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首页> 外文期刊>Journal of applied toxicology >Evaluation of physiologically based pharmacokinetic models for use in risk assessment.
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Evaluation of physiologically based pharmacokinetic models for use in risk assessment.

机译：用于风险评估的基于生理的药代动力学模型的评估。

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摘要

Physiologically based pharmacokinetic (PBPK) models are sophisticated dosimetry models that offer great flexibility in modeling exposure scenarios for which there are limited data. This is particularly of relevance to assessing human exposure to environmental toxicants, which often requires a number of extrapolations across species, route, or dose levels. The continued development of PBPK models ensures that regulatory agencies will increasingly experience the need to evaluate available models for their application in risk assessment. To date, there are few published criteria or well-defined standards for evaluating these models. Herein, important considerations for evaluating such models are described. The evaluation of PBPK models intended for risk assessment applications should include a consideration of: model purpose, model structure, mathematical representation, parameter estimation, computer implementation, predictive capacity and statistical analyses. Model purpose and structure require qualitative checks on the biological plausibility of a model. Mathematical representation, parameter estimation, computer implementation involve an assessment of the coding of the model, as well as the selection and justification of the physical, physicochemical and biochemical parameters chosen to represent a biological organism. Finally, the predictive capacity and sensitivity, variability and uncertainty of the model are analysed so that the applicability of a model for risk assessment can be determined. Published in 2007 by John Wiley & Sons, Ltd.
机译：基于生理学的药代动力学（PBPK）模型是复杂的剂量测定模型，为数据有限的暴露场景建模提供了极大的灵活性。这与评估人类对环境有毒物质的暴露特别相关，这通常需要对物种，途径或剂量水平进行大量推断。 PBPK模型的不断发展确保了监管机构将越来越多地体验到评估可用模型进行风险评估的需求。迄今为止，很少有公开的标准或定义明确的标准来评估这些模型。本文中，描述了评估此类模型的重要考虑因素。用于风险评估应用的PBPK模型的评估应考虑以下因素：模型目的，模型结构，数学表示，参数估计，计算机实现，预测能力和统计分析。模型的目的和结构要求对模型的生物学合理性进行定性检查。数学表示，参数估计，计算机实现涉及对模型编码的评估，以及对代表生物体的物理，物理化学和生物化学参数的选择和证明。最后，分析了模型的预测能力和敏感性，变异性和不确定性，从而可以确定模型用于风险评估的适用性。 John Wiley＆Sons，Ltd.于2007年出版。

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