JSM 2018 A Case Study on Model Based Meta Analysis (MBMA) for Drug Development Decisions Guohui Liu, Zhaoyang Teng, Zoe Hua, Neeraj Gupta, Karthik Venkatakrishnan, Richard Labotka

Clinical Trial Perform Metrics 12% entering phase 1 were approved by FDA 34% phase 3 studies achieve statistical significance Average time > 10 years Average cost: 2.6 billion Oncology: 40% of clinical trial and 19% of participants Average cost per patient: $59,500 ( vs $36,500 in all) disease) Factors associated with late stage oncology study failure lack of a biomarker-driven strategy failure to attain proof of concept in phase II Biopharmaceutical Industry-Sponsored Clinical Trials: Impact on State Economies, 2015 Jardim DL, Groves ES, Breitfeld PP, Kurzrock R. Factors associated with failure of oncology drugs in late-stage clinical development: A systematic review. Cancer Treatment Reviews 52 (2017) 12–21

Objectives: Go/No Go decision Motivation Objectives: Go/No Go decision Publications Phase 2 with ORR Phase 3 with PFS Statistical significant target: HR=0.8 (10 m vs 12.6 m) Minimal detective value: 12.6 m Target PFS: 15 m ORR Model based meta analysis PFS Teng Z, Gupta N, Hua Z, Liu G, Samnotra V, Venkatakrishnan K, Labotka R. Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development. Clin Transl Sci (2017) 00, 1–8 ｜○○○○　|　　DDMMYY

Contemporary RRMM Studies ｜○○○○　|　　DDMMYY

Model-based meta-analysis (MBMA) : Method 1: Linear regression ｜○○○○　|　　DDMMYY

Method 2: Bayesian Method with Linear Regression A prior distribution in linear regression to account for the variability (β0, β1) estimated from linear regression as an empirical Bayesian approach; σ2 follows an inverse Gamma distribution ｜○○○○　|　　DDMMYY

Method 3: Bayesian Predictive Power Two parameters of interest: Assume has the following prior distribution A small number of patients (N; phase II study) and a small number of events (E; phase III study), can be used to define treatment effect and variance in prior distribution For example, 8 patients with ORR of 60% from phase II and 2 patients with median PFS of 10 months from phase III ｜○○○○　|　　DDMMYY

Predict PFS Using ORR: Bayesian Predictive Power ｜○○○○　|　　DDMMYY

If no significant safety concerns, then – Summary If no significant safety concerns, then – If at least 33 patients (out of 50 patients, ORR=66%) have confirmed Responses (≥PR) then team recommendation is: GO. If ≤ 32 (out of 50 patients) confirmed responders – No-Go MBMA is acknowledged as a valuable tool in the drug development process that is widely used for integrating data to enable informed and efficient development decisions. The results of these analyses demonstrate the feasibility of developing a quantitative drug-independent framework for RRMM to predict PFS based upon an endpoint (ORR) for which data are available earlier. ｜○○○○　|　　DDMMYY