Hattis D, White P, Marmorstein L, Koch P
M.I.T. Center for Technology, Policy and Industrial Development, Cambridge, Massachusetts 02139.
Risk Anal. 1990 Sep;10(3):449-58. doi: 10.1111/j.1539-6924.1990.tb00528.x.
In recent years physiologically based pharmacokinetic models have come to play an increasingly important role in risk assessment for carcinogens. The hope is that they can help open the black box between external exposure and carcinogenic effects to experimental observations, and improve both high-dose to low-dose and interspecies projections of risk. However, to date, there have been only relatively preliminary efforts to assess the uncertainties in current modeling results. In this paper we compare the physiologically based pharmacokinetic models (and model predictions of risk-related overall metabolism) that have been produced by seven different sets of authors for perchloroethylene (tetrachloroethylene). The most striking conclusion from the data is that most of the differences in risk-related model predictions are attributable to the choice of the data sets used for calibrating the metabolic parameters. Second, it is clear that the bottom-line differences among the model predictions are appreciable. Overall, the ratios of low-dose human to bioassay rodent metabolism spanned a 30-fold range for the six available human/rat comparisons, and the seven predicted ratios of low-dose human to bioassay mouse metabolism spanned a 13-fold range. (The greater range for the rat/human comparison is attributable to a structural assumption by one author group of competing linear and saturable pathways, and their conclusion that the dangerous saturable pathway constitutes a minor fraction of metabolism in rats.) It is clear that there are a number of opportunities for modelers to make different choices of model structure, interpretive assumptions, and calibrating data in the process of constructing pharmacokinetic models for use in estimating "delivered" or "biologically effective" dose for carcinogenesis risk assessments. We believe that in presenting the results of such modeling studies, it is important for researchers to explore the results of alternative, reasonably likely approaches for interpreting the available data--and either show that any conclusions they make are relatively insensitive to particular interpretive choices, or to acknowledge the differences in conclusions that would result from plausible alternative views of the world.
近年来，基于生理的药代动力学模型在致癌物风险评估中发挥着越来越重要的作用。人们希望这些模型能够有助于将外部暴露与致癌效应之间的“黑匣子”打开，使其接受实验观察的检验，并改善高剂量到低剂量以及种间风险预测。然而，迄今为止，在评估当前建模结果的不确定性方面，仅有相对初步的尝试。在本文中，我们比较了七组不同作者针对全氯乙烯（四氯乙烯）构建的基于生理的药代动力学模型（以及与风险相关的整体代谢的模型预测）。数据得出的最惊人结论是，与风险相关的模型预测中的大多数差异可归因于用于校准代谢参数的数据集的选择。其次，很明显模型预测之间的根本差异相当可观。总体而言，在六组可用的人类/大鼠比较中，低剂量人类与生物测定啮齿动物代谢的比率跨度达30倍，而七组预测的低剂量人类与生物测定小鼠代谢的比率跨度达13倍。（大鼠/人类比较中范围更大是由于一个作者团队对竞争性线性和饱和途径的结构假设，以及他们得出的危险饱和途径在大鼠代谢中占比小的结论。）显然，建模人员在构建用于估计致癌风险评估中“传递的”或“生物有效”剂量的药代动力学模型过程中，有许多机会对模型结构、解释性假设和校准数据做出不同选择。我们认为，在展示此类建模研究的结果时，研究人员重要的是要探索解释现有数据的其他合理可行方法的结果——要么表明他们得出的任何结论对特定解释性选择相对不敏感，要么承认由于对世界的合理替代观点而导致的结论差异。
