Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
J Natl Cancer Inst. 2023 Sep 7;115(9):1092-1098. doi: 10.1093/jnci/djad103.
The traditional more-is-better dose selection paradigm, originally developed for cytotoxic chemotherapeutics, can be problematic when applied to the development of novel molecularly targeted agents. Recognizing this issue, the US Food and Drug Administration initiated Project Optimus to reform the dose optimization and selection paradigm in oncology drug development, emphasizing the need for greater attention to benefit-risk considerations.
We identify different types of phase II/III dose-optimization designs, classified according to trial objectives and endpoint types. Through computer simulations, we examine their operating characteristics and discuss the relevant statistical and design considerations for effective dose optimization.
Phase II/III dose-optimization designs are capable of controlling family-wise type I error rates and achieving appropriate statistical power with substantially smaller sample sizes than the conventional approach while also reducing the number of patients who experience toxicity. Depending on the design and scenario, the sample size savings range from 16.6% to 27.3%, with a mean savings of 22.1%.
Phase II/III dose-optimization designs offer an efficient way to reduce sample sizes for dose optimization and accelerate the development of targeted agents. However, because of interim dose selection, the phase II/III dose-optimization design presents logistical and operational challenges and requires careful planning and implementation to ensure trial integrity.
传统的“剂量越高越好”的剂量选择范式最初是为细胞毒性化疗药物开发而制定的，但在应用于新型分子靶向药物的开发时可能会出现问题。美国食品和药物管理局认识到了这个问题，启动了“Optimus 计划”，以改革肿瘤药物开发中的剂量优化选择范式，强调需要更加关注获益-风险的考虑。
我们确定了不同类型的 II/III 期剂量优化设计，根据试验目标和终点类型进行分类。通过计算机模拟，我们研究了它们的操作特性，并讨论了有效的剂量优化相关的统计和设计考虑因素。
II/III 期剂量优化设计能够控制 I 类错误率，并在比传统方法显著减少样本量的情况下获得适当的统计效力，同时还减少了毒性反应患者的数量。根据设计和情况的不同，样本量的节省范围在 16.6%至 27.3%之间，平均节省率为 22.1%。
II/III 期剂量优化设计为减少剂量优化的样本量并加速靶向药物的开发提供了一种有效的方法。然而，由于中间剂量选择，II/III 期剂量优化设计带来了后勤和操作方面的挑战，需要仔细规划和实施，以确保试验的完整性。
