🤖 AI Summary
In early-phase oncology trials, patient heterogeneity is often unknown a priori, making prespecification of subpopulations infeasible. To address this, we propose a two-stage adaptive dose optimization design: Stage I defines a safe dose set using toxicity data; Stage II jointly models efficacy, baseline covariates, and pharmacokinetic exposure via Bayesian sparse group selection to dynamically identify unknown heterogeneous subpopulations and recommend personalized optimal doses. Our key innovation is the first implementation—within a Phase I trial framework—of fully data-driven, prespecification-free subpopulation identification, integrated with exposure-toxicity modeling and futility assessment to adaptively enrich the target population. Simulation studies demonstrate that the method accurately recovers critical heterogeneity-driving biomarkers, distinguishes multiple biologically distinct subgroups, and assigns differential optimal doses—outperforming conventional designs relying on prespecified subpopulations.
📝 Abstract
Project Optimus, an initiative by the FDA's Oncology Center of Excellence, seeks to reform the dose-optimization and dose-selection paradigm in oncology. We propose a dose-optimization design that considers plateau efficacy profiles, integrates pharmacokinetic data to inform the exposure-toxicity curve, and accounts for patient characteristics that may contribute to heterogeneity in response. 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, employing Bayesian Sparse Group Selection to understand patient heterogeneity. Between stages, the design integrates pharmacokinetic data and uses futility assessments to identify the target population among the general phase I patient population. An optimal dose is recommended for each identified subpopulation within the target population. The simulation study demonstrates that a model-based approach to identifying the target population can be effective; patient characteristics relating to heterogeneity were identified and different optimal doses were recommended for each identified target subpopulation. 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.