Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843-4458, USA.
Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843-4458, USA.
Toxicol Sci. 2021 Aug 3;182(2):168-182. doi: 10.1093/toxsci/kfab057.
Quantification of interindividual variability is a continuing challenge in risk assessment, particularly for compounds with complex metabolism and multi-organ toxicity. Toxicokinetic variability for perchloroethylene (perc) was previously characterized across 3 mouse strains and in 1 mouse strain with various degrees of liver steatosis. To further characterize the role of genetic variability in toxicokinetics of perc, we applied Bayesian population physiologically based pharmacokinetic (PBPK) modeling to the data on perc and metabolites in blood/plasma and tissues of male mice from 45 inbred strains from the Collaborative Cross (CC) mouse population. After identifying the most influential PBPK parameters based on global sensitivity analysis, we fit the model with a hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation. We found that the data from 3 commonly used strains were not representative of the full range of variability in perc and metabolite blood/plasma and tissue concentrations across the CC population. Using interstrain variability as a surrogate for human interindividual variability, we calculated dose-dependent, chemical-, and tissue-specific toxicokinetic variability factors (TKVFs) as candidate science-based replacements for the default uncertainty factor for human toxicokinetic variability of 100.5. We found that toxicokinetic variability factors for glutathione conjugation metabolites of perc showed the greatest variability, often exceeding the default, whereas those for oxidative metabolites and perc itself were generally less than the default. Overall, we demonstrate how a combination of a population-based mouse model such as the CC with Bayesian population PBPK modeling can reduce uncertainty in human toxicokinetic variability and increase accuracy and precision in quantitative risk assessment.
个体间变异性的量化是风险评估中的一个持续挑战，特别是对于具有复杂代谢和多器官毒性的化合物。以前已经在 3 种小鼠品系和 1 种具有不同程度肝脂肪变性的小鼠品系中对全氯乙烯（perc）的毒代动力学变异性进行了描述。为了进一步描述遗传变异在 perc 毒代动力学中的作用，我们应用贝叶斯群体生理基于药代动力学（PBPK）模型来对来自协作交叉（CC）小鼠群体的 45 种近交系雄性小鼠血液/血浆和组织中的 perc 和代谢物数据进行分析。在基于全局敏感性分析确定了最具影响力的 PBPK 参数后，我们使用基于马尔可夫链蒙特卡罗模拟的分层贝叶斯群体分析对模型进行拟合。我们发现，来自 3 种常用品系的数据不能代表 CC 群体中 perc 和代谢物血液/血浆和组织浓度的全部变异性范围。使用种间变异性作为人类个体间变异性的替代物，我们计算了剂量依赖性、化学物质特异性和组织特异性毒代动力学变异性因子（TKVF），作为人类毒代动力学变异性的默认不确定性因子 100.5 的替代物。我们发现，perc 的谷胱甘肽结合代谢物的毒代动力学变异性因子表现出最大的变异性，通常超过默认值，而 perc 本身和氧化代谢物的变异性因子通常小于默认值。总的来说，我们展示了如何将 CC 这样的基于群体的小鼠模型与贝叶斯群体 PBPK 模型相结合，可以减少人类毒代动力学变异性的不确定性，并提高定量风险评估的准确性和精密度。
