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 dose-response surface, and the small sample sizes in early phase trials. Existing methods often restrict the search to pre-defined dose combinations, which may fail to identify regions of optimality in the dose combination space. These difficulties are 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颇具挑战性，原因在于需在灵活建模剂量-反应曲面所需的大量参数精确估计与早期试验的小样本量之间取得平衡。现有方法常将搜索范围限制在预定义的剂量组合，这可能导致无法识别剂量组合空间中的最优区域。当涉及基于患者特征定制最优剂量组合的个体化剂量探索时，这些困难更为突出。为应对这些挑战，我们提出采用贝叶斯优化方法，在标准（"一刀切"式）和个体化多药物剂量探索试验中寻找最优剂量组合。贝叶斯优化是一种用于估计代价高昂的目标函数全局最优值的方法，通过高斯过程等代理模型近似目标函数，并配合序贯设计策略，利用采集函数选择下一个评估点。本研究源自工业界实际问题，聚焦于在低毒性场景中优化双药物联合治疗方案。为比较该场景下标准方法与个体化方法的性能，我们针对多种情景开展了模拟研究。研究结论表明，在存在异质性的情况下，采用个体化方法具有显著优势。