
6.3.1.1 PopulationPKModelingofUstekinumabinPsA 130
6.3.1.2 E–RModelingofUstekinumabinPsA 130
6.3.1.3 ApplicationExampleSummaryofUstekinumabinPsA 134
6.4 StructuralCorrelationModelingBetweenaContinuousEndpoint
andaCategoricalEndpoint 134
6.4.1 ApplicationExample:RheumatoidArthritis 134
6.4.1.1 PopulationPKModelingofmAbXinRheumatoidArthritis 135
(cid:2) 6.4.1.2 E–RModelingofmAbXinRheumatoidArthritis 135 (cid:2)
6.4.1.3 ApplicationExampleSummary 144
6.5 Conclusion 145
References 145
7 ModelingApproachestoCharacterizeTarget-Mediated
PharmacokineticsandPharmacodynamicsforTherapeutic
Proteins 149
LeonidGibianskyandEkaterinaGibiansky
7.1 Introduction 149
7.2 Target-MediatedDrugDispositionModel 150
7.3 DataandPracticalConsiderations 152
7.4 WhattoExpectfromtheConcentration–TimeCourse 154
7.5 ApproximationsoftheTMDDModel 157
7.5.1 Quasi-Steady-StateandRapidBindingApproximations 157
7.5.2 Michaelis–MentenApproximation 160
7.5.3 WagnerEquation 161
7.5.4 IrreversibleBindingApproximation 162
7.5.5 HierarchyofTMDDModelApproximations 163
7.5.6 RelationshipBetweentheQSSApproximationandtheIndirect
ResponseModels 164
7.5.7 Two-TargetTMDDModelandApproximations:Solubleand
MembraneTargets 165
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