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Toxicity Predictions for Mycotoxins: A Combined In Silico Approach on Enniatin-Like Cluster

Abstract : Due to climate change, mycotoxins are expected to become a specific concern worldwide. In the future, predicted changes in environmental conditions will affect the growth of crops and may favor the development of fungi and, therefore, the presence of mycotoxins. In addition to direct human oral exposure to mycotoxins through cereal food products, potential human exposure may also occur as a result of crop contamination with mycotoxins via animal feed and consumption of meat or milk products. Fungi can produce numerous compounds, many of which have not yet been characterized, including in terms of their toxicological potency. A large number of mycotoxins and their metabolites have not been evaluated for their toxicity so far. In this study, an innovative combined strategy based on several validated in silico tools was used to assess specific toxicity endpoints. From a list of 552 mycotoxins, 12 mycotoxins were clustered together based on physico-chemical parameters. On this specific cluster, firstly quantitative structure–activity relationship (QSAR) tools were used to assess the mutagenic and carcinogenic potential of each compound. From this analysis, 12 mycotoxins were found to have a potential activity in cancer promotion. The link between these compounds and cancer activity was further investigated by two complementary approaches: identification of gene pathways involved in the toxic response and a datamining search. Altogether, the results point to a potential association between these mycotoxins and lung cancer.
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https://hal-anses.archives-ouvertes.fr/anses-03707090
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Submitted on : Tuesday, June 28, 2022 - 11:39:32 AM
Last modification on : Wednesday, June 29, 2022 - 3:30:17 AM

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Denis Habauzit, Pierre Lemée, Luis Botana, Valérie Fessard. Toxicity Predictions for Mycotoxins: A Combined In Silico Approach on Enniatin-Like Cluster. Exposure and Health, 2022, ⟨10.1007/s12403-022-00492-2⟩. ⟨anses-03707090⟩

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