Oncology dose optimization paradigms: knowledge gained and extrapolated from approved oncology therapeutics

医学 最大耐受剂量 有效剂量(辐射) 肿瘤科 人口 选择(遗传算法) 临床试验 内科学 核医学 计算机科学 环境卫生 人工智能
作者
Rajendar K. Mittapalli,Cen Guo,Stefanie K. Drescher,Donghua Yin
出处
期刊:Cancer Chemotherapy and Pharmacology [Springer Nature]
卷期号:90 (3): 207-216 被引量:10
标识
DOI:10.1007/s00280-022-04444-0
摘要

There has been increasing attention to dose optimization in the development of targeted oncology therapeutics. The current report has analyzed the dose selection approaches for 116 new molecular entities (NMEs) approved for oncology indications by the US FDA from 2010 to August 2021, with the goal to extract learnings about the ways to select the optimal dose. The analysis showed that: (1) the initial label dose was lower than the maximum tolerated dose (MTD) or maximum studied dose (MSD) in Phase 1 for the majority of approved NMEs, and that the MTD approach is no longer the mainstay for dose selection; (2) there was no dose ranging or optimization beyond Phase 1 dose escalation for ~ 80% of the NMEs; (3) integrated dose/exposure–response analyses were commonly used to justify the dose selection; (4) lack of dose optimization led to dose-related PMRs/PMCs in 14% of cases, but 82% of these did not result in change of the initial label dose; and (5) depending on properties of the NME and specific benefit/risk considerations for the target patient population, there could be different dose selection paradigms leading to identification of the appropriate clinical dose. The analysis supports the need to incorporate more robust dose optimization during oncology clinical development, through comparative assessment of benefit/risk of multiple dose levels, over a wide exposure range using therapeutically relevant endpoints and adequate sample size. On the other hand, in certain cases, data from FIP dose escalation may be adequate to support the dose selection.
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