Structure-activity relationship modelling of REACH-relevant endpoints to predict the toxicity of engineered nanomaterials

Nanotechnology is one of the fastest growing and most promising technologies in our society (Forster et al. 2011), promoting the development a new generation of smart and innovative products and processes that have created tremendous growth potential for a large number of industry sectors such as composites, colouring, ceramics, electronics, nutrition, cosmetics, energy, optics, automotive, as well as numerous other industrial sectors.Currently, there is a need of ensuring a safe and sustainable development of the nanotechnology, which implies a better understanding of the potential harmful effects that ENMs may have on human´s health or the environment.

New paradigms are necessary to identify high concern ENMs and predict relevant endpoints for risk assessment, reducing the cost andtimescale derived from the use of in vivo or in vitro assays.QSAR approaches have only recently been used to predict biological effects of ENMs, with only few Quantitative Nano- Structure Activity Relationships models described in the literature.

The lack of available data explains why there is almost no literature reporting the use of computational modelling techniques applied to ENMs, especially in the area of nanotoxicology.

On the other hand, current toxicological regulation, such as the Registration, Evaluation, Authorisation and Restriction ofChemicals (REACH), strongly promotes the use of these predictive modelling.On the basis of the concept of the project, the main objective of the Nano-QSAR project is to develop new scientifically validated QSARs models to predict REACH relevant toxicological, ecotoxicological and environmental endpoints of a priority list of ENMs such as Metal Oxide Nanoparticles (MOx) and Quantum Dots (QD) on the basis of available literature and own experimental data.