超低氯苯预测性风险评估的工作流程:应用于复杂环烷酸金属混合物的化学信息学库设计、QSAR 和读数交叉方法。

IF 3.6 Q2 TOXICOLOGY Frontiers in toxicology Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI:10.3389/ftox.2024.1452838
A J Prussia, C Welsh, T S Somers, P Ruiz
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引用次数: 0

摘要

环境中常见成分不明或可变的物质、复杂的反应产物和生物材料(UVCBs)。然而,由于其成分多变、成分繁多,评估其对人体的毒理风险具有挑战性。金属环烷酸盐就是这样一类超低纯生物质,是环烷酸与金属反应生成的复杂混合物。金属环烷酸盐经常出现或用于家居和工业材料中,有可能与人体接触,但很少有人评估过这些材料是否会对人体健康造成危害。在此,我们利用读取交叉和定量结构-活性/性质关系(QSAR/QSPR)模型得出的预测结果对金属环烷酸盐进行评估。因此,我们首先通过列举环烷酸的结构建立了一个计算化学库,并得出了 11,850 个可接受的 QSAR 结构;然后,我们在这些结构上使用开放式和商业化的硅学工具来预测一系列理化性质和毒性终点。然后,我们将 QSAR/QSPR 预测结果与现有的环烷酸实验数据进行比较,以便更全面地了解各成分对金属环烷酸混合物毒性特征的影响。所有环烷酸成分的现有系统急性口服毒性值(LD50)和 QSAR LD50 预测值都表明毒性问题不大。根据对大鼠进行的研究,使用 QSAR 模型对环烷酸成分的慢性重复剂量毒性进行的起点预测介于 25 至 50 毫克/千克/天之间。这些数值与环烷酸铜和环烷酸锌的研究结果非常一致,这两种物质的不良影响水平分别为 30 毫克/千克/天和 118 毫克/千克/天。因此,本研究展示了如何利用已公布的硅学方法来确定金属环烷酸盐的潜在成分,以便进行进一步测试,为环烷酸盐等超低氯苯类物质的分组提供信息,以及利用读数交叉和 QSAR 模型填补数据空白,为风险评估提供信息。
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Workflow for predictive risk assessments of UVCBs: cheminformatics library design, QSAR, and read-across approaches applied to complex mixtures of metal naphthenates.

Substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs) are commonly found in the environment. However, assessing their human toxicological risk is challenging due to their variable composition and many constituents. Metal naphthenate salts are one such category of UVCBs that are the reaction products of naphthenic acids with metals to form complex mixtures. Metal naphthenates are often found or used in household and industrial materials with potential for human exposure, but very few of these materials have been evaluated for causing human health hazards. Herein, we evaluate metal naphthenates using predictions derived from read-across and quantitative structure-activity/property relationship (QSAR/QSPR) models. Accordingly, we first built a computational chemistry library by enumerating the structures of naphthenic acids and derived 11,850 QSAR-acceptable structures; then, we used open and commercial in silico tools on these structures to predict a set of physicochemical properties and toxicity endpoints. We then compared the QSAR/QSPR predictions with available experimental data on naphthenic acids to provide a more complete picture of the contributions of the components to the toxicity profiles of metal naphthenate mixtures. The available systematic acute oral toxicity values (LD50) and QSAR LD50 predictions of all the naphthenic acid components indicated low concern for toxicity. The point of departure predictions for chronic repeated dose toxicity for the naphthenic acid components using QSAR models developed from studies on rats ranged from 25 to 50 mg/kg/day. These values are in good agreement with findings from studies on copper and zinc naphthenates, which had no observed adverse effect levels of 30 and 118 mg/kg/day, respectively. Hence, this study demonstrates how published in silico approaches can be used to identify the potential components of metal naphthenates for further testing, inform groupings of UVCBs such as naphthenates, as well as fill the data gaps using read-across and QSAR models to inform risk assessment.

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