Welcome to the webpage of the project: “Development of Robust Computational Models of Chemical Toxicity for Health and Environmental Risk Assessment”
The traditional approaches for in vivo animal chemical safety testing are costly, time consuming, and have a low throughput. Accurate prediction of the adverse effects of chemical substances on living systems, identification of possible toxic alerts, and compound prioritization for animal testing are the primary goals of computational toxicology. Rapid expansion of experimental data sets that combine data on chemical structure and various toxicity end points for numerous environmental agents.
The aim of this project is the development of alternative in silico models for the evaluation of toxicity of chemicals and a prioritized list of compounds for experimental in vivo testing. Recent (Q)SAR developments allow much more accurate prediction of complex toxicological endpoints than a few years ago. This progress is due to (i) the development of improved (Q)SAR methodologies and (ii) by the availability of larger and better curated public databases. The QSAR models that will be developed for predicting the in vivo adverse health effects of compounds will use both biological and chemical descriptors. The ultimate goal of this project is to construct a prioritized list of compounds with low potential for in vivo toxicity. This list of compounds will be proposed for experimental testing and will be of particular interest for the REACH and other similar projects.
The project is funded by Cyprus Research Promotion Foundation. (ΔΙΕΘΝΗ/ΣΤΟΧΟΣ/0308/05)