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环境污染物的 PBPK 建模的定量构效关系(QSARs)。

QSARs for PBPK modelling of environmental contaminants.

机构信息

Departement de sante environnementale et sante au travail, Universite de Montreal, Montreal, Canada.

出版信息

SAR QSAR Environ Res. 2011 Mar;22(1-2):129-69. doi: 10.1080/1062936X.2010.548351.

Abstract

Physiologically-based pharmacokinetic (PBPK) models are increasingly finding use in risk assessment applications of data-rich compounds. However, it is a challenge to determine the chemical-specific parameters for these models, particularly in time- and resource-limiting situations. In this regard, SARs, QSARs and QPPRs are potentially useful for computing the chemical-specific input parameters of PBPK models. Based on the frequency of occurrence of molecular fragments (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure) and exposure conditions, the available QSAR-PBPK models facilitate the simulation of tissue and blood concentrations for some inhaled volatile organic chemicals. The application domain of existing QSARs for developing PBPK models is limited, due to lack of relevant data for diverse chemicals and mechanisms. Even though this approach is conceptually applicable to non-volatile and high molecular weight organics as well, it is more challenging to predict the other PBPK model parameters required for modelling the kinetics of these chemicals (particularly tissue diffusion coefficients, association constants for binding and oral absorption rates). As the level of our understanding of the mechanistic basis of toxicokinetic processes improves, QSARs to provide a priori predictions of key chemical-specific PBPK parameters can be developed to expedite the internal dose-based health risk assessments in data-poor situations.

摘要

生理药代动力学(PBPK)模型在富含数据的化合物的风险评估应用中越来越多地被使用。然而,确定这些模型的化学特异性参数是一个挑战,特别是在时间和资源有限的情况下。在这方面,SARs、QSARs 和 QPPRs 可用于计算 PBPK 模型的化学特异性输入参数。基于分子片段(CH(3)、CH(2)、CH、C、C=C、H、苯环和苯环结构中的 H)和暴露条件的出现频率,现有的 QSAR-PBPK 模型可用于模拟某些吸入挥发性有机化合物的组织和血液浓度。由于缺乏各种化学物质和机制的相关数据,现有的用于开发 PBPK 模型的 QSAR 的应用领域是有限的。尽管这种方法在概念上适用于非挥发性和高分子量有机物,但预测这些化学物质动力学所需的其他 PBPK 模型参数(特别是组织扩散系数、结合的缔合常数和口服吸收速率)更具挑战性。随着我们对毒代动力学过程的机制基础的理解的提高,可以开发提供关键化学特异性 PBPK 参数的先验预测的 QSAR,以加快数据匮乏情况下基于内部剂量的健康风险评估。

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