Comparative evaluation of 11 in silico models for the prediction of small molecule mutagenicity: role of steric hindrance and electron-withdrawing groups.
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.
a Safety Assessment , Genentech Inc. , South San Francisco , CA , USA.
Kevin A
KA
Ford
b Nonclinical Biostatistics , Genentech Inc. , South San Francisco , CA , USA.
Gregory
G
Ryslik
c Discovery Chemistry , Genentech Inc. , South San Francisco , CA , USA.
Bryan K
BK
Chan
b Nonclinical Biostatistics , Genentech Inc. , South San Francisco , CA , USA.
Sock-Cheng
SC
Lewin-Koh
a Safety Assessment , Genentech Inc. , South San Francisco , CA , USA.
Davi
D
Almeida
a Safety Assessment , Genentech Inc. , South San Francisco , CA , USA.
Michael
M
Stokes
a Safety Assessment , Genentech Inc. , South San Francisco , CA , USA.
Stephen R
SR
Gomez
D003198:Computer Simulation; D004249:DNA Damage; D004269:DNA, Bacterial / Q000737:chemistry / Q000235:genetics; D004579:Electron Transport; D004926:Escherichia coli / Q000187:drug effects / Q000235:genetics; D008954:Models, Biological; D062105:Molecular Docking Simulation; D015394:Molecular Structure; D009152:Mutagenicity Tests / Q000379:methods; D009153:Mutagens / Q000737:chemistry / Q000633:toxicity*; D011237:Predictive Value of Tests; D012486:Salmonella typhimurium / Q000187:drug effects / Q000235:genetics; D012680:Sensitivity and Specificity; D054852:Small Molecule Libraries / Q000737:chemistry / Q000633:toxicity*; D013329:Structure-Activity Relationship
D004269:DNA, Bacterial; D009153:Mutagens; D054852:Small Molecule Libraries
Ames II test; DNA-docking; QSAR; comparative study; detoxification group; in silico prediction; mutagenicity