Comparison of criteria used to access carcinogenicity in CPANN QSAR models versus the knowledge-based expert system Toxtree

References

• EPA/630/P–03/001F. Guidelines for carcinogen risk assessment. Risk Assessment Forum, US Environmental Protection Agency, Washington DC, 2005.
   Google Scholar
• N. Fjodorova, M. Novic, M. Vracko, N. Kharchevnikova, Z. Zholdakova, O. Sinitsyna, and E. Benfenati, Regulatory assessment of chemicals within OECD member Countries, EU and in Russia, J. Environ. Health Sci., Part C 26 (2008), pp. 40–88.
   Google Scholar
• EC Corrigendum to Regulation No. 1907/2006 of the European Parliament and of the Council of 18 December 2006, Off. J. Eur. Union, L 136/3 (2007), pp. 3–280.
   Google Scholar
• EC Directive 2003/15/EC of the European Parliament and of the Council of 27 February 2003 amending Directive 78/786/EEC on the approximation of the laws of the member states relating to cosmetic products, Off. J. Eur. Union, L 66 (2003), pp. 26–35.
   Google Scholar
• E.J. Matthews, N.L. Kruhlak, R.D. Benz, J.F. Contrera, C.A. Marchant, and C. Yang, Combined use of MC4PC, MDL–QSAR, BioEpisteme, Leadscope PDM, and Derek for windows software to achieve high-performance, high-confidence mode of action-based predictions of chemical carcinogenesis in rodents, Toxicol. Mech. Meth. 18 (2008), pp. 189–206.
   PubMed Web of Science ®Google Scholar
• Y.T. Woo, D. Lai, J.L. McLain, M.K. Manibusan, and V. Dellarco, Use of mechanism-based structure–activity relationship analysis in carcinogenic potential ranking for drinking water disinfection by-products, Environ. Health Perspect. 110 (Suppl. 1) (2002), pp. 75–87.
   PubMed Web of Science ®Google Scholar
• CPDBAS: Carcinogenic potency database summary tables – All species. Available at: http://www.epa.gov/ncct/dsstox/sdf_cpdbas.html (accessed 3 October 2013).
   Google Scholar
• Y.T. Woo, D. Lai, J.L. McLain, M.K. Manibusan, and V. Dellarco, Use of mechanism-based structure–activity relationships analysis in carcinogenic potential ranking for drinking water disinfection by-products, Environ. Health Perspect. 110 (Suppl 1) (2002), pp. 75–87.
   PubMed Web of Science ®Google Scholar
• R. Benigni C. Bossa, N. Jeliazkova, T.I. Netzeva, and A.P. Worth, The Benigni/Bossa rulebase for mutagenicity and carcinogenicity – a module of Toxtree, EUR 23241 EN (2008), pp. 1–70.
   Google Scholar
• E. Mombelli and J. Devillers, Evaluation of the OECD (Q)SAR Application Toolbox and Toxtree for predicting and profiling the carcinogenic potential of chemicals, SAR QSAR Environ. Res. 21 (2010), pp. 731–752.
   PubMed Web of Science ®Google Scholar
• J. Devillers, E. Mombelli, and R. Samsera, Structural alerts for estimating the carcinogenicity of pesticides and biocides, SAR QSAR Environ. Res. 22 (2011), pp. 89–106.
   PubMed Web of Science ®Google Scholar
• N. Fjodorova, M. Vračko, M. Novič, A. Roncaglioni, and E. Benfenati, New public QSAR model for carcinogenicity, Chem. Cent. J. 4, (Suppl 1) S3 (2010), pp. 1–15. Available at: http://www.journal.chemistrycentral.com/content/4/S1/S3. doi:10.1186/1752–153X–4–S1–S3 (accessed 3 October 2013).
   Google Scholar
• Toxtree at Joint Research Center website: http://ihcp.jrc.ec.europa.eu/our_labs/predictive_toxicology/qsar_tools/toxtree (accessed 11 December 2012).
   Google Scholar
• R. Benigni and C. Bossa, Mechanisms of chemical carcinogenicity and mutagenicity: A review with implications for predictive toxicology, Chem. Rev. 111 (2011), pp. 2507–2536.
   PubMed Web of Science ®Google Scholar
• Y.T. Woo, Mechanisms of action of chemical carcinogens and their role in structure–activity relationships (SAR) analysis and risk assessment. In Quantitative structure–activity relationship (QSAR) models of mutagens and carcinogens, R. Benigni, ed., CRC Press Boca Raton, FL, 2003, pp. 41–80.
   Google Scholar
• M. Vračko, M. Novič, and J. Zupan, Study of structure–toxicity relationship by a counter propagation neural network, Anal. Chim. Acta 384 (1999), pp. 319–332.
   Web of Science ®Google Scholar
• M. Vračko, D. Mills, and S.C. Basak, Structure–mutagenicity modeling using counter propagation neural networks, Environ. Toxicol. Pharmacol. 16 (2004), pp. 25–36.
   PubMed Web of Science ®Google Scholar
• J.F. Contrera, E.J. Matthews, and R.D. Benz, Prediction of the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices, Regul. Toxicol. Pharm. 38 (2003), pp. 243–259.
   PubMed Web of Science ®Google Scholar
• J.R. Votano, M. Parham, L.H. Hall, L.B. Kier, S. Orloff, A. Tropsha, Q. Xie, and W. Tong, Three new consensus QSAR models for the prediction of Ames genotoxicity, Mutagenesis 19 (2004), pp. 365–378.
   PubMed Web of Science ®Google Scholar
• R. Combes, C. Grindon, M.T. Cronin, D.W. Roberts, and J.F. Garrod, Integrated decision-tree testing strategies for mutagenicity and carcinogenicity with respect to the requirements of the EU REACH legislation, ATLA 36 (2008), pp. 43–63.
   Google Scholar
• N. Fjodorova, M. Vračko, M. Tušar, A. Jezierska, M. Novič, R. Kühne, and G. Schüürmann, Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CPANN method for regulatory uses, Mol. Diversity 14 (2010), pp. 581–594.
   PubMed Web of Science ®Google Scholar
• R. Benigni, C. Bossa, and O. Tcheremenskaia, Nongenotoxic carcinogenicity of chemicals: Mechanisms of action and early recognition through a new set of structural alerts, Chem. Rev. 113 (2013), pp. 2940–2957.
   PubMed Web of Science ®Google Scholar
• N. Fjodorova, M. Vračko, A. Jezierska-Mazzarello, and M. Novič, Counter-propagation artificial neural networks: Categorical models for prediction of carcinogenicity for non-congeneric chemicals, SAR QSAR Environ. Res. 21 (2010), pp. 57–75.
   PubMed Web of Science ®Google Scholar
• F. Luan, R. Zhang, C. Zhao, X. Yao, M. Liu, Z. Hu, and B. Fan, Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis, Chem. Res. Toxicol. 18 (2005), pp. 198–203.
   PubMed Web of Science ®Google Scholar