INERIS, Institut National de l'Environnement Industriel et des Risques, Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Parc ALATA, BP2, 60550 Verneuil en Halatte, France.
Regul Toxicol Pharmacol. 2010 Jun;57(1):103-16. doi: 10.1016/j.yrtph.2010.01.005. Epub 2010 Feb 1.
Physiologically based pharmacokinetic (PBPK) models have proven to be successful in integrating and evaluating the influence of age- or gender-dependent changes with respect to the pharmacokinetics of xenobiotics throughout entire lifetimes. Nevertheless, for an effective application of toxicokinetic modelling to chemical risk assessment, a PBPK model has to be detailed enough to include all the multiple tissues that could be targeted by the various xenobiotics present in the environment. For this reason, we developed a PBPK model based on a detailed compartmentalization of the human body and parameterized with new relationships describing the time evolution of physiological and anatomical parameters. To take into account the impact of human variability on the predicted toxicokinetics, we defined probability distributions for key parameters related to the xenobiotics absorption, distribution, metabolism and excretion. The model predictability was evaluated by a direct comparison between computational predictions and experimental data for the internal concentrations of two chemicals (1,3-butadiene and 2,3,7,8-tetrachlorodibenzo-p-dioxin). A good agreement between predictions and observed data was achieved for different scenarios of exposure (e.g., acute or chronic exposure and different populations). Our results support that the general stochastic PBPK model can be a valuable computational support in the area of chemical risk analysis.
生理药代动力学(PBPK)模型已被证明在整合和评估与外源性物质药代动力学相关的年龄或性别依赖性变化方面非常成功,贯穿整个生命周期。然而,为了使毒代动力学模型在化学风险评估中得到有效应用,PBPK 模型必须足够详细,以包括所有可能成为环境中各种外源性物质靶标的多种组织。出于这个原因,我们开发了一个基于人体详细区室化的 PBPK 模型,并使用描述生理和解剖参数随时间演变的新关系进行参数化。为了考虑人体变异性对预测毒代动力学的影响,我们定义了与外源性物质吸收、分布、代谢和排泄相关的关键参数的概率分布。通过将计算预测与两种化学物质(1,3-丁二烯和 2,3,7,8-四氯二苯并对二恶英)的内部浓度的实验数据进行直接比较,评估了模型的可预测性。对于不同的暴露场景(例如急性或慢性暴露和不同的人群),预测和观察数据之间都达到了很好的一致性。我们的研究结果支持一般随机 PBPK 模型可以成为化学风险分析领域有价值的计算支持。