[en] Over the past years, the European Food Safety Authority (EFSA) released to the public domain several databases, with the main objectives of collecting and storing hazard data on the substances considered in EFSA's risk assessment and secondly to serve as a basis for further development of in silico tools such as quantitative structure-activity relationship (QSAR) models. In this work, we evaluated the ability of freely available QSAR models to estimate genotoxicity and carcinogenicity properties and their possible use for screening purposes on three different EFSA's databases. With an accuracy close to 90%, the results showed good capabilities of QSAR models to predict genotoxicity in terms of bacterial reverse mutation test, while statistics for in vivo micronucleus test are not satisfactory (accuracy in the predictions close to 50%). Interestingly, results on the carcinogenicity assessment showed an accuracy in prediction close to 70% for the best models. In addition, an example of the potential application of in silico models is presented in order to provide a preliminary screening of genotoxicity properties of botanicals intended for use as food supplements.
Disciplines :
Environmental sciences & ecology
Author, co-author :
Carnesecchi, Edoardo; Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy ; Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
Raitano, Giuseppa; Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Gamba, Alessio ; Université de Liège - ULiège > GIGA > GIGA In silico medecine - Biomechanics Research Unit ; Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Benfenati, Emilio; Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Roncaglioni, Alessandra; Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Language :
English
Title :
Evaluation of non-commercial models for genotoxicity and carcinogenicity in the assessment of EFSA's databases.
This work has been financed by the European Food Safety Authority (EFSA) under the contract number OC/EFSA/SCER/2018/01. Authors would like to acknowledge colleagues in S-IN (Soluzioni Informatiche) for providing the extract of OpenFoodTox database and Jean Lou Dorne for providing the EFSA Compendium on Botanicals.
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
J.A., Swets, Measuring the accuracy of diagnostic systems, Science 240 (1988), pp. 1285–1293. doi:10.1126/science.3287615.
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
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
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
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
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.
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
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
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
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
Risk Assessment Information System (RAIS), Toxicity values database. Available at https://rais.ornl.gov/cgi-bin/tools/TOX_search?select=chem.
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
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
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
The Carcinogenic Potency Database (CPDB), Available at https://toxnet.nlm.nih.gov/cpdb/.
