The potential (eco)toxicological hazard posed by engineered nanoparticles is a major scientific and societal concern since several industrial sectors (e.g. electronics, biomedicine, and cosmetics) are exploiting the innovative properties of nanostructures resulting in their large-scale production. Many consumer products contain nanomaterials and, given their complex life-cycle, it is essential to anticipate their (eco)toxicological properties in a fast and inexpensive way in order to mitigate adverse effects on human health and the environment. In this context, the application of the structure-toxicity paradigm to nanomaterials represents a promising approach. Indeed, according to this paradigm, it is possible to predict toxicological effects induced by chemicals on the basis of their structural similarity with chemicals for which toxicological endpoints have been previously measured. These structure-toxicity relationships can be quantitative or qualitative in nature and they can predict toxicological effects directly from the physicochemical properties of the entities (e.g. nanoparticles) of interest. Therefore, this approach can aid in prioritizing resources in toxicological investigations while reducing the ethical and monetary costs that are related to animal testing. The purpose of this review is to provide a summary of recent key advances in the field of QSAR modelling of nanomaterial toxicity, to identify the major gaps in research required to accelerate the use of quantitative structure-activity relationship (QSAR) methods, and to provide a roadmap for future research needed to achieve QSAR models useful for regulatory purposes.
Keywords: Chemical category; Computational toxicology; Nanotoxicology; QSAR; Regulation.
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