This is the collection of sources on which our training courses on in silico toxicology are built and constantly updated. This list is not intended to be a complete list of publications on computational toxicology, but rather a selection of what we found relevant for our work. The list will be periodically updated, therefore, come back.
In the NAMs Webinar section, you'll find our selection of webinars to keep up to date with the latest developments in the use of New approach methodologies (NAMs) in chemical risk assessment scheduled in the next few months.
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Cosmetics, NAMs, read-across, Next generation risk assessment (NGRA)
A 10-Step Framework for Use of Read-across (RAX) in next Generation Risk Assessment (NGRA) for Cosmetics Safety Assessment
Alexander-White, C.; Bury, D.; Cronin, M.; Dent, M.; Hack, E.; Hewitt, N. J.; Kenna, G.; Naciff, J.; Ouedraogo, G.; Schepky, A.; Mahony, C.; Europe, C. Regulatory Toxicology and Pharmacology 2022, 129, 105094.
Current Concepts in Quantitative Risk Assessment for Skin Sensitization
NICEATM, the Swiss Centre for Applied Human Toxicology, and the Swiss State Secretariat for Economic Affairs.
A framework for chemical safety assessment incorporating new approach methodologies within REACH
Ball, N., Bars, R., Botham, P.A. et al. Arch Toxicol 96, 743–766 (2022).
VEGA-QSAR: AI inside a platform for predictive toxicology, E. Benfenati, A. Manganaro, G. Gini, IRCCS- Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy, DEIB, Politecnico di Milano, Italy
REACH, read-across, ToolBox
New developments and regulatory applications of the OECD QSAR Toolbox
Andrea Gissi (ECHA), Patience Browne (OECD), Doris Hirmann (ECHA), Darina Yordanova (LMC), Stanislav Temelkov (LMC), Martin B. Philips (US EPA), Brianne Raccor (US EPA), Andrea Richarz (ECHA), Tiago Pedrosa (ECHA)
QSAR, read-across, cosmetics
A Review of In Silico Toxicology Approaches to Support the Safety Assessment of Cosmetics-Related Materials.
Cronin, M. T. D.; Enoch, S. J.; Madden, J. C.; Rathman, J. F.; Richarz, A.-N.; Yang, C. Computational Toxicology 2022, 100213.