Literature DB >> 23702385

Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling.

Stefano Cassani1, Simona Kovarich, Ester Papa, Partha Pratim Roy, Leon van der Wal, Paola Gramatica.   

Abstract

Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD). These models are based on theoretical molecular descriptors, and are statistically robust, externally predictive and characterized by a verifiable structural applicability domain. They have been applied to predict acute toxicity for over 300 (B)TAZs without experimental data, many of which are in the pre-registration list of the REACH regulation. Additionally, a model based on quantitative activity-activity relationships (QAAR) has been developed, which allows for interspecies extrapolation from daphnids to fish. The importance of QSAR/QAAR, especially when dealing with specific chemical classes like (B)TAZs, for screening and prioritization of pollutants under REACH, has been highlighted.
Copyright © 2013 Elsevier B.V. All rights reserved.

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Year:  2013        PMID: 23702385     DOI: 10.1016/j.jhazmat.2013.04.025

Source DB:  PubMed          Journal:  J Hazard Mater        ISSN: 0304-3894            Impact factor:   10.588


  7 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.  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

3.  Supervised extensions of chemography approaches: case studies of chemical liabilities assessment.

Authors:  Svetlana I Ovchinnikova; Arseniy A Bykov; Aslan Yu Tsivadze; Evgeny P Dyachkov; Natalia V Kireeva
Journal:  J Cheminform       Date:  2014-05-07       Impact factor: 5.514

4.  Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error.

Authors:  Lorentz Jäntschi; Donatella Bálint; Sorana D Bolboacă
Journal:  Comput Math Methods Med       Date:  2016-12-07       Impact factor: 2.238

5.  Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure.

Authors:  Liang-Yong Xia; Yu-Wei Wang; De-Yu Meng; Xiao-Jun Yao; Hua Chai; Yong Liang
Journal:  Int J Mol Sci       Date:  2017-12-22       Impact factor: 5.923

6.  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

7.  QSARINS-Chem standalone version: A new platform-independent software to profile chemicals for physico-chemical properties, fate, and toxicity.

Authors:  Nicola Chirico; Alessandro Sangion; Paola Gramatica; Linda Bertato; Ilaria Casartelli; Ester Papa
Journal:  J Comput Chem       Date:  2021-05-11       Impact factor: 3.376

  7 in total

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