Using In Silico Approach for Metabolomic and Toxicity Prediction of Alternariol

• Published in: Toxins Vol. 15; no. 7; p. 421
• Main Authors: Marin, Daniela Eliza, Taranu, Ionelia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.06.2023; MDPI
• Subjects: Alternaria - metabolism; alternariol; Biocompatibility; Biomedical materials; Blood-brain barrier; Carcinogens; Cereals; Computational neuroscience; Cytotoxicity; Diarrhea; DNA methylation; Evaluation; Feeds; Fruits; Glycoproteins; Hydrogen bonds; In vivo methods and tests; Intestinal absorption; Isoforms; Lactones - metabolism; Metabolism; Metabolites; Metabolomics; Methods; Mycotoxins; Mycotoxins - metabolism; Oilseeds; Oxidative Stress; Permeability; Pharmacokinetics; Predictions; Protein kinase C; Software; Substrates; Toxicity; Romania; toxicity; alternariol; metabolism
• ISSN: 2072-6651; 2072-6651
• DOI: 10.3390/toxins15070421

Alternariol is a metabolite produced by fungus that can contaminate a variety of food and feed materials. The objective of the present paper was to provide a prediction of Phase I and II metabolites of alternariol and a detailed ADME/Tox profile for alternariol and its metabolites using an in silico working model based on the MetaTox, SwissADME, pKCMS, and PASS online computational programs. A number of 12 metabolites were identified as corresponding to the metabolomic profile of alternariol. ADME profile for AOH and predicted metabolites indicated a moderate or high intestinal absorption probability but a low probability to penetrate the blood-brain barrier. In addition to cytotoxic, mutagenic, carcinogenic, and endocrine disruptor effects, the computational model has predicted other toxicological endpoints for the analyzed compounds, such as vascular toxicity, haemato-toxicity, diarrhea, and nephrotoxicity. AOH and its metabolites have been predicted to act as a substrate for different isoforms of phase I and II drug-metabolizing enzymes and to interact with the response to oxidative stress. In conclusion, in silico methods can represent a viable alternative to in vitro and in vivo tests for the prediction of mycotoxins metabolism and toxicity.