Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_m97+1537). 2020.

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Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_m97+1537). 2020.

QDB archive DOI: 10.15152/QDB.249   DOWNLOAD

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  • Lawless, M. Data for: ADMET Predictor - Bacterial mutagenicity model (MUT_m97+1537). QsarDB repository, QDB.249. 2021. http://dx.doi.org/10.15152/QDB.249

  • Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_m97+1537). 2020.


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Title: Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_m97+1537). 2020.
Abstract:MUT_m97+1537 The Ames bacterial mutagenicity test is an important regulatory screen for potential carcinogenicity. In silico prediction of Ames positivity plays two important but somewhat distinct roles in the discovery and development of biologically active compounds. A positive Ames test does not necessarily derail development of an active ingredient (AI), because some bacterial mutagens are not carcinogenic. As a practical matter, however, demonstrating non-carcinogenicity is challenging enough that having a robust predictive model for Ames mutagenicity is a very cost-effective alternative to automatically testing every active compound, especially since the model can be used to help decide which particular compounds to synthesize or purchase. In addition, regulatory guidance now allows the use of in silico predictions of mutagenicity to help justify waiving Ames testing requirements for impurities; ADMET Predictor®’s MUT_Risk qualifies as a “statistical model” under ICH M7. MUT_m97+1537 is one of the 11 models that provide input to MUT_Risk. It is an artificial neural network ensemble (ANNE) classification model built on literature data for 308 positive examples and 1902 negative examples from Ames tests run using Salmonella typhimurium strain TA97 or TA1537 with metabolic activation by S9 cell fractions. Of those, 64 and 378, respectively, were set aside as an external test set for which the sensitivity was 0.813 and the specificity was 0.902 with an overall concordance of 0.889. The corresponding training set performance statistics were 0.840, 0.917, and 0.907, respectively. Predictions for any compounds for which any descriptor falls more than 10% outside the range of that descriptor seen in the training data are flagged as out-of-scope, and statistically rigorous confidences (doi 10.1186/1758-2946-6-34) are provided for all in-scope predictions.

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