Coalition against Major Diseases (CAMD) Work Scope 1.1 JULY 2009
Coalition against Major Diseases
(CAMD)
Work Scope 1.1JULY 2009
Coalition against Major Diseases Work Scope
CAMD �
MeMBers
• AbbottLaboratories• AllianceforAgingResearch• Alzheimer’sAssociation• Alzheimer’sFoundationofAmerica• AstraZenecaPharmaceuticalsLP• Bristol-MyersSquibbCompany• DaiichiSankyo• EliLillyandCompany• F.HoffmannLaRoche,Ltd.• ForestResearchInstitute• Genentech,Inc.• GlaxoSmithKline• Johnson&Johnson• NationalHealthCouncil• NovartisPharmaceuticalCorporation• Parkinson’sActionNetwork• Parkinson’sDiseaseFoundation• Pfizer,Inc.• sanofi-aventis,US,Inc.• Schering-Plough• WyethPharmaceuticals,Inc.
NON-VOTING PARTICIPANTS
•U.S.Food&DrugAdministration(FDA)• EuropeanMedicineAgency(EMEA)•NationalInstituteonAging(NIA)•NationalInstituteofNeurologicalDisorders(NINDS)
IN COLLABORATION WITH
• EngelbergCenterforHealthCareReformattheBrookingsInstitution
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taBle oF Contents
OVERVIEW...............................................................................................................................................3
CHAPTER I: INTRODUCTION.............................................................................................................4
CAMDCaseStatement..........................................................................................................................4 Collaboration..........................................................................................................................................7 Background............................................................................................................................................7 InitialWorkgroups...............................................................................................................................10 WorkflowAmongWorkgroups.............................................................................................................11 StructureofWorkgroups......................................................................................................................12CHAPTER II: PROPOSED WORKGROUPS FOR RESEARCH.....................................................13
Workgroup1–Data.............................................................................................................................13 SubgroupA:DataSources,StandardsandSharing.............................................................14 SubgroupB:DataInfrastructure.........................................................................................19 Workgroup2–Disease-ProgressionModeling....................................................................................26 ProcesstoDetermine/CompileDataNeeds........................................................................28 QuantitativeNeurodegenerativeDiseaseModelCreation...................................................29 Workgroup3–BiomarkerEvaluation.................................................................................................32 Process.................................................................................................................................32 Deliverables.........................................................................................................................34 Workgroup4–HealthAuthoritiesSubmissions...................................................................................35 AllianceswithPatientCommunities....................................................................................................36CHAPTER III: SUPPORT AND COMMUNICATIONS TEAM.......................................................37 AdministrationandProjectManagement............................................................................................37 AnnouncementsandOngoingExternalCommunications...................................................................38
APPENDICES..........................................................................................................................................40 AppendixA:GlossaryofTermsandAcronyms..................................................................................40 AppendixB:OverviewandPurposeoftheCAMDLegalAgreement................................................42 AppendixC:CAMDParticipantRoles................................................................................................44
REFERENCES CITED...........................................................................................................................45
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oVerVieW
ThegoaloftheCoalitionAgainstMajorDiseases(CAMD)istobringtogethermajorpharmaceuti-calcompanies,theU.S.FoodandDrugAdministration(FDA),theEuropeanMedicineAgency
(EMEA),theNationalInstituteonAging(NIA),theNationalInstituteofNeurologicalDisordersandStroke(NINDS),andpatientgroupsinacollaborationtodevelopnewknowledgethatwillenhancetheindustry’sabilitytodevelopinnovativenewtherapies.CAMDwillfocusfirstonAlzheimer’sandParkinson’sdiseasesandthenexpandtootherdiseases.
Thisworkscopedefinestheroleoftheinitialfourworkgroupsthatwillgeneratenewknowledgeresult-ingintoolstoimprovethemedicalproductdevelopmentprocess.Themajordeliverablesofthecoalitionare:
• TosubmitbiomarkerstotheFDAforqualificationtoaccuratelydiagnosedisease,stratifypatient populations,andpredictpatientoutcomes. • TosubmitmultiparametermodelsofdiseaseprogressiontotheFDAforqualificationthatcan beusedtoprojecttheeffectsofpotentialdiagnosticsandtreatments,aswellasinfluencethe designofclinicaltrials. • TodevelopanintegrateddatabasefromcompletedtrialsinacommonClinicalDataInterchange StandardsConsortium(CDISC)standardformatusableforresearchbycoalitionmembersand others.
Aworkgrouphasbeencreatedtoworkoneachmajordeliverable.AfourthworkgroupwillbeformedtoassistinthecreationofthedossiersforsubmissiontotheFDA.
Itisnottheintentofthecoalitiontoduplicatecurrenteffortsalreadyunderwayintheseareas,butin-steadtoleverageexistingdataandknowledge,createconsensusonmethodstoadvanceproductdevel-opment,andmakethemethodsavailableforbroadapplications.Whereappropriate,theresultingapplieddataandnewinformationwillbesubmittedforFDAreview,withthegoaltohavethemqualified,andinallcases,tohavethemwidelyavailableforuseinnewmedicalproductdevelopment.
ThecoalitionisbeingfoundedandsupportedbytheCriticalPathInstitute(C-Path)incollaborationwiththeEngelbergCenterforHealthCareReformattheBrookingsInstitution.CAMDisaself-govern-ingentityadvisedbyscientistsfromtheFDA,EMEA,andtheNationalInstitutesofHealth(NIH)anddirectedbyitsmembers,whoarepharmaceuticalandbiotechcompaniesandpatientgroupscommittedtoadvancingthecareofpatientswithneurodegenerativediseases.
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CAMD CASE STATEMENT
Oneofthegreatestchallengesfacingbiomedicalsciencesinthe21stcenturyisthedevelopmentoffun-damentallybettertreatmentsforneurodegenerativediseases.Thetwomostprevalentofthese,Alzheim-er’sdiseaseandParkinson’sdisease,exertaheavyandrapidlygrowinghumanandeconomicburdenonoursociety.Ourlackofknowledgeaboutthespecificcause(s)ofeitherdiseaseisamajorobstacletothedevelopmentoftreatmentsthatwouldhavethepotentialtoeithercureorprevent.
InordertoreplicatethesuccessfulprocessthatwasusedtodevelopHIV/AIDSdrugsduringthe1980s,theCriticalPathInstitute(C-Path),incollaborationwiththeEngelbergCenterforHealthCareReformattheBrookingsInstitution,hasformedtheCoalitionAgainstMajorDiseases(CAMD),whichincludesadvisorsfromtheFDA,EMEA,NIH(NIAandNINDS),andacademia.Membersincludepatientgroupsandthemedicalproductsindustry.Acoordinatingcommitteewithrepresentationfromeachmemberorganizationwilldirect,prioritize,coordinate,andoverseetheworkofthecoalition.CAMDwillfocusfirstonAlzheimer’sandParkinson’sdiseasesandthenexpandtootherdiseaseswithsignificantpublichealthimplications.
Theinitial,veryambitiousgoalofCAMDistoestablishacommonresearchsupportinfrastructureforusingpooledcontrolorplacebopatientdatafromclinicaltrialstocreatequantitativedisease-progressionmodelsforbothAlzheimer’sdiseaseandParkinson’sdisease.Thesemodelswillutilizealreadydefineddatastandardswhenpossible,andthecoalitionwilldevelopnewdatastandardswhengapsexist.Coalitionmembersalsowillworktogethertoincorporatedataonimagingandbiochemicalandmolecularbiomark-ersthathavethegreatestpotentialtoidentifythosepatientswiththemaximumlikelihoodofderivingben-efitfromand/orthelikelihoodofharmfromspecifictherapies.Importantly,whereappropriateanduseful,CAMDmemberswillcollaboratetoassemble,evaluate,andsubmittheevidencesupportingrequestsfortheFDAtodesignatesuchtoolsasqualifiedforuseindrugdevelopment.Thesenewlyqualifiedtoolsthenwillbemadepubliclyavailableforallscientistsandcommercialdeveloperstoutilize.
TheCAMDCoordinatingCommittee,with21membersrepresented,metonSeptember22,2008,inWashington,D.C.,andauthorizedC-Pathtoenrolladditionalparticipantsandtodraftadetailedworkscopeofactivitiestobeexecutedoverthenextfewyears.ByJanuary2009thedraftworkscopewascompletedandbeganwiththecreationoffourmajorworkgroupsthatwillbesupportedbyateamfo-cusedonprovidingtheinformationtechnologyinfrastructureandtheinterorganizationalcommunicationnecessaryforeffectivecollaboration.
Itisnottheintentofthecoalitiontoduplicatecurrenteffortsalreadyunderwayinanyoftheseareas,butinsteadtogather,integrate,shareandleverageexistingdataandknowledgebymakingthemavail-ableforbroadapplicationandtohavetheresultinganalyticaltoolsandprocessesqualifiedbytheFDAwhereappropriate,sotheymaybewidelyutilizedinnewmedicalproductdevelopment.
cHApTer I: INTroDUcTIoN
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Alzheimer’sandParkinson’sdiseaseshavebeenchoseninitiallybecauseoftheirprevalence,impactonmorbidityandmortality,extensivepriordataavailable,andcurrentinvestmentbyacademia,governmentandindustryforbetterdiseaseunderstandingandinterventionsforpreventionorbettertreatment.TheoverallroadmapfortheCAMDactivitiesisshowninthefollowingdiagram(Figure1).
Theexecutionphaseofthecoalitionbeganinearly2009,withameetingofthecoordinatingcounciltolaunchtheinitialworkgroups.TheevidencewillbeevaluatedbytheworkinggroupsandsubmittedtotheFDAasvoluntaryexploratorydatasubmissions(VXDS).OnceaninternalconsensusisreachedwithinCAMDthatthereisadequateevidencetosupportthequalificationofagivenbiomarker,aBio-markerQualificationReviewTeam(BQRT)willbeformedattheFDAtoreviewthesubmissionpack-ageforqualification.Acceptancebythehealthauthorityagencieswillmeanthatthenewmethodsarethenqualifiedforuseindrugdevelopmentinadefinedmanner.
ThenewmethodsandtoolsfromCAMDareexpectedtohaveasignificanteffectonthedrugdevelop-mentprocess.Today,mostdrugcandidatesareidentifiedbecausetheyhavedemonstratedsomemea-surablechangeinalaboratorymodelthatisconsideredrelevanttoadisease.Thislabassay,ifitcanbelinkedtopatientswiththedisease,oftenhasthepotentialtobeausefulbiomarkerduringdrugdevelop-mentandperhapsinclinicalpractice.Biomarkerscanidentifysubsetsofpatientswithadiseasewhohavedistinctpatternsofprogressionoroutcomes.InCAMD,dataintegrationandsharingareplannedtocreateaquantitativedisease-progressionmodelthatincludesbiomarkersthatpotentiallyidentifydiscretepatientsubsetsofthedisease.Increasingly,a“diseasemodel”anda“drugmodel”areintegrated,andmodelingandsimulationareusedtosimulateanddesignclinicaltrialswithagreaterchanceofsuccess.Thedrugmodelincludesinformationaboutitspharmacokinetics,pharmacodynamics,andthepatient-specificfactorsthatinfluenceitsactions(e.g.,CYPisoformmetabolism).Diseasemodelsenablegreateraccuracyinpredictingtheoccurrenceofclinicallysignificanteventsandtheinfluenceofpotentiallyconfoundingfactors.
Figure1.CAMDRoadmap(VXDS=VoluntaryeXploratoryDataSubmission,BQR=BiomarkerQualificationReview).
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CAMDwilladdresstheneedforamorereliableandpredictableprocessofdrugdevelopmentforthesediseasesbycreatingscientificconsensusfornewtoolsandmethodsqualifiedbytheFDAforuseindrugdevelopment.AsshownbelowinFigure2,inputsforcreatingthediseasemodelwillcomefrommanyestablishedandnewsources.ThediseasemodelandbiomarkerswillbereviewedbytheFDAand“qualifiedforuse”wherepossible,andwillbecomepartofthedrugdevelopmentprocess.Thecombina-tionofqualifiedbiomarkersandquantitativediseasemodelswillprovideimportanttoolsforthephar-maceuticalindustrytousetoidentifypotentialnewtherapies,tolearnmoreaboutwhentomorequicklyterminatecandidatedrugsthathavealowprobabilityofsuccess,andtoensurethatwhenanewtherapyisfound,ithasamuchhigherprobabilitytomovethroughthedevelopmentandregulatorysystemsuc-cessfullyandmorerapidly.
CAMDalsowillprovideaframeworkforcontinuouslearningaboutthediseasesbecausethedatabaseandthediseasemodelswillbeenrichedasnewinformationbecomesavailable.Furthermore,theexperi-encewithAlzheimer’sandParkinson’sdiseasewillbereadilyexpandedtoasystematicexplorationformanyotherimportantdiseases.
Figure 2. CAMD’s contribution to integrated drug development.
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COLLABORATION
ThereisagreatdealofresearchunderwaytodayinbothAlzheimer’sandParkinson’sdiseases.TheAlzheimer’sStudyGrouprecentlypublishedareportonthechallengesofeffectivelyutilizingallofthisnewsciencetogenerateeffectivetherapiesforpatients.1TheCAMDstaff,aspartofthedevelopmentofthisWorkScope,hasmaintainedclosecommunicationwiththoseorganizationsinvolvedinactiveareasofresearch.TheBiomarkerConsortium,Alzheimer’sDiseaseNeuroimagingProject(ADNI),Parkin-son’sStudyGroup(PSG),andAlzheimer’sDiseaseCooperativeStudy(ADCS)haveallbeencontactedandinvitedtocollaboratewithCAMD.
CAMD’sinitialworkinvolvesthesharingofclinicaltrialinformationbyourcorporatememberstogen-eratethecoreinformationneededtobuildnewdiseasemodelsthatrepresentpopulations.Inaddition,wewillalsoseektoincorporatedatafromconcurrentresearchbyotherstosupplementthesediseasemodels,aswellastoenrichthesemodelswithprioritybiomarkerinformationfromotherresearchef-forts.TheactiveparticipationoftheFDAandtheircommitmenttoreviewdataforpossiblequalificationmakeCAMDnovelandespeciallyimportant.Wewillseekqualificationforthesemodelsandbiomark-ersandplacethemintothepublicrecordsoeveryonecanusethemtoenhanceresearchanddrugdevel-opmentforthesediseases.
BACKGROUND
ALZHEIMER’S DISEASE
Ithasbeenmorethan100yearssinceDr.AloisAlzheimerfirstdescribedthecaseofAugusteD.,apatientwithrapidlyfailingmemory,confusion,disorientation,troubleexpressingherthoughts,andunfoundedsuspicionsaboutherfamilyandhospitalstaff.Today,Alzheimer’sdisease(AD)—themostcommonformofdementia—affects4.6millionnewpatientsworldwideeachyear.Thereare5.1mil-lioncasesintheU.S.,mostofthemreceivingcareunderMedicare.By2030,thenumberofAmericans65andolderwithADwillhavegrownby50percentto7.7million.2ADisestimatedtoafflictabout10percentofpeopleoverage65and30to50percentofthoseoverage85.Asacauseofdeath,ADgrewby33percentfrom2000to2004,comparedtodeclinesinthepercentagesofdeathscausedbyheartdisease,stroke,andbreastcancer.ThedirectandindirectcostsofADandotherdementias,includingMedicareandMedicaidcostsandtheindirectcosttoemployersofcaregivers,ismorethan$148billionannuallyintheU.S.2Forthe10millionAmericanscaringforapersonwithADorotherdementia,theannualburden(intermsofreducedproductivityandlowerhealthstatus)hasbeenestimatedat$60bil-lion.2
Inthepast20years,morethan300drugshaveenteredtestingforAD,yetonlyfivehavebeenapprovedindevelopedcountries.Whilethesefivedrugshavesomeimpactonsymptomsforpatientswhohaveal-readydevelopedsignsofdisease,theydonotfundamentallyalteritscourse,andtheircost-effectivenesshasbeenquestioned.3,4ThougheffectiveADdrugsarebadlyneeded,5progresshasbeendifficult.
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ThereiscurrentlyaworldwideefforttoidentifyandvalidatebiomarkersofAD.Theidentificationofbiomolecules—proteins,genesandtheirpathways—combinedwithadvancedimagingtechnologies,promisestoinformonrisk,prevention,identificationoftreatmenttargetsandresponse,anddiseasepro-gression.ItisbelievedthatADisaheterogeneousdisease,comprisingseveralsubtypes,eachofwhichmayhavedifferentetiopathogeneticmechanismsleadingultimatelytoasimilarpathology.Inaddition,thetwoanatomicalhallmarksofAD,amyloidplaquesandneurofibrillarytangles,arenowknown,re-spectively,tobeassociatedwithagingintheabsenceofdementiaandarefoundinotherdiseasestatesaswellasAD.
Forthesereasons,itishighlylikelythatpanelsofbiomarkers,combinedwithimagingtechnologies,willbeneededtospeedprogressinaccurateearlydiagnosisandsuccessfultreatmentstrategies.6Therealpushistowarddevelopmentofantecedentmarkerstoenableearlydetectionorpreventionofdisease,basedonthehypothesisthatbiochemicalchangesoccurfarinadvanceofcognitivedeclineandbraindamage.7
Todate,fourgenesassociatedwithanincreasedriskofADhavebeenidentified.Mutationsinpreseni-lin1(PS1)andpresenilin2(PS2)arethoughttocauseearly-onsetAD.Mutationsingenesexpressingamyloidprecursorprotein(APP)associatedwithβ-andγ-secretaseareanotherrarecauseofearly-onsetfamilialAD.MutationsinPS1,PS2andAPPthatareassociatedwithADallresultinincreasedβ-amy-loid-42(Aβ42)production.Thesemutations,however,arerare,andnogenestodatehavebeenfoundtobeassociatedwiththecommonsporadicformofAD.However,certainallelesoftheapolipoproteinE(ApoE)genehavebeenassociatedwithriskofdisease;theE4allelewithincreasedrisk,andtheE2allelewithdecreasedrisk.
Overthelastdecade,biomarkersderivedfromcerebrospinalfluid(CSF)wereshowntocorrelatewithpathogenicprocessesinADandhavehighpotentialasdiagnosticmarkers.ThecombinationsofelevatedtotalTau(t-Tau),orphosphorylatedTauproteins(p-Tau),andlowβ-amyloid-42(Aβ42)arecurrentlytheonlyCSFbiomarkerswithhighsensitivityandspecificityfordifferentiatingearly-stageADfromotherdementias.Theirstabilityinindividualpatientsovertimealsomakesthempromisingmarkersformonitoringtreatmentresponseinclinicaltrialswithpotentialdisease-modifyingdrugs.OtherresearchsuggeststhatlevelsofP-Tauandisoprostanescombinedwithimaging(MRIorFDG-PET)maybemoresensitiveinassessingdruginterventions.8,9Onthehorizonareadditionalproteinssuchasvisinin-likeprotein1(VLP-1),abraininjurymarker,andtheenzymeBACE1.Inarecentstudy,CSFVLP-1concentrationswereshowntocorrelatewithtauproteinsandwithscoresontheMini-MentalStateExamination,astandardclinicaldiagnostictool.10WhencombinedwithAβ42andtauproteins,VLP-1improveddiagnosticaccuracy.10Similarly,BACE1correlateswithbetaamyloidandtheApoE4allele,11andwhencombinedmaybeusefulasapredictivepanelofdiseasemarkers.
Animportantgoalistodevelopnoninvasiveearlydiagnosticmarkers.Whileconfirmatorytrialsareneeded,CD69valuesinwhitebloodcellsallowedresearcherstodifferentiatebetweenpatientswithAlzheimer’sdiseaseandthosewithParkinson’sdiseasewithgreaterthan90percentaccuracy.12ThemeasurealsodistinguishednormalsubjectsfrompatientswithADwith88percentsensitivity,and82
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percentofthetimewhentheindividualshadnocognitivedeficits.Severaladditionalblood-basedbio-markersforpredictingonsetofdiseaseanddiagnosis,includingAβ42,APP,andBACE,arecurrentlyindevelopment.
Neuroimaging(MRIandPET)technologiesandmarkersalsohavereceivedmuchattentionandresearcheffort.TheabilitytoproduceimagesofamyloidinthebrainsoflivingpeoplerepresentsagreatadvanceindiagnosingAlzheimer’sdisease.Inaddition,imagingcanhelpconfirmthevalueofsurrogatemark-ers13andmayservetoassesstheefficacyofdruginterventions.Whiletherearesomelimitationsduetothehalf-lifeofsomeradiotracers,requiringthemtobemadeon-site,improvedtracersthatcouldbeusedinmorecommunitysettingsarebeingdeveloped.12,14
PARKINSON’S DISEASE
Parkinson’sdisease(PD)isachronicdegenerativedisorderofthecentralnervoussystem.Britishphy-sicianJamesParkinsonfirstdescribeditas“theshakingpalsy”in1817.TheU.S.prevalenceofPDisestimatedatonemillioncases,secondonlytoADamongneurodegenerativediseases.15Theaverageageofonsetis60years,thoughPDcanstrikeadultsatanyage.Thetotalcosttothenationisestimatedtoexceed$25billionannually.TheriskofPDincreaseswithage,soanalystsexpectthefinancialandpublichealthimpactofthisdiseasetoincreaseasthepopulationages.
Parkinson’sdiseaseischaracterizedbyspecificmovementdisorders,includingtremor,rigidity,slowmovement,andposturalinstability.Thesesymptomsusuallybegingraduallyandworsenwithtime.Astheybecomemorepronounced,patientsmayhavedifficultywalking,talking,orcompletingothersimpletasks.AdvancedPDpatientsalsomaysufferfromdementia,adisconcerting,butperhapsinfor-mative,nexusbetweenthesetwodisablingdiseases.
ThespecificcauseofPDremainsunknown.PDoccurswhenneuronsinanareaofthebrainknownasthesubstantianigradieorbecomeimpaired,reducingproductionofdopamine—aneurotransmitterrequiredtoproducesmooth,purposefulmovement.RecentstudieshaveshownthatpeoplewithPDalsohavelossofthenerveendingsthatproducenorepinephrine,whichmayexplainthenonmotorfeaturesofPD,includingfatigueandabnormalitiesofbloodpressureregulation.ManybraincellsofpeoplewithPDcontainLewybodies—unusualdepositsofproteins—butresearchersdonotyetknowwhatroletheyplayindevelopmentofthedisease.SeveralgeneticmutationshavebeenassociatedwithPD,andmanymoregeneshavebeententativelylinkedtothedisorder.AlthoughtheimportanceofgeneticsinPDisincreasinglyrecognized,mostresearchersbelieveenvironmentalexposuresalsoincreaseaperson’sriskofdevelopingthedisease.Certaintoxicchemicals,trauma,andsomevirusesareknownenvironmentaltriggersforPD.Atacellularlevel,mitochondrialdysfunction,oxidativestress,inflammation,andmanyotherprocessesmaycontributetoPD,buttheactualcauseofthedopaminecelldeathformostpatientsisstillundetermined.16Unmetclinicalneedsthusincludethevalidationofbiomarkersandimagingtech-niques,suchas18F-dopaPET,17fortheirabilitytobetterdiscriminatediseasetypes,leadingtoabetterunderstandingofetiology,andultimately,treatment.18
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Inthe1990s,therewasoptimismamongpatientadvocatesandresearchersthatemergingsciencewouldenablePDtobecuredinaslittleasfivetotenyears.In2000,theNIHissuedanambitiousParkinson’sDiseaseResearchAgendathatwouldleveragenewresearchcapabilities,suchashigh-throughputdrugscreening,arraytechnologies,andtissuerepositories,tobetterunderstandPDandtodevelopnewphar-macological,surgical,cellimplantation,gene,andrehabilitationtherapies.19However,thatoptimismhasnotyetturnedintotheneededeffectivenewtherapies.OfthefivenewdrugsapprovedforPDbytheFDAsince2004(rasagiline,rotigotinetransdermal,ropineroleextendedrelease,selegilinedissolvingtablets,andcarbidopa/levodoparapidlydissolvingtablets),onlyrasagilineisanovelcompound—theotherfourarereformulationsofexistingproducts.CurrenttreatmentsforPD,likethoseforAD,aretorelievesymptoms,ratherthanreverseorpreventunderlyingcauses,andtheylosetheireffectivenessovertime.
INITIAL WORKGROUPS
Thisworkscopedocumentdescribestheprocessesthatwillguideactivitytobegininearly2009.Inthisdocument,anumberofworkgroupsareidentifiedtoconductthetasksrelatedtovariousprojects.Theyarelistedbelow,andothersmaybecreatedasnecessary.Finally,ateamwillprovidelogisticalandcom-municationssupportforthecoalition.
Workgroup (WG) 1: Data • CompiledataidentifiedbyWorkgroups2and3 • Establishdatastandardsanddataremapping • Providedatamanagementinfrastructure
Workgroup (WG) 2: Disease-Progression Modeling • Determineclinicaltrialdatarequirements • Createquantitativediseasemodels • Determinedatarequirementsforinclusionofelectronichealthrecords
Workgroup (WG) 3: Biomarker Evaluation • Determinebiomarkerandimagingdatarequirements • Selectbiomarkerstosubsetpatientsinthemodel
Workgroup (WG) 4: Health Authorities Submissions
Support and Communications Team
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WORKFLOW AMONG WORKGROUPS
WG 2 (Disease Progression Modeling) and WG 3 (Biomarker Evaluation) will identify for WG 1 (Data) what data it will need and will assist with compiling these data. WG 1 will request the data from the member companies, NIH, FDA (if feasible and as permitted), etc.; standardize/remap the data; put the data into an infrastructure; and make the data available to WG s 2 and 3 as the data are ready. WG 2 will define the data needed for model building and work with the data as they become available to create
Figure3.Workflowamongworkgroups.
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a disease model (empirical now - mechanistic at first, based on clinical trial data), while WG 3 works with the data to evaluate and identify the most promising biomarkers, including imaging. Candidate biomarkers identified by WG 3 will be referred back to WG 2 for the development of more sophisticated mechanistic disease models. Once biomarkers and disease models have been adequately developed with sufficient evidence, WG 4 will submit them to the FDA for consideration and possible qualification.
STRUCTURE OF WORKGROUPS
Thecurrentstructureoftheworkgroupsisshownabove.Eachofthethreeinitialworkgroupshasadirectliaisonwiththecoordinatingcommittee.Theworkgroupsareledbytwoco-chairs,onefromC-Pathandtheotherfromanindustrymember.Workgroups2and3haveformedseparateAlzheimer’sandParkinson’sdiseaseteamsinordertofocustheknowledgeandexpertiseinthedistinctareas.
Figure4.Structureofworkgroups.
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WORKGROUP 1: DATA
OVERVIEW
TheDataWorkgroupwillfocusonenablingtheeffortsoftheBiomarkerEvaluationandDisease-Pro-gressionModelingworkgroups.Thiswillbeaccomplishedbyworkingwiththesegroupstounderstandtheirneedsandprioritiesforinformationandthenworkingwiththemembercompaniestocompilethisinformationfromthesourcedata.Thissectionaddressesthetechnicalrequirementsofgathering,as-sessing,standardizing,andpoolingdisparatesourcesofclinicalandlaboratorydataintoanintegrateddatabase.Itdescribestheworkneededtodevelopstandardsforremappingandintegratingdatafromavarietyofsourcesandformatsintoacommonformatbasedonopen,consensus-basedstandardswherefeasible(e.g.,CDISCandHealthLevel7[HL7]).Italsoaddressesthearchitecturespecifications:howallpiecesofthecoalition’sinformationtechnology(IT)interacttointegrate,submit,storeandaccessthedataforanalysisineitheracentralizedorfederatedsystem.
OBJECTIVES
TheDataWorkgroupwilladdresstwoprimaryobjectives,possiblyviaseparatecollaborativesubgroups:1)toconvertalldatadomainsandindividualdataelementsrequestedbytheDisease-ProgressionMod-elingandBiomarkerEvaluationworkgroupstoastandardusableformattopopulatetheintegrateddata-base,and2)toprovidetheinfrastructure/architectureoftheentireITsystemforthecoalition.
Insupportoftheseprimaryobjectives,theDataWorkgroupwillmakelinkagestokeypersonnelwithinthemembercompanies,FDA,EMEA,NIH,NIH-fundedefforts,academia,patientgroups,andotherconsortiatocollecttheinformationanddatathatarerelevanttothescopeofCAMD.ThisworkgroupwilldevelopdatausabilitycriteriainconsultationwiththeDisease-ProgressionModelingWorkgroup,andwillusethesecriteriatoassessfitnessofcandidatesourcedataforinclusionintheproject.TheDataWorkgroupwillworkcloselywiththeotherworkgroupstounderstandtheirevolvingdataneedsandtoanticipatefutureneedssodataconversioncanoccurinatimelymanner.TheDataWorkgroupwillneedtoaddressqualitycontrolthroughouttheprocessofdataconversionandwillcoordinate,whenappli-cable,submissionofdatatoFDAforqualification.
Inconsiderationofdevelopingtheinfrastructure,theDataWorkgroupwillevaluateandrecommendpotentialhardwareandtechnologyproductsandvendorsfortheirabilitytomeetthesecurityandfunc-tionalityrequirementsofCAMD.Additionally,inconsultationwiththeDisease-ProgressionModelingWorkgroup,theDataWorkgroupwillassistindecisionsastowhetherornottoacquireanyavailablecommercialoff-the-shelf(COTS)analysistools,ortodevelopcustomanalysistoolsthatwillfittherequirementsoftheproject.
cHApTer II: propoSeD WorkGroUpS For reSeArcH
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SUBGROUP A: DATA SOURCES, STANDARDS AND SHARING
OverviewThissubsectiondescribesthefirstroleoftheDataWorkgroup(WG1):developingtechnicalstandardsforremappingandintegratingdisparatesourcesofclinicaldata.Anoverviewoftheworkflowispresent-edinFigure3,anddeliverablesandresourcesrequiredformeetingtimelinegoalsareoutlinedbelow.
IntroductionWhenconsideringthevariousdataneedsofthecoalition,itisimportanttodistinguishbetweendatarequirementsofthediseasemodels,thestandardsforintegratingthedata,therequirementsfordatastorageinfrastructure,andthetoolsfordataanalysis.Theprimaryobjectiveofthedatateamdescribedinthissectionistocompileandconvertsourcedataintoastandardizedformatforsemanticinteroper-abilityandintegrationinthediseasemodeldatabase.Thisteamwillalsoplayasignificantroleintheinfrastructureconsiderationsaddressedinthenextsection.ThedatarequirementsofthediseasemodelrefertothoseindividualelementsordomainsdeemednecessarybytheDisease-ProgressionModelingandBiomarkerEvaluationworkgroupstodrawmeaningfulscientificconclusions,andshouldthereforebeconsidereddeliverablesofthoseworkgroups.Itwillbeimportanttoestablishbestpracticesstandardsforbothdatamining/modelingandreportingmodels.Thesestandardswilllikelyevolveovertime,buttheyareimportantconsiderationsinsettingtheframeworkforhowtaskswillbeaccomplished.
TheDataWorkgroupwillworkwithnationalstandardsorganizations,suchastheClinicalDataInter-changeStandardsConsortium(CDISC)andHealthLevel7(HL7),toensurethatitseffortsareneitherredundantnorcontradictorytothestandardizationeffortscurrentlybeingdevelopedandimplementedbytheFDAandinthepharmaceuticaland,ifpossible,healthcareindustries.TheworkgroupwillworkwiththeFDAtoensurealldataforCAMDareformattedinamannerthatisaccessiblebytheagency.
Specific AspectsAbroadoverviewoftheworkflowamongtheworkgroupsispresentedinFigure3.TheDisease-Pro-gressionModelingWorkgroup’sprocessforconstructingmodelsisiterative;eachmodelproducedwillbuildonthepreviousmodelsasmoreconclusionsaredrawn,morehypothesesaregenerated,andmoretypesofdataareincluded.AstheDisease-ProgressionModelingandBiomarkerEvaluationworkgroupsidentifynewormorecomplexcandidateprocesses,toolsandbiomarkers,theywillrequesttherepresen-tativedatarequiredformodelingthesefactorsfromtheDataWorkgroup,whichthenwillproducethesedatainausableformat.Consideringthattheusabilityofpreexistingdatawilllimitallfuturemodelingactivities,anessentialearlyandongoingtaskoftheDataWorkgroupwillbetoestablishaprocessforassessingtheusabilityofclinicalstudydataandstudyacceptanceinaprospectivemanner.TheDataWorkgroupalsowillneedtoconsiderthatwhilethefirstsourcesofdatafortheDisease-Pro-gressionModelingWorkgroupwillbelimitedtoclinicaltrialdatafromthemembercompanies,addi-tionalsourcesofdatasuchastheADNI(Alzheimer’sDiseaseNeuroimagingInitiative)willbeusedtoenrichthemodelsastheworkprogresses.
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Asthecomplexityofthemodelsincrease,thescopeofcontributingdatawillalsoincrease.Figure5belowillustrateshowthevarioussourcesofdatabuildontheprevioussourcesasdatafromNIHclinicalstudiesandconsortia(e.g.,ADNI),availablebiomarkersdata,andultimately,personalhealthrecordandelectronichealthrecorddatathatareincorporatedintothemodel.Thislastsourceofdataisalater-phasegoalbutisworthpursuingsincethedataobtainedmayhelpcorroboratetheclinicalobservationsandtheconclusionsdrawnbythemodel,andmayverifytheirrelevancetothegeneralpopulation.Additionally,theelectronicmedicalrecordandpersonalhealthrecorddataalsomayhelptheCAMDeffortserveasavaluablepatientresourceandaclinicaltrialrecruitmenttool.
Theapproachofconvertingonlytherequesteddataforagivenexerciseisthemostefficientsinceitallowstheminimumsetofdataelementsordomainstoundergothesignificanttaskofconversion,reducingtherequiredeffortandresources.Con-versionofentirebodiesofdatafromtheavailablestudiesisanenormoustaskthatislikelyunnecessary;manyofthedatacollectedinthesestudieswillnotberequiredbythemodels.Regardlessofthechosenformat,theworkloadissignificant,sowork-loadaloneshouldnotbeafactorintermsofchoosingthespecificnewstandard.ThefirstmodelinggoalofCAMDwillbetocreatearelativelybasicempiricalmodelfoundedonrelativelyfewerclinicaldomainsandclinicaldataelementsthanarerepresentedinanentireclinicaltrial.TheDisease-ProgressionModelingWorkgroupalsowillrequestbasicdemographicinformationandavarietyofothersupportingdata.Formuchofthisdata,therearelikelytobeexistingCDISCstandards.TheDataWorkgroupwillworkwithitsCDISCexperts,and,whennecessary,CDISCleaderstodeterminehowbesttofitCAMD-requesteddataintoStudyDataTabulationModel(SDTM).
CAMDwillsubmitmodelsandrealdatatotheFDAforassessmentandqualification,andbyusingCDISCandHL7standards,thecoalitioncanensurethattheFDAwillbeabletoreceiveandworkwiththesubmissionsandwiththestandardinteractiveanalyticaltoolsbeingimplemented.Additionally,com-paniescurrentlyconductingstudieswillbenefitfromknowingthattheproactiveconversionofdatawillhelpthemrespondtoanypotentialregulatoryrequeststhatmaycomeyearsafterthestudyissubmit-ted.ConsideringthatCAMDeffortswillcontinueformanyyears,itisinevitablethatthecoalitionwilleventuallyreceivedatafromitsmembersinSDTMformat.Itwouldbeunreasonabletoexpectthatthesefuturedatashouldberetrofittedtosomeotheroutdatedstandardusedbythecoalition.Finally,remap-
Figure 5. Sources of data (Obtaining de-identified data from personal health records and electronic medical records is a future goal of CAMD.)
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pingexistingdatatoCDISCstandardsisbecomingaprescribedprocess.Thechanceoffindingmeta-data-drivenandfullyauditableCOTSanalyticaltools,in-houseexpertise,and/orqualifiedconsultantstoassistintheprocessisthereforeincreased.
WhetherornotnewCDISCdomainstandardsforParkinson’sdiseaseandAlzheimer’sdiseasewillneedtobecreated,orwhethertheexistingstandardscanaccommodatethediseasemodeldatarequirementsremainstobeseenasthecomplexityofthemodelsgrows.Mostlikely,asnewcandidatebiochemicalandimagingbiomarkersemerge,newtherapeutic-areastandardstoaccommodatetheseelementswillneedtobecreated.CAMDwillnotdelaymodelingprogresswhileawaitingfinaldevelopmentofthesenewdomainstandards(assumingnewdomainsarenecessary).Therefore,itwillbenecessarytokeepCDISCabreastofcoalitionactivitiesasitmovesforwardtoavoidcontradictoryorduplicatedeffortsinthefuture.ItisalsoimportanttonotethatthedefinedCDISCprocessfordevelopingnewdisease-spe-cificdomainsinvolvesassemblingsubject-matterexperts,manyofwhomlikelywillhavebeeninvolvedintheCAMDeffort,therebyreducingthechanceofconflictingefforts.
Realisticdetailedestimationsoftimeandresourcerequirementstoachievethegoalsabovearenotfeasi-blewithoutfirstknowingthequantityandcurrentstateofthesourcedatathatwillneedtobeconvertedandintegrated.However,severalgeneralizationscanbemadeandextrapolatedbyapplyingtheexam-plesrelayedtousbyexpertswhohaveembarkedonsimilarefforts.Generallyspeaking,theamountofeffortrequiredinconvertingthedatadependsonanswerstothefollowingquestions: • Howmanyclinicalstudiesneedtoberemapped? • Howmanydifferentsponsorsareinvolved? • Howmanydomainsneedtobeconvertedtothenewstandard? • Howmanystandardvariablesareneeded? • Willnewdomainsneedtobecreated? • Whatdegreeofterminologymappingisrequired? • Howwelldocumentedaretheavailablestudydata? • Howaccessiblearethepeoplewhoarefamiliarwiththedataandthestudyprotocol? • Whatisthecurrentstateanddegreeofstandardizationoflegacydatasetstobetransformed? • Howusefularetheanalysistoolstodetecterrorsinthedata(datevariablescontainingdates, plusotherforeigndatafromshiftedcolumns)?
Thelargesteffortinvolvedintheseundertakingswillbethepreliminarymetadataanalysis.Therefore,thelasttwoitemsabovetouchuponresourcesthataregermanetotheconversionproject.TheFDAandCDISCrecentlyengagedinasimilareffort—TheIntegratedPilotDatabaseProject—whereinitwasat-temptedtointegratedatafrom29clinicalstudiesinvolving8clinicaldomains,8sponsors,andatotalof13compounds.Thiswork,thefirstofitskind,took4FTEs(full-timeequivalentemployees)andnearly6monthstocomplete,andonly8oftheoriginal29studieswereultimatelyintegrated.OtherestimatesfromCDISC“registeredsolutionproviders”indicatethatatypicalconversionof~40sourcedatado-mains,resultingin20to25SDTMdomains,takesapproximately4weeksforawell-documented,morerecentstudy,andupto3monthsforolderstudies.Intheirestimation,thetaskrequires4“roles”(though
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notnecessarily4FTEs),whichtheydefineasjobdeveloper,projectmanager,statistician,andaclini-calleadwhoisfamiliarwiththedata,aswellasanexpertmedicalreviewertoassessthesoundnessoftheintegrateddata.Theworkofthisreviewerwillbefacilitatedbyhavinginteractiveaccesstographicdisplaysof“PatientProfileViews”ofindividualpatientdataandgraphicdisplaysofaggregateddata,includingthe“Napoleon’sMarch”graphicdisplay.
Giventheaboveconsiderations,itisrealistictoexpectthatwithinthefirst5months,theDataWork-groupwillassemble,agreeuponastandard,andinventory/characterizethefirstsetsofsourcedatatomeetthemodelers’needsfortheempiricalmodelbasedonclinicaldataelements.Implementationofanerror-checkingtoolearlyintheprocesswillminimizefindingproblemsattheendoftheanalyticalpro-cess.However,allowingtimeforteamandworkflownormalization,andthepossibilityofencounteringsomepoor-qualitydata,itisreasonabletoallowanother3to6monthsfortheconversionofthefirstsetofrequesteddomainsandtheirintegrationintothedisease-modeldatabase.Inotherwords,withinthefirst8to11months,thecoalitioncanexpecttohavethenecessarydatatocreateitsfirstempiricalmodel,basedonaninitialdatasetresidinginasharedinfrastructure.Itisimportanttonotethatmoreaggressivetimelinescanbeachievedbytakingleanerapproachestodocumentationandvalidation,relyinginsteadonCOTSvalidationtools.ThefeasibilityofthiswillbediscussedwiththeFDA.Inaddition,thedegreeofcommitmentfromparticipantscandrasticallyaffectthetimeline.Foroptimumprogress,asmallteamofpeopleworkingnearlyfulltimeintheinitialstagesisideal.CAMDwillconsultwiththeappropriateexpertstoensurethecoalitioncanmeetitsgoalswithqualityandtimeliness.
Withintwoyearsoflaunch,thecoalitioncanexpecttohavegainedeconomiesofscale.Awell-estab-lishedprocessforcategorizingandremappingsourcedatashouldbeinplace,andtheDataWorkgroupcanbeexpectedtobeproactiveandresponsivetotheneedsoftheDisease-ProgressionModelingWork-grouptodeliverthedataitrequires.Specificoptimizedanalyticaltoolsmayneedtobecreatedtodoadditionalassessments.Also,bythistime(ifnotsooner)amorerobustinfrastructureshouldbeinplacewithsuitabledata-storagesolutions,andGUI(graphicaluserinterface)portalstothedataandanalysistoolsthathavebeenchosenbytheDisease-ProgressionModelingWorkgroupwillbeintegratedintotheinfrastructure.Withinfiveyears,anynewCDISCdomainsthatwerenecessarywillhavebeenlongcompleted,thecoalitionwillhaveprogressedthroughthehierarchyofdatasourcestoincludeEHR(electronichealthrecord)data,anditwillpossiblybeacceptingPHR(personalhealthrecord)data.Thedatabasemaybeservingasaclinicaltrialrecruitmenttoolforinterestedpatientsbythistime.
Deliverables, Considerations, and Resource Requirements I. Datadeliverables A. Surveyofcurrentlyavailabledata,includingalistofmembercompaniesthathavegenerated oraregeneratingstudydata,brokendownby: 1. Numberofstudiesperdisease(ADandPD) 2. Typesofstudiesbydisease(phaseII,III,failedstudies,etc.) 3. Numberofinvestigatorsandnumberofpatientsperinvestigator B. Allfinal,annotatedblankCRFs(casereportforms)fromcandidatestudies C. Allfinalapprovedprotocolsfromcandidatestudies
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D. FinalrawclinicaldatasetsthatmatchtheannotatedCRFs(eventhoughnotalldatawillbe converted) E. Alldiagnosticsandsupplementaldata(labs,ECG,ePRO,etc.) F. Possiblyuseful:analysisdatasetsusedforclinicalstudyreports(CSRs) II. Data-assessmenttasks A. Verifyalldatasetscanbeaccessedandarenotcorrupted B. Developandimplementcriteriaforassessingstudydataofvaluetotheproject 1. Datateamrole:criteriaforqualityofdatawithregardstotechnicalstandards (flagsuspiciousdata;donotattemptcleaningatthisstage) 2. Modelingteamrole:communicatecriteriaforscientificutilityofdata(smallnumber, dropoutrates,trialduration,etc.,thatmayrenderdataundesirable) C. Evaluateandunderstandtheprotocols;metadataanalysis III.Dataconversion A. Personnelrequirements(roles) 1. Ofthecoalition a. Facilitator(s)tocoordinatetheactivitiesamongthevariouscontributinggroups b. Administrativesupportstaff 2. Ofeachcontributinggroup a. Projectmanager-preferablyexperiencedindataconversion b. LeadSDTManalyst c. SDTMprogrammingleader(SASprogrammer—workswithleadanalysttocommu- nicateandresolveinconsistencies) e. Statistician(familiarwitheachstudytobeconverted) f. Clinicallead(familiarwiththestudyobjectives) g. Anysupportstaffrequired,tobedeterminedbyeachgroup B. Specialconsiderations 1. Otherpossiblesourcesofcollaborativedatasharing a. ADNI/LONI (Laboratory of NeuroImaging) b. Other NIA databases c. PD-DOC (Parkinson’s Disease Data and Organizing Center) d. EHRs e. Others (ADCS, REGARDS Study, etc.) 2. Overall workflow considerations a. Each member company converts its own data before submitting to CAMD, or b. A coordinated, shared resource work plan to convert all contributed data will be established.
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SUBGROUP B: DATA INFRASTRUCTURE
The data life cycle needs to accommodate the varied ac-tivities to support transformation of data into information and knowledge, accounting for future data modalities and complex analysis.
The data model and database architecture must accom-modate individual contributing sites and must include workflows that can integrate with the downstream analysis. This section describes the robust infrastructure meeting these criteria. It is important to note that less sophisticated systems can and should be used in earlier stages of the CAMD effort. See Figure 6 to the right for an outline of data infrastructure goals.
The data infrastructure must be designed to support:
• Efficient data infusion: receipt and ETL (extract, transform and load) operations• Quality assurance steps before incorporating the data into the repository• Partitioning and mapping data for integrity and quality benchmarking• Data integration across disparate sources through the use of standards—with extensibility in mind as new standards emerge• A mapping of data provenance and dating, incorporated with tools for data-quality rating and archiving features for old or infrequently used data sets• Risk avoidance via proper backup and recovery proce- dures• Security within the transmission, storage, and manipu- lation of data• Efficient structure for data analyses and reporting• Workflow, visualization, and collaboration tools for consumers of the data• An ability to integrate with other external sources and repositories for greater knowledge aggregation and broader data analysis• Information in multiple standard-compliant formats for data analytics and data-mining activities• Sharing both directly derived and inferred content• Monitoring for availability, performance, and security
Figure6.Datainfrastructuregoals.
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Our proposed basic model for the receipt and infusion of data from disparate sources into a single, cohe-sive data warehouse is shown in Figure 7 below.
This model assumes source data are coming from multiple sources (e.g., pharmaceutical companies “A” through “Z” and others). The pertinent concepts of the data flow of this model are shown in the table on the following page.
Figure7:Proposeddatawarehousemodel.
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Though it is extremely important to capture quality data in a consistent model, of equal importance is the ability for coalition members to access, view, and manipulate that data in a reliable, secure manner once it has been consolidated.
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The basic model for the access and use of the consolidated data set is shown in the diagram below, and described in the following table. Note: The data-access model begins with “Step 9” (from the previous data consolidation flow model), meaning the data-access model assumes that the data have already been transferred, quality-checked, transformed as necessary, and are resident in the CAMD repository. See Figure 8 and the table below for the CAMD data access model and flow description.
Figure8.Proposeddataaccessmodel.
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To accomplish the Data Consolidation and Data Access flow models as described above, the following infrastructure concept is proposed. Figure 9 below is meant to be a conceptual model, not a representa-tion of physical connectivity.
Incomingdatatransfersandrequestsforinformationwillarrive,encryptedattheperimeteroftheC-Pathinfrastructure.Theseservicerequestswillbehandledbyredundant(nosinglepoint-of-failure)infrastructurethroughout.Theperimetersecurity(e.g.,firewalls,intrusiondetection,andproxies)willpassservicerequeststoload-balancedWebservicesthroughalimitedsetofcoalition-agreed-uponports,protocolsandservices.TheWebserversaloneshalltalktoapplicationservices,whichinturnshalltalkalonetodatabaseservices.Theserviceswillbeseparatedbyenclaveboundariesusinga“defense-in-depth”approach,sothatminimalservicesneedtobeopenedbetweenenclaves.
ThisstructurewilllimitCAMD’sriskfor: • Unauthorizeddataaccess • Dataloss • Datatheft • Datatampering • Downtime/outages • Otherproblems
Detailsoftheinfrastructure-specificconfigurationsandsettingswillbedeterminedbyC-PathasCAMDparticipantsformdefinitiveopinionsonanswerstothefollowingquestions:
Figure9.Proposeddataconsolidationmodel.
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Availability • Whatistheallowabledowntime,i.e.,meantimebetweenfailure(MTBF)andmeantimeto repair(MTTR)? • Isredundancyacrossallinfrastructurecomponentswarrantedattheassociatedcost? • Whatisthecontinuityofoperationsplan? - Howoftenmustconfigurationsbebackedup? • Willdailyincrementalbackupsandweeklyfullbackupssuffice? - Howquicklymustrestoreproceduresbeexecuted? - Isoffsitebackupwarranted? - Isadisaster-recoverysitewarranted? • OtherconsiderationsProcessing • Areopen-sourceproducts(e.g.,Linux,Apache,PostgreSQL)acceptabletotheCAMDpartici- pantsinordertominimizecostwhilestillprovidingascalablesolution? • Whataretheresponserequirements? • Howmanysimultaneoususersareenvisioned? • Whattypeofloadisenvisioned?CPUbound?DiskI/Ointensive? • OtherconsiderationsStorage • Whatvolumeofmaterialsisenvisioned? • Whatperformancemetricsareenvisioned? • Underwhatconditions(e.g.,age,quality,infrequencyofuse)woulddatabetakenoffline? • Isdataarchivingnecessary? • Inadditiontodataprovenanceissues,whatadditionalmetadataarerequired? • OtherconsiderationsData Mining • Willthevolumeandusagebesuchthataseparatesetofinfrastructuresfordataminingiswarranted? • Whatquality-validationrulesdoesthecoalitiondesiretoenforce? • Whatisthenatureofthedata,andwhatsortsofreportsandqueriesareofgreatestinterest? • OtherconsiderationsSecurity • Howstringentshouldthecoalitionbewithregardtoauthenticationandauthorization? • Whatspecificports,protocols,andservicesshouldbeallowableattheperimeterandbetween enclaves? • WhatlevelofautomatedmonitoringandcorrectiveactionwouldCAMDliketosee? • Whatforumwillevaluatesecurityviolations,andwhatarethepunitiveactions,ifany,forviolations? • Otherconsiderations
Furtherdetailregardingthedatainfrastructureisleftpendinguntilagreementisreachedregardingthesebasicconcepts,namelytheDataConsolidationFlowModel,theDataAccessFlowModel,andthebasicdesignofthesupportinginfrastructure.Oncethesebasicshavereachedconsensus,thisworkgroupwillholdrequirementsmeeting(s)withCAMDrepresentativestofleshoutfurtherdetailsofthedatainfra-structuredesign.
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WORKGROUP �: DISEASE-PROGRESSION MODELING
OVERVIEW
This section describes the application of mathematical models to characterize disease progression, and how the modeling process is a continuum that depends on the existing knowledge about specific condi-tions, as well as the different data needed to develop such models. In the case of Alzheimer’s and Par-kinson’s diseases, the existing knowledge provides information about clinically observable phenomena, which can be used to create empirical disease-progression models. A particular application for quantita-tive disease, drug, and trial models is to help make more efficient decisions in the drug development pro-cess. The two key strategic goals of this workgroup are to provide a library of trial designs, endpoints, and analysis options for 1) early, exploratory, or phase II clinical trials and 2) late, adequate and well-controlled, or phase III, clinical trials.
Previous models developed by FDA or member companies will act as the basis for further model de-velopment. The work will be conducted considering the differences and similarities of Parkinson’s and Alzheimer’s disease in the context of neurodegeneration, from the clinical to the biological spectrum of both conditions (see Figure 10 below).
Figure 10. AD and PD in the context of neurodegeneration
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Asfurtherknowledgeregardingbiomarkers,mechanisticallydefinedsubpopulations,andimagingpa-rametersbecomeincorporatedintothemodelsandassociatedwithoutcomesthathavebeendefinedasclinicallyrelevant,itwillbepossibletoexaminepotentialrelationshipsandassociations.
Oncetheoverallmodelscontainsufficientdataaboutthecomplexinteractionsamongparametersintheindividualdiseasemodels,asystemsdynamicsapproachcanbeappliedtocreatewholesystembiologymodels.Criteriathencanbeestablishedforconsensusidentificationofcandidateparameters(biomark-ers,imagingmarkers,etc.)thatcanbesubmittedtotheFDAwitharequestthattheybedeemed“quali-fied”forspecificuse(s)indrugdevelopment.Thesubsequentresultsfromtheparameters’applicationindrugdevelopmentcanbeconsideredconfirmationofknowledgethatisthenusedtoenrichthemodelandimprovingitspredictiveaccuracy.OBJECTIVES
Thisworkgroupwillpooldatatocreaterobust(intermsofscopeandpredictiveaccuracy),quantitativedisease-progressionmodelsforuseindrugdevelopmentforAlzheimer’sandParkinson’sdiseases.ThedatausedtocreatesuchmodelsinitiallywillbepooledfromcontrolarmsofclinicaltrialsperformedbyCAMDmembercompaniesandfullclinicaltrialsthatwereconsideredfailuresduetoeitherefficacyorsafetyconcerns,aswellaspubliclyavailablesourcesliketheANDIdatabase.
Additionaldataonrelevantbiomarkers(laboratorytests,imagingparameters,etc.)willbeprogressivelyincorporatedintothemodelsastheyaregeneratedbytherespectiveteamofthecoalition.Otherdatasourcesthatcouldhavefuturevaluerelevanttotheworkgroupincludeelectronichealthrecordsoflesssystematicallyidentified,characterized,andcontrolledpopulationscomparedtoindividualsparticipatinginindustryorNIH-sponsoredclinicaltrials.
Further Objectives • Describemodelingtechniquesandtheircontextofapplication • Describehowandwhenthosemodelingtechniquescouldbeappliedtogeneraterobustquantita- tivedisease-progressionmodelsforuseindrugdevelopmentforAlzheimer’sandParkinson’s diseases • Definetheclinicaltrialendpointsthatwillbeincorporatedintothemodel • Evaluatethelevelofcorrelationamongmultipleendpoints(ADAS-Cogvs.DADvs.NTB,etc.) • Definethelevelofcorrelationamongthesubscaleswithineachscale • Evaluatethelongitudinalpredictabilityofeachscaleanditsapplicabilityindrugdevelopment
Milestones Needed to Achieve the Workgroup’s Objectives • Identifyprospectiveorretrospectivedatasetsthatwillinformthemodeldevelopment • Designapredefinedmodelscope,aswellasadataandknowledgeanalysisplanforall drugresponseanddiseasescalesofinterest • Designapredefinedplanformodeldevelopment,evaluation,calibration,andimplementation
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• Continuouslyincorporatecandidatebiomarkersofdiseasestratification,activity/severity,and progression,bothforParkinson’sandAlzheimer’sdiseases,asidentifiedandqualifiedbythe BiomarkerEvaluationWorkgroup
PROCESS TO DETERMINE/COMPILE DATA NEEDS
Asexplainedbelow,theprocesstodeveloprobustquantitativediseasemodelswillfollowadownwardlyintegrativeapproach,beginningwithempiricalmodelsbasedonclinicallyobservedphenomena(seeFigure11below.)
Theprocesstodeterminewhichvariablesarerelevantforthedevelopmentofrobustmodelswillbebasedontheirstrengthsofassociationtodiseaseprogression,whichincludesthefollowing:
• Medicalhistory,includingageoffirstdiagnosis • Basicdemographicinformation • Standardtherapyanddosetracking • Non-pharmacologicalactions(food,rest,andexercise) • Frequencyoffollow-upvisitsandprocedures • Clinicalscoreevolutionovertime(medicalandself-assessed) • Additionallaboratoryandimagingtestsperformed • Patientdropoutrates
Additionalpathophysiologicdata,suchasbiomarkersofrisk,diseasestratification,activity,progres-sion,andprognosis,willbeincorporatedatfurtherstagesintheevolutionofthemodels.Theprocesstodeterminetherelevanceofsuchdatawillco-evolvewiththedevelopmentofCAMD,astheBiomarkerEvaluationWorkgroupandotherworldwidescientificgroupsgeneratenewinformation.
Figure 11. Disease-progression modeling.
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QUANTITATIVE NEURODEGENERATIVE DISEASE MODELS
Robustneurodegenerativedisease-progressionmodelsofferquantitativeinsightsintodiseasebehaviorandhowitcanbemodified.Thesemodelscanincreaseefficiencyanddecreaseriskoferrorsindrug-developmentdecisionsbyovercomingthecomplexityanduncertaintiesofthedisease-drug-treatmentinteraction.
Manyfactorsthatinfluenceoutcomescanbeconsideredsimultaneouslywiththeuseofsimulations,andtheweightofknowledgeanddatagapsmaybemoresystematicallyrankedandprioritized.Amodel-baseddrug-developmentapproachcanincreaseefficiencybyintegratingallpertinentpriorinformationintoapredictivemodel,whichinturncanbeusedtoguidethedesignofclinicaltrialsanddrugdevelop-mentstrategy.20-23
Asystematicmodel-basedframeworktomaximizeknowledgeanddatainterpretationfrompriorandongoingclinicaltrialinformation,aswellasrelevantpreclinicalandlaboratoryresearch,iscriticallyneeded.24Suchanapproachcanbeusedtocharacterizeandquantifynaturaldiseaseprogression,placeboanddrugeffects,informeddoseselection,aswellastrialexecutionvariables(patientdiscontinuationrates,compliance,selfmedication,specificdesign,etc.)frommultipletrialsusingpatient-leveldata.25,26
Alargespectrumofmodelingmethodsandtechniqueswillbeconsideredandmaybeused,dependingondifferentfactors,suchasthequestionstoberesolved;thestageofdrugresearchanddevelopmentatwhichthesequestionsarise;dataavailability;lengthandtimescalesoftheproblem(moleculartopa-tients,weekstoyears);andmanpowerandtimeconstraints.Thesemethodsincludethefollowing:
• Empiricalmodels,whichoftenmayoriginatefroma“top-down”approach(frompatientpopulation dataintopathophysiologicalphenomena,andasdrivenbydrugdoseandpharmacokinetics.• Semi-empiricalorsemi-mechanistic,withincreasinglyexplicitrepresentationofpathophysiological, cellular,molecular,marker,and/ordrugpharmacokineticsmechanisms.Thesemayincludemath- ematicaltransformstogeneratemeasurableoutputsthatcanberelatedtomeasurableevents.• Mechanisticor“firstprinciple”models,whichoftenarebuiltupona“bottom-up”approach(from molecularpathwaystopathophysiologyandclinicalendpoints,andasdrivenbydrugdoseandphar- macokinetics).• Searchandinferencealgorithms,whichmaybeappliedacrosslengthandtimescalesandareaimed atestablishinglinksamongvariablesofinterest,forwhichmeasurementsareavailable.• Acombinationorintegrationthereoftogeneratesystemsbiologymodelsofhighcomplexity.20,23,25,26
Asanillustration,anempiricalmodelmaydescribetheshapeortrajectoryofdiseasemarkers(bio-chemicaltests,imagingpatterns,clinicalscores,etc.)overtime,andmaytrytorelatechangesinthesemarkerstotreatmentinterventions.Thesemodelsmayhavelittleornorelevancetobiologicalbasisofdiseaseprogression.Thesetrajectoriesandtheirvariationscanbeusefulforsimulatingclinicaltrialsanddetectingpotentialdrugeffects.27,28Atthislevelofobservation,bothlinearandnonlinearapproachescanbeappliedtoprovideanestimateofdiseasestatesthatrelatetoprogression.29ExamplesofAlzheimer’s
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diseasemodelsincludetheworkofHolfordandPeace,21,22aswellasLockwoodetal.30Anothermoredetailedapproachusesmathematicaltransformsthatprovideanoutputwithoutnecessarilyincorporatingexplicitinterconnectionsofunderlyingbiologicalmechanisms.20Thesekindsofmodelsareparticularlyusefulinoscillating,periodic,oraperiodiccontexts,whereinthemostdetailedlevel,suchastherela-tionshipbetweenheartratevariabilityandcardiacevents,30-32cannotbeadequatelydefined.
Amechanisticallyorientedmodelincorporatesarepresentationofkeymolecularsignaling,cellularre-sponses,orpathophysiologicalprocesses(oracombinationofthese)involvedindisease-statemanifes-tationandprogression.Sucharepresentationoftenincorporatesfeedbackandfeed-forwardregulatoryloopsthatmaintainhomeostasisinnonlinear,adaptivesystems,thusprovidingsignificantinsightsintodiseaseprogression.20Suchmodelsmaysimulatealterationsinoneormoreparametersoveradefinedtimeperiodofinterest.Inaddition,thefunctionofmultipleparametersmaybeassessedsimultaneouslytoprovidesignificantinsightsintothedynamicsofthesystem.33-35
Allapproachesrequiredatafromwell-designedandwell-executedstudies,whetherinvitro,invivoorclinical,forthemodeltobeadequatelyinformedanduseful.Model-drivenexperimentaldesign,withatargetedcollectionoftime-series(longitudinal)data,canhelpuncoverunderlyingmechanismsandtheirassociatedkineticswithinbiologicalandpathophysiologicalregulatoryloops.20Amodel-drivenexperi-mentaldesignshouldconsiderimportantaspectssuchasthetimingandpatternofpharmacokineticandpharmacodynamiceffects,response-hysteresisphenomena,e.g.,thetimescaleforthebiologicalsystemtoshiftfromandreturntoabaselinestateafteraneventorintervention,etc.20,29Allthesecomponentsshouldbeintegratedcontinuouslyandprogressivelyintoamodel-basedframework.
Inthecaseofquantitativeneurodegenerativediseasemodels,itisessentialtotakeintoaccountade-tailedclinicalscoreevolution(UnifiedParkinson’sDiseaseRatingScale,orUPDRS,andAlzheimer’sDiseaseAssessmentScale-Cognitivesubscale,orADAS-Cog),patientdropoutrates,genotypeandotherbiomarkers,aswellasenvironmentalhistorydataasextractedfromhumanandpossiblyinvivo/invitrostudies,bothpreviousandongoing.Themodelalsoshouldconsiderallrelevantpharmacokineticandpharmacodynamicpathways(e.g.,CytochromeP450polymorphisms,etc.)thatmayaffecttreatmentresponseandcontainrelevantcovariatessuchasacuteandchronictreatmenteffects.21,36,37
Thequalificationofcandidateefficacyandsafetybiomarkersisafundamentalcomponentofanevolv-ingquantitativedisease-progressionmodel.Sincebiomarkerqualificationiscontext-dependent,andthecontextisprovidedbythedrug-diseasemodelandthedatajustifyingthemodel,biomarkerqualificationanddrug-diseasemodelingareintimatelylinked.Informationabouthowthemodelresponds(ornot)toadruginterventionrepresentsafundamentalopportunitytoenrichanyevolvingquantitativedisease-progressionmodel.
Withoverallfeaturessuchasdifferentdiseasestatesandprogressionsbeingadequatelyrepresented,anintegrateddrug-diseasemodelshouldreproduceclinicallyobservedtrendsortrajectoriescorrectly,byaccuratelymodelingcontrolandsubsequenttreatmentresponsestovariousdrugclasses.20-23,25-29Akeyaspectinthecaseofneurodegenerativediseasesistheneedforthemodeltobeabletocorrectlyrepre-sentdifferingresponseandprogressionratestonoveltherapiesincharacterizedpatientsubpopulations.
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Asbrieflydescribedabove,different—andoftencomplementary—modelingmethodsandtechniquesmaybeusedtocreatequantitativediseasemodels.Inthespecificcaseof Parkinson’sandAlzheimer’sdiseases,giventhelimitedunderstandingofthemolecularandphysiologicalcausalchainsthatleadtothediseasesandtheirprogression,adownwardlyintegrativeapproachmaybepreferredinitially.20Satel-litemodelingefforts(e.g.,modelsborrowingfrominvitroand/orinvivostudiesandscalinguptohu-man,molecularmarkerkineticmodelsinvariousbodycompartments,model-basedscalingfromanimaltopatients,etc.)maybeusedtoindirectlysupportthetop-downapproach.
Theproposedapproachwouldstartbyconsideringclinicallyobservedphenomena,whichareincludedineachdisease’sclinicalscoremetrics(UPDRSandADAS-Cog),followedbyanattempttorepresentthesetoplevelswithkeypathophysiologicalfunctionsthatfeedintoclinicalscoresortheirsubcompo-nents.Inthisdownwardlyintegrativeattempt,theroleofcertainbiomarkersmaybeascertainedmorefully,therebycontributingtoprovidingabasisfortheirpotentialqualification.
Certainly,therewillbesomeknowledgeandevidencegaps,whichwillbenotedandconsideredforfur-theranalysis,e.g.,viahypothesistestingthroughsimulationsofthelargermodelingframework,aswellastargetedexperimentalworkorevaluationviathesatellitemodelsdescribedabove.Insomeinstances,physiologicallyreasonableassumptionscantemporarilyattempttofillsomeofthosegaps.Modelcalibrationandrobustnessneedtobetestedateachstageofdevelopmentandateachscaleofinterestagainstavailableoutputdata.
AlthoughParkinson’sandAlzheimer’sdiseasesare,accordingtothebestunderstandingavailable,twodifferentmedicalconditions,thereareareasofpathophysiologicalsimilarityandclinicaloverlap.38-42Forthisreason,aconstantinteractionbetweentheteamsfocusingoneachdiseaseiscrucialforthesuccessofCAMD.Specificteamscancarryoutworkineachparticularcondition,butwitharegularandperiod-icalefforttowardcommonstandardsandintegrationofadvances.Suchacontinuousprocessofinforma-tion-sharingcanprovideusefulinsightsforscientistsworkingoneachdisease.
SinceFDAscientistshavedevelopedaninitialquantitativediseasemodelforPDwhilemembercompa-nieshaveworkedonotherin-housemodelsforAD,theproposedapproachforthisworkgroupistostartbycross-testingandenrichingtheexistingmodels,withacontinuousfeedbackbasedontheexperiencegatheredateachstage.
Ultimately,theapplicationofquantitativemathematicalmodelingcanprovideusefulandsignificantinsightsintothenatureofneurodegenerativediseasesandtheirpotentialresponsetopharmacologicalinterventions.Thelevelofdetailrequiredbythemodelswillbedictatedbythenatureofthequestionsposedregardingbothdiseasesand,byextension,thelevelofknowledgerequiredinordertosufficientlyaddressthese.
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WORKGROUP 3: BIOMARKER EVALUATION
OVERVIEW
ThefocusoftheBiomarkerEvaluationWorkgroupwillbetoestablishaprocessandexecutetheplanforcompilingandevaluatingthescientificmeritofreportedbiomarkers(includingimagingdata)thatarepotentiallyusefulindrugordiagnostictestdevelopment.Initially,throughcollaborationwiththeDataWorkgroup,dataformatandrequirements,aswellasITsupportforthedatabase,willbeestablished.Aninitiallistofcandidatebiomarkerswillbecompiledfrommembersandotheravailabledatasources,andevidencewillbethoroughlyandsystematicallyreviewed.Gapanalysiswillbeperformed,andprogramswillbeimplementedtoaddressgaps.Whenbiomarkersaredeterminedtobeadequatelysupportedbyevidence,andifdeemedappropriate,theywillmoveintothebiomarkerqualificationprocessdescribedinChapter1.Theultimateobjectiveistoidentifyandqualifybiomarkersthathavepromiseinthedevel-opmentofnewmedicalproductstoimprovethemanagementandoutcomeofthesediseases.
OBJECTIVES
• Establishaprocessforcollatingacomprehensivelistofpotentialbiomarkersusingpubliclyavail- abledataandCAMDmembers’proprietarydata• Establishanevidence-basedprocessincorporatingcurrentmethodstoassessscientificstrengthof candidatebiomarkers• Determinewhichbiomarkersshouldhavepriorityforfurtherdevelopmentefforts,aswellasspecific usecontexts• Determinewhenbiomarkershavesufficientevidencetobesubmittedtothebiomarkerqualification processwithFDAandEMEA(EuropeanMedicinesAgency)• Integratebiomarkerdataintoquantitativedisease-progressionmodels,anduseevolvingdisease modelstohelpwiththeevaluationofbiomarkers
PROCESS
TheBiomarkerEvaluationWorkgroupwillevaluateandprioritizecandidatebiomarkersforfurtherde-velopment,submission,andqualificationbasedonanalysisofestablisheddatabases,incorporationintodisease-progressionmodels,andinternalCAMDmemberunpublisheddataandpublishedreports.TheBiomarkerEvaluationWorkgroupwillincluderepresentativesfromthemembercompanies,memberpatientorganizations,NIH,academiaandotherresearchorganizations,FDA,andC-Path.Themissionofthisworkgroupwillbetoestablishandexecuteasystematicprocessforreviewofcandidatebiomark-ersandtoevaluatethescientificmerit/strengthofevidencesupportingbiomarkersfordiseasedetection,activity,progression,predisposition,predictionofresponsetotreatment,negativeresponsemarkers,andsafetybiomarkers.
Thefirstphaseofthisbiomarkerrevieweffortwillfocusonprocessdefinition,e.g.,definingthedatasourcesforcandidatebiomarkersbycompilingacomprehensivelistofexistentimagingandbiochemi-
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calbiomarkers,definingtheprocessforaddingnewbiomarkerstothelist,determiningcriteriaforevaluation,andestablishingthemethodofratingthedegreeofconfidenceineachbiomarkeraccordingtoestablishedmethodsofreview.43,44DataonexploratorybiomarkerswillbesharedwiththeFDAinmultipleVoluntaryExploratoryDataSubmissions(VXDS)meetings.WorkingtogetherwiththeDataWorkgroup,informationmanagementalsowillbeaddressed,includingdevelopmentofastandardizedformatforthebiomarkerevidencedatabase,aswellastheinfrastructureandmethodforstorageandsharingofbiomarkerinformation.Figure12belowoutlinesthisprocess.
Thesecondphasewillbetheongoingevaluationofevidenceandtheselectionofthemostpromis-ingbiomarkersforregulatoryreview,qualification(whereappropriate),andacceptanceandusebythescientificcommunityforaspecificpurpose.Theworkgroupwillevaluateexternalreportsandmemberorganizationdatathroughliteraturepresentationsandinternaldatapresentations,withtheparticipationofadditionalinvitedexperts.Evidencewillbethoroughlyreviewed,includingrigorousstatisticalanaly-sisofdata.Candidatebiomarkerswillberanked,andevaluationoftop-tierbiomarkerswillcontinue
Figure 12. Biomarker evaluation process and estimated timeline.
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withformulationoftheclaimofspecificpurpose(s)foreachbiomarker,reviewofevidencesupportingclaims,andidentificationofgapsinsupportingevidence.Aclinicalvalidationprogram,foreitherdenovoand/orre-miningofretrospectivedata,willbedesignedtoaddressgaps.Itisdesirabletodevelopclinicalprogramtemplatesthatcanbeusedasatooltoguideclinicalprogramdevelopmentinastan-dardizedapproach,butitisalsoanticipatedthatthespecificprogramforqualificationwillbebiomarkeranddrugdevelopmentclaim-specific.Whenclaimsaredeterminedtobeadequatelysupportedbyevi-dence,thesewillbeconsideredforsubmissiontothebiomarkerqualificationprocessbytheHealthAuthoritiesSubmissionWorkgroup,asdescribedinthesectionbelowonWorkgroup4.
Thisprocessasdescribedisopenanddynamic,withdatasupportingcandidatebiomarkersenteringtheevaluationprocessonanongoingbasis.Asdatabecomeavailabletheworkgroupwillre-evaluatethemandreprioritizebiomarkersbasedoncumulativeevidence.
Note:Thetimelineabove(Figure12)isabestestimateforevaluationandqualificationoftop-tierbio-markersexpectedtobefurtheralongindevelopment.Qualificationofadditionalbiomarkers,withlesssupportingevidenceandthereforerequiringmoreclinicalprograms,isexpectedtohaveanextendedtimeline.
DELIVERABLES
Nine months • Standardizedformatforbiomarkerdatabase• Criteriaandprocessforevaluationofscientificmeritofevidence• Infrastructuretosupportdataandaccess• Comprehensivelistofexistentcandidatebiomarkerswithexistingscientificevidenceminedfrom publicandCAMDmemberdatabases,includingformulatingclaimsofuse• Initialprioritizationofbiomarkers• Processforupdatingdatabase
Nine to fifteen months• Datafromadditionalsourcesoncurrentbiomarkersincorporatedintotheexistingdatabase• Gapanalysisoftop-tier• Definitionandinitiationofclinicalplantoaddressgaps
Fifteen to twenty-four months• SubmissionofbiomarkerstoWorkgroup4forsubmissiontoFDA/EMEAforqualificationforspe- cificusethroughtheBQRTattheFDA
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WORKGROUP 4: HEALTH AUTHORITIES SUBMISSIONS
OVERVIEW
TheHealthAuthoritiesSubmissionsWorkgroup(HASWG)hasasitsmandatetobetheinterfacebe-tweentheotherworkinggroupsandgovernmenthealthauthorities(agencies)forthepurposeofregula-torysubmissions.Thusthisgroupdoesnotdeveloporanalyzebiomarkerdataordiseasemodels,butratherassuresthattheprogressoftheseeffortsisinformedbythecurrentthinkingrelevanttobiomarkerandmodelqualificationsofhealthauthorities.Importantly,whensuchbiomarkerdataordiseasemodelsreachalevelofmaturitythatwarrantsregulatoryreview,theHASWGwillberesponsibleforseeingthatthecollationofsuchinformationisreadyforsubmissiontothehealthauthorities.TheHASWGwillpre-parethesubmissionpackage,followingcurrenthealthauthoritysubmissionpractices,andwillserveastheprimaryinterfaceforcommunicationamongtheCAMDandthehealthauthoritiesduringthereviewprocess.
FDApersonnelwillparticipateinthefunctionalworkgroupsasadvisorsand,insomecases,itwillhavedirectscientificinvolvement.Ontheotherhand,therewillbenoFDAdirectparticipationontheHASWG.Certainly,theHASWGmayhavekeycontactswithinthehealthauthorities,andthesecontactsmayofferadviceregardingdataformatandproceduralissues,buttomaintainobjectivityinthereviewprocess,thesecontactswillnotparticipateinthesubmissionpreparation;inasimilarfashion,reviewersattheFDAmustnothavebeeninvolvedinCAMDactivity.
OBJECTIVES
TheHASWGhasasitsprimaryobjectivethepreparationandsubmissionofdata-setpackagesforre-viewbyhealthauthorities,andthesuccessfulqualificationofthesubmittedbiomarkersand/ordiseasemodelsforuseinregulateddrugdevelopment.
Aseriesofregular“grandrounds”eventsattheFDA,examiningthestateofthefieldandengagingout-sideexperts,maybepursuedpriortoformaldatasubmissiontotheFDAifthegrandroundsserveasavaluableopportunitytocommunicatethinkingandtoengageindiscussionwithalargenumberofFDAregulatoryscientistsandstaffmembers.
Submissionofdisease-progressionmodelsandbiomarkersdevelopedoutofCAMDworkwillfollowthepathdevelopedforbiomarkersubmissionthatthePredictiveSafetyTestingConsortiumpresentedtoboththeFDAandEMEA.45-48Forbothagencies,theprocesswillbeginwithabrieftwo-pagenotifi-cationoftheintenttoseekqualification.TheprocesswillproceedinthemannerplannedbyFDAandEMEAfortheirownfunctions.
Asidefromtherequirementsofthework,adedicatedteamfromthemembersofregulatoryaffairsde-partments,experiencedinbothFDAandEMEAsubmissions,isexpectedtomanagethepreparationofsubmissionmaterials.Theseindividualsshouldserveastheprimarypointsofcontactonthesubmissionforbiomarkerqualification.
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ALLIANCES WITH PATIENT COMMUNITIES
OVERVIEW
TheCriticalPathInstitute(C-Path)strivestopromotecollaborationamongbiopharmaceuticalcom-paniesandincludesfederalagenciessuchasFDAandNIH.ThefirstpriorityfortheC-PathCoalitionAgainstMajorDiseases(CAMD)istodevelopandqualifyforusethemostmoderntoolsandmethodsfordrugdevelopmentforAlzheimer’sandParkinson’sdiseases.Inrecognitionthatthepatientcom-munitiesrepresentavaluableresourceofknowledge,experience,service,advocacy,andsupportforresearch,CAMDincludestheparticipationbyandcontributionsfrompatientgroupsthathavebecomecoalitionmembers.
ThepatientgroupswillactivelyparticipateintheCAMDtechnicalworkgroupstoacceleratethede-velopmentofnewtherapiesforAlzheimer’sandParkinson’sdiseases.ThepatientgroupsconsistofAlzheimer’sandParkinson’sorganizations,aswellasothernot-for-profitorganizationsthatpromotehealth.
OBJECTIVES
ThepatientgroupswillbringtoCAMDtheircollectiveknowledgeoftheentireAlzheimer’sandParkin-son’sdiseasecommunities,includingpatients,researchers,scientists,andcompaniesworkingonthesediseases.Undermostcircumstances,thememberpatientgroupswillrepresentthevoicesofpatients—peoplelivingwithAlzheimer’sandParkinson’sdiseases—andtheircaregivers.Thepatientgroupswillensurethatpatientissuesareattheforefrontofalldiscussionsandconveyasenseofurgency.Ratherthanbeingaseparateworkgroup,theCAMDpatientgroupswillseektobeintegratedwithandcontrib-utetotheCAMDtechnicalworkgroups,whichinturnwillstrivetoimprovethedevelopmentofthera-piesforAlzheimer’sandParkinson’sdiseases.ThepatientgroupsalsowillserveinaleadershiproleincommunicatingbetweenCAMDandthebroaderpatientcommunitywhenappropriateandallowableundertheconfidentialityrestrictionsofthelegalagreement.
Aspartofthisprocess,CAMDwillinvitescientistssupportedbythepatientgroupsandthefederalgovernmenttopresenttheirdatatothetechnicalworkgroupsinthehopesofincreasingthetransparencyofresearch,promotingdialogueamongmultidisciplinaryscientists,andspeedingthetransitionfromdiscoverytotherapeutics.
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ADMINISTRATION AND PROJECT MANAGEMENT
OVERVIEW
TheSupportandCommunicationsTeamisresponsibleforsupportingtheoverallCAMDprojectcoordi-nationandcommunication.
OBJECTIVES
• Establishaprojectmanagementstaffsupportandinfrastructurethatwillmaintainprojectplans foreachworkgroup,aswellastrackandreportprogress • Facilitatecollaborationandinformation-sharingamongmembersofCAMD
SPECIFIC TASKS
1. Provideoverallcoordinationofworkgroupefforts.Tasksinclude: • Developandmaintainworkgroupschedulesandmilestones • Maintaindetailedworkplans,schedulemeetings,anddocumentactionsandthestatusofprojects • StandardizetheuseofMicrosoftProject;usingabaselinefortracking,assigntaskownersfor accountability,templatesforfilters,andaprocessforkeepinginformationuptodate • Standardizeageneralcommunicationandcollaborationtool • Manageexpensesandotherresourcesneededbytheworkgroups • Createtemplatesforimportantdocuments • Maintainadocumentationdatabasewithnecessarysecurityandcheck-outcontrols
2. Establishawebdomainandpassword-protectedsiteformembersofthecoalition.This“intranet” willhaveabasicstructureforcommunicationwithinthecoalition,includingamainpageforan- nouncementsofgeneralinterest,andpageswhereeachworkgroupcanpostdraftdocumentsand sharecomments.MicrosoftSharePointwillbethecollaborationsoftware.
AllmemberswillbegivenanIDandpasswordforaccesstotheCAMDsite.Workgroupleaderswill approveadditionalusers.
3. Establishteleconference,webconference,andvideoconferencecapabilitiesasrequiredtosupport CAMDworkgroupactivities.
4. Toreviewprogress,C-PathandBrookingswillhostformalannualmeetings(perhapsmorefre- quent)oftheCAMDcoordinatingcommittee.TokeepCAMDmembersinformedofprog- ressonanongoingbasis,morefrequentcommunicationswillbeconductedthroughe-mail.
cHApTer III: SUpporT AND coMMUNIcATIoNS TeAM
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ANNOUNCEMENTS AND ONGOING EXTERNAL COMMUNICATIONS
OVERVIEW
TheSupportandCommunicationsTeamisalsoresponsibleforannouncementsandongoingexternalcommunicationsofCAMDbenefitsandprogresstotheindustry,medicalandbusinesscommunities,andthemedia.ExternalcommunicationswillmaximizetheimpactofCAMDannouncements.Thiswillincludesecur-ingmediacoverageandindustry/medicalpublicationofsignificantaccomplishments.CAMDwillbehighlightedasanexampleofeffectivegovernment/privatepartnership.ThisexternalcommunicationisintendedtogeneratesignificantvisibilityforCAMDandtoleadtoenhancedopportunitiestobringad-ditionalsupporttotheproject.
OBJECTIVES
• GatherbroadexposureandpresscoveragefromCAMDlaunchannouncementandfutureprog- ress,developingawarenessintheindustry,patientcommunities,medicalcommunity,academia, andthegeneralpublic • ShowtheuniqueandcomplementaryvalueoftheCAMDeffort • Developapre-andpost-launchCAMDpressplan • Developapre-andpost-launchCAMDcommunicationsplantogovernmentofficials,industry andmedicalpublications,etc.
SPECIFIC TASKS
Specifictasksandresourcesrequiredforeachareoutlinedbelow.
CAMDlauncheventinWashington,D.C.,in2009: • PrepareCAMDlaunchviabroadpressreleaseacrossmediaplatforms
Post-launchcommunicationsplan:
CAMDcommunicationswillinvolveatieredapproachtoinformation,beginningwithamacromessageandbecomingprogressivelymoretechnicalasneeded.ThemacromessagewillallowCAMDtoemergeasamajorhealth-careinitiativethatisvaluedandrespectedbythepublic.Communicationswillbedirectedtowardthefollowingaudiences/constituencies:
• Media - Establishadetailedplanforperiodicpressreleasestoshowmetrics,transparency,and accountabilitythroughouttheCAMDprocess
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- Developcontinuousandupdatedcontentfromscientistsandothermemberstostayactive withmediaandprovidespecificmessagingforsophisticatedortargetedaudiences - Secureprominentplacementandcontinuedcoverageinindustryandmedicalpublications • Members Memberswillbekeptapprisedofnews,successesanddevelopmentsthroughtheCAMD projectmanagementteam.
• PatientCommunity Patientgroupswillbeakeyresourceforcommunicatingimportantfindingstothepatient communitythroughoutthetimelineofthecoalition.
• GeneralPublic ThegeneralpublicwillbeeducatedregardingthemagnitudeoftheAlzheimer’sandParkinson’s problemsandthesuccessesofCAMD.
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APPENDIX A: GLOSSARY OF TERMS AND ACRONYMS
AD: Alzheimer’sDisease
ADaM: AnalysisDataModel(aCDISCstandard)
ADAS-Cog(Alzheimer’sDiseaseAssessmentScale-Cognitivesubscale):Themostcommonlyusedprimaryoutcomeinstrumentinclinicaltrialsfortreatmentsofdementia.Itfocusesonmemoryloss,soitisoftencomplementedbytheapplicationoftheMini-MentalStateExamination(MMSE).
ADNI: Alzheimer’sDiseaseNeuroimagingInitiative(partofLONI)
BQRT: BiomarkerQualificationReviewTeam
CAMD:CoalitionAgainstMajorDiseases(partofC-Path)
CDISC:ClinicalDataInterchangeStandardsConsortium
CFIC:CenterforFDAandIndustryCollaboration
COTS:CommercialOff-the-Shelf(software)
C-Path:CriticalPathInstitute
CPI: CriticalPathInitiative(partoftheFDA)
CRF: CaseReportForm
CSR:ClinicalStudyReport
CSF:CerebrospinalFluid
EHR:ElectronicHealthRecords
EMEA:EuropeanMedicinesAgency
FDA: U.S.FoodandDrugAdministration
FTE:Full-timeequivalent(employee)
AppeNDIceS
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GUI:GraphicalUserInterface
HL-7:HealthLevel7
HHS:U.S.DepartmentofHealthandHumanServices
IT:InformationTechnology
LONI:LaboratoryofNeuroImaging(UCLA)
MMSE:Mini-MentalStateEvaluation.Ascreeningtoolfrequentlyusedbyhealth-careproviderstoassessoverallbrainfunction.ItisoftenusedtoevaluatepatientswithpossibleAlzheimer’sdiseaseoranotherrelateddementia.
NIA:NationalInstituteonAging
NINDS:NationalInstituteforNeurologicalDisordersandStroke
NIH: NationalInstitutesofHealth(partofHealthandHumanServices)
PD:Parkinson’sDisease
PD-DOC: Parkinson’sDiseaseDataandOrganizingCenter
PK:Pharmacokinetics
PRO: Patient-ReportedOutcomesSDTM: StudyDataTabulationModel(ACDISCstandard)
UPDRS(UnifiedParkinson’sDiseaseRatingScale):RatingscoreusedtofollowtheprospectivecourseofParkinson’sdisease,basedonthepatient’sexperiencewiththediseaseandamotorexamination.Itiscomposedofthefollowingsections:mentation,behaviorandmood;experiencesofdailyliving;motorexamination;andcomplicationsoftherapy.
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APPENDIX B: OVERVIEW AND PURPOSE OF CAMD LEGAL AGREEMENT
TheCAMDlegalagreementprovidestheframeworkfortheoperationoftheCoalitionAgainstMajorDiseases.Belowisabriefsummaryofimportantprovisionscontainedinthelegalagreement.
Section 2: Statement of Purpose: Research Plan
2.1:StatementofPurposeToformacoalitionofmembersrepresentinganyofthefollowing: 1. Patientorganizations 2. Pharmaceuticalcompanies 3. Biotechnologycompanies 4. Diagnosticandmedicaldevicecompanies 5. FoodandDrugAdministration 6. NationalInstituteonAgingorotherunitofNIH 7. Othergovernmentalentities 8. Researchinstitutions 9. NonprofitorganizationsToenablesharingofinformationaboutneurodegenerativediseases: Alzheimer’sdisease Parkinson’sdisease
Toexpeditethedevelopmentofsafenewtreatments,curesandpreventionsbycreatingquantitativedis-ease-progressionmodels
Toqualifybiomarkersforuseindrugdevelopmentandtoincorporatetheiruseindrugdevelopmentasclinicaldiagnostictools
Eachsignatoryoftheagreementwillappointonevotingrepresentativetoserveonacoordinatingcom-mitteethatwillgovernandguidetheworkoftheconsortium.Althoughnotexplicitintheagreement,itisexpectedthatthecoordinatingcommitteewillencourageeachmembertoassignscientiststopartici-pateineachworkinggroup(seeSection2.2).Memberswillbeexpectedtocontributedata,expertguid-ance,advocacyforthemission,orotherimportantcontributions.
2.2:ResearchProgramTheoverallresearchprogramoftheCAMDwillbeconductedthroughoneormoreresearchprojects.TheresearchplanandbudgetforeachresearchprojectwillbesubjecttoapprovalbytheCAMD’sCo-ordinatingCommittee,whichwillbeincorporatedintoaprojectagreementthatismutuallyagreedtobyC-Path(onbehalfoftheCAMD),themember(s)participatingintheresearchproject,and,ifapplicable,anyapprovedparticipatingthird-partycontractor(s).
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Eachresearchprojectwillbeoverseenbyaworkinggroup,composedofrepresentativesofeachofthepartiestosuchprojectagreement.2.3:MaterialsTransferInordertofacilitatetheresearchproject,itisexpectedthatbiologicalsamples,reagentsorchemicalsmaybeexchangedbythemembersfromtimetotime—thissectionallowsthememberstosharemateri-alsthroughouttheresearchprojectwithouttheneedforaseparatematerialstransferagreementforeachitemshared.2.4:PolicyregardingcompliancewithantitrustpolicyByreferencetoExhibitA,thisinformsallmembersandatteststhatthememberswillactincompliancewithantitrustlaws.2.5:DisclaimerofpartnershipThisstatestheintentofthememberstointeractasindependentcontractors,andnottocreateanyagency,jointventure,orsimilararrangement.
Section 4. Management4.1:ConsortiumManagement Director–willbeaC-Pathemployee Co-Director–willbeamember’srepresentativeelectedbymembers CoordinatingCommittee–Eachmemberhasonevotingrepresentativeonthecommittee.4.2:CoordinatingCommittee–Thissectiondefinesitsresponsibilities,e.g.: Developresearchplan Overseeresearchgroups Approveresearchprojectsandbudgets WillincludenonvotingFDAorEMEArepresentative(s)
Section 6. Confidentiality
6.1:Confidentiality–Thissectionenablesallmemberstosharetheirintellectualpropertywithoutlossoftheirrights.Thisisanessentialelementtoensurethedevelopmentofnewtherapies.6.2:Exceptions–standardlanguageforconfidentialityagreement6.3:Authorizeddisclosure–standardlanguagetodiscloseinformation6.4:Publications–standardlanguagethatencouragespromptpublicationandrespectsconfidentialityofinformationbeingsharedandhasstandardlanguagetoallowtimeforfilingpatents.6.5:Publicity–Permissionrequiredformentionofpartnersinpublicityregardingresearchortechnicalpublicity,butnotrequiredifonlymentioningtheirname.
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APPENDIX C: CAMD PARTICIPANT ROLES
Voting members of the Coordinating Council:
• Designated representatives of the member patient groups and industry. Responsible to be the conduit and champions for CAMD activities within their organizations. Together, they are the governing group for CAMD work plans and all matters pertaining to the coalition. • Critical Path Institute - Director of CAMD
Nonvoting participants and advisors:
• FDA, EMEA and NIA representatives (technical advisors to the Coordinating Council and workgroups) • Brookings Institution - cosponsor (with C-Path) of CAMD public meetings
Workgroup members:
• Scientists from relevant disciplines within the industry and patient group members to define and, in some cases, execute activity defined by that workgroup’s work scope • Patient group members to provide guidance on the direction and priority of work and assist in public policy messaging • Scientific and advisory personnel from the FDA, EMEA and NIH
Additional CAMD members:
• To be determined by the Coordinating Council and as defined by the CAMD Consortium Legal Agreement
Additional CAMD participants:
• Other organizations or individuals that are brought into workgroups to provide information, services, advice, etc., have a confidential disclosure agreement in place with C-Path, and are approved by the director of CAMD.
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