In vitro to in vivo extrapolation for predicting human equivalent dose of phenolic endocrine disrupting chemicals: PBTK model development, biological pathways, outcomes and performance

体内 外推法 化学 体外 计算生物学 药理学 环境化学 生化工程 生物 生物化学 生物技术 工程类 数学 数学分析
作者
Ruili Xie,Xiaodan Wang,Yiping Xu,Lei Zhang,Mei Ma,Zijian Wang
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:897: 165271-165271 被引量:3
标识
DOI:10.1016/j.scitotenv.2023.165271
摘要

In vitro to in vivo (IVIVE) leverages in vitro high-throughput biological responses to predict the corresponding in vivo exposures and further estimate the human safe dose. However, for phenolic endocrine disrupting chemicals (EDCs) linked with complicated biological pathways and adverse outcomes (AO), such as bisphenol A (BPA) and 4-nonylphenol (4-NP), plausible estimation of human equivalent doses (HED) by IVIVE approaches considering various biological pathways and endpoints is still challenging. To explore the capabilities and limitations of IVIVE, this study conducted physiologically based toxicokinetic (PBTK)-IVIVE approaches to derive pathway-specific HEDs using BPA and 4-NP as examples. In vitro HEDs of BPA and 4-NP varied in different adverse outcomes, pathways, and testing endpoints and ranged from 0.0013 to 1.0986 mg/kg bw/day and 0.0551 to 1.7483 mg/kg bw/day, respectively. In vitro HEDs associated with reproductive AOs initiated by PPARα activation and ER agonism were the most sensitive. Model verification suggested the potential of using effective in vitro data to determine reasonable approximation of in vivo HEDs for the same AO (fold differences of most AOs ranged in 0.14–2.74 and better predictions for apical endpoints). Furthermore, system-specific parameters of cardiac output and its fraction, body weight, as well as chemical-specific parameters of partition coefficient and liver metabolic were most sensitive for the PBTK simulations. The results indicated that the application of fit for-purpose PBTK-IVIVE approach could provide credible pathway-specific HEDs and contribute to high throughput prioritization of chemicals in a more realistic scenario.
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