Our services for agrochemicals
Metabolites, degradates, transformation products or impurities
Genotoxicity assessment integrating predictions from different (Q)SAR methods.
Use of in silico predictions in a weight of evidence approach.
Tools that we employ are:
Leadscope Model Applier
Lhasa (Derek and Sarah)
Other (VEGA/OECD QSAR Toolbox/T.E.S.T./Toxtree)
In Silico Toxicology
In silico toxicology (also referred to as computational toxicology) aims at predicting toxicity generally from chemical structure.
In silico toxicology includes category formation (grouping) and read-across, Structure-Activity Relationship (SAR), Quantitative Structure-Activity Relationship (QSAR), and Expert Systems.
In silico toxicology methods play a crucial role to generating additional information for complementing and ultimately enhancing or supporting a risk assessment, including an understanding of the structural and/or mechanistic basis that may contribute ideas for the rational design of new chemicals, development of a testing strategy or an overall weight-of-evidence evaluation.
Myatt, G. et al. (2018). In silico toxicology protocols. Regul Toxicol Pharmacol, 96, 1-17.
Benigni, A. et al. (2019). 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 supporting publication 2019:EN-1598. 221 pp.
Pavan, M. et al. (2016). The consultancy activity on in silico models for genotoxic prediction of pharmaceutical impurities. In: Benfenati E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 1425. Humana Press, New York, NY.
Bassan, A., Worth, A. P. (2007). Computational Tools for Regulatory Needs. In: Ekins S. (eds) Risk Assessment For Pharmaceutical And Environmental Chemicals, , Wiley & Sons, Inc.