SpheraCosmolife: a new tool for the risk assessment of cosmetic products

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Gianluca Selvestrel
Federica Robino
Diego Baderna
Serena Manganelli
David Asturiol
Alberto Manganaro
Matteo Zanotti Russo
Giovanna Lavado
Cosimo Toma
Alessandra Roncaglioni
Emilio Benfenati


A new, freely available software for cosmetic products has been designed considering the regulatory framework for cosmetics. This software allows an overall toxicological evaluation of cosmetic ingredients without the need of additional testing and, depending on the product type, it applies defined exposure scenarios to derive risk for consumers. It takes regulatory thresholds into account and uses either experimental values, if available, or predictions.  Based on experimental or predicted no observed adverse effect level (NOAEL), the software can define a point of departure (POD), which is useful to calculate the margin of safety (MoS) of the query chemicals. The software also provides other toxicological properties, such as mutagenicity, skin sensitization and the threshold of toxicological concern (TTC) to provide an overall evaluation of the potential chemical hazard. Predictions are calculated using in silico models implemented within the VEGA software. The full list of ingredients of a cosmetic product can be processed at the same time, at the effective concentration in the product given by the user. SpheraCosmolife is designed as a support tool for the safety assessors of cosmetic products and can be used to prioritize the cosmetic ingredients or formulations according to their potential risk for the consumers. The major novelty of the tool is that it wraps a series of models (some of them new) into a single user-friendly software system.

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Selvestrel, G., Robino, F., Baderna, D., Manganelli, S., Asturiol, D., Manganaro, A., Zanotti Russo, M., Lavado, G., Toma, C., Roncaglioni, A. and Benfenati, E. (2021) “SpheraCosmolife: a new tool for the risk assessment of cosmetic products”, ALTEX - Alternatives to animal experimentation. doi: 10.14573/altex.2010221.

Asturiol, D., Casati, S. and Worth, A. (2016). Consensus of classification trees for skin sensitisation hazard prediction. Toxicology in Vitro 36, 197–209. doi:10.1016/j.tiv.2016.07.014.

Baderna, D., Gadaleta, D., Lostaglio, E. et al. (2020). New in silico models to predict in vitro micronucleus induction as marker of genotoxicity. J Hazard Mater 385, 121638. doi:10.1016/j.jhazmat.2019.121638.

Benfenati, E., Golbamaki, A., Raitano, G. et al. (2018). A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity. SAR QSAR Environ Res 29, 591-611. doi:10.1080/1062936X.2018.1497702.

Berggren, E., White, A., Ouedraogo, G. et al. (2017). Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods. Comput Toxicol 4, 31–44. doi:10.1016/j.comtox.2017.10.001.

Carnesecchi, E., Raitano, G., Gamba, A. et al. (2020). Evaluation of non-commercial models for genotoxicity and carcinogenicity in the assessment of EFSA’s databases. SAR QSAR Environ Res 31, 33-48. doi:10.1080/1062936X.2019.1690045.

Cassano, A., Raitano, G., Mombelli, E. et al. (2014). Evaluation of QSAR models for the prediction of Ames genotoxicity: a retrospective exercise on the chemical substances registered under the EU REACH regulation. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 32, 273–298. doi:10.1080/10590501.2014.938955.

Chaudhry, Q., Piclin, N., Cotterill, J. et al. (2010). Global QSAR models of skin sensitisers for regulatory purposes. Chem Cent J 4 Suppl 1, S5. doi:10.1186/1752-153X-4-S1-S5.

Cramer, G. M., Ford, R. A. and Hall, R. L. (1978). Estimation of toxic hazard--a decision tree approach. Food Cosmet Toxicol 16, 255–276. doi:10.1016/s0015-6264(76)80522-6.

EC - European Commission (2009). Regulation (EC) No.1223/2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic products. Official Journal of European Union. L, 342, 59-209. http://data.europa.eu/eli/reg/2009/1223/2020-05-01

EC - European Commission (2008). Regulation (EC) No. 1272/2008 of the European Parliament and of the Council of 16 December 2008 on classification, labelling and packaging of substances and mixtures, amending and repealing Directives 67/548/EEC and 1999/45/EC, and amending Regulation (EC) No 1907/2006. Official Journal L 353, 31/12/2008, p. 1-1355. http://data.europa.eu/eli/reg/2009/1223/2020-05-01

Gadaleta, D., Lombardo, A., Toma, C. et al. (2018). A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications. J Cheminformatics 10, 60. doi:10.1186/s13321-018-0315-6.

Gellatly, N. and Sewell, F. (2019). Regulatory acceptance of in silico approaches for the safety assessment of cosmetic-related substances. Comput Toxicol 11, 82–89. doi:10.1016/j.comtox.2019.03.003.

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. doi:10.1093/mutage/gey031.

ICH guideline M7 (R1) on assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk. EMA/CHMP/ICH/83812/2013. (2017) https://www.ema.europa.eu/en/documents/scientific-guideline/ich-guideline-m7r1-assessment-control-dna-reactive-mutagenic-impurities-pharmaceuticals-limit_en.pdf

Kroes, R., Renwick, A. G., Cheeseman, M. et al. (2004). Structure-based thresholds of toxicological concern (TTC): guidance for application to substances present at low levels in the diet. Food Chem Toxicol 42, 65–83. doi:10.1016/j.fct.2003.08.006.

Kroes, R., Renwick, A. G., Feron, V. et al. (2007). Application of the threshold of toxicological concern (TTC) to the safety evaluation of cosmetic ingredients. Food Chem Toxicol 45, 2533–2562. doi:10.1016/j.fct.2007.06.021.

Manganelli, S., Schilter, B., Benfenati, E. et al. (2018). Integrated strategy for mutagenicity prediction applied to food contact chemicals. ALTEX 35, 169–178. doi:10.14573/altex.1707171.

Manganelli, S., Roncaglioni, A., Mansouri, K. et al. (2019). Development, validation and integration of in silico models to identify androgen active chemicals. Chemosphere 220, 204–215. doi:10.1016/j.chemosphere.2018.12.131.

Munro, I. C., Ford, R. A., Kennepohl, E. et al. (1996). Correlation of structural class with no-observed-effect levels: a proposal for establishing a threshold of concern. Food Chem Toxicol 34, 829–867. doi:10.1016/s0278-6915(96)00049-x.

Patlewicz, G., Jeliazkova, N., Safford, R. J. et al. (2008). An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res 19, 495–524. doi:10.1080/10629360802083871.

Potts, R. O. and Guy, R. H. (1992). Predicting skin permeability. Pharm Res 9, 663–669. doi:10.1023/a:1015810312465.

Raitano, G., Roncaglioni, A., Manganaro, A. et al. (2019). Integrating in silico models for the prediction of mutagenicity (Ames test) of botanical ingredients of cosmetics. Comput Toxicol 12, 100108. doi:10.1016/j.comtox.2019.100108.

Rogiers, V., Benfenati, E., Bernauer, U. et al. (2020). The way forward for assessing the human health safety of cosmetics in the EU - Workshop proceedings. Toxicology 436, 152421. doi:10.1016/j.tox.2020.152421.

SCCS - Scientific Committee on Consumer Safety (2018). SCCS Notes of Guidance for the Testing of Cosmetic Ingredients and their Safety Evaluation 10th revision, 24-25 October 2018, SCCS/1602/18. Available at: https://ec.europa.eu/health/sites/health/files/scientific_committees/consumer_safety/docs/sccs_o_224.pdf

SCCS - Scientific Committee on Consumer Safety (2015). SCCS Notes of Guidance for the Testing of Cosmetic Ingredients and their Safety Evaluation 9th revision, Revised version of 25 April 2016, SCCS/1564/15. Available at: https://ec.europa.eu/health/scientific_committees/consumer_safety/docs/sccs_o_190.pdf

Shen, J., Kromidas, L., Schultz, T. et al. (2014). An in silico skin absorption model for fragrance materials. Food Chem Toxicol 74, 164–176. doi:10.1016/j.fct.2014.09.015.

Taylor, K. and Rego Alvarez, L. (2020). Regulatory drivers in the last 20 years towards the use of in silico techniques as replacements to animal testing for cosmetic-related substances. Comput Toxicol 13, 100112. doi:10.1016/j.comtox.2019.100112.

Ten Berge, W. (2009). A simple dermal absorption model: derivation and application. Chemosphere 75, 1440–1445. doi:10.1016/j.chemosphere.2009.02.043.

Therneau, T. M., Atkinson, E. J. (2015). An Introduction to Recursive Partitioning Using the RPART Routines. Mayo Foundation, Rochester. https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf

Toropov, A. A., Toropova, A. P., Raitano, G. et al. (2019). CORAL: Building up QSAR models for the chromosome aberration test. Saudi J Biol Sci 26, 1101–1106. doi:10.1016/j.sjbs.2018.05.013.

Toropov, A. A., Toropova, A. P., Pizzo, F. et al. (2015). CORAL: model for no observed adverse effect level (NOAEL). Mol Divers 19, 563–575. doi:10.1007/s11030-015-9587-1.

Vecchia, B.E. and Bunge A.L. (2002a). Skin absorption databases and predictive equations. Chapter 3 in Transdermal Drug Delivery, edited by Guy RH and Hadgraft J, Publisher Marcel Dekker. https://www.researchgate.net/publication/272149756_Skin_Absorption_Databases_and_Predictive_Equations

Williams, F. M., Rothe, H., Barrett, G. et al. (2016). Assessing the safety of cosmetic chemicals: Consideration of a flux decision tree to predict dermally delivered systemic dose for comparison with oral TTC (Threshold of Toxicological Concern). Regul Toxicol Pharmacol 76, 174–186. doi:10.1016/j.yrtph.2016.01.005.

Worth, A., Cronin, M., Enoch, S., et al. (2012). Applicability of the Threshold of Toxicological Concern (TTC) approach to cosmetics – preliminary analysis. European Union. doi:10.2788/5059

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