ИСТИНА

Behaviour of nano-sized materials is frequently different from the bulk counterparts, even opposite in some cases. Due the unique properties nanoparticles found wide commercial applications. Application area of nanoparticles include various sectors of industry such as medicine and pharmaceutics, textile, shoes and home appliances, personal care and cosmetics, electronics, etc [i]. Although nanomaterials are already present in many commercial products, the full potential of nanotechnology has yet to be realised. However, the potential harmful effects of nanomaterials on human’s health or the environment have not yet been identified. In recent years, the interest of the scientist to toxicity of nanoparticles has increased and nanotoxicity data are becoming rapidly available. The evaluation of such properties using experimental means is time-consuming and costly and yet a lot of gaps and uncertainty persist. It is clear that individual testing of all nanomaterials currently available, industrially or in the lab, will never be possible. Therefore computational models will be crucial to establish a generic understanding, in order to safeguard safety and to support regulation. Thus, researchers are investigating the potential of using Quantitative Nanostructure–Activity Relationship (QNAR) models to predict the properties of nanoparticles prior to their manufacturing. Traditional QSAR (Quantitative Structure–Activity Relationship) evaluate functional dependence between structure and activity of substances. Using QSARs for nanoparticles is a new and still developing area of research. Within our study, we have estimated toxicity of a number of nanoparticles (currently, metals and metal oxides) for different species: daphnids, planaria worms, mussels, zebra fishes. Additionally, we have collected toxicity data of the number of nanoparticles for on vivo and in vitro tests. The data has been uploaded to the Online Chemical Modeling Environment (www.ochem.eu) and is publicly accessible by everyone on the Web. In our studies, we plan to use this data to develop predictive QSAR models for nanoparticles toxicity. The further work will include development of new descriptors to discribe nano-particles according to their chemical composition, size,shapes and surface chemistry; and desing of the basis for algorithms linking physicochemical substance properties to the observed toxicity of the NPs.