Dose Optimization Design Accounting for Patient Heterogeneity

Music City Center 

Project Optimus, an initiative by the FDA's Oncology Center of Excellence, seeks to reform the dose-optimization and dose-selection paradigm in oncology by integrating more data sources in the estimation of optimal doses. Most designs that account for patient heterogeneity are intended for trials where heterogeneity is known and pre-defined subpopulations are specified. However, given the limited information at such an early stage, subpopulations should be learned through the design. We propose a dose-optimization design that integrates toxicity, pharmacokinetic, patient characteristic, and response data to inform dose recommendations. The dose-optimization design is carried out in two stages. First, a toxicity-driven stage estimates a safe set of doses. Then, a dose-ranging efficacy-driven stage explores the set using response and patient characteristic data by employing Bayesian sparse group selection to understand patient heterogeneity. An optimal dose is recommended for each identified subpopulation within the target population. The simulation studies show that a model-based approach to identifying the target population can be effective; patient characteristics relating to heterogeneity were identified correctly and different optimal doses were recommended for each identified target subpopulation. Our findings show that accounting for heterogeneity is advantageous even in the more realistic setting of not knowing the source of heterogeneity.