利用双阶段 δ 全球敏感性分析方法,在PBPK模型中为参数估计提供信息
Marina Cuquerella-Gilabert1,2,3,4, Alessandro De Carlo4, Sergio Sánchez Herrero3
1Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain.
Pharmaceutical statistics
|March 10, 2026
概括
本研究介绍了一个计算框架,将PhysPK和Python连接起来,用于高级PBPK模型分析. 它集成了全球敏感性分析 (GSA) 和个别参数估计,提高了药理动力学建模的准确性.
科学领域:
- 药理动力学和药理计量学
- 计算生物学和生物信息学
- 系统生物学 系统生物学
背景情况:
- 生理学基础的药理动力学 (PBPK) 建模对于药物开发至关重要,但往往缺乏用于敏感性分析和参数估计的可靠方法.
- 目前的做法不充分解决全球敏感性分析 (GSA) 和个别参数的精细化,限制PBPK模型的准确性和适用性.
研究的目的:
- 建立一个集成PhysPK和Python的计算框架,用于高级PBPK分析.
- 实现半机械PBPK模型的双阶段三角形GSA和代双阶段 (ITS) 方法.
- 评估参数不确定性和相关性对关键药理动力学终点的影响.
主要方法:
- 开发了一个框架,将PhysPK与Python连接起来,用于GSA和参数估计.
- 应用双阶段三角形GSA来确定影响AUC,Cmax和Tmax的有影响力的参数.
- 在各种模拟场景和优化算法 (Nelder-Mead,Powell,BFGS) 中利用ITS方法进行单个参数估计.
主要成果:
- 两个阶段的三角形GSA确定了分布体积,清除量和胃排空率作为关键参数.
- 参数估计性能使用AFE,AAFE和PEE指标进行评估.
- 大多数估计实现了AFE和AAFE值在0.8和1.25之间,Nelder-Mead显示出更高的准确性.
结论:
- 综合框架成功地将相关性意识的GSA与PBPK模型中的单个参数估计相结合.
- 这种方法增强了PBPK模型的简化,并支持受数据限制的单个参数估计.
- 集成代表了PhysPK平台的重大进步,为药理动力学建模提供了一个强大的工具.
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