The Promise of Novel Clinical Trial Designs Michael Parides, Ph.D. Mount Sinai School of Medicine
Productivity New Drug Approvals (NMEs) R&D Spending (billions) $12 $13 $13 $15 $49 $38 $39 $43 $30 $32 $33 $27 $26 $19 $21 $23 26 25 22 28 53 39 30 35 27 24 17 21 31 18 18 19 Source: FDA and Burrill & Company
FDA NDA Approval Rates 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 82% 67% 71% 1988-1992 1993-1997 1998-2002
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
Cost
Number of academic citations per target in drug development More targets but less validated 120 100 80 60 40 100 High throughput sequencing 50% higher failure rate for Novel targets 20 0 8 1990 1999 Booth and Zemmel, Nature Review Drug Discovery (2004)
Cause of failure? Wrong compound dose patients (heterogeneity) study design developmental plan
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
One approach: Adaptive Designs Interim data used to modify trial design, without undermining trial validity and integrity
b Adaptations are planned FLEXIBLE Planned ADAPTIVE Sample Size Re-estimation Adaptive doseresponse Seamless Phase II/III Adaptive Randomization Test statistics, eligibility, primary endpoint
New Idea? Biometrika 1933; Adaptive randomization
Group Sequential Designs are Adaptive
What is new? Increased use of Bayesian methods Reliance on probability models Frequent Updating Learning Useful for exploratory trials 1702-1761
??????? Thanks to Mehran Sahami of Stanford for his insight
What is new? Bolder Implementation Un-blinded sample size reestimation Changing primary endpoint Patient enrichment Seamless phase II/III designs
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
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)
Probability of toxicity Dose Toxicity Curves 0.5 0.4 0.3 0.2 Pt 1: 160 - Pt 2: 320 + Pt 3: 160 - Pt 4: 160 - Pt 5: 160 - Pt 6: 160-0.1 0 40 80 160 320 640 Dose mg/kg Target DLT = 10% CRM more efficient: fewer patients + fewer toxicities
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
Results: 13 vs 10 failures (C:A) Treatment Failure Proportion 95% Confidence Interval Control.65 0.14 0.56 RR reduction Difference Fisher s exact p=0.52 Active.50 0.28 0.72 0.77 0.15 0.30 1.90 0.15 0.45
0 1 2 3 Bayesian summary posteriors Prior Active Control 0.0 0.2 0.4 0.6 0.8 1.0 Success Probability p Probability that Active is better than control> 0.75
Cautious use acceptable by many Seamless Phase II/III designs Some un-blinded sample size re-estimation
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
Still very controversial Enrichment of subpopulations Change in choice of test statistic Change of primary hypothesis Change of primary endpoint
Still very controversial Inconsistent with Confirmatory Trial Enrichment of subpopulations Change in choice of test statistic Change of primary hypothesis Change of primary endpoint
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
CAPRICORN g Type I error partitioned: Mortality.005 Mortality or CV Hosp.045
CAPRICORN Results Carvedilol (n=975) Placebo (n=984) p All cause mortality 11.9% 15.3% 0.031 All cause mortality or CV hosp 34.9% 37.3% 0.30 Correct conclusion: No difference Interpretation is not so clear
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
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
Summary g Some ideas are useful Others are impractical