Comparative evaluation of 11 in silico models for the prediction of small molecule mutagenicity: role of steric hindrance and electron-withdrawing groups

Kevin A. Ford, Gregory Ryslik, Bryan K. Chan, Sock Cheng Lewin-Koh, Davi Almeida, Michael Stokes, Stephen R. Gomez

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

The goal of this investigation was to perform a comparative analysis on how accurately 11 routinely-used in silico programs correctly predicted the mutagenicity of test compounds that contained either bulky or electron-withdrawing substituents. To our knowledge this is the first study of its kind in the literature. Such substituents are common in many pharmaceutical agents so there is a significant need for reliable in silico programs to predict precisely whether they truly pose a risk for mutagenicity. The predictions from each program were compared to experimental data derived from the Ames II test, a rapid reverse mutagenicity assay with a high degree of agreement with the traditional Ames assay. Eleven in silico programs were evaluated and compared: Derek for Windows, Derek Nexus, Leadscope Model Applier (LSMA), LSMA featuring the in vitro microbial Escherichia coli–Salmonella typhimurium TA102 A-T Suite (LSMA+), TOPKAT, CAESAR, TEST, ChemSilico (±S9 suites), MC4PC and a novel DNA docking model. The presence of bulky or electron-withdrawing functional groups in the vicinity of a mutagenic toxicophore in the test compounds clearly affected the ability of each in silico model to predict non-mutagenicity correctly. This was because of an over reliance on the part of the programs to provide mutagenicity alerts when a particular toxicophore is present irrespective of the structural environment surrounding the toxicophore. From this investigation it can be concluded that these models provide a high degree of specificity (ranging from 71% to 100%) and are generally conservative in their predictions in terms of sensitivity (ranging from 5% t o 78%). These values are in general agreement with most other comparative studies in the literature. Interestingly, the DNA docking model was the most sensitive model evaluated, suggesting a potentially useful new mode of screening for mutagens. Another important finding was that the combination of a quantitative structure–activity relationship and an expert rules system appeared to offer little advantage in terms of sensitivity, despite of the requirement for such a screening paradigm under the ICH M7 regulatory guideline.

Original languageEnglish (US)
Pages (from-to)24-35
Number of pages12
JournalToxicology Mechanisms and Methods
Volume27
Issue number1
DOIs
StatePublished - Jan 2 2017

All Science Journal Classification (ASJC) codes

  • Toxicology
  • Health, Toxicology and Mutagenesis

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