Optimizing the Design of Clinical Programs

1. Optimizing the Design of Clinical Programs Adaptive Programs (AP) workstream Carl-Fredrik Burman, Ph.D., Assoc. Prof. AstraZeneca R&D and Chalmers Univ. Tech. Fredrik Öhrn, Ph.D. AstraZeneca R&D

2. Thanks to the rest of the AP core team: • Zoran Antonijevic, Quintiles • Alun Bedding, GSK • Christy Chuang-Stein, Pfizer • Chris Jennison, Bath Univ. • Martin Kimber, Tessella • Olga Marchenko, Quintiles • Nitin Patel, Cytel • José Pinheiro, J&J • … and to the entire AP network with 30+ members R & D | Global Medicines Development

3. Topics for today • Background: Adaptive Program workstream • Joint optimization of Phase II and Phase III • Adaptive Program subteam work R & D | Global Medicines Development

4. Background:Adaptive Program (AP) workstream • Carl-Fredrik Burman

5. DIA’s Adaptive Design Scientific Working Group (ADSWG) • Ongoing workstreams • KOL lectures • Adaptive Programs (AP) • Communication • Simulations • Survey • Material supply (sunsetting) • Upcoming workstreams • DMC • NIH • Personalized medicine • Portfolio R & D | Global Medicines Development

6. Adaptive Programs (AP) Background Past: Standard trials Recent: Optimization of single trials • Increased interest in study design (optimal design theory, cross-over, dose choices, etc) • In particular, much work on Adaptive Designs (including group-sequential, dose-finding, sample size re-estimation) Sometimes: Consider a study in its context • Modeling – what can be learned from previous information? • Hand-over – what questions need answers in this trial, to provide a solid basis for the design of the next phase? AP: Optimize the whole program • Optimal Phase IIB depends on design rule for Phase III • Optimal Phase III depends on Phase IIB results R & D | Global Medicines Development

7. From study to program perspective Focus on one single trial One trial in program context Program focus

8. Adaptive Programs (AP) Key ideas • See the whole picture • Global optimization, not sub-optimization • Efficacy & safety • Be specific about values and costs • Stakeholder analysis • Clear science-based quantitative assumptions • Bayesian in, frequentist out • Focus on key decisions • Go / No Go • Sample size • Dose • Biomarkers • Population R & D | Global Medicines Development

9. Adaptive Programs (AP) Ongoing activities • Main model • Specific applications • Neuropathic pain • Diabetes • Oncology • Supporting • Algorithms & soft-ware • Academic collaborations • Communication • Six scientific articles planned • Some 20 conference presentations R & D | Global Medicines Development

10. Potential model components (have to prioritize) Ph IIb Go / No Go Design Ph III Go / No Go Design Benefit/risk (enters everywhere) Phase IIb Regulatory evaluation Commercial Phase III Prior information Project Prioritiza- tion Adaptation Dose adaptation Depends on efficacy, safety, timing Biomarker in ph IIb? Two doses? Seamless? Uncertain response (even given data)

11. Grand (mathematical) problem Phase II model • Parametric models (based in pharmacology): • E(d) for efficacy • S(d) for safety • One safety variable may be sufficient for our purposes • Typically Emax models • Prior on all parameters • Ethical constraints on doses d • Cost c2(N2), e.g. linear in N2 • Time t2(N2), e.g. linear in N2 • Design parameters • Go / No Go • Doses d1,…,dk • Sizes n1,…,nk (N2=ni) • (Adaptation) R & D | Global Medicines Development

12. Grand problem Phase III model • Parameters of efficacy & safety depends on Phase II data • Posterior • May allow different endpoints in Phase II and III, with correlated parameters • Cost c3(N3), e.g. linear in N3 • Time t3(N3), e.g. linear in N3 • Design parameters • Go / No Go • Dose d* • Size N3 (per trial) R & D | Global Medicines Development

13. Grand problem Regulatory / Commercial model • Value depends on • Efficacy E (true mean efficacy at selected dose, vs. placebo) • Safety S • Total time to market T • E.g. probit function of clinical utility index (CUI), linear in time • No value unless regulatory acceptance, e.g. • Statistical significant efficacy in two phase III trials • Pooled estimate of CUI is positive R & D | Global Medicines Development

14. Grand problem Goal function • Expectation over net value minus total cost • Optimization over both Phase II and Phase III design parameters • Optimal Phase III design depends on Phase II outcome • Optimal Phase II design depends on optimal Phase III designs in all possible scenarios, reflecting Phase II outcome • Many potential extensions, e.g. • Subpopulation / Personalized Health Care • Several Phase III trials in different sub-indications • >1 dose in Phase III • Earlier phases R & D | Global Medicines Development

15. Joint Optimization of Phase II and Phase III • Fredrik Öhrn • (special thanks to Chris Jennison)

16. Topics • Introduction • Model • Design of Phase III • Design of Phase II with Phase III in mind • Group Sequential Phase III • Extensions with multiple doses

17. A Common Situation • Different endpoints in Phase II and Phase III • Differences in StudyLength, Population etc. • Perhaps a broader population in phase III • Longer follow-up in Phase III • Phase III oftenexpensivePhase III go/no go decisionimportant • Phase II trial can reduce uncertainty but comes at a price: • Cost of running trial • Limited patent life

18. Relevant Questions • How to model Phase II and Phase III endpoints? • Phase III sample size? • Phase II sample size? • Quantitative support for go/no go decision • Further efficiencies could be achieved throughseamless Phase II/III trials but not focus here • Focus on one active does and a control in Phases II and III

19. A Bayesian Framework • Express prior belief and associated uncertainty • Denote the mean treatment effect by  in Phase II and  in Phase III • Suppose  and  follow bivariate normal distribution • Correlation r is crucial for the properties of the model • Express prior belief about  and  before start of Phase II • Update for posterior distribution after Phase II

20. A Mixed Bayesian/Frequentist Approach • Frequentist requirement at end of Phase III • May not be as important in Phase II • Bayesian approach to model endpoints • Specifying prior distributions may be difficult • Ideally supported by previous studies • Cost of phase II trial a2 + c2 n2 • Cost of phase III trial a3 + c3 n3 • Gain function g(z3,,n2,n3) • Can depend on sample sizes in Phase II and Phase III • True and estimated treatment effect in Phase III

21. Expected Utility to be Maximised

22. Probability of Success (PoS) • Account for uncertaintyabouttreatmenteffect • Integrate over prior distribution for effectsize, (|z2) : • (|z2) is posterior distribution after Phase II • Can be thought of as prior beforePhase III • PoS is in this contextsometimescalledaverage power or assurance • PoS given progress to Phase III is an importantmetric: • Crucial to avoidcostlyfailures in Phase III • Exampleshown on a subsequentslide

23. Sample Size of Phase III Trial • Assume prior distributions for relevant parameters • Define gain function and cost of sampling • We let gain function depend on • Time (sample sizes), true effect size and/or estimated effect size • Fixed gain function for illustrative purposes on next slide • Can find expected utility of running trial • Use decision analysis to find phase III sample size n3

24. Optimisation of Phase III Sample Size

25. But… • Many other factors to consider • Phase III sample size often driven by safety • Suppose Phase III sample size is either • Fixed due to constraints not in our model • Optimised based on prior after Phase II • Move one step back • Focus on design of Phase II • With approach to choosing Phase III sample size in mind

26. Scope for Phase II Trial • Today, focus on trial with two treatment arms • Support go/no go decision • Guide Phase III sample size • Briefly mention approach for multidose Phase II • Four parameter Emax model • More computationally challenging

27. Phase II Sample Size • Can reduce uncertainty • A more informed investment decision • Can derive decision rule for progression • Similar approach possible with multiple doses • Important to consider other factors • Information collected for a certain cost

28. PoS vs Phase II Sample Size Solid and dashed lines display biomarkers with high and low correlation, respectively. We note a substantial increase in PoS in phase III for the former, but little impact for the latter.

29. Phase II Investment Decision • Increasing Phase II sample size can help to reduce Phase III attrition but… • More expensive Phase II • Longer duration delay to start of Phase III and potential launch • Other differences compared to Phase III • Endpoint is often not the same • Shorter follow-up time • Information may be cheaper

30. Choice of Biomarker • Suppose different biomarkers are considered • Different costs of sampling and correlation • May represent different follow-up times in Phase II • Natural to focus on decrease in posterior variance of  • Adjust for Phase II cost • Can be expressed analytically as function f(r,c2,parameters in prior distribution) • Calculate for different biomarkers • For a given cost, which biomarker decreases posterior variance the most?

31. Choice of Biomarker More strongly correlated biomarkers dominate for large investments!

32. Group Sequential vs Fixed Sample Phase III • Consider group sequential design (GSD) with K interim analyses (IA) • Each IA provides a go/no go decision rule • Role of Phase II becomes less important • Can still be helpful if observations are very cheap • Not obvious how to model cost of stopping at IA • Savings may be small due to large start-up cost • Survival setting may be different • In what follows expected cost under prior is charged

33. Benefits with GSD

34. GSD Summary • Modest gains by updating Phase III sample size based on Phase II • Greater efficiency gains by adding more IA • In keeping with results within trials • Schmitz designs versus group sequential designs • Now consider if this holds for multidose Phase II

35. Extending to Multiple Doses • Chris Jennison studied 4-parameter Emax model • Treatment effect increasing with dose • Needs to model safety • Calculate posterior distribution • Right dose as trade-off between efficacy and safety • Impact of GSD in Phase III similar to for two-arm Phase II trial • Brief results on next slide, courtesy of Chris Jennison

36. Optimal Phase II Sample Size for 7-arm Phase II Trial

37. For GSD in Phase III, Similar as for Two-arm Trials Optimal Phase II sample size is substantially lower than for fixed sample Phase III trial!

38. Summary • A mixed Bayesian and frequentist framework • Can solve decision problem to optimise various quantities • Phase II and Phase III sample sizes n2 and n3 • Choice of endpoint in Phase II • Optimal Phase II sample sizes lower with GSD in Phase III • Number of groups more important than updating sample size • Holds also for multidose Phase II • Latter problem more complex to solve

39. Work in AP subteams • Carl-Fredrik Burman • (thanks to Nitin Patel, Zoran Antonijevic, Olga Marchenko and other subteam members)

40. Diabetes subteam Team members Martin Kimber, Tessella (lead) Zoran Antonijevic, Quintiles (ex-lead, driving ms #1) Klas Bergenheim, AZ José Pinheiro, J&J David Manner, Lilly Carl-Fredrik Burman, AZ R & D | Global Medicines Development

41. Diabetes subteam Framework • Key endpoints • Efficacy: HbA1c, at 24 weeks in phase III • Safety: Hypoglycemic events • Dose-limiting in phase IIB • More a payer than regulatory concern. • Safety: CV events • Regulatory requirements both pre- and post- • Trial program • Phase IIB dose-finding • Phase III, 1st line treatment • X vs Placebo (superiority) • Phase III, 2nd line treatment • X vs Placebo vs Active control (superiority + non-inferiority) • Phase III, 3rd line treatment • X vs Placebo vs Active control (superiority + non-inferiority) • CV safety (meta-analysis above trials + CV safety study) R & D | Global Medicines Development

42. Diabetes subteam Model • Costs • Linear in sample size • Cost per patient depending on phase • Value • No value unless regulatory acceptance • At least two significant trials • CV fulfils regulatory requirements • Value depends heavily on claims (superiority / non-inferiority) in different market segments (1st, 2nd, 3rd line) • Value decreases with hypoglycemic event (AE) rate • Interaction between claim and AE rate • Time factor • Prior uncertainty in key parameters R & D | Global Medicines Development

43. Diabetes subteam Sketching the problem • Design parameters to optimize • Phase II • Sample size • Treatment duration, 12 vs 24 weeks • Adaptive vs. fixed • Phase III • Go / No Go criteria • Sample size • (CV safety study) R & D | Global Medicines Development

44. Diabetes subteam Sketching the solution • Lots of simulations (thanks Martin and David!) • Check different (discrete) design scenarios • MCMC to update prior for parameter vector • Note: complicated dependence structure • For results … see Zoran’s presentations R & D | Global Medicines Development

45. Neuropathic Pain subteam Team members Nitin Patel, Cytel (lead) Christy Chuang-Stein, Pfizer Jim Bolognese, Cytel José Pinheiro, J&J David Hewitt, Merck + a number of associated members R & D | Global Medicines Development

46. Neuropathic Pain subteam Sketching the problem More mature project than Diabetes – but I’m no expert • Impact of Phase II • Sample size • Number of doses • Adaptive designs • on probability of success (PoS), expected net present value (ENPV), financial risk • Value depends on both efficacy & safety • Time factor • Discrete doses R & D | Global Medicines Development

47. Oncology subteam Team members Olga Marchenko, Quintiles (lead) Don Berry, MD Anderson Cancer Center Joel Miller, ImClone Tom Parke, Tessella Inna Perevozskaya, Pfizer Yanping Wang, Lilly R & D | Global Medicines Development

48. Oncology subteam Newly started work Plan to finalize simulation plan in October May cover other questions than other subteams R & D | Global Medicines Development

49. Discussion • We’ve come a long way … but the journey’s just begun • In 10 years, people will hopefully think what we’ve done now is very immature • Future work • Increased realism (Apply to real projects – Confidentiality) • More continuous models • Benefit / risk (connect with evolving regulatory B/R thinking) • Subpopulations • Etc. etc. • Any volunteers? R & D | Global Medicines Development

50. To be continued …