Dadashova Kamala, Smith Ralph C, Haider Mansoor A, Reich Brian J
Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA.
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
Philos Trans A Math Phys Eng Sci. 2025 Mar 13;383(2292):20240219. doi: 10.1098/rsta.2024.0219.
Physiologically based pharmacokinetic (PBPK) models use a mechanistic approach to delineate the processes of the absorption, distribution, metabolism and excretion of biological substances in various species. These models generally comprise coupled systems of ordinary differential equations involving multiple states and a moderate to a large number of parameters. Such models contain compartments corresponding to various organs or tissues in the body. Before employing the models for treatment, the quantification of uncertainties for the parameters, based on information or data for a specific response, is necessary. This requires the determination of identifiable parameters, which are uniquely determined by data, and uncertainty analysis based on frequentist or Bayesian inference. We introduce a strategy to integrate parameter subset selection, based on identifiability analysis, with Bayesian inference. This approach further refines the subset of identifiable parameters, quantifies parameter and response uncertainties, enhances model prediction and reduces computational cost.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.
基于生理学的药代动力学（PBPK）模型采用机械方法来描述生物物质在不同物种中的吸收、分布、代谢和排泄过程。这些模型通常由涉及多个状态和中等到大量参数的常微分方程耦合系统组成。此类模型包含对应于体内各种器官或组织的隔室。在将这些模型用于治疗之前，基于特定反应的信息或数据对参数的不确定性进行量化是必要的。这需要确定可识别参数，这些参数由数据唯一确定，并基于频率论或贝叶斯推断进行不确定性分析。我们引入一种策略，将基于可识别性分析的参数子集选择与贝叶斯推断相结合。这种方法进一步优化了可识别参数的子集，量化了参数和反应的不确定性，增强了模型预测并降低了计算成本。本文是主题为“医疗保健和生物系统的不确定性量化（第1部分）”的一部分。
