Ada ptive T r ia l De sig n a nd Inc or por a tion of Bioma r - PowerPoint PPT Presentation

Ada ptive T r ia l De sig n a nd Inc or por a tion of Bioma r ke r s to Ma ximize Ac hie va ble Obje c tive s In Early Phase Clinical Studies

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Polling Question #1

Ke y Obje c tive s Ove r the c our se of this we binar , we will aim to: 01 Discuss adaptive designs and strategies for incorporation in early phase studies. 02 Discuss the utility of biomarker evaluation and its influence on drug development in early stages. 03 Present relevant examples from previous WCCT programs in which the aforementioned strategies were implemented. Address the relevant statistical issues that arise in this setting, and discuss strategies to ensure that valid 04 statistical inferences can be drawn for each of the objectives.

Adaptive Clinic al T r ial De sign

Adaptive Clinic al T r ial De sign FDA (2010): “An adaptive design clinical study is defined as a study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (usually interim data) from subjects in the study.” Are our assumptions correct? Is the study worth continuing, or in need of modifications?

Adaptive Clinic al T r ial De sign Str uc tur e of an Adaptive De sign • Stop trial early Sc ope of • Resize the trial Adaptations • Modify endpoints, etc. • Event rate • Frequentist/Bayesian De c ision Assumptions • Effect size • Blinded/Unblinded to Che c k Rule s • Variability, etc. • Probability-based Ada ptive De sig n • Control type 1 error • Study governance Valid T r ial • Combine • Objective, blinded Infe r e nc e Inte gr ity before/after info assessment

Baye sian De c ision Rule Histor ic al Data Ave r age of Public ations Past & Pr e se nt info E xpe r t Knowle dge Conte xtual Update d = + Obse r ve d Data E vide nc e E vide nc e “Pr ior ” “L ike lihood” “Poste r ior ”

Adaptive De signs in E ar ly Phase T r ials Sc ope s  Futility  Dose Finding  Adaptive randomization  Sample-size Re-estimation  Enrichment  Seamless Phase 2/3

F utility via Conditional Powe r Are we going anywhe re ?  At interim, calculate the probability of success, given the data so far. If the probability is low then stop the study.  Can be accomplished by a frequentist or a Bayesian calculation.  No type-1 error penalty  Need to consider: Expected sample size vs. Max sample size

Adaptive Dose F inding A host of de signs:  Goal is to identify the maximum tolerable dose (MTD)  Design choices:  Escalating Cohort Design:  Assign 6 subjects to dose 1  If toxicity < 0.2 (<= 1 DLT in 6 subjects) then assign 6 new subjects to dose 2  Otherwise stop, and declare MTD at lower dose  “3+3” Design  Assign 3 subjects to dose 1  If 0 DLT in 3 subjects then assign 6 new subjects to dose 2  If 1 DLT in 3 subjects then add 3 new subjects to dose 1  1/6  go to dose 2  2 or more  stop, and declare MTD at lowerdose  If 2-3 DLT in first 3, then stop, and declare MTD at lower dose  3 + 3 converges on MTD defined with Pr [DLT] = 20%  That is changeable (e.g. for a target Pr [DLT] = 10%, one can use a 5+5 Design

Adaptive Dose F inding Additional De sign Choic e s:  Up and Down Design  Search can go in both directions  Continual Re-assessment Method (CRM)  MTD -= a dose associated with Pr [DLT] = x%  Model-based

Adaptive Dose E sc alation 1. Select a mathematical model to describe the relationship between dose and PR [DLT] 2. After each patient, update the model ,and estimate the probability of toxicity for each dose level 3. Treat the next patient at the dose who estimate is closes to some pre-specified target, for example, 20% 4. Stop when a maximum sample size is reached T obability of DL Pr 20% MT D Dose

Adaptive Randomization Updating T r e atme nt Assignme nts  Baseline adaptive randomization  A large number of stratification variables  Balancing treatment arms for all stratification variables is impossible  Balance marginally  Adaptive minimization  Response adaptive randomization  Based urn models  Biomarker-adaptive randomization

Polling Question #2

Inc or por ation of Biomar ke r s

Utility of Biomar ke r s In E ar ly Phase Clinic al T r ials  Increase likelihood for success  Evaluate the population who can benefit  Exclude population with off-target effects  Multiple barometers of PD  Better define mechanism of action  More clearly understand disease  Identify targets for future development  Strongly encouraged by regulators  Personalized medicine  FDA Biomarker Qualification Program

Biomar ke r Cate gor ie s Each biomarker category can have a variety of “Context of Uses” (e.g., a prognostic biomarker can be used for patient stratification of enrichment in clinical trials). Patie nt Se le c tion Diagnostic De te c t a c hange in the de gr e e or e xte nt of a dise ase Indic ate toxic ity or asse ss safe ty Monitor ing Conte xt of Use E Pr ovide e vide nc e of e xposur e ie s* Ide ntify individuals on ba sis of e ffe c t fr om Cate gor Pr e dic tive a spe c ific inte r ve ntion or e xposur e Str atify Patie nts Pr ognostic ke r E nr ic hme nt: inc lusion/ e xc lusion data xample s Biomar E ffic ac y biomar ke r / sur r ogate e ndpoint Pharmac odynamic Show biolog ic al r e sponse r e la te d to a n / Re sponse inte r ve ntion/ e xposur e Indic a te the pr e se nc e or e xte nt of toxic ity Safe ty r e la te d to a n inte r ve ntion or e xposur e Susc e ptibility/ Indic a te the pote ntia l for de ve loping a dise a se or se nsitivity to a n e xposur e Risk *Source: FDA Biomarker Qualification Program

Biomar ke r Cate gor ie s Diagnostic Monitor ing ie s Cate gor Pr e dic tive Pr ognostic ke r Biomar Pharmac odynamic / Re sponse Safe ty Susc e ptibility/ Risk

Biomar ke r Cate gor ie s Use in T ria l De sig n  If the evidence suggests that the benefit of a treatment is Diagnostic limited to the biomarker-positive sub-population, an enrichment design strategy with only biomarker-positive patients may be appropriate Monitor ing  If there is sufficient reason to suggest that a biomarker can ie s predict that therapy will be more effective in biomarker- Cate gor Pr e dic tive positive patients, but the evidence is not compelling enough to rule out clinical efficacy in biomarker-negative patients, a biomarker-stratified trial design or an adaptive Pr ognostic ke r enrichment trial design may be more appropriate Biomar  In the biomarker-stratified trial design , biomarkers are Pharmac odynamic used to guide analysis but not treatment assignment / Re sponse  In the adaptive enrichment trial design , biomarkers are Safe ty used to guide the enrollment and not treatment assignment Susc e ptibility/ Risk

E nr ic hme nt De sign T r e atme nt A Biomarker Positive Asse ss Bioma rke r T r e atme nt B Biomarker Negative Off study

Biomar ke r - Str atifie d De sign T r e atme nt A Biomarker Positive T r e atme nt B Asse ss Bioma rke r T r e atme nt A Biomarker Negative T r e atme nt B

Case Studie s

Case Study AL S Pilot T r ial In this Amytrophic Lateral Sclerosis pilot study, over 10 biomarkers were measured across five different biomarker categories: Diagnostic 4 ALS Target biomarkers across 2 modalities (CSF & Plasma)—SOD1, phosphorylated neurofilament heavy Monitor ing chain (pNFH), total tau, and phosphorylated tau ie s* Cate gor Pr e dic tive 4 efficacy biomarkers/surrogate endpoints—ALS Functional Rating Scale (ALSFRS-R), Force Vital Pr ognostic Capacity (FVC), Time Up and Go (TUG), and Hand-Held ke r Dynamometry (HHD) Biomar Pharmac odynamic / Re sponse Several safety biomarkers including QT measurement Safe ty and hematology parameters Susc e ptibility/ Risk

Multipar ame te r T e c hnologie s Maximizing Pote ntial for Biomar ke r Disc ove r in E ar ly Phase T r ials Proteomics  Single-cell proteomics (FACS, CyTOF) Metabolomics  Metabolism-related small molecules Multiplexed immunoassays  Cytokines  Chemokines  Growth factors Microbiome  18S rRNA sequencing Genetics  Whole genome sequencing  mRNA expression  Single-cell sequencing