Summary
In order to evaluate human carcinogenic risks, genotoxicity data such as animal cancer bioassay are often not available. In this study, to assess the relevance of indicator of carcinogenic risks, we used the “molecular diversity approach” to estimate the genotoxicity based upon Salmonella genotoxicity test using the umu test and systemic toxicity data of the 82 environmental chemicals predicted by neural network simulation. The 82 environmental chemicals were randomly selected for this study according to the production and usage in Japan. Even in this challenging trial for QSTR (Quantitative Structure Toxicity Relationship) study, approaches using artificial neural networks can account for about 94% of the variation in the genotoxicity results derived by the umu-test.
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Shoji, R., Sakoda, A., Sakai, Y. and Suzuki, M., Hazard assessment of micropollutants and endocrine disruptors – preservation of human cell bioassay device for the on-site evaluation of environmental waters, Wat. Sci. Technol., 46 (2002) 355–362.
Shoji, R., Current computer-aided drug design, The potential performance of artificial neural networks in QSTRs for predicting ecotoxicity of environmental pollutants, 2 (2004) 563–569.
Kawakami, M., Nanri, T. and Shoji, R.J., Estimation of cytotoxicity of organic chemicals by quantitative structure activity relationships (QSARs) based on a neural network adaptation, Ecotechnol. Res., 10 (2004) 119–124.
Shoji, R., Miyazaki, T. and Nishimiya, T., Estimation of cytotoxicity to HEP-G2 cells of 255 kinds of environmental pollutants and landfill leachate using QSAR (Quantitative structure activity relationship), J. Environ. Sci. Health, A38 (2003) 2807–2823.
Enslein, K. and Gombar, U.K., TOPKAT 5.0 and modulation of toxicity, Mut. Res., 379 (1997) 514–519.
Klopman, G., Stefan L.R. and Saiakhov, R.D., ADME evaluation 2: A computer model for the prediction of intestinal absorption in humans, Euro. J. Pharm. Sci., 17 (2002) 253–258.
Lewis, D.F.V., On the recognition of mammalian microsomal cytochrome P450 substrates and their characteristics: Towards the prediction of human p450 substrate specificity and metabolism, Biochem. Pharmacol., 60 (2000) 293–296.
Moriguchi, I., Hirano, H. and Hirano, S., Prediction of the rodent carcinogenicity of organic compounds from their chemical structures using the FALS method, Environ. Health Perspect., 104 (1996) 1051–1060.
Kaiser, K.L.E., Dearden, J.C., Klein, W. and Schultz, T.W., Note of caution to users of ECOSAR, Water Qual. Res. J. Can., 34 (1999) 1–6.
Little, J.W. and Mount, D.W., The SOS regulatory system of Escherichia coli, Cell, 29 (1982) 11–22.
Oda, Y., Nakamura, S., Ise, T., Makino, K. and Nakata, A., Evaluation of the new system (umu-test) for the detection of environmental mutagens and carcinogens, Mut. Res., 23 (1985) 219–229.
Reifferscheid, G., Heil, J., Oda, Y. and Zahn, R. K., A microplatge version of the SOS/umu-test for rapid detection of genotoxins and genotoxic potentials of environmental samples, Mutat. Res., 253 (1991) 215–222.
Justus, T. and Thomas, S.M., Construction of a umuC'-luxAB plasmid for the detection of mutagenic DNA repair via luminescence, Mutat. Res., 398 (1998) 131–141.
Cupples, C.G., Miller, J.H. and Huber, R.E.J., Determination of the roles of Glu-461 in β-galactosidase (Escherichia coli) using site-specific mutagenesis, Biol. Chem., 265 (1990) 5512–5518.
Oda, Y., Yamazaki, H., Watanabe, M., Nohmi, T. and Shimada, T., Highly sensitive umu test system fot the detection of mutagenic nitroarenes in Salmonella typhimurium NM3009 having high O-acetyltransferase and nitroreductase activities, Environ. Mol. Mutagen., 21 (1993) 297–302.
Turner, J.V., Maddalena, D.J. and Cutler, D.J., Pharmacokinetic parameter prediction from drug structure using artificial neural networks, Int. J. Pharmaceutics, 36 (2004) 17.
Hawkins, D.M., Basak, S.C. and Mills, D., QSARs for chemical mutagens from structure: ridge regression fitting and diagnostics, Environ. Toxicol. Pharmacol., 16 (2004) 37–44.
Garg, A., Bhat, K.L. and Bock, C.W., Mutagenicity of aminoazobenzene dyes and related structures: A QSAR/QPAR investigation, Dyes Pigments, 55 (2002) 35–52.
Gonzalez-Mancebo, S., Gaspar, J., Calle, E., Pereira, S., Mariano, A., Rueff, J. and Casado, J., Stereochemical effects in the metabolic activation of nitrosopiperidines: Correlations with genotoxicity, Mut. Res., 558 (2004) 45–51.
Barratt, M.D. and Rodford, R.A., The computational prediction of toxicity, Curr. Opt. Chem. Biol., 5 (2001) 383–388.
Sztandera, L., Garg, A., Hayik, S., Bhat, K.L. and Bock, C.W., Mutagenicity of aminoazo dyes and their reductive-cleavage metabolites: A QSAR/QPAR investigation, Dyes Pigments, 59 (2003) 117–133.
Taguchi, K., Tanaka, Y., Imaeda, T., Hirai, M., Mohri, S., Yamada, M. and Inoue, Y., Development of a genotoxicity detection system using a biosensor, Environ. Sci., 11 (2004) 293–302.
Rosenkranz, H.S., Synergy between systemic toxicity and genotoxicity: Relevance to human cancer risk, Mut. Res. 529 (2003) 117–127.
White, A.C., Mueller, R.A., Gallavan, R.H., Aaron, S. and Wilson, A.G.E., A multiple in silico program approach for the prediction of mutagenicity from chemical structure, Mut. Res. 593 (2003) 77–89.
Cash, G.G., Prediction of the genotoxicity of aromatic and heteroaromatic amines using electrotopological state indices, Mut. Res., 491 (2001) 31–37.
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Shoji, R., Kawakami, M. Prediction of genotoxicity of various environmental pollutants by artificial neural network simulation. Mol Divers 10, 101–108 (2006). https://doi.org/10.1007/s11030-005-9005-1
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DOI: https://doi.org/10.1007/s11030-005-9005-1