A0390
Title: PKBOIN-12: Integrating pharmacokinetics into Bayesian dose-finding for oncology drug development
Authors: Hao Sun - Bristol Myers Squibb (United States)
Jieqi Tu - University of Illinois Chicago (United States) [presenting]
Abstract: In oncology drug development, early-phase trials are increasingly focused on identifying the optimal biological dose (OBD) rather than the maximum tolerated dose (MTD). This shift is driven by the need to balance efficacy and safety and maximize the risk-benefit trade-off, especially for therapies with non-monotonic dose-response relationships, such as immunotherapies and targeted therapies. These trials often collect multiple pharmacokinetics (PK) outcomes to assess drug exposure, which has shown correlations with toxicity and efficacy outcomes but has not been utilized in the current dose-finding designs for OBD selection. Moreover, PK outcomes are usually available sooner than toxicity and efficacy outcomes. To bridge this gap, the innovative model-assisted PKBOIN-12 design is introduced, which enhances BOIN12 by integrating PK information into both the dose-finding algorithm and the final OBD determination process. Simulation results demonstrate that PKBOIN-12 more effectively identifies the OBD and allocates more patients to it than BOIN12. Additionally, PKBOIN-12 reduces the probability of selecting suboptimal doses as the OBD by excluding those with low drug exposure. Comprehensive simulation studies and sensitivity analysis confirm the robustness of both PKBOIN-12 in various scenarios.
