Identification of optimal dose combinations in early phase dose-finding trials is challenging, due to the trade-off between precisely estimating the many parameters required to flexibly model the possibly non-monotonic dose-response surface, and the small sample sizes in early phase trials. This difficulty is even more pertinent in the context of personalized dose-finding, where patient characteristics are used to identify tailored optimal dose combinations. To overcome these challenges, we propose the use of Bayesian optimization for finding optimal dose combinations in standard ("one size fits all") and personalized multi-agent dose-finding trials. Bayesian optimization is a method for estimating the global optima of expensive-to-evaluate objective functions. The objective function is approximated by a surrogate model, commonly a Gaussian process, paired with a sequential design strategy to select the next point via an acquisition function. This work is motivated by an industry-sponsored problem, where focus is on optimizing a dual-agent therapy in a setting featuring minimal toxicity. To compare the performance of the standard and personalized methods under this setting, simulation studies are performed for a variety of scenarios. Our study concludes that taking a personalized approach is highly beneficial in the presence of heterogeneity.

翻译：早期剂量探索试验中识别最优剂量组合具有挑战性，这源于精确估计灵活建模可能非单调剂量-反应曲面所需的大量参数与早期试验小样本量之间的权衡。在利用患者特征识别定制化最优剂量组合的个性化剂量探索背景下，这一困难更为突出。为克服上述挑战，我们提出采用贝叶斯优化方法，用于标准（"一刀切"式）与个性化多药物剂量探索试验中最优剂量组合的识别。贝叶斯优化是一种对评估代价高昂的目标函数进行全局最优估计的方法。该目标函数通过代理模型（通常为高斯过程）近似，并配合序贯设计策略，通过采集函数选择下一个采样点。本研究受行业赞助课题驱动，聚焦于在最小毒性场景下优化双药物联合疗法。为比较该场景下标准方法与个性化方法的性能，我们在多种情景下开展了仿真研究。研究结论表明，在存在异质性的情况下，采用个性化方法具有显著优势。