Three steps towards dose optimization for oncology dose finding

最大耐受剂量 医学 药代动力学 药物开发 临床试验 药效学 药品 选择(遗传算法) 药理学 肿瘤科 计算机科学 内科学 机器学习
作者
Jason J. Z. Liao,Ekaterine Asatiani,Qingyang Liu,Kevin Hou
出处
期刊:Cornell University - arXiv
标识
DOI:10.48550/arxiv.2309.15333
摘要

Traditional dose selection for oncology registration trials typically employs a one- or two-step single maximum tolerated dose (MTD) approach. However, this approach may not be appropriate for molecularly targeted therapy that tends to have toxicity profiles that are markedly different to cytotoxic agents. The US Food and Drug Administration launched Project Optimus to reform dose optimization in oncology drug development and has recently released a related Guidance for Industry. In response to these initiatives, we propose a "three steps towards dose optimization" procedure and discuss the details in dose optimization designs and analyses in this manuscript. The first step is dose-escalation to identify the MTD or maximum administered dose with an efficient hybrid design, which can offer good overdose control and increases the likelihood of the recommended MTD being close to the true MTD. The second step is the selection of appropriate recommended doses for expansion (RDEs), based on all available data including emerging safety, pharmacokinetics, pharmacodynamics, and other biomarker information. The third step is dose optimization, which uses data from a randomized fractional factorial design with multiple RDEs explored in multiple tumor cohorts during the expansion phase to ensure a feasible dose is selected for registration trials, and that the tumor type most sensitive to the investigative treatment is identified. We believe using this three-step approach can increase the likelihood of selecting the optimal dose for registration trial, one that demonstrates a balanced safety profile while retaining much of the efficacy observed at the MTD.
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