剂量
医学
最大耐受剂量
食品药品监督管理局
药品
临床试验
药理学
重症监护医学
选择(遗传算法)
医学物理学
肿瘤科
内科学
计算机科学
人工智能
作者
Wei Gao,Jiang Liu,Cynthia J. Musante,Hao‐Jie Zhu,Matthew D. Thompson,Mirat Shah,Yanguang Cao,Vijay Ivaturi,Mark R. Conaway,Dean Bottino,Donghua Yin,Dorothée Sémiond,Aram Oganesian,Mark J. Ratain,Chunze Li,Li Zhu,Ying Ou,Xiling Jiang,Jonathon Vallejo,Rajanikanth Madabushi
摘要
Ongoing efforts to optimize the dosages of oncology drugs have largely focused on the initial indication, with emphasis placed on maximizing the utility of all available evidence to improve dose finding, dose selection, and trial design; however, optimizing dosages for new combinations or subsequent indications is more complex and warrants further discussion. For example, the dose–response (DR) or exposure–response (ER) relationships can change when multiple drugs are used (combination therapies) and can differ between tumor types, patient populations, and treatment settings (subsequent indications). Quantitative approaches can help address the challenges of optimizing dosages for new combinations or subsequent indications. To further this dialogue, the US Food and Drug Administration's Office of Clinical Pharmacology and the International Society of Pharmacometrics co‐sponsored a workshop to discuss the development of investigational and approved drugs in new combinations or for subsequent indications using model‐informed approaches to investigate, support, and select optimized dosages for oncology drugs.
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