Phase I oncology trials aim to identify a safe dose - often the maximum tolerated dose (MTD) - for subsequent studies. Conventional designs focus on population-level toxicity modeling, with recent attention on leveraging pharmacokinetic (PK) data to improve dose selection. We propose the Precision Dose-Finding (PDF) design, a novel Bayesian phase I framework that integrates individual patient PK profiles into the dose-finding process. By incorporating patient-specific PK parameters (such as volume of distribution and elimination rate), PDF models toxicity risk at the individual level, in contrast to traditional methods that ignore inter-patient variability. The trial is structured in two stages: an initial training stage to update model parameters using cohort-based dose escalation, and a subsequent test stage in which doses for new patients are chosen based on each patient's own PK-predicted toxicity probability. This two-stage approach enables truly personalized dose assignment while maintaining rigorous safety oversight. Extensive simulation studies demonstrate the feasibility of PDF and suggest that it provides improved safety and dosing precision relative to the continual reassessment method (CRM). The PDF design thus offers a refined dose-finding strategy that tailors the MTD to individual patients, aligning phase I trials with the ideals of precision medicine.

翻译：I期肿瘤试验旨在确定后续研究的安全剂量——通常为最大耐受剂量（MTD）。传统设计侧重于群体水平的毒性建模，近期研究关注利用药代动力学（PK）数据改进剂量选择。我们提出精准剂量探索（PDF）设计，这是一种新颖的贝叶斯I期试验框架，将个体患者的PK特征整合至剂量探索过程。通过纳入患者特异性PK参数（如分布容积和消除速率），PDF可在个体水平建模毒性风险，这与忽略患者间差异的传统方法形成对比。试验采用两阶段结构：初始训练阶段通过队列剂量递增更新模型参数，后续测试阶段则根据每位患者自身的PK预测毒性概率为新患者选择剂量。这种两阶段方法在保持严格安全监督的同时，实现了真正个性化的剂量分配。大量模拟研究验证了PDF的可行性，并表明相较于持续再评估方法（CRM），该设计能提升安全性与剂量精准度。因此，PDF设计提供了一种精细化的剂量探索策略，使MTD能够适配个体患者，推动I期试验向精准医学理念迈进。