Evaluation of non-commercial models for genotoxicity and carcinogenicity in the assessment of EFSA’s databases

References

• EFSA CEF Panel (EFSA Panel on Food Contact Materials, Enzymes, Flavourings and Processing Aids), Note for guidance for the preparation of an application for the safety assessment of a substance to be used in plastic food contact materials, EFSA J. 6(7) (2008), pp. 21r, 41. doi:10.2903/j.efsa.2008.21r
   Google Scholar
• A. Bassan, L. Ceriani, J. Richardson, A. Livaniou, A. Ciacci, R. Baldin, S. Kovarich, E. Fioravanzo, M. Pavan, D. Gibin, G. Di Piazza, L. Pasinato, S. Cappé, H. Verhagen, T. Robinson, and J.L. Dorne, OpenFoodTox: EFSA’s chemical hazards database, Zenodo (2018). doi:10.5281/zenodo.780543.
   Google Scholar
• F. Metruccio, I. Castelli, C. Civitella, C. Galbusera, F. Galimberti, L. Tosti, and A. Moretto, Compilation of a database, specific for the pesticide active substance and their metabolites, comprising the main genotoxicity endpoints, EFSA Support. Publ. 2017(EN–1229) (2017), pp. 125. doi:10.2903/sp.efsa.2017.EN-1229
   Google Scholar
• EFSA Scientific Committee, S. More, V. Bampidis, D. Benford, J. Boesten, C. Bragard, T. Halldorsson, A. Hernandez-Jerez, S. Hougaard-Bennekou, K. Koutsoumanis, H. Naegeli, S.S. Nielsen, D. Schrenk, V. Silano, D. Turck, M. Younes, G. Aquilina, R. Crebelli, R. Gurtler, K.I. Hirsch-Ernst, P. Mosesso, E. Nielsen, R. Solecki, M. Carfì, C. Martino, D. Maurici, J. Parra Morte, and J. Schlatter, Statement on the genotoxicity assessment of chemical mixtures, EFSA J. 17(1) (2019), pp. 5519, 11. doi:10.2903/j.efsa.2019.5519
   Web of Science ®Google Scholar
• R. Benigni, C.L. Battistelli, C. Bossa, A. Giuliani, E. Fioravanzo, A. Bassan, M.F. Gatnik, J. Rathman, C. Yang, and O. Tcheremenskaia, Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across, EFSA Support. Publ. EN-1598 (2019), pp. 221. doi:10.2903/sp.efsa.2019.EN-1598
   Google Scholar
• E. Benfenati, F. Como, M. Marzo, D. Gadaleta, A. Toropov, and A. Toropova, Developing innovative in silico models with EFSA’s OpenFoodTox database, EFSA Support. Publ. 2017(EN–1206) (2017), pp. 19. doi:10.2903/sp.efsa.2017.EN-1206
   Google Scholar
• M. Honma, A. Kitazawa, A. Cayley, R.V. Williams, C. Barber, T. Hanser, R. Saiakhov, S. Chakravarti, G.J. Myatt, K.P. Cross, E. Benfenati, G. Raitano, O. Mekenyan, P. Petkov, C. Bossa, R. Benigni, C.L. Battistelli, A. Giuliani, O. Tcheremenskaia, C. DeMeo, U. Norinder, H. Koga, C. Jose, N. Jeliazkova, N. Kochev, V. Paskaleva, C. Yang, P.R. Daga, R.D. Clark, and J. Rathman, Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: Outcomes of the Ames/QSAR, international challenge project, Mutagenesis 34 (2019), pp. 3–16. doi:10.1093/mutage/gey031
   PubMed Web of Science ®Google Scholar
• G. Raitano, A. Roncaglioni, A. Manganaro, M. Honma, L. Sousselier, Q.T. Do, E. Paya, and E. Benfenati, Integrating in silico models for the prediction of mutagenicity (Ames test) of botanical ingredients of cosmetics, Comput. Toxicol. 12 (2019), pp. 100108. doi:10.1016/j.comtox.2019.100108
   Google Scholar
• T. Ferrari and G. Gini, An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts, Chem. Cent. J. 4(Suppl. 1) (2010), pp. S2, 1–6. doi:10.1186/1752-153X-4-S1-S2
   Google Scholar
• T. Ferrari, D. Cattaneo, G. Gini, N. Golbamaki Bakhtyari, A. Manganaro, and E. Benfenati, Automatic knowledge extraction from chemical structures: The case of mutagenicity prediction, SAR QSAR Environ. Res. 24 (2013), pp. 365–383. doi:10.1080/1062936X.2013.773376
   PubMed Web of Science ®Google Scholar
• R. Benigni and C. Bossa, Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology, Mutat. Res. 659 (2008), pp. 248–261. doi:10.1016/j.mrrev.2008.05.003
   PubMed Web of Science ®Google Scholar
• A. Manganaro, F. Pizzo, A. Lombardo, A. Pogliaghi, and E. Benfenati, Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm, Chemosphere 144 (2016), pp. 1624–1630. doi:10.1016/j.chemosphere.2015.10.054
   PubMed Web of Science ®Google Scholar
• T. Morita, S. Hamada, K. Masumura, A. Wakata, J. Maniwa, H. Takasawa, K. Yasunaga, T. Hashizume, and M. Honma, Evaluation of the sensitivity and specificity of in vivo erythrocyte micronucleus and transgenic rodent gene mutation tests to detect rodent carcinogens, Mutat. Res. Genet. Toxicol. Environ. Mutagen. 802 (2016), pp. 1–29. doi:10.1016/j.mrgentox.2016.03.008
   PubMed Web of Science ®Google Scholar
• J.A. Cooper, R. Saracci, and P. Cole, Describing the validity of carcinogen screening tests. Br. J. Cancer 39 (1979), pp. 87–89. doi:10.1038/bjc.1979.10
   PubMed Web of Science ®Google Scholar
• P. Dao, K. Wang, C. Collins, M. Ester, A. Lapuk, and S.C. Sahinalp, Optimally discriminative subnetwork markers predict response to chemotherapy, Bioinformatics 27 (2011), pp. 205–213. doi:10.1093/bioinformatics/btr245
   Web of Science ®Google Scholar
• J.A. Swets, Measuring the accuracy of diagnostic systems, Science 240 (1988), pp. 1285–1293. doi:10.1126/science.3287615.
   PubMed Web of Science ®Google Scholar
• J.L. Dorne, J. Richardson, G. Kass, N. Georgiadis, M. Monguidi, L. Pasinato, S. Cappe, H. Verhagen, and T. Robinson, Editorial: OpenFoodTox: EFSA’s open source toxicological database on chemical hazards in food and feed, Efsa J. 15 (2017). doi:10.2903/j.efsa.2017.e15011
   Web of Science ®Google Scholar
• F. Como, E. Carnesecchi, S. Volani, J.L. Dorne, J. Richardson, A. Bassan, M. Pavan, and E. Benfenati, Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model, Chemosphere 166 (2017), pp. 438–444. doi:10.1016/j.chemosphere.2016.09.092
   PubMed Web of Science ®Google Scholar
• A.A. Toropov, A.P. Toropova, M. Marzo, J.L. Dorne, N. Georgiadis, and E. Benfenati, QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA’s OpenFoodTox database, Environ. Toxicol. Pharmacol. 53 (2017), pp. 158–163. doi:10.1016/j.etap.2017.05.011
   PubMed Web of Science ®Google Scholar
• A.P. Toropov, A.A. Toropova, M. Marzo, S.E. Escher, J.L. Dorne, N. Georgiadis, and E. Benfenati, The application of new HARD-descriptor available from the CORAL software to building up NOAEL models, Food Chem. Toxicol. 112 (2018), pp. 544–550. doi:10.1016/j.fct.2017.03.060
   PubMed Web of Science ®Google Scholar
• E. Carnesecchi, C. Svendsen, S. Lasagni, A. Grech, N. Quignot, B. Amzal, C. Toma, S. Tosi, A. Rortais, J. Cortinas-Abrahantes, E. Capri, N. Kramer, E. Benfenati, D. Spurgeon, G. Guillot, and J.L. Dorne, Investigating combined toxicity of binary mixtures in bees: Meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment, Env. Int. 133 Part B (2019). doi:10.1016/j.envint.2019.105256.
   Google Scholar
• N. Fjodorova, M. Vračko, M. Novič, A. Roncaglioni, and E. Benfenati, New public QSAR model for carcinogenicity, Chem. Central J. 4 (2010), pp. S3. doi:10.1186/1752-153X-4-S1-S3
   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. doi:10.1021/cr300206t
   PubMed Web of Science ®Google Scholar
• A. Golbamaki, E. Benfenati, N. Golbamaki, A. Manganaro, E. Merdivan, A. Roncaglioni, and G. Gini, New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds, J. Environ. Sci. Health C Environ. Carcinog. Ecotoxicol. Rev. 34 (2016), pp. 97–113. doi:10.1080/10590501.2016.1166879
   PubMed Web of Science ®Google Scholar
• D. Kirkland, M. Aardema, L. Henderson, and L. Müller, 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, Mutat. Res. Genet. Toxicol. Environ. Mutag. 584 (2005), pp. 1–256. doi:10.1016/j.mrgentox.2005.02.004
   PubMed Web of Science ®Google Scholar
• Risk Assessment Information System (RAIS), Toxicity values database. Available at https://rais.ornl.gov/cgi-bin/tools/TOX_search?select=chem.
   Google Scholar
• European Food Safety Authority, Compendium of botanicals reported to contain naturally occurring substances of possible concern for human health when used in food and food supplements, EFSA J. 2663 (2012), pp. 60. doi:10.2903/j.efsa.2012.2663
   Google Scholar
• E. Benfenati, A. Golbamaki, G. Raitano, A. Roncaglioni, S. Manganelli, F. Lemke, U. Norinder, E.L. Piparo, M. Honma, A. Manganaro, and G. Gini, A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity, SAR QSAR Environ. Res. 29 (2018), pp. 591–611. doi:10.1080/1062936X.2018.1497702
   PubMed Web of Science ®Google Scholar
• C. Milan, O. Schifanella, A. Roncaglioni, and E. Benfenati, Comparison and possible use of in silico tools for carcinogenicity within REACH legislation, J. Environ. Sci. Health C Environ. Carcinog. Ecotoxicol. Rev. 29 (2011), pp. 300–323. doi:10.1080/10590501.2011.629973
   PubMed Web of Science ®Google Scholar
• The Carcinogenic Potency Database (CPDB), Available at https://toxnet.nlm.nih.gov/cpdb/.
   Google Scholar