最大耐受剂量
贝叶斯概率
猎枪
计算机科学
优化设计
医学
药理学
机器学习
药代动力学
生物
人工智能
生物化学
基因
作者
Xin Chen,Jingyi Zhang,Liyun Jiang,Fangrong Yan
标识
DOI:10.1177/09622802221129049
摘要
For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named “shotgun-2,” to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional “phase I + cohort expansion” design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.
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