MicotoXilico

MicotoXilico predicts mutagenicity, genotoxicity, and carcinogenicity of mycotoxins using QSAR models and a curated database of 4,360 mycotoxins across 170 groups to support in silico toxicological assessment.


Key Features:

  • Extensive Mycotoxin Database: MicotoXilico contains a curated collection of 4,360 mycotoxins classified into 170 distinct groups for structure-based analyses.
  • Quantitative Structure-Activity Relationship (QSAR) Models: Employs QSAR models tailored to predict mutagenicity, genotoxicity, and carcinogenicity with reported high accuracy, precision, sensitivity, and specificity.
  • Regulatory Compliance: QSAR models adhere to OECD regulatory criteria enabling use in regulatory contexts.

Scientific Applications:

  • Research: Screening non-regulated mycotoxins for potential toxicological effects to prioritize compounds for experimental follow-up.
  • Industry: Assessing mycotoxin-related risk in food products to inform safety evaluations.
  • Regulatory Agencies: Providing preliminary in silico screening to support regulatory decision-making and monitoring priorities.

Methodology:

Uses a curated mycotoxin database (4,360 compounds, 170 groups) and QSAR modeling validated under OECD criteria to perform in silico predictions of mutagenicity, genotoxicity, and carcinogenicity.

Topics

Details

Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Added:
2/23/2024
Last Updated:
11/24/2024

Operations

Publications

Tolosa J, Serrano Candelas E, Vallés Pardo JL, Goya A, Moncho S, Gozalbes R, Palomino Schätzlein M. MicotoXilico: An Interactive Database to Predict Mutagenicity, Genotoxicity, and Carcinogenicity of Mycotoxins. Toxins. 2023;15(6):355. doi:10.3390/toxins15060355. PMID:37368656. PMCID:PMC10301946.

PMID: 37368656
Funding: - Torres Quevedo MicotoXilico: AEST/2021/077, PID2020-115871RB-I00, PTQ2020-011477 - Ministerio de Ciencia e Innovación of Spain: AEST/2021/077, PID2020-115871RB-I00, PTQ2020-011477 - Conselleria d’Innovació, Universitats, Ciència i Societat Digital: AEST/2021/077, PID2020-115871RB-I00, PTQ2020-011477