在暴露-反应分析中解决因果关系和同质性假设
Mats O Karlsson1, Divya Brundavanam1
1Department of Pharmacy, Uppsala University, Uppsala, Sweden.
Clinical pharmacology and therapeutics
|November 25, 2025
概括
新的仪器变量 (IV) 模型通过评估因果关系和同质性来改善药理动力学分析 (PKPD). 分区效应 (PE) 模型准确地估计了各种混场景中的药物效应,与标准PKPD模型不同.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 生物统计学 生物统计学
- 药物开发 药物开发
背景情况:
- 暴露-反应 (PKPD) 分析对于药物开发决策至关重要.
- 当前的PKPD模型往往缺乏对因果关系和同质性的评估,这可能导致偏见的结果.
- 仪表变量 (IV) 分析提供了一种方法来确定因果关系.
研究的目的:
- 适应和评估IV模型 (预测器替代和控制功能) 用于重复测量分析.
- 将这些IV模型与标准PKPD模型和新型分区效应 (PE) 模型进行比较.
- 在各种混杂情景下评估模型性能,包括共享潜变量,未测量的活性代谢物和逆向因果关系.
主要方法:
- 调整预测器替代 (PS) 和控制函数 (CF) IV模型的重复测量数据.
- 与PKPD模型 (具有和没有PK-PD随机效应相关性) 和分区效应 (PE) 模型进行比较.
- 模拟六种情景:没有混,三种类型的混,未测量的活性代谢物和反向因果关系.
主要成果:
- 在大多数模拟场景中,标准PKPD模型产生了偏差的参数估计和不适当的剂量建议.
- 在大多数场景中,PS和CF模型提供了足够的估计.
- 新的PE模型始终在所有模拟场景中提供足够的估计,包括复杂的混情况.
结论:
- PE模型为因果暴露-反应建模提供了强大的方法,在混下优于传统的PKPD方法.
- PE模型支持基于度或反应的剂量足够个性化.
- 适应的IV方法,特别是PE模型,对于可靠的药物开发决策,无论是单一的还是重复的措施,都是有价值的.
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