Exploratory Modeling And Simulation To Support Development Of Motesanib In Asian Patients With Non-Small Cell Lung Cancer (Nsclc) Based On Monet1 Study Results.

e19103 Background: Motesanib failed to improve overall survival (OS) in the MONET1 phase III study when combined to carboplatin/paclitaxel (CP) vs. CP in first-line NSCLC cancer patients (JCO 30:2829-2836, 2011). However, outcome of the study was favorable in a sub-population of Asian patients. We performed exploratory modeling and simulations based on the MONET1 data in order to support further development of motesanib in Asians. Methods: We developed 1) a longitudinal tumor growth inhibition model to estimate time to tumor regrowth (TTG)) using data from 934 out of 1,090 (86%) included in MONET1 and 2) a multivariate parametric model for OS including baseline prognostic factors and tumor size metrics to capture treatment effect. OS model was assessed in simulating OS distribution and hazard ratios (HR) in multiple replicates of MONET1 and comparing 95% predictive distribution (PI) with observed data. Multiple replicates of virtual phase III studies in Asian patients were simulated to assess probability of success of alternative designs. Results: Baseline tumor size (TS) and smoking history (former and current smokers vs. never smokers) were significant independent prognostic factors for OS with Asian ethnicity and log (TTG). Logarithm of OS (in days) was defined by a linear model. Parameter estimates of the OS model are given below (Table). The model successfully predicted OS distributions and HR in the full populations and in Asian patients. Simulations suggested that a 500 patient phase III study would exceed 80% power to demonstrate motesanib combination OS superiority in Asian with an expected HR of 0.74. Conclusions: A model-based estimate of TTG captured treatment effect in Asian and simulations suggested superiority of motesanib combination in these patients. The OS model could be used to simulate expected OS based on longitudinal NSCLC tumor size data with new investigational agents and support development decisions and study design. [Table: see text]