Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches

Silva, Luana de Morais e; Alves, Mateus Feitosa; Scotti, Luciana; Lopes, Wilton Silva; Scotti, Marcus Tullius

doi:10.1016/j.ecoenv.2018.01.054

Cited by 14 publications

(1 citation statement)

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“…The application of such methods is a useful alternative to deal with large quantities of data for assessing the substance properties, evaluate their fate‐determining processes, and predict their effects, reducing expensive and time‐consuming experiments and showing a strong potential to reduce the use of animals in experiments . In a decision‐making context, comprehensive protection of public health and the environment with the use of computational predictive methods supports on‐going efforts to develop information in order to fill gaps in the (eco)toxicity data and is needed for improving the understanding of assessing the hazards of chemicals as required by various government agencies across the globe, including the US EPA and by European Legislation on Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) …”

Section: Introductionmentioning

confidence: 99%

Predictive Computational Tools for Assessment of Ecotoxicological Activity of Organic Micropollutants in Various Water Sources in Brazil

Silva

Lorenzo

Lopes

et al. 2019

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement.Abstract: The increase of chemical pollutants detected in different aquatic environments over the past few years has been in the focus of several studies related to their occurrence, transport, fate, and hazards, or risks to human and environmental health. In Brazil, recent studies have been conducted on the occurrence of a series of organic micropollutants (OMPs) in aquatic environments. Nevertheless, the toxicological information and environmental behavior for most of these pollutants are still difficult to evaluate. We obtained consensus models using a combina-tion of VolSurf and Dragon descriptors and Random Forest approach to predict the toxicity against Daphnia magna and Pimephales promelas for compounds identified in different sources of water in Brazil. The models showed satisfactory performance when compared with other approaches, such as Toxicity Estimation Software Tool (T.E.S.T.), etc. Both models can be used as complementary tools to aid in the prediction of organic compounds against both organisms.

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Section: Introductionmentioning

confidence: 99%

Predictive Computational Tools for Assessment of Ecotoxicological Activity of Organic Micropollutants in Various Water Sources in Brazil

Silva

Lorenzo

Lopes

et al. 2019

Molecular Informatics

Self Cite

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Ecotoxicological Modeling, Ranking and Prioritization of Pharmaceuticals Using QSTR and i‐QSTTR Approaches: Application of 2D and Fragment Based Descriptors

Khan

Kar

Sanderson

et al. 2018

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement.Abstract: There is a huge lack of experimental data on ecotoxicity of pharmaceuticals, while existing resources are insufficient to gather these data against all possible environmental endpoints. Computational tools such as quantitative structure-toxicity relationship (QSTR) can help us to a great extent to overcome this problem through filling of data gaps. In the current study, QSTR models have been developed for toxicity of 260 diverse pharmaceuticals on three different trophic level species namely algae, daphnia and fish, using partial least squares (PLS) regression approach with 2D descriptors selected through a genetic algorithm approach in order to study underlying chemical features responsible for the observed acute toxicity. The final obtained statistically reliable QSTR models were extensively validated following the OECD guidelines. Interspecies quantitative structure-toxicity-toxicity (QSTTR) models were also developed using genetic algorithm followed by multiple linear regression (GA-MLR) approach to check for the pattern of responses observed as we move across the hierarchy of genetics in different taxonomical class. The obtained interspecies models were finally utilized to fill the data gaps for 260 pharmaceuticals, where experimental data were missing for at least one of the endpoints. Finally, a prioritized list for 7106 existing drug like substances was prepared by predicting their acute toxicity using developed QSTR models.

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