The main objective of the NanoPUZZLES project is to develop, within three years, a package of computational algorithms for the comprehensive modelling of the relationships between the structure, properties, molecular interactions and toxicity of selected classes of engineered nanoparticles (NPs). The package (i) will serve as a proof-of-the-concept that the risk related to NPs can be comprehensively assessed with use of computational techniques and (ii) will define a basis for development of further modelling techniques for a large variety of nanoparticles.
The project will focus on two groups of compounds: (i) inorganic engineered nanoparticles (metal nanooxides) and (ii) carbon nanoparticles (carbon nanotubes (single-walled and multi-walled), fullerenes and fullerene derivatives). That choice was dictated by the wide application of these nanoparticles in everyday household products, and by the fact that these compounds are commercially available in the market whch eliminates the necessity for their synthesis (reduction of costs).
Computational algorithms will be developed within four work packages related to the following thematic areas (Fig. 1):
• Quality assessment of physicochemical and toxicological data available for nanomaterials and data exploration (NanoDATA),
• Development of novel descriptors for nanoparticles’ structure (NanoDESC),
• Simulating interactions of nanoparticles with biological systems (NanoINTER),
• Quantitative and qualitative structure-activity relationship modelling, grouping and read across (NanoQSAR).
Application of the methods developed within the four thematic areas will allow for predicting toxicity and the behaviour of novel nanoparticles from their structure and/or physicochemical properties without the necessity of performing extensive empirical testing (reduction of costs and need for animal testing). Moreover, it will result in a framework being established to categorise nanoparticles according to the potential for exposure, as well as physicochemical, structural and toxicological properties (based on available empirical data and computationally predicted results). This, in the longer perspective, should lead to designing and engineering nanomaterials that are of low risk for human and the environment.