Computational Chemistry: In Silico (Eco)toxicology
The increasing efforts to reduce in vivo tests for active substances, metabolites and impurities to a minimum, increase the importance of computational testing methods. In addition to grouping of chemicals, these methods allow to estimate or predict the (eco)toxicity of compounds and model toxicodynamic and toxicokinetic properties.
In Silico Models
(Quantitative) structure-activity relationship ((Q)SAR) software tools are computational tools used to predict the activity, property and toxicity of new classes of compounds based on the knowledge of their chemical structure. We are skilled in generating in silico data and performing knowledge- and statistically-based QSAR models (e.g. Toxtree, Vega and U.S. EPA T.E.S.T) to fulfil regulatory hazard information requirements regarding metabolites and impurities.
The OECD QSAR Toolbox is used by our experts for profiling and data-gap-filling approaches, such as read-across.
We provide detailed descriptions of the models used, ensuring the reliability and applicability domains of the model.
Our team of experts interpret and discuss the data in a weight of evidence (WoE) approach based on expert judgement.
We can help with the evaluation of specific toxicity endpoints such as genotoxicity or endocrine discruption. For the latter, effects on specific targets as receptors are predicted and evaluated using state-of-the-art models and databases, e.g. ToxCast Pathway Model Prediction, Danish (Q)SAR Database, Endocrine Disruptome).
Please do not hesitate to contact us to learn how we can help you with your specific needs.