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用于风险评估的基于生理的药代动力学模型的开发与特性描述。

Development and specification of physiologically based pharmacokinetic models for use in risk assessment.

作者信息

Clewell Rebecca A, Clewell Harvey J

机构信息

The Hamner Institutes for Health Sciences, Research Triangle Park, NC 27709, USA.

出版信息

Regul Toxicol Pharmacol. 2008 Feb;50(1):129-43. doi: 10.1016/j.yrtph.2007.10.012. Epub 2007 Nov 6.

Abstract

Risk assessments are performed to estimate the conditions under which individuals or populations may be harmed by exposure to environmental or occupational chemicals. In the absence of quantitative data in the human, this process is often dependent upon the use of animal and in vitro data to estimate human response. To reduce the uncertainty inherent in such extrapolations, there has been considerable interest in the development of physiologically based pharmacokinetic (PBPK) models of toxic chemicals for application in quantitative risk assessments. PBPK models are effective tools for integrating diverse dose-response and mechanistic data in order to more accurately predict human risk. Yet, for these models to be useful and trustworthy in performing the necessary extrapolations (species, doses, exposure scenarios), they must be thoughtfully constructed in accordance with known biology and pharmacokinetics, documented in a form that is transparent to risk assessors, and shown to be robust using diverse and appropriate data. This paper describes the process of PBPK model development and highlights issues related to the specification of model structure and parameters, model evaluation, and consideration of uncertainty. Examples are provided to illustrate approaches for selecting a "preferred" model from multiple alternatives.

摘要

进行风险评估是为了估计个体或人群在接触环境或职业化学物质时可能受到伤害的条件。在缺乏人体定量数据的情况下,这一过程通常依赖于使用动物和体外数据来估计人体反应。为了减少此类外推法固有的不确定性,人们对开发用于定量风险评估的有毒化学物质的基于生理的药代动力学(PBPK)模型产生了浓厚兴趣。PBPK模型是整合各种剂量反应和机制数据以更准确预测人类风险的有效工具。然而,为了使这些模型在进行必要的外推(物种、剂量、暴露场景)时有用且可靠,它们必须根据已知的生物学和药代动力学进行精心构建,以一种风险评估人员能够理解的形式记录,并使用多样且合适的数据证明其稳健性。本文描述了PBPK模型的开发过程,并强调了与模型结构和参数的设定、模型评估以及不确定性考虑相关的问题。提供了示例来说明从多个备选方案中选择“首选”模型的方法。

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