New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments
| Autor: | Claudia Ileana Cappelli, Cosimo Toma, Jürgen Arning, Alberto Manganaro, Emilio Benfenati, Anna Lombardo |
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| Rok vydání: | 2021 |
| Předmět: |
Raphidocelis subcapitata
Daphnia magna Pharmaceutical Science Biology Ecotoxicology Models Biological Article Analytical Chemistry Machine Learning QD241-441 Statistical quality Chlorophyceae Drug Discovery Animals Physical and Theoretical Chemistry Trophic level fish Aquatic ecosystem Organic Chemistry Fishes biology.organism_classification applicability domain quantitative structure-activity relationship (QSAR) Daphnia Chemistry (miscellaneous) Aquatic environment Molecular Medicine Fish Biochemical engineering biological databases Water Pollutants Chemical random forest Applicability domain |
| Zdroj: | Molecules Volume 26 Issue 22 Molecules, Vol 26, Iss 6983, p 6983 (2021) |
| ISSN: | 1420-3049 |
| Popis: | To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to avoid or reduce the need for animal models and speed up the process when there are many substances to be tested. We developed predictive models for Raphidocelis subcapitata, Daphnia magna, and fish for acute and chronic toxicities. The random forest machine learning approach gave the best results. The models gave good statistical quality for all endpoints. These models are freely available for use as individual models in the VEGA platform and for prioritization in JANUS software. |
| Databáze: | OpenAIRE |
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