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Evaluating Pharmacokinetic and Pharmacodynamic Interactions with Computational Models in Supporting Cumulative Risk Assessment

机译:用计算模型评估药代动力学和药效相互作用以支持累积风险评估

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摘要

Simultaneous or sequential exposure to multiple chemicals may cause interactions in the pharmacokinetics (PK) and/or pharmacodynamics (PD) of the individual chemicals. Such interactions can cause modification of the internal or target dose/response of one chemical in the mixture by other chemical(s), resulting in a change in the toxicity from that predicted from the summation of the effects of the single chemicals using dose additivity. In such cases, conducting quantitative cumulative risk assessment for chemicals present as a mixture is difficult. The uncertainties that arise from PK interactions can be addressed by developing physiologically based pharmacokinetic (PBPK) models to describe the disposition of chemical mixtures. Further, PK models can be developed to describe mechanisms of action and tissue responses. In this article, PBPK/PD modeling efforts conducted to investigate chemical interactions at the PK and PD levels are reviewed to demonstrate the use of this predictive modeling framework in assessing health risks associated with exposures to complex chemical mixtures.
机译:同时或顺序暴露于多种化学物质可能会导致各个化学物质的药代动力学(PK)和/或药效学(PD)相互作用。此类相互作用可能会导致混合物中一种化学物质的内部或目标剂量/反应被其他一种或多种化学物质改变,从而导致毒性与使用剂量加和法将单个化学物质的作用总和预测的毒性发生变化。在这种情况下,很难对作为混合物的化学物质进行定量的累积风险评估。 PK相互作用引起的不确定性可以通过开发基于生理学的药代动力学(PBPK)模型来描述化学混合物的处理方式来解决。此外,可以开发PK模型来描述作用机制和组织反应。在本文中,对PBPK / PD建模工作进行了研究,以研究PK和PD层面的化学相互作用,以证明该预测建模框架在评估与复杂化学混合物接触相关的健康风险中的用途。

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