The Promise of Novel Clinical Trial Designs. Michael Parides, Ph.D. Mount Sinai School of Medicine

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1 The Promise of Novel Clinical Trial Designs Michael Parides, Ph.D. Mount Sinai School of Medicine

2 Productivity New Drug Approvals (NMEs) R&D Spending (billions) $12 $13 $13 $15 $49 $38 $39 $43 $30 $32 $33 $27 $26 $19 $21 $ Source: FDA and Burrill & Company

3 FDA NDA Approval Rates 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 82% 67% 71%

4 Failure is expensive the cost of drug development is not in the cost of success but in the cost of failure Robert Hemmings Statistics Unit Manager MHRA and CHMP

5 Cost

6 Number of academic citations per target in drug development More targets but less validated High throughput sequencing 50% higher failure rate for Novel targets Booth and Zemmel, Nature Review Drug Discovery (2004)

7 Cause of failure? Wrong compound dose patients (heterogeneity) study design developmental plan

8 Can CT Designs be Improved? Better information Fewer failures Earlier detection of inadequate benefit Better recognition of potential benefit Some improvement in success rate Better Faster Cheaper

9 One approach: Adaptive Designs Interim data used to modify trial design, without undermining trial validity and integrity

10 b Adaptations are planned FLEXIBLE Planned ADAPTIVE Sample Size Re-estimation Adaptive doseresponse Seamless Phase II/III Adaptive Randomization Test statistics, eligibility, primary endpoint

11 New Idea? Biometrika 1933; Adaptive randomization

12 Group Sequential Designs are Adaptive

13 What is new? Increased use of Bayesian methods Reliance on probability models Frequent Updating Learning Useful for exploratory trials

14 ??????? Thanks to Mehran Sahami of Stanford for his insight

15 What is new? Bolder Implementation Un-blinded sample size reestimation Changing primary endpoint Patient enrichment Seamless phase II/III designs

16 Commonly accepted Most useful early in development Adaptive dose finding Adaptive randomization Adding/dropping treatment arms More caution in confirmatory trials Early stopping for futility or benefit Blinded sample size re-estimation

17 Continual Reassessment Method Adaptive dose finding method (MTD) Choose prior dose toxicity curve and target DLT Curve refit as outcomes observed Patients assigned dose most likely to be MTD Usually, but not necessarily, Bayesian Continual Reassessment Method (CRM)

18 Probability of toxicity Dose Toxicity Curves Pt 1: Pt 2: Pt 3: Pt 4: Pt 5: Pt 6: Dose mg/kg Target DLT = 10% CRM more efficient: fewer patients + fewer toxicities

19 Example: Bayesian approach Exploratory trial of stem cell therapy in LVAD recipients Goal: Adequate efficacy signal to continue study and development 40 patients randomized: 20 with active therapy, 20 with control

20 Results: 13 vs 10 failures (C:A) Treatment Failure Proportion 95% Confidence Interval Control RR reduction Difference Fisher s exact p=0.52 Active

21 Bayesian summary posteriors Prior Active Control Success Probability p Probability that Active is better than control> 0.75

22 Cautious use acceptable by many Seamless Phase II/III designs Some un-blinded sample size re-estimation

23 Adaptive Seamless Designs Dose A Dose B Dose C white space Placebo Phase II Phase III Time Stage A (learning) Stage B (confirming) Dose A Dose B Dose C Placebo Eliminating the white space

24 Still very controversial Enrichment of subpopulations Change in choice of test statistic Change of primary hypothesis Change of primary endpoint

25 Still very controversial Inconsistent with Confirmatory Trial Enrichment of subpopulations Change in choice of test statistic Change of primary hypothesis Change of primary endpoint

26 Example: CAPRICORN g Carvedilol versus placebo for acute MI Primary Endpoint: all-cause mortality Blinded SSR: Lower than assumed event rate CV hospitalization added to endpoint Lancet 2001

27 CAPRICORN g Type I error partitioned: Mortality.005 Mortality or CV Hosp.045

28 CAPRICORN Results Carvedilol (n=975) Placebo (n=984) p All cause mortality 11.9% 15.3% All cause mortality or CV hosp 34.9% 37.3% 0.30 Correct conclusion: No difference Interpretation is not so clear

29 Summary g The potential appeal of adaptive designs is understandable, and motivates the high level of interest Adaptation is NOT always better Difficult logisitical issues

30 Summary g May not answer the question faster, or at all the question itself may become unclear Need to understand operating characteristics by conducting simulations to assess performance under realistic scenarios

31 Summary g Some ideas are useful Others are impractical