Risk assessments in nanotoxicology: bioinformatics and computational approaches

Abstract A massive-scale production of engineered nanoparticles (ENPs) becomes one of the most important environmental issues. The mechanisms of ENPs' (eco)toxic action are not fully understood, and the estimation of those mechanisms is a complicated task because even slight changes in particle characteristics could dramatically change their toxicity. As a result of continuous manufacturing of ENPs with specific functionality and different physicochemical properties, conventional methods of in vivo and in vitro testing would not be able to fill the existing knowledge gap in nanotoxicology. The objectives of this review are to overlook the current achievements based on the new approaches of ENPs' risk assessment, such as bioinformatics approaches and machine learning tools. These methods confirmed their ability to reliable prediction and evaluation of ENPs' behavior and their toxic endpoints. Databases and projects based on these methods and approaches would be highly useful in addressing the problem of ENPs’ regulation.

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