Mathematical Sciences Unit, Health and Safety Laboratory Derbyshire, UK.
Front Pharmacol. 2011 Jun 23;2:31. doi: 10.3389/fphar.2011.00031. eCollection 2011.
Physiologically based pharmacokinetic (PBPK) models have a potentially significant role in the development of a reliable predictive toxicity testing strategy. The structure of PBPK models are ideal frameworks into which disparate in vitro and in vivo data can be integrated and utilized to translate information generated, using alternative to animal measures of toxicity and human biological monitoring data, into plausible corresponding exposures. However, these models invariably include the description of well known non-linear biological processes such as, enzyme saturation and interactions between parameters such as, organ mass and body mass. Therefore, an appropriate sensitivity analysis (SA) technique is required which can quantify the influences associated with individual parameters, interactions between parameters and any non-linear processes. In this report we have defined the elements of a workflow for SA of PBPK models that is computationally feasible, accounts for interactions between parameters, and can be displayed in the form of a bar chart and cumulative sum line (Lowry plot), which we believe is intuitive and appropriate for toxicologists, risk assessors, and regulators.
生理药代动力学(PBPK)模型在开发可靠的毒性预测测试策略方面具有潜在的重要作用。PBPK 模型的结构是理想的框架,可以将不同的体外和体内数据整合并利用,将使用替代动物毒性测量和人体生物监测数据生成的信息转化为合理的相应暴露。然而,这些模型通常包括对众所周知的非线性生物学过程的描述,如酶饱和和参数之间的相互作用,如器官质量和体重。因此,需要一种适当的敏感性分析(SA)技术,该技术可以量化与单个参数、参数之间的相互作用以及任何非线性过程相关的影响。在本报告中,我们定义了用于 PBPK 模型敏感性分析的工作流程的要素,该工作流程在计算上是可行的,考虑了参数之间的相互作用,并可以以条形图和累积和线(Lowry 图)的形式显示,我们认为这对于毒理学家、风险评估人员和监管机构来说是直观和合适的。