SAMBa ITT – Reducing the Size of Clinical Trials Jonathan Bartlett SAMBa ITT 4 Strictly Confidential 31 st May 2016

Why Reduce the Size of Clinical Trials To make drug development more efficient and ethical. Reduction in the size of a trial means improvement ethically and most cost effective. The cost of R&D is ever increasing with the number of approvals reducing. 2

Paul et al Nat Rev Drug Disc. 2010;9: The Cost of Drug Development Eli Lilly R&D Productivity Model

One way might be to combine trials Combine more than one pre-clinical experiment (Pre-Clinical) Combine single dose and multiple dose ascending studies (Phase I) Proof of concept and dose finding (Phase II) Seamless Phase II/II trials 4

Pre-Clinical Discovery Studies Address the 3Rs - Replacement, Reduction and Refinement Property of GlaxoSmithKline Experiment 1 Experiment 2 Experiment 3 Experiment 4 Decision Experiment 1 Experiment 2 Experiment 3 Experiment 4 Decision One big design

Typical looking First in Human Studies Single Ascending Dose – 7 doses in cohorts 3 D1 + 1 P 3 D2 + 1 P 3 D3 + 1 P 3 D4 + 1 P 3 D5 + 1 P 3 D6 + 1 P 3 D7 + 1 P Total n = 28 6 MD1 + 2 P 6 MD2 + 2 P 6 MD3 + 2 P Multiple Ascending Dose – 3 doses in cohorts Total n = 24 Dose Follow-Up

Typical looking Proof of Concept Study then Dose Finding in 2b POC – 1 doses versus placebo High dose n = 50 Placebo n = 50 Total n = 100 Dose finding Total n = 200 High dose n = 50 Mid dose n = 50 Low dose n = 50 Placebo n = 50

Proposal for First in Human and Proof of Concept/Dose Finding Study Design FIH POC/DF Repeat dose adaptive dose escalation study in a patient population but not the target (N=20) Adaptive repeat dose in target population (N = 120) Traditional Paradigm = N = 352 Lean Paradigm N = 140

DoseFollow-Up Analysis N=2 SD MD Combined SD and MD Study

Combined POC/DF Design Initial Randomisation Placebo (n = 30) (+historical data) Interim analysis at n = 60 Is Dose 5 (highest) better than placebo? Dose 4 (n = 60) Dose 3 (n = 60) Dose 2 (n = 60) Dose 1 (n = 60) Placebo (n = 30) (+historical data) Add arms to explore the dose response Stop No Yes Dose 5 (n = 30)

Seamless Phase II/III 11

Another way is to use historical control data A large proportion of studies compare an experimental arm with an existing treatment or placebo. Interest has increased on using historical information for the control arm. Opportunity: Reduce the need for excessive control patients – reducing the size of clinical trials. Threat: Need to be aware of when the current data and not consistent with the historical data. 12

Where might we use historical control data? Rare diseases Single arm trials Non-inferiority trials Exploratory trials (not the subject of this talk/discussion) 13

Some issues Lack of consistency between prior and trial data Selection bias in the historical data Inflation of type I error and/or reduced power Increase in MSE due to bias 14

15 Motivating Paper

16 Use of Historical Model (sometimes called Bayesian Augmented Control) The Bayesian Hierarchical model as outline in Viele et al (2014). If  0 is the logit of the response rate for the current study,  1-H is the logit of the response rate from H historical studies (H can = 1). We assume that: -  0,  1,  2,  3,  4………,  H ~ N( ,  2 ) - Where  g is the study to study standard deviation. This is the term which allows for the dynamic borrowing between the New Study and the historical data. - We allow priors for , and  2 -  ~N(  0,  0 ) and  2 ~ Inverse-Gamma( ,  )  2 a llows for the dynamic borrowing. The larger  2 the less the borrowing

Summary Two ideas for reducing the size of trials Combining trials Use of historical data It would be good to see what other ideas people have 17

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