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Quantitative structure-activity relationship to predict acute fish toxicity of organic solvents

机译：定量构效关系预测有机溶剂对鱼类的急性毒性

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摘要

Reach regulation requires ecotoxicological data to characterize industrial chemicals. To limit in vivo testing, Quantitative Structure-Activity Relationships (QSARs) are advocated to predict toxicity of a molecule. In this context, the topic of this work was to develop a reliable QSAR explaining the experimental acute toxicity of organic solvents for fish trophic level. Toxicity was expressed as log(LC50), the concentration in mmol.L~(-1) producing the 50% death of fish. The 141 chemically heterogeneous solvents of the dataset were described by physico-chemical descriptors and quantum theoretical parameters calculated via Density Functional Theory. The best subsets of solvent descriptors for LC50 prediction were chosen both through the Kubinyi function associated with Enhanced Replacement Method and a stepwise forward multiple linear regressions. The 4-parameters selected in the model were the octanol-water partition coefficient, LUMO energy, dielectric constant and surface tension. The predictive power and robustness of the QSAR developed were assessed by internal and external validations. Several techniques for training sets selection were evaluated: a random selection, a LC50-based selection, a balanced selection in terms of toxic and non-toxic solvents, a solvent profile-based selection with a space filling technique and a D-optimality onions-based selection. A comparison with fish LC50 predicted by ECOSAR model validated for neutral organics confirmed the interest of the QSAR developed for the prediction of organic solvent aquatic toxicity regardless of the mechanism of toxic action involved.
机译：达到法规需要生态毒理学数据来表征工业化学品。为了限制体内测试，提倡定量结构-活性关系（QSAR）来预测分子的毒性。在这种情况下，这项工作的主题是开发一种可靠的QSAR，解释有机溶剂对鱼类营养级的实验急性毒性。毒性表示为log（LC50），浓度以mmol.L〜（-1）表示鱼死亡50％。通过理化描述符和通过密度泛函理论计算的量子理论参数描述了数据集中的141种化学异质溶剂。通过与增强替代方法相关的Kubinyi函数和逐步正向多元线性回归选择了LC50预测的溶剂描述符的最佳子集。在模型中选择的4个参数是辛醇-水分配系数，LUMO能量，介电常数和表面张力。通过内部和外部验证评估了开发的QSAR的预测能力和鲁棒性。评估了几种用于训练集选择的技术：随机选择，基于LC50的选择，有毒和无毒溶剂方面的平衡选择，带有空间填充技术的基于溶剂特性的选择以及D-最佳洋葱-基于选择。与通过ECOSAR模型预测的鱼类LC50进行的中性有机物验证相比较，证实了开发的QSAR对预测有机溶剂水生毒性的兴趣，无论涉及的毒性作用机理如何。

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  • 来源
    《Chemosphere》 |2013年第6期|1094-1103|共10页
  • 作者

    A. Levet; C. Bordes; Y. Clement; P. Mignon; H. Chermette; P. Marote; C. Cren-Olive; P. Lanteri;

  • 作者单位

    Universite de Lyon, F-69622 Villeurbanne, France, Universite Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR CNRS 5280, F-69622 Villeurbanne, France;

    Universite de Lyon, F-69622 Villeurbanne, France, Universite Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR CNRS 5280, F-69622 Villeurbanne, France;

    Universite de Lyon, F-69622 Villeurbanne, France, Universite Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR CNRS 5280, F-69622 Villeurbanne, France;

    Universite de Lyon, F-69622 Villeurbanne, France, Universite Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR CNRS 5280, F-69622 Villeurbanne, France;

    Universite de Lyon, F-69622 Villeurbanne, France, Universite Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR CNRS 5280, F-69622 Villeurbanne, France;

    Universite de Lyon, F-69622 Villeurbanne, France, Universite Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR CNRS 5280, F-69622 Villeurbanne, France;

    Universite de Lyon, F-69622 Villeurbanne, France, Universite Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR CNRS 5280, F-69622 Villeurbanne, France;

    Universite de Lyon, F-69622 Villeurbanne, France, Universite Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR CNRS 5280, F-69622 Villeurbanne, France;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Ecotoxicity; QSAR; Organic solvents; Fish LC50; DFT; ECOSAR;

    机译：生态毒性;QSAR;有机溶剂;鱼LC50;DFT;CO;

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