Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, USA.
Pharm Stat. 2024 Nov-Dec;23(6):1181-1205. doi: 10.1002/pst.2417. Epub 2024 Aug 9.
Dose-finding studies play a crucial role in drug development by identifying the optimal dose(s) for later studies while considering tolerability. This not only saves time and effort in proceeding with Phase III trials but also improves efficacy. In an era of precision medicine, it is not ideal to assume patient homogeneity in dose-finding studies as patients may respond differently to the drug. To address this, we propose a personalized dose-finding algorithm that assigns patients to individualized optimal biological doses. Our design follows a two-stage approach. Initially, patients are enrolled under broad eligibility criteria. Based on the Stage 1 data, we fit a regression model of toxicity and efficacy outcomes on dose and biomarkers to characterize treatment-sensitive patients. In the second stage, we restrict the trial population to sensitive patients, apply a personalized dose allocation algorithm, and choose the recommended dose at the end of the trial. Simulation study shows that the proposed design reliably enriches the trial population, minimizes the number of failures, and yields superior operating characteristics compared to several existing dose-finding designs in terms of both the percentage of correct selection and the number of patients treated at target dose(s).
剂量探索研究在药物开发中起着至关重要的作用，通过考虑耐受性来确定后续研究的最佳剂量。这不仅可以节省时间和精力进行 III 期试验，还可以提高疗效。在精准医学时代，在剂量探索研究中假设患者具有同质性并不理想，因为患者对药物的反应可能不同。为了解决这个问题，我们提出了一种个性化的剂量探索算法，将患者分配到个体化的最佳生物学剂量。我们的设计遵循两阶段方法。首先，根据广泛的入选标准招募患者。基于第一阶段的数据，我们拟合了一个关于剂量和生物标志物的毒性和疗效结果的回归模型，以确定治疗敏感的患者。在第二阶段，我们将试验人群限制在敏感患者中，应用个性化剂量分配算法，并在试验结束时选择推荐剂量。模拟研究表明，与几种现有的剂量探索设计相比，所提出的设计能够可靠地富集试验人群，最大限度地减少失败人数，并在正确选择的百分比和在目标剂量治疗的患者数量方面具有优越的操作特征。
