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基于结构的预测器评估发育和生殖毒性潜能的价值和局限性。

Value and limitation of structure-based profilers to characterize developmental and reproductive toxicity potential.

机构信息

Chemical Food Safety Group, Nestlé Research, Lausanne, Switzerland.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche "Mario Negri"-IRCCS, Milan, Italy.

出版信息

Arch Toxicol. 2020 Mar;94(3):939-954. doi: 10.1007/s00204-020-02671-z. Epub 2020 Feb 25.

DOI:10.1007/s00204-020-02671-z
PMID:32100055
Abstract

The uncertainty regarding the safety of chemicals leaching from food packaging triggers attention. In silico models provide solutions for screening of these chemicals, since many are toxicologically uncharacterized. For hazard assessment, information on developmental and reproductive toxicity (DART) is needed. The possibility to apply in silico toxicology to identify and quantify DART alerts was investigated. Open-source models and profilers were applied to 195 packaging chemicals and analogues. An approach based on DART and estrogen receptor (ER) binding profilers and molecular docking was able to identify all except for one chemical with documented DART properties. Twenty percent of the chemicals in the database known to be negative in experimental studies were classified as positive. The scheme was then applied to 121 untested chemicals. Alerts were identified for sixteen of them, five being packaging substances, the others structural analogues. Read-across was then developed to translate alerts into quantitative toxicological values. They can be used to calculate margins of exposure (MoE), the size of which reflects safety concern. The application of this approach appears valuable for hazard characterization of toxicologically untested packaging migrants. It is an alternative to the use of default uncertainty factor (UF) applied to animal chronic toxicity value to handle absence of DART data in hazard characterization.

摘要

食品包装中化学物质浸出的安全性存在不确定性,引起了人们的关注。由于许多化学物质的毒理学特性尚未得到充分研究,因此需要使用计算毒理学模型来筛选这些化学物质。对于危害评估,需要了解发育和生殖毒性 (DART) 的信息。本文研究了应用计算毒理学来识别和量化 DART 警报的可能性。本研究使用开源模型和分析器对 195 种包装化学物质及其类似物进行了分析。基于 DART 和雌激素受体 (ER) 结合分析器和分子对接的方法,能够识别出具有 DART 特性的所有化学物质,但有一个除外。数据库中已知具有实验研究阴性结果的 20%的化学物质被归类为阳性。然后,该方案被应用于 121 种未经测试的化学物质。其中 16 种化学物质发出了警报,其中 5 种是包装物质,其余为结构类似物。随后进行了读码分析,将警报转化为定量毒理学值。可以使用这些值来计算暴露量比 (MoE),其大小反映了安全问题。该方法的应用对于毒理学未测试的包装迁移物的危害特征描述具有重要意义。它是在危害特征描述中处理缺乏 DART 数据时应用动物慢性毒性值的默认不确定性因子 (UF) 的替代方法。

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The rapid development of computational toxicology.计算毒理学的快速发展。
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