医学
精确肿瘤学
肿瘤科
医学物理学
内科学
计算机科学
癌症
作者
David Dejardin,Bo Huang,Ying Yuan,Ulrich Beyer,Jane Fridlyand,Jiawen Zhu
标识
DOI:10.1080/19466315.2024.2308856
摘要
Over the past decade, drug development in oncology has shifted from cytotoxic agents to drugs with new mechanisms of action, such as cancer immunotherapies, targeted therapeutics, T-cell engagers and others. The conventional maximum tolerated dose (MTD) based dose-finding paradigm is not suitable for the development of these new agents. Further, health authorities, especially the FDA, are requesting more thorough dose optimization prior to the initiation of pivotal trials, and initiatives such as the FDA’s project Optimus have been launched to accelerate this paradigm shift. Dose optimization is more complicated than finding the MTD and requires consideration of complex mechanisms of action, schedule optimization, long-term drug tolerability, and possibly novel pharmacodynamic endpoints. Thus, thoughtful study designs, translational data, and statistical modelling play an increasingly important role to achieve the goal of dose optimization. This paper captures opinions from the 2022 ASA biophamaceutical section regulatory-industry statistics workshop session” Dose finding and optimization for novel oncology agents - the new challenges and novel technologies”. We present general design options for dose optimization. Pros and cons of these design options are discussed, and a real-world case study is provided to illustrate a strategy of dose optimization. Discussions focused on practical considerations are included.
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