Acute aquatic toxicity of organic solvents modeled by QSARs

• Published in: Journal of molecular modeling 2016-12, Vol.22 (12), p.288-14, Article 288
• Main Authors: Levet, A., Bordes, C., Clément, Y., Mignon, P., Morell, C., Chermette, H., Marote, P., Lantéri, P.
• Format: Article
• Language: English
• Subjects: Algae; Algorithms; Amines; Analytical chemistry; Animals; Biochemistry; Biochemistry, Molecular Biology; Biocompatibility; Characterization and Evaluation of Materials; Chemical Sciences; Cheminformatics; Chemistry; Chemistry and Materials Science; Computer Appl. in Life Sciences; Computer Applications in Chemistry; Datasets as Topic; Density functional theory; Festschrift in Honor of Henry Chermette; Invertebrates; Least squares method; Life Sciences; Models, Theoretical; Molecular biology; Molecular Medicine; Organic Chemicals - chemistry; Organic Chemicals - toxicity; Original Paper; Performance prediction; Quantitative Structure-Activity Relationship; Solvents; Solvents - chemistry; Solvents - toxicity; Surface tension; Theoretical and Computational Chemistry; Toxicity; Water Pollutants, Chemical
• ISSN: 1610-2940; 0948-5023
• DOI: 10.1007/s00894-016-3156-0

To limit in vivo experiments, the use of quantitative structure-activity relationships (QSARs) is advocated by REACH regulation to predict the required fish, invertebrate, and algae EC50 for chemical registration. The aim of this work was to develop reliable QSARs in order to model both invertebrate and algae EC50 for organic solvents, regardless of the mechanism of toxic action involved. EC50 represents the concentration producing the 50 % immobilization of invertebrates or the 50 % growth inhibition of algae. The dataset was composed of 122 organic solvents chemically heterogeneous which were characterized by their invertebrate and/or algae EC50. These solvents were described by physico-chemical descriptors and quantum theoretical parameters calculated via density functional theory. QSAR models were developed by multiple linear regression using the ordinary least squares method and descriptor selection was performed by the Kubinyi function. Invertebrate EC50 was well-described with LogP, dielectric constant, surface tension, and minimal atomic Mulliken charges while algae EC50 of organic solvents (except amines) was predicted with LogP and LUMO energy. To evaluate robustness and predictive performance of the QSARs developed, several strategies have been used to select solvent training sets (random, EC50-based selection and a space-filling design) and both internal and external validations were performed.