Literature DB >> 28970106

Assessment of predictivity of volatile organic compounds carcinogenicity and mutagenicity by freeware in silico models.

Lília Ribeiro Guerra1, Alessandra Mendonça Teles de Souza2, Juliana Alves Côrtes1, Viviane de Oliveira Freitas Lione2, Helena Carla Castro3, Gutemberg Gomes Alves4.   

Abstract

The application of in silico methods is increasing on toxicological risk prediction for human and environmental health. This work aimed to evaluate the performance of three in silico freeware models (OSIRIS v.2.0, LAZAR, and Toxtree) on the prediction of carcinogenicity and mutagenicity of thirty-eight volatile organic compounds (VOC) related to chemical risk assessment for occupational exposure. Theoretical data were compared with assessments available in international databases. Confusion matrices and ROC curves were used to evaluate the sensitivity, specificity, and accuracy of each model. All three models (OSIRIS, LAZAR and Toxtree) were able to identify VOC with a potential carcinogenicity or mutagenicity risk for humans, however presenting differences concerning the specificity, sensitivity, and accuracy. The best predictive performances were found for OSIRIS and LAZAR for carcinogenicity and OSIRIS for mutagenicity, as these softwares presented a combination of negative predictive power and lower risk of false positives (high specificity) for those endpoints. The heterogeneity of results found with different softwares reinforce the importance of using a combination of in silico models to occupational toxicological risk assessment.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Carcinogenicity; Computational toxicology; In silico methods; Mutagenicity; QSAR

Mesh:

Substances:

Year:  2017        PMID: 28970106     DOI: 10.1016/j.yrtph.2017.09.030

Source DB:  PubMed          Journal:  Regul Toxicol Pharmacol        ISSN: 0273-2300            Impact factor:   3.271


  1 in total

1.  Oil sludge washing with surfactants and co-solvents: oil recovery from different types of oil sludges.

Authors:  Diego Ramirez; Liz J Shaw; Chris D Collins
Journal:  Environ Sci Pollut Res Int       Date:  2020-09-25       Impact factor: 5.190

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.