optimizing the design of clinical programs

Optimizing the Design of Clinical Programs

Carl-Fredrik Burman, Ph.D., Assoc. Prof.AstraZeneca R&D and Chalmers Univ. Tech.

Fredrik Öhrn, Ph.D.AstraZeneca R&D

Adaptive Programs (AP) workstream

• 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

Thanks to the rest of the AP core team:

2 Burman & Öhrn | 14 October 2011 R & D | Global Medicines Development

• Background: Adaptive Program workstream• Joint optimization of Phase II and Phase III• Adaptive Program subteam work

Topics for today


Background:Adaptive Program (AP) workstream

Carl-Fredrik Burman

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


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


From study to program perspective

7

Focus on one single trial

One trial in program context

Program focus


• 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

Key ideas



• Main model

• Specific applications- Neuropathic pain- Diabetes- Oncology

• Supporting- Algorithms & soft-ware- Academic collaborations

• Communication- Six scientific articles planned- Some 20 conference presentations

Ongoing activities


Potential model components (have to prioritize)

Phase IIb Phase IIIRegulatoryevaluation

CommercialPrior

information

Doseadaptation

Adaptation

Ph IIbGo / No Go

Design

Ph IIIGo / No Go

Design

ProjectPrioritiza-

tion

Biomarker in ph IIb?

Benefit/risk(enters everywhere)

Two doses? Seamless?

Uncertain response(even given data)

Depends on efficacy, safety, timing

Grand (mathematical) problem

• 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

Phase II model


50max0 EDd

dEE

Design parameters• Go / No Go• Doses d1,…,dk

• Sizes n1,…,nk (N2=ni)• (Adaptation)

Grand problem

• 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

Phase III model


Design parameters• Go / No Go• Dose d*• Size N3 (per trial)

Grand problem

• 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

Regulatory / Commercial model


patentT

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Grand problem

• 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

Goal function


Joint Optimization of Phase II and Phase III

Fredrik Öhrn

(special thanks to Chris Jennison)

Topics

• Introduction

• Model

• Design of Phase III

• Design of Phase II with Phase III in mind

• Group Sequential Phase III

• Extensions with multiple doses

A Common Situation

• Different endpoints in Phase II and Phase III

• Differences in Study Length, Population etc.- Perhaps a broader population in phase III- Longer follow-up in Phase III

• Phase III often expensive Phase III go/no go decision important

• Phase II trial can reduce uncertainty but comes at a price: - Cost of running trial - Limited patent life

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

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

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

Expected Utility to be Maximised

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Probability of Success (PoS)

• Account for uncertainty about treatment effect

• Integrate over prior distribution for effect size, (|z2) : - (|z2) is posterior distribution after Phase II- Can be thought of as prior before Phase III

• PoS is in this context sometimes called average power or assurance

• PoS given progress to Phase III is an important metric:- Crucial to avoid costly failures in Phase III- Example shown on a subsequent slide

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

Optimisation of Phase III Sample Size

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

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

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

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.

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

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?

Choice of Biomarker

More strongly correlated biomarkers dominate for large investments!

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

Benefits with GSD

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

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

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

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!

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

Work in AP subteams

Carl-Fredrik Burman

(thanks to Nitin Patel, Zoran Antonijevic, Olga Marchenko and other subteam members)

Diabetes subteam

Team members

Martin Kimber, Tessella (lead)Zoran Antonijevic, Quintiles (ex-lead, driving ms #1)Klas Bergenheim, AZJosé Pinheiro, J&JDavid Manner, LillyCarl-Fredrik Burman, AZ


Diabetes subteam

• 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)

Framework


Diabetes subteam

• 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

Model


Diabetes subteam

• 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)

Sketching the problem


Diabetes subteam

• 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

Sketching the solution


Neuropathic Pain subteam

Team members

Nitin Patel, Cytel (lead)Christy Chuang-Stein, PfizerJim Bolognese, CytelJosé Pinheiro, J&JDavid Hewitt, Merck

+ a number of associated members


Neuropathic Pain subteam

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

Sketching the problem


Oncology subteam

Team members

Olga Marchenko, Quintiles (lead)Don Berry, MD Anderson Cancer CenterJoel Miller, ImCloneTom Parke, TessellaInna Perevozskaya, PfizerYanping Wang, Lilly


Oncology subteam

Newly started work

Plan to finalize simulation plan in October

May cover other questions than other subteams


• 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?

Discussion


To be continued …


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