[en] Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools as alternatives to animal testing. The main interest is not solely on the use of in silico tools, or in read-across, but on better toxicological safety assessment of substances, and for this purpose more advanced, integrated strategies have to be implemented. VEGAHUB wants to promote this broader view, not necessarily focused on a specific approach. Applying multiple tools and complementary approaches instead of one technique may provide more elements for a more robust evaluation, but at the same time it is important to have a conceptual scheme to integrate multiple, heterogeneous lines of evidence. We will show how the user can benefit from the diversity of tools available within the platform VEGAHUB for assessing the biological properties of chemical substances on an example of (non)mutagenicity.
VEGA HUB—virtual models for property. Evaluation of chemicals within a global architecture. https://www.vegahub.eu/. Accessed 20 Apr 2021
Toropov AA, Toropova AA, Benfenati E et al (2015) QSPR/QSAR analyses by means of the CORAL software. In: Roy K (ed) Quantitative structure-activity relationships in drug desing, predictive toxicology, and risk assessment. IGI Global, pp 560–585
Gadaleta D, Marzo M, Toropov A et al (2021) Integrated in silico models for the prediction of no-observed-(adverse)-effect levels and lowest-observed-(adverse)-effect levels in rats for sub-chronic repeated-dose toxicity. Chem Res Toxicol 34:247–257. https://doi.org/10. 1021/acs.chemrestox.0c00176
Toropov AA, Toropova AP, Carnesecchi E et al (2020) The index of ideality of correlation and
Toropova AP, Toropov AA, Benfenati E (2020) QSAR-models, validation, and IIC-paradox for drug toxicity. Int J Quant Struct Prop Relatsh 5:22–43. https://doi.org/10.4018/IJQSPR. 2020010102
Toma C, Gadaleta D, Roncaglioni A et al (2018) QSAR development for plasma protein binding: influence of the ionization state. Pharm Res 36:28. https://doi.org/10.1007/s11095-018-2561-8
Toropova AP, Toropov AA, Benfenati E (2019) Semi-correlations as a tool to build up categorical (active/inactive) model of GABAA receptor modulator activity. Struct Chem 30: 853–861. https://doi.org/10.1007/s11224-018-1226-x
Toropova AP, Toropov AA, Benfenati E et al (2019) Virtual screening of anti-cancer compounds: application of Monte Carlo technique. Anti Cancer Agents Med Chem 19:148–153. https://doi.org/10.2174/1871520618666181025122318
Toropov AA, Toropova AP, Benfenati E, Sal-mona M (2018) Mutagenicity, anticancer activity and blood brain barrier: similarity and dissimilarity of molecular alerts. Toxicol Mech Methods 28:321–327. https://doi.org/10. 1080/15376516.2017.1422579
Vukovic K, Gadaleta D, Benfenati E (2019) Methodology of aiQSAR: a group-specific approach to QSAR modelling. J Chem 11:27. https://doi.org/10.1186/s13321-019-0350-y
Ferrari T, Cattaneo D, Gini G et al (2013) Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction. SAR QSAR Environ Res 24:365–383. https://doi.org/10.1080/1062936X.2013. 773376
Benfenati E, Lombardo A (2020) VEGAHUB for ecotoxicological QSAR modeling. In: Roy K (ed) Ecotoxicological QSARs. Springer, New York, NY, pp 759–787
Benfenati E, Roncaglioni A, Lombardo A, Manganaro A (2019) Integrating QSAR, read-across, and screening tools: the VEGA-HUB platform as an example. In: Hong H (ed) Advances in computational toxicology: methodologies and applications in regulatory science. Springer, Cham, pp 365–381
Annex III inventory—ECHA. https://echa. europa.eu/information-on-chemicals/annex-iii-inventory. Accessed 20 Apr 2021
European Chemicals Agency (2016) How to use and report (Q)SARs. Practical guide 5. Publications Office, LU
Gini G, Franchi AM, Manganaro A et al (2014) ToxRead: a tool to assist in read across and its use to assess mutagenicity of chemicals. SAR QSAR Environ Res 25:999–1011. https://doi. org/10.1080/1062936X.2014.976267
Hansen K, Mika S, Schroeter T et al (2019) Benchmark data set for in silico prediction of ames mutagenicity. J Chem Inf Model 49: 2077–2081
Chemical Carcinogenesis Research Information System. National Health Institute. https://www.nlm.nih.gov/databases/down load/ccris.html. Accessed22 Apr 2012
Helma C, Cramer T, Kramer S, De Raedt L (2004) Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. J Chem Inf Comput Sci 44:1402–1411. https://doi.org/10.1021/ci034254q
Kazius J, McGuire R, Bursi R (2005) Derivation and validation of toxicophores for mutagenicity prediction. J Med Chem 48:312–320. https://doi.org/10.1021/jm040835a
Feng J, Lurati L, Ouyang H et al (2003) Predictive toxicology: benchmarking molecular descriptors and statistical methods. J Chem Inf Comput Sci 43:1463–1470. https://doi. org/10.1021/ci034032s
Judson PN, Cooke PA, Doerrer NG et al (2005) Towards the creation of an international toxicology information centre. Toxicology 213:117–128. https://doi.org/10.1016/j.tox.2005.05.014
Matthews EJ, Kruhlak NL, Cimino MC et al (2006) An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints. Regul Toxicol Pharmacol 44:83–96. https://doi.org/10. 1016/j.yrtph.2005.11.003
Benigni R, Bossa C, Jeliazkova N et al (2008) Benigni/Bossa rulebase for mutagenicity and carcinogenicity—a module of toxtree. JRC scientific and technical reports
Benfenati E, Manganelli S, Giordano S et al (2015) Hierarchical rules for read-across and in silico models of mutagenicity. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 33: 385–403. https://doi.org/10.1080/10590501.2015.1096881
Floris M, Raitano G, Medda R, Benfenati E (2017) Fragment prioritization on a large mutagenicity dataset. Mol Informat 36: 1600133. https://doi.org/10.1002/minf. 201600133
Bakhtyari NG, Raitano G, Benfenati E et al (2013) Comparison of in silico models for prediction of mutagenicity. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 31:45–66. https://doi.org/10.1080/10590501.2013. 763576
Daylight Theory: SMARTS—a language for describing molecular patterns. https://www. daylight.com/dayhtml/doc/theory/theory. smarts.html. Accessed 20 Apr 2021
More SJ, Bampidis V, Benford D et al (2019) Guidance on harmonised methodologies for human health, animal health and ecological risk assessment of combined exposure to multiple chemicals. EFSA J 17:e05634. https://doi.org/10.2903/j.efsa.2019.5634
Benfenati E, Chaudhry Q, Gini G, Dorne JL (2019) Integrating in silico models and read-across methods for predicting toxicity of chemicals: a step-wise strategy. Environ Int 131: 105060. https://doi.org/10.1016/j.envint. 2019.105060
Benfenati E, Pardoe S, Martin T et al (2013) Using toxicological evidence from QSAR models in practice. ALTEX 30:19–40. https://doi. org/10.14573/altex.2013.1.019
ICH M7(2018) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk. In: European medicines agency. https://www.ema.europa.eu/en/ich-m7-assessment-control-dna-reactive-mutagenic-impurities-pharmaceuticals-limit-potential. Accessed 20 Apr 2021
Manganelli S, Schilter B, Benfenati E et al (2018) Integrated strategy for mutagenicity prediction applied to food contact chemicals. ALTEX 35:169–178. https://doi.org/10. 14573/altex.1707171
Floris M, Manganaro A, Nicolotti O et al (2014) A generalizable definition of chemical similarity for read-across. J Chem 6:39. https://doi.org/10.1186/s13321-014-0039-1
Honma M, Kitazawa A, Cayley A et al (2019) Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project. Mutagenesis 34:3–16. https://doi.org/10.1093/mutage/gey031
