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Physiological modeling and extrapolation of pharmacokinetic interactions from binary to more complex chemical mixtures.

机译:从二元化合物到更复杂的化学混合物的药代动力学相互作用的生理模型和外推法。

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摘要

The available data on binary interactions are yet to be considered within the context of mixture risk assessment because of our inability to predict the effect of a third or a fourth chemical in the mixture on the interacting binary pairs. Physiologically based pharmacokinetic (PBPK) models represent a potentially useful framework for predicting the consequences of interactions in mixtures of increasing complexity. This article highlights the conceptual basis and validity of PBPK models for extrapolating the occurrence and magnitude of interactions from binary to more complex chemical mixtures. The methodology involves the development of PBPK models for all mixture components and interconnecting them at the level of the tissue where the interaction is occurring. Once all component models are interconnected at the binary level, the PBPK framework simulates the kinetics of all mixture components, accounting for the interactions occurring at various levels in more complex mixtures. This aspect was validated by comparing the simulations of a binary interaction-based PBPK model with experimental data on the inhalation kinetics of m-xylene, toluene, ethyl benzene, dichloromethane, and benzene in mixtures of varying composition and complexity. The ability to predict the kinetics of chemicals in complex mixtures by accounting for binary interactions alone within a PBPK model is a significant step toward the development of interaction-based risk assessment for chemical mixtures.
机译:由于我们无法预测混合物中第三种或第四种化学物质对相互作用的二元对的影响,因此在混合物风险评估的背景下仍需考虑有关二元相互作用的可用数据。基于生理学的药代动力学(PBPK)模型代表了一种潜在有用的框架,可用于预测复杂性不断增加的混合物中相互作用的后果。本文着重介绍了PBPK模型的概念基础和有效性,该模型用于推断从二元化学混合物到更复杂化学混合物的相互作用的发生和程度。该方法包括为所有混合物成分开发PBPK模型,并在发生相互作用的组织水平上将它们互连。一旦所有组分模型在二元级上互连,PBPK框架就会模拟所有混合物组分的动力学,从而说明在更复杂的混合物中各个级别上发生的相互作用。通过将基于二元相互作用的PBPK模型的模拟与间苯二酚,甲苯,乙苯,二氯甲烷和苯在不同组成和复杂度的混合物中的吸入动力学的实验数据进行比较,从而验证了这一方面。通过仅考虑PBPK模型中的二元相互作用来预测复杂混合物中化学物质动力学的能力,是朝着基于相互作用的化学混合物风险评估发展的重要一步。

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