A0275
Title: An extended Bayesian semi-mechanistic dose-finding design for phase I oncology trials using PK and PD information
Authors: Chao Yang - Eli Lilly (United States)
Yisheng Li - The University of Texas MD Anderson Cancer Center (United States) [presenting]
Abstract: A model-based, semi-mechanistic dose-finding (SDF) design is proposed for phase I oncology trials that incorporate pharmacokinetic/pharmacodynamic (PK/PD) information when modeling the dose-toxicity relationship. This design is motivated by a phase Ib/II clinical trial of anti-CD20/CD3 T cell therapy in non-Hodgkin lymphoma patients; it extends a recently proposed SDF model framework by incorporating measurements of a PD biomarker relevant to the primary dose-limiting toxicity (DLT). Joint Bayesian modeling of the PK, PD, and DLT outcomes is proposed. Extensive simulation studies show that on average the proposed design outperforms some common phase I trial designs, including modified toxicity probability interval (mTPI) and Bayesian optimal interval designs, the continual reassessment method (CRM), and an SDF design assuming a latent PD biomarker (SDF-woPD), in terms of percentage of correct selection of maximum tolerated dose (MTD) and average number of patients allocated to MTD, under a variety of dose-toxicity scenarios. When the working PK model and the class of link function between the cumulative PD effect and DLT probability are correctly specified, the proposed design also yields better estimated dose-toxicity curves than CRM and SDF-woPD. Sensitivity analyses suggest that the design's performance is reasonably robust to prior misspecification for the parameter in the link function, as well as misspecification of the PK model and class of the link function.
