Bayesian Phase I/II Biomarker-Based Dose Finding for Precision Medicine With Molecularly Targeted Agents

ABSTRACT

The optimal dose for treating patients with a molecularly targeted agent may differ according to the patient's individual characteristics, such as biomarker status. In this article, we propose a Bayesian phase I/II dose-finding design to find the optimal dose that is personalized for each patient according to his/her biomarker status. To overcome the curse of dimensionality caused by the relatively large number of biomarkers and their interactions with the dose, we employ canonical partial least squares (CPLS) to extract a small number of components from the covariate matrix containing the dose, biomarkers, and dose-by-biomarker interactions. Using these components as the covariates, we model the ordinal toxicity and efficacy using the latent-variable approach. Our model accounts for important features of molecularly targeted agents. We quantify the desirability of the dose using a utility function and propose a two-stage dose-finding algorithm to find the personalized optimal dose according to each patient's individual biomarker profile. Simulation studies show that our proposed design has good operating characteristics, with a high probability of identifying the personalized optimal dose. Supplementary materials for this article are available online.

KEYWORDS:

• Bayesian adaptive design
• Dose finding
• Partial least squares
• Personalized dose finding
• Personalized medicine

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Erratum

Supplementary Materials

The supplementary materials contain additional tables and figures, as well as, an evaluation of the robustness of the CPLS components and posterior full conditionals.

Acknowledgments

The authors thank the editor, the associate editor, and two anonymous referees for their insightful and constructive comments that substantially improved the article.

Funding

Yuan's research was supported by NCI grant R01 CA 154591 and 5P50CA098258.