预测大鼠口服吡虫啉和多菌灵后体内和体外剂量水平的 PBK 模型

IF 3.1 Q2 TOXICOLOGY
Bohan Hu, Hans J.H.J. van den Berg, Ivonne M.C.M. Rietjens, Nico W. van den Brink
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引用次数: 0

摘要

监测野生动物口服农药的情况对于评估环境风险和防止对非目标物种造成不利影响至关重要。传统上,这需要进行侵入性组织采样,从而引发伦理、监管和经济方面的问题。为了弥补这一不足,我们的研究旨在根据吡虫啉和多菌灵这两种农药及其一种主要代谢物的血药浓度水平,利用基于生理学的动力学(PBK)模型,开发一种评估大鼠外部口服剂量水平的方法。我们利用肝微粒体和 S9 培养的体外代谢动力学数据为模型提供信息。然后,我们将这些模型的预测结果与现有文献中的体内实验数据进行了比较,从而对这些模型进行了评估。我们的结果表明,这些模型提供了准确的预测,为农药的环境暴露和风险评估提供了一种新颖的体外和硅学方法。这种方法有可能应用于野生动物物种,从而推进非侵入性农药暴露评估的知识前沿。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PBK models to predict internal and external dose levels following oral exposure of rats to imidacloprid and carbendazim

Monitoring oral exposure to pesticides in wildlife is crucial for assessing environmental risks and preventing adverse effects on non-target species. Traditionally, this requires invasive tissue sampling, raising ethical, regulatory, and economic concerns. To address this gap, our study aims to develop a method for assessing external oral dose levels in rats using physiologically-based kinetic (PBK) modeling based on blood concentration levels of two pesticides, imidacloprid and carbendazim, and one of their primary metabolites. We utilized in vitro metabolic kinetic data from hepatic microsomal and S9 incubations to inform our models. These models were then evaluated by comparing their predictions with existing in vivo experimental data from the literature. Our results demonstrate that the models provide accurate predictions, presenting a novel in vitro and in silico approach for environmental exposure and risk assessment of pesticides. This methodology has the potential for application in wildlife species, advancing the frontier of knowledge in non-invasive pesticide exposure assessment.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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