PDF(Pubmed)
Abstract：
Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.
摘要：
基于生理的动力学（PBK）模型广泛用于药理学和毒理学，用于预测暴露后物质的内部配置。自愿或不。由于其复杂性,需要估计大量的模型参数，要么通过硅片工具，体外实验或通过将模型拟合到体内数据。在后一种情况下，在体内数据上拟合复杂的结构模型可能导致过度参数化并产生不切实际的参数估计。为了解决这些问题,我们提出了一种新的参数分组方法，通过共同估计跨隔室的参数组来减少参数空间。参数分组使用遗传算法进行，并且是完全自动化的，基于一种新颖的拟合优度度量。为了说明拟议方法的实际应用，进行了两个案例研究。第一个案例研究展示了一种新的PBK模型的开发，而第二个侧重于模型细化。在第一个案例研究中，建立了PBK模型，以阐明静脉注射后大鼠中二氧化钛（TiO2）纳米颗粒的生物分布。采用了多种参数估计方案。基于拟合优度指标的比较分析表明，所提出的方法产生的模型优于标准估计方法，同时使用减少数量的参数。在第二个案例研究中，现有的大鼠全氟辛酸（PFOA）PBK模型扩展到纳入其他组织，为PFOA生物分布提供了更全面的描述。两种模型均通过独立的体内研究进行验证，以确保其可靠性。
