The GenoQSAR Project has as a main goal the development, through machine learning techniques, of QSAR models capable of predict the ability of a chemical agents of damaging the genetic information within a cell (genotoxicity). During the project we will try to pay special attention to the nanomaterials, due to the important role that they are taking nowadays in the research and development environment, and the main role that they would obtain in the industry in a few years.
The concern to protect human health and the environment has prompted significant changes in EU regulation on chemical substances. The European Chemicals Agency (ECHA) plays the role in implementing the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) legislation, that requires industry to evaluate the toxicity of chemical substances that are in use but have never been subjected to regulatory testing.
REACH regulation has also raised strong criticism and concern from society and industrials because of ethical and economic reasons. The toxicity evaluation of chemicals requires costly, time-consuming and ethically questionable animal experiments. As consequence, this European regulation promotes scientific innovation and encourages the use of results generated by alternative methods, including especially non-testing methods (NTMs), also referred to as in silico tools.
Among them, “Quantitative Structure-Activity Relationships” (QSAR) methods are one of the most recognized machine learning methods in drug design, toxicology, industrial and environmental chemistry. Nowadays, they can be included in integrated testing strategies (ITS), to provide information for hazard and risk assessment, classification and labelling.
We propose the development of an ensemble of QSAR predictive models for several parameters related with the different kinds of genotoxicity damage. These chemoinformatic will be implemented on a proprietary computational technological platform. Particular attention will be also paid to the generation of models for nanomaterials, taking into account their high and growing impact nowadays on industry in general. The QSAR models and integration algorithms will be characterized by their reliability, and will be developed according to the rules set out by the OECD, therefore guaranteeing their validity in REACH.
Period
2021-2023
Financing entities
This project will be funded thanks to the European Grant Program “Marie Skłodowska-Curie Actions, Individual Fellowships (MSCA-IF)”, and more specifically through the “Society and Enterprise panel (MSCA-IF-EF-SE)”.
