评估含咪唑和吡啶离子液体毒性的新 QSTR 模型

IF 3.1 Q2 TOXICOLOGY
Ivan Semenyuta, Vasyl Kovalishyn, Diana Hodyna, Yuliia Startseva, Sergiy Rogalsky, Larysa Metelytsia
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引用次数: 0

摘要

我们介绍了专门用于创建含咪唑和吡啶离子液体毒性评估预测模型的机器学习研究。我们使用 OCHEM 开发了新的预测模型。通过交叉验证测试了模型的预测能力,结果表明决定系数 q2 = 0.77-0.82。这些模型被应用于筛选虚拟化学库,以确定惰性惰性物质在真鲷和大型蚤生物测定中的毒性。利用模型预测了 25 种 IL 的毒性,然后合成了这些 IL 并进行了体内测试。体内毒性研究发现,大型蚤是一种比红腹锦蛇更敏感的水生试验生物--67%的所研究的ILs被归类为毒性极强,半数致死浓度范围为0.005至0.01毫克/升。同时,只有一种 LC50 值为 0.08 mg/l 的 1-dodecylpyridinium bromide 被归类为剧毒,而以 D. rerio 为测试生物的 76% 被归类为轻微和中等毒性化合物。将毒性最强的 ILs 5 和 19 与人类 AChE 活性中心对接,计算出的结合能值分别为-9.5 和-9.3 kcal/mol,与人类 AChE 抑制剂多奈哌齐的络合能值相当,这有助于深入了解 ILs 毒性的潜在分子机制。所创建的 QSTR 模型是一种成功的工具,可用于分析有潜力的新型 ILs 的毒性。QSTR 模型不仅具有很高的预测指标,而且在体内研究中正确预测毒性值的比例也很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

New QSTR models to evaluation of imidazolium- and pyridinium-contained ionic liquids toxicity

New QSTR models to evaluation of imidazolium- and pyridinium-contained ionic liquids toxicity

We present machine learning studies devoted to the creation of predictive models for toxicity evaluation of imidazolium- and pyridinium-containing ionic liquids. New created predictive models were developed using the OCHEM. The predictive ability of the models was tested by cross-validation, giving a coefficient of determination q2 = 0.77–0.82. The models were applied to screen a virtual chemical library to the toxicity of ILs in Danio rerio and Daphnia magna bioassays. Models were used to predict toxicity for 25 ILs, which were then synthesized and tested in vivo. The in vivo toxicity studies found that D. magna is a more sensitive aquatic test organism than D. rerio – 67 % of the studied ILs are classified as extremely toxic with an LC50 range from 0.005 to 0.01 mg/l. At the same time, only one IL 1-dodecylpyridinium bromide with an LC50 of 0.08 mg/l is classified as extremely toxic, and 76 % are classified as slightly and moderately toxic compounds using D. rerio as a test organism. The most toxic ILs 5 and 19 were docked into the human AChE active center and demonstrated calculated binding energy values −9.5 and −9.3 kcal/mol that is comparable with the complexation of the human AChE inhibitor Donepezil, which provides insight into the potential molecular mechanisms of ILs toxicity. The created QSTR models are a successful tool for the toxicity analysis of new promising ILs. QSTR models demonstrated not only high predictive indicators but also a high percentage of correctly predicted toxicity values in vivo studies.

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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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