Quantitative Decision Making (QDM) in Phase I/II studies Kevin Gan, Jon Haddad, GlaxoSmithKline 2019 JSM 1

Advantage of QDM based Adaptive Trial Design More information earlier in development, with increased efficiency for quicker decision making Kill ineffective compounds earlier Take the right doses into Phase III Improve characterization of dose response Increase “probability of technical success” To market faster – shift to seamless combined phase studies Optimize patient treatment within a study Minimize patient exposure to ineffective/unsafe treatment Maximize patient exposure to effective treatment Enhance design efficiency by learning as you go Most valuable in situations of prior uncertainty Answers the right question 2

FTIH Study Design FTIH MAD – Part B FTIH SAD – Part A Healthy Subjects FTIH MAD – Part B Healthy Subjects Option: Combine Phase 1 and POC study Dose 1 SD Dose x QD FTIH MAD – Part C Patient Subjects Bayesian decision after each dose Safety & PK Review Dose 2X QD Dose 2 SD Safety and PK Review Dose 4X QD Dose 4X QD Dose 3 SD Dose 4 SD Bayesian decision Rule: Eg. Dose escalation in Part A (SAD) will be stopped in the event that the Bayesian predicted probability is >50% that any participant’s Cmax for the next subsequent dose will exceed X.xx μg/mL 3

Endpoint Considerations Endpoint: R = test/reference, the ratio of the geometric least square mean (GLSmean) of PK parameters of test treatment vs reference treatment Evaluation criteria (through simulations): 90% CI: (0.8-1.25) or (0.7-1.43): Not chosen as required large sample size R >0.8 or R >0.9 for the point estimate: Chosen when small sample size Based on posterior probability Non-informative prior Different decision rules Interaction with clinical team 4

Example: POC Study Design Part 1 Part 2 Dose (Max) N=6~10 Dose 3 (ED90) Dose 4 (ED75) Dose 2 (ED50) n = 6~10 Dose 5 ED30 PBO Unblinded Interim Analysis Conducted by Study Team (Including PK, safety and HIV Viral Load data) PBO Day 1 Randomization Day x Day 1 Randomization Day x

Operating Characteristics for QDM framework (Mock data) Decision Rules: EOS Go if P(∆>MV)>80% STOP if P(∆≤TV)>90% Else CONSIDER MV: Minimum Value TV: Target Value Combined No Go decision Interim decision rule: STOP if Predictive Probability of Stop at EOS is high True VLD Possibility of achieving each decision Interim NO GO (%) Final No GO (%) Final Consider (%) Final Go (%) No effect 1.0 82.5% 11.4% 6.1% 0.0% MV 18% 3.5% 75.1% 3.4% TV 2.0% 0% 48.9% 49.1% High effect 0.2% 17.5% 82.3% 93.9% 6