Bayesian optimization design for finding a maximum tolerated dose combination in phase I clinical trials

Takahashi Ami () and Suzuki Taiji
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Takahashi Ami: Tokyo Institute of Technology, School of Computing, Meguro-ku, Tokyo, Japan
Suzuki Taiji: The University of Tokyo, Bunkyo-ku, Tokyo, Japan

The International Journal of Biostatistics, 2022, vol. 18, issue 1, 39-56

Abstract: The development of combination therapies has become commonplace because potential synergistic benefits are expected for resistant patients of single-agent treatment. In phase I clinical trials, the underlying premise is toxicity increases monotonically with increasing dose levels. This assumption cannot be applied in drug combination trials, however, as there are complex drug–drug interactions. Although many parametric model-based designs have been developed, strong assumptions may be inappropriate owing to little information available about dose–toxicity relationships. No standard solution for finding a maximum tolerated dose combination has been established. With these considerations, we propose a Bayesian optimization design for identifying a single maximum tolerated dose combination. Our proposed design utilizing Bayesian optimization guides the next dose by a balance of information between exploration and exploitation on the nonparametrically estimated dose–toxicity function, thereby allowing us to reach a global optimum with fewer evaluations. We evaluate the proposed design by comparing it with a Bayesian optimal interval design and with the partial-ordering continual reassessment method. The simulation results suggest that the proposed design works well in terms of correct selection probabilities and dose allocations. The proposed design has high potential as a powerful tool for use in finding a maximum tolerated dose combination.

Keywords: Bayesian optimization; combination therapy; maximum tolerated dose; nonparametric method; phase I clinical trials (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2020-0147

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