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采用定量构效关系(QSAR)和定量构效传递关系(QSTR)方法研究新兴关注污染物(CECs)在日本三角涡虫体内的水生毒性及其与水蚤和鱼类的种间相关性。

Chemometric modeling of aquatic toxicity of contaminants of emerging concern (CECs) in Dugesia japonica and its interspecies correlation with daphnia and fish: QSTR and QSTTR approaches.

机构信息

Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Kolkata 700032, India.

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.

出版信息

Ecotoxicol Environ Saf. 2018 Dec 30;166:92-101. doi: 10.1016/j.ecoenv.2018.09.068. Epub 2018 Sep 22.

Abstract

The contaminants of emerging concern (CEC) are universally detected in surface water and soil. They can affect the wild life, and their subsequent translocation through the food chain can affect human health, which is an issue of serious concern. Very few amounts of ecotoxicological data are available on the environmental behavior and ecotoxicity of CEC, thus modeling approaches are essential to bridge the existing gap in experimental data. In this present study, we have developed quantitative structure-toxicity relationship (QSTR) models using a data set of 75 compounds for the prediction of aquatic ecotoxicity of CECs on fresh water planarian (Dugesia japonica) by partial least squares (PLS) regression algorithm using simple molecular descriptors selected by genetic algorithm approach. We also explore the correlations between toxicity against D. japonica and those against daphnia (D. magna) and fish (P. promelas), and these were improved on addition of a few molecular descriptors (B08[C-O] and B09[N-O] in case of daphnia and C-006 and H-052 in case of fish) which allowed us to develop predictive interspecies quantitative structure toxicity-toxicity relationship (QSTTR) models, allowing to extrapolate data from one endpoint to another endpoint. The QSTR (Q ranging from 0.630 to 0.720 and R ranging from 0.723 to 0.798) and QSTTR (Q = 0.60 and 0.67, R = 0.88 and 0.84) models have desirable statistical qualities and acceptable internal and external validation measures, meeting rigorous criteria of different validation metrics and showing acceptability for regulatory purposes as proposed by Organization for Economic Cooperation and Development (OECD). Consensus predictions were also performed based on multiple models generated in this study by using the "Intelligent Consensus Predictor" (ICP) tool to enhance the prediction quality for external set compounds.

摘要

新兴关注污染物(CEC)普遍存在于地表水和土壤中。它们会影响野生动物,而它们随后通过食物链的转移会影响人类健康,这是一个严重关切的问题。关于 CEC 的环境行为和生态毒性,仅有很少的毒理学数据可用,因此建模方法对于弥合现有实验数据差距至关重要。在本研究中,我们使用 75 种化合物的数据集,通过偏最小二乘(PLS)回归算法,使用遗传算法方法选择简单的分子描述符,开发了定量结构 - 毒性关系(QSTR)模型,用于预测淡水涡虫(Dugesia japonica)对 CEC 的水生生态毒性。我们还探索了对 D. japonica 的毒性与对水蚤(D. magna)和鱼类(P. promelas)的毒性之间的相关性,并在添加少数分子描述符(水蚤情况下的 B08[C-O]和 B09[N-O],鱼类情况下的 C-006 和 H-052)后,这些相关性得到了改善,这使我们能够开发预测种间定量结构毒性 - 毒性关系(QSTTR)模型,从而能够将数据从一个终点外推到另一个终点。QSTR(Q 值范围为 0.630 至 0.720,R 值范围为 0.723 至 0.798)和 QSTTR(Q 值为 0.60 和 0.67,R 值为 0.88 和 0.84)模型具有良好的统计质量和可接受的内部和外部验证措施,符合经济合作与发展组织(OECD)提出的不同验证指标的严格标准,并显示出对监管目的的可接受性。还根据本研究中生成的多个模型,使用“智能共识预测器”(ICP)工具进行共识预测,以提高外部化合物集的预测质量。

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