Enhanced prediction of internal concentrations of phenolic endocrine disrupting chemicals and their metabolites in fish by a physiologically based toxicokinetic incorporating metabolism (PBTK-MT) model.
Enhanced prediction of internal concentrations of phenolic endocrine disrupting chemicals and their metabolites in fish by a physiologically based toxicokinetic incorporating metabolism (PBTK-MT) model.
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DOI：
10.2139/ssrn.4160650
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发表时间：
2022-09
影响因子：
8.9
通讯作者：
Yue-Hong Liu;Li Yao;Zheng Huang;Yuan-Yuan Zhang-Yuan;Chang-Er Chen;Jian-Liang Zhao;G. Ying
中科院分区：
文献类型：
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作者：
Yue-Hong Liu;Li Yao;Zheng Huang;Yuan-Yuan Zhang-Yuan;Chang-Er Chen;Jian-Liang Zhao;G. Ying
双酚A（BPA）、4-壬基酚（4-NP）和三氯生（TCS）是酚类内分泌干扰物（EDCs），广泛存在于水环境中，并在鱼类体内积累和代谢。基于生理学的毒代动力学（PBTK）模型已被用于描述母体化合物在鱼类中的吸收、分布、代谢和排泄（ADME），而代谢产物的研究较少。在这项研究中，PBTK纳入代谢（PBTK-MT）模型的BPA，4-NP和TCS的建立，以提高传统的PBTK模型的性能。PBTK-MT模型由16个隔室组成，在预测鱼类中三种化合物及其葡萄糖醛酸化和硫酸化缀合物的内部浓度方面显示出很高的准确性。通过优化推导血液和肝脏之间分配系数的机制，成功解决了典型肝脏代谢对PBTK-MT模型的影响。PBTK-MT模型通过参数的后向外推法对未知参数表现出潜在的数据缺口填补能力。模型敏感性分析表明，在至少两个PBTK-MT模型中，只有5个参数是敏感的，而大多数参数是不敏感的。PBTK-MT模型将有助于更好地理解污染物在水生生物中的环境行为和风险。
Bisphenol A (BPA), 4-nonylphenol (4-NP), and triclosan (TCS) are phenolic endocrine disrupting chemicals (EDCs), which are widely detected in aquatic environments and further bioaccumulated and metabolized in fish. Physiologically based toxicokinetic (PBTK) models have been used to describe the absorption, distribution, metabolism, and excretion (ADME) of parent compounds in fish, whereas the metabolites are less explored. In this study, a PBTK incorporating metabolism (PBTK-MT) model for BPA, 4-NP, and TCS was established to enhance the performance of the traditional PBTK model. The PBTK-MT model comprised 16 compartments, showing great accuracy in predicting the internal concentrations of three compounds and their glucuronidated and sulfated conjugates in fish. The impact of typical hepatic metabolism on the PBTK-MT model was successfully resolved by optimizing the mechanism for deriving the partition coefficients between the blood and liver. The PBTK-MT model exhibited a potential data gap-filling capacity for unknown parameters through a backward extrapolation approach of parameters. Model sensitivity analysis suggested that only five parameters were sensitive in at least two PBTK-MT models, while most parameters were insensitive. The PBTK-MT model will contribute to a well understanding of the environmental behavior and risks of pollutants in aquatic biota.