Assessing the ecotoxicity of multicomponent nanomaterials using a classification SAR approach

The emerging applications of nanotechnology have led to the synthesis, production and use of a continuously increasing number of nanomaterials. In recent years, the focus is being shifted to multicomponent nanomaterials (MCNMs), due to the control over their functional properties. At the same time, the increasing exposure of ecosystems to such materials has raised concerns over their environmental hazard, with several in vivo and in vitro studies used to assess the ecotoxicity of MCNMs. The demanding nature of such methods has also led to the increasing development of in silico methods, such as structure–activity relationship (SAR) models. Although such approaches have been developed for single component nanomaterials, models for the ecotoxicity of MCNMs are still sparse in scientific literature. In this paper, we address the case of MCNM ecotoxicity by developing an in silico classification SAR computational framework. The models are built over a dataset of 652 ecotoxicity measurements for 214 metal and metal oxide MCNMs, towards bacteria, eukaryotes, fish, plants and crustaceans. This dataset is, to the best of the authors' knowledge, the largest dataset used for MCNM ecotoxicity. It is found that two descriptors can adequately classify different MCNMs based on their ecotoxicity over the whole heterogeneous dataset. These descriptors are the hydration enthalpy of the metal ion and the energy difference between the MCNM conduction band and the redox potential in biological media. Although the classification does not allow a quantitative ecotoxicity assessment, the heterogeneous nature of the dataset can reveal key MCNM features that induce toxic action, allowing a more holistic understanding of MCNM ecotoxicity, as well as the nature of interaction between the different MCNM components.