Abstract：
The primary objective of an oncology dose-finding trial for novel therapies, such as molecularly targeted agents and immune-oncology therapies, is to identify the optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. Pharmacokinetic (PK) information is considered an appropriate indicator for evaluating the level of drug intervention in humans from a pharmacological perspective. Several novel anticancer agents have been shown to have significant exposure-efficacy relationships, and some PK information has been considered an important predictor of efficacy. This paper proposes a Bayesian optimal interval design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients allocated to OD in various realistic settings.
摘要：
肿瘤治疗新疗法的剂量发现试验的主要目标，如分子靶向药物和免疫肿瘤治疗，是为了确定在随后的临床试验中对受试者可耐受和治疗有益的最佳剂量(OD)。药代动力学（PK）信息被认为是从药理学角度评估人体药物干预水平的适当指标。几种新型抗癌药物已被证明具有显著的暴露-疗效关系，一些PK信息被认为是疗效的重要预测因子。本文提出了一种贝叶斯最佳间隔设计，用于基于肿瘤学中PK结果的随机化方案的剂量优化。模拟研究表明，与其他设计相比，所提出的设计在正确OD选择的百分比和在各种现实设置中分配给OD的患者平均数量方面具有优势。
