Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA.
Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.
Clin Trials. 2024 Jun;21(3):298-307. doi: 10.1177/17407745231220661. Epub 2024 Jan 11.
Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.
靶向药物和免疫疗法彻底改变了癌症治疗方式,为多种癌症类型提供了有前途的治疗选择。与传统疗法不同,由于这些新疗法具有独特的生物医学机制,“越多越好”的原则并不总是适用于它们。因此,已经提出了各种 I- II 期临床试验设计,以通过共同监测疗效和毒性结果来确定最佳的生物学剂量,从而最大限度地提高靶向治疗和免疫疗法的治疗效果。
这篇综述文章探讨了几种利用累积疗效和毒性结果来适应性地确定后续患者剂量并确定最佳生物学剂量、最大限度地提高整体治疗效果的创新 I- II 期临床试验设计。具体来说,我们强调了三类 I- II 期设计:疗效驱动、基于效用和纳入多个疗效终点的设计。对于每种设计,我们回顾了剂量-结果模型、最佳生物学剂量的定义、剂量发现算法以及试验实施的软件。为了说明这些概念,我们还介绍了两个利用 EffTox 和 ISO 设计的真实 I- II 期临床试验示例。最后,我们提供了一个分类树来总结本文讨论的设计。