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Prioritization of mycotoxins based on mutagenicity and carcinogenicity evaluation using combined in silico QSAR methods
- Source :
- Environmental Pollution, Environmental Pollution, 2023, 323, pp.121284. ⟨10.1016/j.envpol.2023.121284⟩
- Publication Year :
- 2023
- Publisher :
- Elsevier BV, 2023.
-
Abstract
- International audience; Mycotoxins and their metabolites are a family of compounds that contains a great diversity of both structure and biological properties. Information on their toxicity is spread within several databases and in scientific literature. Due to the number of molecules and their structure diversity, the cost and time required for hazard evaluation of each compound is unrealistic. In that purpose, new approach methodologies (NAMs) can be applied to evaluate such large set of molecules. Among them, quantitative structure-activity relationship (QSAR) in silico models could be useful to predict the mutagenic and carcinogenic properties of mycotoxins. First, a complete list of 904 mycotoxins and metabolites was built. Then, some known mycotoxins were used to determine the best QSAR tools for mutagenicity and carcinogenicity predictions. The best tool was further applied to the whole list of 904 mycotoxins. At the end, 95 mycotoxins were identified as both mutagen and carcinogen and should be prioritized for further evaluation.
- Subjects :
- Génotoxicité
Health, Toxicology and Mutagenesis
metabolite
MESH: Mutagenicity Tests
Toxicology
MESH: Carcinogens
MESH: Computer Simulation
métabolite
MESH: Mutagens
carcinogenicity
mutagénicité
prédiction
Toxicity prediction
database
base de données
Mycotoxin
MESH: Quantitative Structure-Activity Relationship
QSAR
mutagenicity
toxicologie
cancérogénicité
General Medicine
MESH: Carcinogenesis
Pollution
NAMs
NAM
[SDV.TOX]Life Sciences [q-bio]/Toxicology
Genotoxicity
Mycotoxine
Subjects
Details
- ISSN :
- 02697491 and 18736424
- Volume :
- 323
- Database :
- OpenAIRE
- Journal :
- Environmental Pollution
- Accession number :
- edsair.doi.dedup.....7438940b31e9934c3cfb87162b30d533