In silico assessment of chemical mutagenesis in comparison with results of Salmonella microsome assay on 909 chemicals.

Genotoxicity is one of the important endpoints for risk assessment of environmental chemicals. Many short-term assays to evaluate genotoxicity have been developed and some of them are being used routinely. Although these assays can generally be completed within a short period, their throughput is not sufficient to assess the huge number of chemicals, which exist in our living environment without information on their safety. We have evaluated three commercially available in silico systems, i.e., DEREK, MultiCASE, and ADMEWorks, to assess chemical genotoxicity. We applied these systems to the 703 chemicals that had been evaluated by the Salmonella/microsome assay from CGX database published by Kirkland et al. We also applied these systems to the 206 existing chemicals in Japan that were recently evaluated using the Salmonella/microsome assay under GLP compliance (ECJ database). Sensitivity (the proportion of the positive in Salmonella/microsome assay correctly identified by the in silico system), specificity (the proportion of the negative in Salmonella/microsome assay correctly identified) and concordance (the proportion of correct identifications of the positive and the negative in Salmonella/microsome assay) were increased when we combined the three in silico systems to make a final decision in mutagenicity, and accordingly we concluded that in silico evaluation could be optimized by combining the evaluations from different systems. We also investigated whether there was any correlation between the Salmonella/microsome assay result and the molecular weight of the chemicals: high molecular weight (>3000) chemicals tended to give negative results. We propose a decision tree to assess chemical genotoxicity using a combination of the three in silico systems after pre-selection according to their molecular weight.

[1]  L. Hall,et al.  Three new consensus QSAR models for the prediction of Ames genotoxicity. , 2004, Mutagenesis.

[2]  Ronald D Snyder,et al.  Evaluation of DNA intercalation potential of pharmaceuticals and other chemicals by cell‐based and three‐dimensional computational approaches , 2004, Environmental and molecular mutagenesis.

[3]  Alan G. E. Wilson,et al.  A multiple in silico program approach for the prediction of mutagenicity from chemical structure. , 2003, Mutation research.

[4]  R. Snyder,et al.  Assessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical molecules , 2004, Environmental and molecular mutagenesis.

[5]  G M Pearl,et al.  Integration of computational analysis as a sentinel tool in toxicological assessments. , 2001, Current topics in medicinal chemistry.

[6]  J. Cummins,et al.  Mutagenic activity of epoxy embedding reagents employed in electron microscopy. , 1979, Environmental mutagenesis.

[7]  John D. Walker,et al.  Use of QSARs in international decision-making frameworks to predict health effects of chemical substances. , 2003, Environmental health perspectives.

[8]  P N Judson,et al.  Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. , 1999, SAR and QSAR in environmental research.

[9]  H S Rosenkranz,et al.  Development, characterization and application of predictive-toxicology models. , 1999, SAR and QSAR in environmental research.

[10]  Neal F. Cariello,et al.  Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity. Deductive Estimate of Risk from Existing Knowledge. Toxicity Prediction by Komputer Assisted Technology. , 2002, Mutagenesis.

[11]  Lutz Müller,et al.  Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens I. Sensitivity, specificity and relative predictivity. , 2005, Mutation research.