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In an attempt to describe the underlying causes of mutagenicity mainly due to organic chemicals, quantitative structure-activity relationship (QSAR) models have been developed using two different Salmonella typhimurium mutagenicity endpoints with or without presence of liver metabolic microsomal enzymes (S9) namely TA98-S9 and TA98 + S9. The models were developed using simple 2D variables having definite physicochemical meaning calculated from Dragon, SiRMS, and PaDEL-descriptor software tools. Stepwise regression followed by partial least squares (PLS) regression was used in model development following the strict OECD guidelines for QSAR model development and validation. The models were validated using coefficient of determination R2, cross-validation coefficient Q2LOO (leave one out) while the test set predictions were analyzed using Q2F1 (coefficient of determination for the test set). Several other internationally accepted validation metrics like MAE95%train, average rm(LOO)2 and Δrm(LOO)2 (for the training set) were used to check model robustness while predictive efficiency was evaluated using MAE95%test, average rm2 and Δrm2 (for the test set). The scope of predictions was defined by applicability domain analysis using the DModX approach, a recommended tool for PLS models. The major contributing features related to mutagenicity include lipophilicity, electronegativity, branching and unsaturation, etc. The present manuscript is the first attempt to undertake modeling of two different endpoints (TA98-S9 and TA98 + S9) in order to explore major contributing molecular features linked directly or indirectly to mutagenicity. Copyright © 2020 Elsevier Ltd. All rights reserved.

Citation

Gopala Krishna Jillella, Kabiruddin Khan, Kunal Roy. Application of QSARs in identification of mutagenicity mechanisms of nitro and amino aromatic compounds against Salmonella typhimurium species. Toxicology in vitro : an international journal published in association with BIBRA. 2020 Jun;65:104768

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PMID: 31926304

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