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Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches.
- Source :
- Ecotoxicology & Environmental Safety; May2018, Vol. 153, p151-159, 9p
- Publication Year :
- 2018
-
Abstract
- Persistent organic products are compounds used for various purposes, such as personal care products, surfactants, colorants, industrial additives, food, pesticides and pharmaceuticals. These substances are constantly introduced into the environment and many of these pollutants are difficult to degrade. Toxic compounds classified as MoA 1 (Mode of Action 1) are low toxicity compounds that comprise nonreactive chemicals. In silico methods such as Quantitative Structure–Activity Relationships (QSARs) have been used to develop important models for prediction in several areas of science, as well as aquatic toxicity studies. The aim of the present study was to build a QSAR model-based set of theoretical Volsurf molecular descriptors using the fish acute toxicity values of compounds defined as MoA 1 to identify the molecular properties related to this mechanism. The selected Partial Least Squares (PLS) results based on the values of cross-validation coefficients of determination ( Q cv 2 ) show the following values: Q cv 2 = 0.793, coefficient of determination ( R 2 ) = 0.823, explained variance in external prediction ( Q ext 2 ) = 0.87. From the selected descriptors, not only the hydrophobicity is related to the toxicity as already mentioned in previously published studies but other physicochemical properties combined contribute to the activity of these compounds. The symmetric distribution of the hydrophobic moieties in the structure of the compounds as well as the shape, as branched chains, are important features that are related to the toxicity. This information from the model can be useful in predicting so as to minimize the toxicity of organic compounds. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01476513
- Volume :
- 153
- Database :
- Supplemental Index
- Journal :
- Ecotoxicology & Environmental Safety
- Publication Type :
- Academic Journal
- Accession number :
- 128128446
- Full Text :
- https://doi.org/10.1016/j.ecoenv.2018.01.054