Adapting for Success: The Genesis of Adaptive Designs

1. Adapting for Success: The Genesis of Adaptive Designs Andy Grieve SVP Clinical Trials Methodology, Innovation Centre, Aptiv Solutions.

2. Outline • Basic Principles of Adaptive Designs • Why adaptive trials? • Differences Between Early / Late Phase Adaptive Designs • Categories, benefits and regulatory feedback • Practical Issues

3. Adaptive Ideas Are Not New Biometrika, 1933 • Ethical Design – concentrating on delivering the best treatment to the most patients • Thall and Wathen (Eur J Cancer, 2007)

4. Basic Principles of Adaptive Designs 4

5. Terminology Adaptive Designs

6. Adaptive Design: Definition An Adaptive Trial usesaccumulating data to decide how to modify aspects of the study without undermining the validityandintegrityof the trial. (PhRMA) • Validity • providing correct statistical inference: • adjusted p-values, estimates, confidence intervals • providing convincing results to a broader scientific community • minimizing statistical bias Integrity • Pre-planningbased on intended adaptations • maintaining confidentiality of data • assuring consistency between different stages ofthe study • minimizing operational bias

7. General Structure – Dragalin (DIJ, 2006) • An adaptive design requires the trial to be conducted in several stages with access to the accumulated data • An adaptive design may have one or more rules: • Allocation Rule: how subjects will be allocated to available arms • Sampling Rule: how many subjects will be sampled at next stage • Stopping Rule: when to stop the trial (for efficacy, harm, futility) • Decision Rule: the terminal decision rule and interim decisions pertaining to design change not covered by the previous three rules • At any stage the following stages can be redesigned taking account of all available data

8. Aspects of the Study Modifiable • Number of Subjects • Study Duration • Endpoint Selection • Treatment Duration • Patient Population • Number of Treatments • Number of Interim Analyses • Hypotheses

9. The Role Of Adaptive Trials in the Drug Development Process Standard Development Process Phase 1 Phase 2 Phase 3 New Product Long periods of information “blackout” No opportunity to: - verify trial assumptions - adjust dosing - make minor adjustments to trial design - stop for futility Adaptive Development Process • Option to: • Select best dose • Submit application early • Stop for futility • Option to: • Verify trial assumptions • Explore additional doses • Stop for futility early

10. Types of Adaptive Design: Learn CRM: Continual Reassessment Method; MTD: Maximum Tolerated Dose; MAD: Multiple Ascending Dose; SAD: Single Ascending Dose; MED: Minimum Effective Dose; EDp: Dose achieving 100p% of maximum effect

11. Types of Adaptive Design: Confirm

12. Failure in Pharmaceutical Drug Development Biomed Tracker February 2011 Kola and Landis (2004)Nature Reviews Drug Discovery

13. Why are Adaptive Clinical Trials Essential for Development of New Products? • Pharmaceutical industry facing a major pipeline challenge • Fewer approvals, escalating development costs, tougher regulatory environment, re-imbursement hurdles, expiring patents for existing blockbusters • Failure rate in Phase III estimated at 50% • Traditional development paradigm is not sustainable • Innovative designs are key prioritiesfor improving R&D productivity and increasing the probability of success at Phase III.

14. The Need for Sample Size Re-Estimation in Late Phase Studies 15

15. Impact of key parameters on trial success Delta: the treatment effect – measures the difference between drug group and placebo group Sigma: the standard deviation - measures the dispersion of patient responses around the mean Power (90%): probability of success Error (5%): probability of success although no effect (aka type-1 error or false positive) Sample size needed, depends on all the above SS SS SS

16. Impact of Incorrect Assumptions: treatment effect and standard deviation 3.0 Data from 39 studies (phase 2 and 3) performed in a 2 year period  observed 2.0 design correct assumption 1.0 observed design Good news 0.0 0.0 1.0 2.0 3.0 + Primary Endpoint met - Primary Endpoint not met

17. Uncertainties and Adaptive Insurance Solutions • Uncertainty about treatment effect or variability of data Early stopping for futility Sample size re-estimation • Uncertainty about dose arm to take forward Dose response adaptive Dose selection (seamless 2/3) • Uncertainty about (sub)population Population enrichment • Drug simply doesn’t work Early stopping for futility

18. Regulatory Environment • EMA released (2007) the “Reflection paper” on adaptive designs in confirmatory trials • The emphasis is on control of Type I error rate • Consistency of treatment effect before and after adaptation • PMDA started adaptive discussions and workshops with PhRMA since 2007 • Wants to discuss pros and cons of adaptive designs • In summary (*), they will consider adaptive trials when there is a compelling medical need unanswered otherwise. • Oncology (group sequential trials are more common) • Orphan drugs, rare disease indications • Severity of disease or difficulty of trial conduct • CNS with uncertainty about subjective endpoints and past trial results (*) Ando et al. (2011). Adaptive Clinical Trials for new drug applications in Japan, European Neuropsycopharmacology,

19. Categories and Benefits *CbC: Case by Case FDA EMA FDA FDA FDA CbC CbC CbC CbC *CRM: Continual Re-assessment Method

20. Phase I Dose-Escalation Designs in Oncology 21

21. The Background (Oncology) • Given several doses of a new compound, determine an acceptable dose for treating patients in future trials • Assumptions • Definition of Dose Limiting Toxicity (DLT) • Definition of Maximum Tolerated Dose (MTD) • Prob ( DLT | MTD) = * • Prob (Response)  with dose A) • Prob (Toxicity)  with dose B) • These conflict : A) is good; B) is bad

22. Adaptive Dose-Escalation in Phase I Studies

23. Schematic of a 3+3 Design Enter 3 patients < 1/3 DLTs  1 /3 and < 2/3 DLTs > 2/3 DLTs Dose Level i Add 3 patients  2/6 DLTs < 2/6 DLTs Escalate to Dose Level i + 1 Dose Level (i-1) is MTD The 3+3 design has been used in approximately 95% of the published phase I oncology trials over the last two decades (Tourneau, 2009)

24. Candidate Dose Response Shapes

25. Probabilities of Selecting Appropriate Doses • tendency of the 3+3 designs to underestimate the target dose • increases the chance of failure in terms of efficacy later • Other characteristics - # number of patients with DLT’s, # patients dosed above the MTD ….. Figure 4: Probabilities of selecting appropriate doses

26. Current MRC Developmental Pathway Funding Scheme (DPFS) Statisticians “Bad Cop” “Good Cop” It would be a good idea if the applicants considered a model-based approach The applicants should use a model-based approach

27. Phase II / III Seamless Adaptive Design in Kidney Transplant Patients : Orphan Condition 28

28. Placebo 80mgs 120 mgs 160 mgs 40 mgs 2.5 mgs 10 mgs Phase 2b Dose Selection Design Circa 1993 25 20 15 10 5 Reduction from Baseline 0 Dose • More Efficient • wider range of doses, smaller numbers of patients per group • followed by one large parallel group study focusing on the doses showing promise in exploratory study. 29

29. Designing a Dose-Response Trial Clinician 1 Clinician 2 Clinician 3 0 0.5 1 1.5 Dose

30. Phase 2b Dose Response/Finding/Selection Designs2005-2010 / 2011-2013 2005-2010 2011-2013

31. Seamless Phase II/III Designs • Seamless design • A clinical trial design which combines into a single trial objectives which are traditionally addressed in separate trials (operationally seamless) • Adaptive Seamless design • A seamless trial in which the final analysis will use data from patients enrolled before and after the adaptation(inferentially seamless) • Primary objective – combine “dose selection” and “confirmation” into a single trial • Key Benefits: Efficiency; faster and more informed decision-making • Key Challenges: Effective and Efficient Implementation 32

32. Efficiency of Adaptive Seamless Phase II/III Designs Development Timeline Separate Phase II and phase III trials Dose C Confirmatory Analysis I. Dose B Dose B Dose A Placebo Placebo Phase II End of Phase III Operationally Seamless Phase II/III trial Dose C Confirmatory Analysis II. Dose B Dose B Dose A Placebo Placebo Phase II Inferentially Seamless Phase II/III trials Dose C Confirmatory Analysis Confirmatory Analysis III. Dose B Dose B Dose A Confirmatory Analysis Placebo Placebo Phase II Interim analysis: Trigger for phase III 33 33 33

33. Phase II/III Seamless Design – checklist for feasibility • The hypotheses are pre-defined and will not change • There is positive data from proof-of-concept studies • The remaining uncertainty primarily concerns dose • The primary endpoint for confirmation is pre-specified and will be measured on all patients • Patient population will stay the same in both phases • The marketing formulation is available • There is sufficient animal data to allow longer drug exposure: Phase II decision may be based on a biomarker believed to be predictive of the clinical endpoint for confirmation 34

34. Example: Seamless Phase II/III study in an Orphan Condition (on-going) • Two-stage group sequential design with O’Brien & Fleming boundaries • Dunnett intersection test (dose selection methodology) • Three doses of a drug with pre-specified effect sizes • Primary endpoint: Short-term response (0 or 1) (7 days) Assumptions • One-sided type I error 0.025 • Power 80% • Placebo rate 45% (tobereducedbytreatment) 35

35. Comparison • An inferentiallyseamless adaptive Phase II/III design with stopping after stage 1 only for efficacy using an O’Brien and Fleming boundary. The interim is assumed to be conducted at an information rate of 1/3rd • An operationally seamless Phase II/III study with the same decision rule, and sample size (in stage 1), as the inferentially seamless design followed by a phase III study powered at 80% 36

36. Seamless Phase II/IIIIncreasedSample Size forOperationallySeamless Design 30% increase 37

37. Seamless Phase II/III study in an Orphan Condition • Inferentially seamless design chosen • Design approved by EMA and FDA • Other case studies have shown similar savings • Orphan status would have been a potential reason for PMDA to accept the design

38. Practical Issues in Execution of Adaptive Designs 39

39. Endpoints in Adaptive Trials

40. Delayed Response in Group Sequential TestsLisa Hampson & Chris Jennison (JRSSB, 2013) Pipeline Data • Develop designs which maximise the use of the pipeline data to increase the test’s power

41. Modelling Approach Basic Principle – Ian Marschner  first measurement  second measurement third measurement enrolment times  6  5  4 participant   3    2    1 start end of accrual interim analysis end of study

42. Link to Missing Data • Little & Rubin (2002) – “Analysis of Missing Data” • the concept of “monotone missing data” • for K measurements Y1,Y2,…,YK • Yj+1,Yj+2,…,YK are missing when Yj is missing • The advantage of this structure is that the likelihood can be partitioned allowing simplified estimation

43. Assumptions Normally distributed data, Two treatments n patients per treatment have data on short-term and long-term endpoints m patients per treatment have data on short-term endpoint only Variances equal and known, correlation known “Simple” Illustration of Efficiency Gains

44. Assumptions Normally distributed data, Two treatments n patients per treatment have data on short-term and long-term endpoints m patients per treatment have data on short-term endpoint only Variances equal and known, correlation known • Treatment 1 Complete data : Short-term only: • Treatment 2 • Complete data : • Short-term only: Short-term Long-term Short-term Long-term “Simple” Illustration of Efficiency Gains

45. Using only long-term data • Using all data (long-term and short-term) “Simple” Illustration of Efficiency Gains • Interested in estimating long-term treatment effect : 12 - 22 cf. Galbraith & Marschner Section 2.3 generalized to k measurements

46. Efficiency = 100V1/ V2 ; m=an a 400 10 8 350 6 300 4 250 Efficiency (%) 2 200 1 150 ½ ¼ 100 0 0.2 0.4 0.6 0.8 1 r

47. 60 Mean Scandinavian Stroke Score 2 55 1 2 3 50 3 5 5 1 3 6 6 4 0 5 6 4 2 7 10 10 5 7 7 10 4 7 10 10 10 7 6 45 10 8 5 8 10 8 4 7 8 6 7 11 8 11 11 10 3 11 11 6 9 8 9 11 9 4 9 11 5 8 40 10 11 9 11 9 12 8 11 9 12 6 10 8 12 7 12 11 12 9 12 9 12 35 12 12 9 12 12 11 30 12 12 25 0 2 4 6 8 10 12 14 Week (Post Stroke) Data from Copenhagen Stroke Database (Jorgensen et al, 1995):Mean Response by Length of stay in the Acute Stroke Unit (0-12 Weeks)