Precision medicine is an emerging approach for disease treatment and prevention that accounts for individual variability in genes, environment, and lifestyle. Cancer is a genomic disease; therefore, the dose-efficacy and dose–toxicity relationships for molecularly targeted agents in cancer most likely differ, based on the genomic mutation pattern. The individualized optimal dose – the maximal efficacious dose with a clinically acceptable safety profile – may vary depending on the genomic mutation patterns and should be determined prior to the use of these agents in precision medicine. In addition, genes that influence the individualized optimal doses should be identified in early-phase development. In this study, we propose a novel dose-finding approach to identify the individualized optimal dose for molecularly targeted agents in phase I cancer trials. Individualized optimal dose determination and gene selection were conducted simultaneously based on L ₁ and L ₂ penalized regression. Similar to most reported dose-finding approaches, this study considers non-monotonic patterns for dose-efficacy and dose–toxicity relationships, as well as correlations between efficacy and toxicity outcomes based on multinomial distribution. Our dose-finding algorithm is based on the predictive probability calculated with an estimated penalized regression model. We compare the operating characteristics between the proposed and existing methods by simulation studies under various scenarios.

精准医学是一种新兴的疾病治疗和预防方法，可以解释基因、环境和生活方式的个体差异。癌症是一种基因组疾病；因此，基于基因组突变模式，癌症中分子靶向药物的剂量-疗效和剂量-毒性关系很可能不同。个体化的最佳剂量——具有临床可接受安全性的最大有效剂量——可能因基因组突变模式而异，应在精准医学中使用这些药物之前确定。此外，应在早期开发阶段确定影响个体化最佳剂量的基因。在这项研究中，我们提出了一种新的剂量发现方法，以确定 I 期癌症试验中分子靶向药物的个体化最佳剂量。个体化最佳剂量确定和基因选择同时进行L ₁和L ₂惩罚回归。与大多数报道的剂量发现方法类似，本研究考虑了剂量-疗效和剂量-毒性关系的非单调模式，以及基于多项分布的疗效和毒性结果之间的相关性。我们的剂量寻找算法基于使用估计的惩罚回归模型计算的预测概率。我们通过在各种场景下的模拟研究来比较所提出的方法和现有方法之间的操作特性。