Literature DB >> 24216263

Development and validation of theoretical linear solvation energy relationship models for toxicity prediction to fathead minnow (Pimephales promelas).

Felichesmi Lyakurwa1, Xianhai Yang, Xuehua Li, Xianliang Qiao, Jingwen Chen.   

Abstract

The acute toxicity predictive models are vitally important for the toxicological information used in the ecological risk assessments. In this study, we used Verhaar classification scheme to group compounds into five modes of toxic action. The quantum chemical descriptors that characterize the electron donor-acceptor property of the compounds were introduced into the theoretical linear solvation energy relationship (TLSER) models. The predictive models have relatively larger data sets, which imply that they cover a wide applicability domain (AD). All models were developed following the Organization for Economic Co-operation and Development (OECD) QSAR models development and validation guidelines. The adjusted determination coefficient (Radj(2)) and external explained variance (QEXT(2)) of the models were ranging from 0.707 to 0.903 and 0.660 to 0.858, respectively, indicating high goodness-of-fit, robustness and predictive capacity. The cavity term (McGowans volume) was the most significant descriptor in the models. Moreover, the electron donor-acceptor (E-TLSER) models are comparable to the TLSER models for the toxicity prediction to fathead minnow. Thus, the E-TLSER models developed can be used to predict acute toxicity of new compounds within the AD.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Applicability domain; Electron donor–acceptor interaction; Fathead minnow; Mode of action; TLSER models

Mesh:

Substances:

Year:  2013        PMID: 24216263     DOI: 10.1016/j.chemosphere.2013.10.039

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  6 in total

1.  QSAR model for predicting the toxicity of organic compounds to fathead minnow.

Authors:  Qingzhu Jia; Yunpeng Zhao; Fangyou Yan; Qiang Wang
Journal:  Environ Sci Pollut Res Int       Date:  2018-10-22       Impact factor: 4.223

2.  In silico prediction of pesticide aquatic toxicity with chemical category approaches.

Authors:  Fuxing Li; Defang Fan; Hao Wang; Hongbin Yang; Weihua Li; Yun Tang; Guixia Liu
Journal:  Toxicol Res (Camb)       Date:  2017-07-31       Impact factor: 3.524

3.  Modeling the toxicity of chemical pesticides in multiple test species using local and global QSTR approaches.

Authors:  Nikita Basant; Shikha Gupta; Kunwar P Singh
Journal:  Toxicol Res (Camb)       Date:  2015-12-10       Impact factor: 3.524

4.  MOA-based linear and nonlinear QSAR models for predicting the toxicity of organic chemicals to Vibrio fischeri.

Authors:  Shengnan Zhang; Ning Wang; Limin Su; Xiaoyan Xu; Chao Li; Weichao Qin; Yuanhui Zhao
Journal:  Environ Sci Pollut Res Int       Date:  2020-01-08       Impact factor: 4.223

5.  New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments.

Authors:  Cosimo Toma; Claudia I Cappelli; Alberto Manganaro; Anna Lombardo; Jürgen Arning; Emilio Benfenati
Journal:  Molecules       Date:  2021-11-19       Impact factor: 4.411

6.  Machine learning-based prediction of toxicity of organic compounds towards fathead minnow.

Authors:  Xingmei Chen; Limin Dang; Hai Yang; Xianwei Huang; Xinliang Yu
Journal:  RSC Adv       Date:  2020-10-01       Impact factor: 4.036

  6 in total

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