Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Secondary Appointment, Department of Environmental and Occupational Health, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Chem Res Toxicol. 2021 Nov 15;34(11):2298-2308. doi: 10.1021/acs.chemrestox.1c00193. Epub 2021 Oct 27.
Physiologically based pharmacokinetic (PBPK) modeling is a powerful technique to inform risk assessment of xenobiotic substances such as perfluorooctanoic acid (PFOA). In our previous study, a permeability-limited PBPK model was developed to simulate the toxicokinetics and tissue distribution of PFOA in male rats. However, due to limited information on some key model parameters (, protein binding and active transport rates), the uncertainty of the permeability-limited PBPK model was quite high. To address this issue, a hierarchical Bayesian analysis with Markov chain Monte Carlo (MCMC) was applied to reduce the uncertainty of parameters and improve the performance of the PBPK model. With the optimized posterior parameters, the PBPK model was evaluated by comparing its prediction with experimental data from three different studies. The results show that the uncertainties of the posterior model parameters were reduced substantially. In addition, most of the PBPK model predictions were improved: with the posterior parameters, most of the predicted plasma toxicokinetics (, half-life) and tissue distribution fell well within a factor of 2.0 of the experimental data. Finally, the Bayesian framework could provide insights into the molecular mechanisms driving PFOA toxicokinetics: PFOA-protein binding, membrane permeability, and active transport.
生理药代动力学（PBPK）模型是一种强大的技术，可以为评估全氟辛酸（PFOA）等外源性物质的风险提供信息。在我们之前的研究中，开发了一个渗透限制的 PBPK 模型来模拟 PFOA 在雄性大鼠中的毒代动力学和组织分布。然而，由于一些关键模型参数（蛋白结合和主动转运速率）的信息有限，渗透限制的 PBPK 模型的不确定性非常高。为了解决这个问题，应用分层贝叶斯分析和马尔可夫链蒙特卡罗（MCMC）来降低参数的不确定性并提高 PBPK 模型的性能。使用优化后的后验参数，通过将其预测与来自三个不同研究的实验数据进行比较来评估 PBPK 模型。结果表明，后验模型参数的不确定性大大降低。此外，大多数 PBPK 模型的预测都得到了改善：在后验参数的情况下，大多数预测的血浆毒代动力学（半衰期）和组织分布都很好地落在实验数据的 2.0 倍以内。最后，贝叶斯框架可以深入了解驱动 PFOA 毒代动力学的分子机制：PFOA-蛋白结合、膜通透性和主动转运。
