day 1 p2 brumfield consortia-based strategies in … · 2017. 12. 15. · duchenne regulatory...
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Consortia-BasedStrategiesinNeurodegenerativeDiseases:CriticalPathInstitute’sTrackRecordinCollaborativeEffortsMarthaA.Brumfield,PhDPresident&CEO
Agenda
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• C-PathModelforCollaborationandWhereWeFocus
• InnovativeDrugDevelopmentTools
• WhatIsInScopeforThisCollaboration
• CriticalImportanceofSharingData
HowC-PathWorks:APublic-PrivatePartnership
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• Actasatrusted,neutralthirdparty• Convenescientificconsortiaofindustry,academia,andgovernmentforsharingofdata/expertiseü Thebestscienceü Thebroadestexperience
ü Activeconsensusbuildingü Sharedriskandcosts
• EnableiterativeEMA/FDA/PMDAparticipationindevelopingnewmethodstoassessthesafetyandefficacyofmedicalproducts
• Officialregulatoryendorsementofnovelmethodologiesanddrugdevelopmenttools
C-PathConsortia
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Coalition Against Major DiseasesFocusing on diseases of the brain
Coalition For Accelerating Standards and TherapiesData standards
Critical Path for Parkinson’s ConsortiumEnabling clinical trials in Parkinson’s Disease
Critical Path to TB Drug RegimensAccelerating the development of TB drug regimens and diagnostics
Duchenne Regulatory Science ConsortiumDuchenne Muscular Dystrophy
International Neonatal ConsortiumNeonatal clinical trials
Polycystic Kidney Disease Outcomes ConsortiumNew imaging biomarker for PKD
Multiple Sclerosis Outcome Assessments Consortium Drug Effectiveness in MS
Patient-Reported Outcome ConsortiumAssessing treatment benefit
Electronic Patient-Reported Outcome ConsortiumElectronic capture of treatment benefit
Predictive Safety Testing ConsortiumDrug safety
Pediatric Trials ConsortiumDeveloping effective therapies for children
BiomarkersClinicaloutcomeassessmentinstruments
ClinicaltrialsimulationtoolsDatastandardsInvitrotools
Transplant Therapeutics ConsortiumNew drug development tools for transplantation
Type 1 Diabetes ConsortiumQualifying biomarkers for type 1 diabetes
Huntington's Disease Regulatory Science ConsortiumExpediting approval of Huntington's therapeutics
C-PathRegulatorySuccesses
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ADclinicaltrialdatabase
ADclinicaltrialsimulationtool
EMAqualifiedADbiomarker
FDALettersofSupport- ADbiomarkers
30therapeuticareausersguidesacross25differentdiseaseareas,CDISC
FDAandEMAletterofsupport- PDbiomarker
EMAqualifiedHollowFiberSystemforTuberculosis
ReSeqTB dataplatform
TotalKidneyVolumeImaging(TKV)biomarkerqualifiedwithEMAandFDA
FDAletterofsupport(TKV)
PKDclinicaldatabase
EMA/FDA/PMDAqualifiednon-clinicalkidneysafetybiomarkers
FDAandEMAlettersofsupport:- Kidneybiomarkers
- Skeletalmuscleinjurybiomarkers
- Drug-inducedliverinjury
EXAMPLES OF HOW BIOMARKERS ARE USED IN DRUG DEVELOPMENT
Basic Research
Prototype Design or Discovery
Preclinical Development
Clinical Development FDA Filing/ Approval
and LaunchPhase 1 Phase 2 Phase 3
Molecular Pathways Leading to Disease
• Preclinical Safety Assessment
• Mechanism of Action• Dose Selection
• Stratification• Patient Selection• Enrichment
• Dose Selection• Safety Assessment• Efficacy Assessment
• Mechanism of Action• Drug Target Selection
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CourtesyofShashiAmur,OfficeofTranslationalSciences,FDACDER
SomeEnablersforBiomarkerDevelopment
• Datastandards
• Dataquality
• Datareproducibility
• Statisticalconsiderations
• Assay/imagingconsiderations/validation
• Assay/imagingprotocols
• Establishingcutpoints
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CourtesyofShashiAmur,OfficeofTranslationalSciences,CDER
Huntington’sDiseaseRegulatoryScienceConsortium:Scope
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Ourworkinvolvesmulti-stakeholdercollaborationinapre-competitivespace.Clarityregardingwhatisinandoutofscopeisveryimportant.Herearehigh-levelprinciples:
INSCOPE
• PromotedevelopmentofinnovativedrugdevelopmenttoolstobeincorporatedintoregulatoryapproachescriticalforadvancingsafeandeffectiveHDtherapies
• Enableprotectedmechanismsforinformationanddatasharinginthecollaborationamongindustry,academiccenters,patients,regulatoryandothergovernmentagencies
• Fosterawarenessoftheimportanceofdatastandardizationandtheneedtofocusonearlystageofdisease
• FocusonneedsofpeoplewithHD,needsoftherapeuticdevelopers,needsofregulators
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Huntington’sDiseaseRegulatoryScienceConsortium:Scope
• Discussions,evaluations,analysis,etc.thatpertaintospecific:• Productsortherapeuticsindevelopment• Clinicaltrialsspecifictoasingletherapeutic• Protocolsspecifictoasingletrial• Vendors• Researchsites• Investigators• HD-RSCMembers• Governmentadvocacy
• Discoveryof/basicresearchregardingnewbiomarkersandotherclinicaldevelopmenttools;rather,wefocusonassuringappropriateanalyticalandclinicalvalidationandadequateevidencegatheringtosupporttheiruseindrugdevelopment
OUTOFSCOPE
CriticalFactorsThatEnableCollaboration
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• MemberLegalAgreement
• Governance
• Scientific,Regulatory,andProjectManagementExpertise
• DataCollaborationCenter- DataContribution&DataUseAgreements
HistoryandEvolutionofDataSharing
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Past
• Data sharing among industry was extremely rare
• Academics misconstrued publishing of trial summary results as “data sharing”
• Trial participants always assumed their data were being shared
HistoryandEvolutionofDataSharing
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Past
• Extremely rare for industry• Academics misconstrued
publishing of trial summary results as “data sharing”
Present
• Many databases with patient-level data exist, from both industry and academic sources
• Trial participants are now adamant their data be shared
• ICMJE and many funders require that actual data be published
• Big data initiatives promise much, but can they deliver?
• Smart data can lead to more insights and answers
DATACOLLABORATIONNEUTRALSPACE
HistoryandEvolutionofDataSharing
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Past
• Extremely rare for industry• Academics misconstrued
publishing of trial summary results as “data sharing”
Present
• Many databases with patient level data from both industry and academic sources
• ICMJE and many funders requiring actual data be published
• Big data initiatives promise much but can they deliver?
• Smart data can lead to more insights and answers
DATACOLLABORATIONNEUTRALSPACE
The Emerging Future
• Disease-specific databases containing 10,000+ patient-level data fields that can be tapped to inform decisions
• Databases containing data from safety biomarkers, collected across multiple diseases, from multiple INDs, NDAs, that inform our learning
• Real-world data structured in a consistent manner
C-PathDataMappingandIntegrationProcess
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TherapeuticAreaDataStandards:KeytoCombiningDatasets
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ClinicalDataContributedtoC-Path
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Clinicaldata:97studies54,044subjects
Note:nonclinical119studies.6296subjects.ReSeqTB:5628IndividualIsolates
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5000
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15000
20000
25000
30000
35000
40000
45000
50000
55000
60000
Jun2014 Jun2015 Jun2016 Jun2017
Subjects
T1D
DuchenneMuscularDystrophy
Kidneyhealthyvolunteerstudy
Polycystickidneydisease
Multiplesclerosis
Tuberculosis
Parkinson'sdisease
Alzheimer'sdisease
SuccessfulC-PathDataCollaborationProjects
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ADplacebo-arm,patient-leveldatausedtodevelopdiseaseprogressionmodelandClinicalTrialsimulationtoolPKDdatabasetosupportregulatoryqualificationofprognosticbiomarkerCPTR:TBclinicalandpre-clinicaldataaggregationusedtosupportmodellingandsimulationdevelopmentMSplacebo-armdatatosupportregulatoryqualificationofPerformanceOutcomeMeasureParkinson’sdiseasedatabase(inprogress)
InsupportofFitforPurposediseasemodelDuchennemusculardystrophydatabase(inprogress)InsupportofFitforPurposediseasemodel
RoleofPublic-PrivatePartnerships
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Astheproductsofthesepartnershipsmature,itisincumbentuponallstakeholderstosharethecollectiveknowledgeandimpactfromintegratingthedeliverablesintodrugdevelopmentprograms.Cataloguingandsharingtheseexperiencesandsuccessespubliclywillenablefurtherlearningandencouragetheadoptionofthesenovelapproachestoachieveunmetpublichealthneeds.Withcontinuedpartnership,multisectorcollaborationscancontinuetoadvanceinnovationsinmedicalproductdevelopmentandthehealthofallAmericans.
ThankYouwww.c-path.org
C-PathCoreCompetencies
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• Regulatoryqualificationofpreclinicalandclinicalbiomarkersforsafety,efficacy,andtrialenrichment
• Outcomeassessmentinstrumentdevelopment
• Comprehensivemodeling&simulationprograms
• Novelinvitro toolstoexpediteproof-of-concept
• Clinicaldatastandardsdevelopment
• Securedatamanagement,standardization,curation,databasedevelopment,andhostingforexternaluse
• Formingandmanaginglargeinternationalconsortia
• Formingcollaborationswithnon-C-Pathconsortia(e.g.,IMI,FNIH)