整合人口方法与生理学基础的药动力学模型:参数估计的新框架
Donato Teutonico1, David Marchionni2, Marc Lavielle3,4
1Pharmacometrics, Translational Medicine Unit, Sanofi, Vitry-sur-Seine, France.
CPT: pharmacometrics & systems pharmacology
|January 16, 2026
概括
这项研究为生理学基础药理动力学 (PBPK) 模型引入了一种新的人口方法,改进了参数估计和减少计算时间. 这种方法通过利用个人数据来提高药物开发,以获得更准确的药理动力学预测.
科学领域:
- 药理动力学和药物开发
- 计算生物学和生物信息学
- 系统药理学系统药理学
背景情况:
- 生理学基础的药理动力学 (PBPK) 建模对于预测开发过程中的药物度至关重要.
- 在PBPK模型中估计参数是具有挑战性的,因为参数众多,数据有限.
- 现有的方法很难有效地估计生理学相关参数的个体间变异性.
研究的目的:
- 为增强参数估计引入一种新型的人口全身PBPK (popWB-PBPK) 建模方法.
- 为了利用个体患者数据,更准确地进行PBPK模型参数化和可变性评估.
- 提出一个优化的随机近似预期-最大化 (SAEM) 算法,用于高效的PBPK参数估计.
主要方法:
- 将全身PBPK (WB-PBPK) 模型与人口估计技术结合起来.
- 实现一个优化的SAEM算法与自适应参数网格优化和线性插值.
- 使用theophylline作为一个案例研究来估计药物特异性参数和共变效应 (例如,吸烟状态).
主要成果:
- popWB-PBPK方法准确地估计了药物特定的参数,如CYP1A2清除率和脂性.
- 与标准SAEM相比,优化的SAEM算法显著减少了计算运行时间.
- 该方法成功地结合了共变量效应,证明了其实际实用性.
结论:
- 开发的popWB-PBPK框架提供了一个可访问的R包 (saemixPBPK),用于可靠的PBPK参数估计.
- 这种方法可以同时估计种群参数,可变性和不确定性,同时保持生理相关性.
- 机械模型的进步使得使用个人数据进行更可靠的药理动力学预测.
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