优化瘤药物剂量的FDA-AACR策略:为首次人体试验选择剂量
Hao Zhu1, Alex N Phipps2, Ying Yuan3
1U.S. Food and Drug Administration, Silver Spring, Maryland.
概括
优化癌症药物剂量对于疗效和安全至关重要. 创新方法,包括建模和模拟,可以在早期试验中确定最佳剂量,避免过量或过少剂量.
科学领域:
- 在瘤学瘤学.
- 药理学 药理学是指药理学的学科.
- 药物开发 药物开发
背景情况:
- 传统的瘤药物开发依赖于最大耐受剂量,适合细胞毒药物.
- 这种方法对于高剂量增加毒性而不提高疗效的向疗法效果较低.
- 需要新的策略来优化剂量,并最大限度地降低次治疗或过度暴露的风险.
研究的目的:
- 突出预期和建议,以首次在人体 (FIH) 瘤学试验中选择和优化剂量.
- 讨论超越传统方法的创新策略,以确定最佳药物剂量.
- 在FIH试验中强调数据集成和基于模型的设计的重要性.
主要方法:
- 利用累积的药物开发数据的总和来优化剂量.
- 在背景数据有限的情况下,采用建模和模拟技术来确定合理的起始剂量.
- 实施创新的基于模型的临床试验设计,以统计指导剂量升级.
主要成果:
- 目前用于瘤学中剂量优化的技术尚未得到充分利用.
- 基于模型的设计允许实时更新以最大限度地提高患者的益处.
- 数据集成和先进的建模可以导致更合理和有效的剂量选择.
结论:
- 剂量优化是现代瘤学药物开发的一个重点,受到像Project Optimus这样的倡议的推动.
- 在首次人体试验中选择适当的剂量对于有效识别最佳剂量至关重要.
- 在瘤学药物开发中,创新,数据驱动的方法对于降低风险和最大化益处至关重要.
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