Genotoxicity prediction by means of QSAR methods for regulatory purposes

The concern to protect human health and the environment has prompted significant changes in EU regulation on chemicalsubstances.

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 chemicalsubstances 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 economicreasons.

The toxicity evaluation of chemicals requires costly, time-consuming and ethically questionable animalexperiments.

As consequence, this European regulation promotes scientific innovation and encourages the use of resultsgenerated 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 machinelearning methods in drug design, toxicology, industrial and environmental chemistry.

Nowadays, they can be included inintegrated 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 differentkinds of genotoxicity damage.

These chemoinformatic will be implemented on a proprietary computational technologicalplatform.

Particular attention will be also payed to the generation of models for nanomaterials, taking into account their highand growing impact nowadays on industry in general.The QSAR models and integration algorithms will be characterized bytheir reliability, and will be developed according to the rules set out by the OECD, therefore guaranteeing their validity inREACH.