Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs

Abstract

Background: The enormous physicochemical and structural diversity of metal oxide nanoparticles (MeONPs) poses significant challenges to the testing of their biological uptake, biodistribution, and effects that can be used to develop understanding of key nano-bio modes of action. This has generated considerable uncertainties in the assessment of their human health and environmental risks and has raised concerns about the adequacy of their regulation. In order to surpass the extremely resource intensive case-by-case testing, intelligent strategies combining testing methods and non-testing predictive modeling should be developed.

Methods: The quantitative structure-activity relationship (QSARs) in silico tools can be instrumental in understanding properties that affect the potencies of MeONPs and in predicting toxic responses and thresholds of effects.

Results: The present study proposes a predictive nano-QSAR model for predicting the cytotoxicity of MeONPs. The model was applied to test the relationships between 26 physicochemical properties of 51 MeONPs and their cytotoxic effects in Escherichia coli. The two parameters, enthalpy of formation of a gaseous cation (▵H_me+) and polarization force (Z/r), were elucidated to make a significant contribution for the toxic effect of these MeONPs. The study also proposed the mechanisms of toxic potency in E. coli through the model, which indicated that the MeONPs as well as their released metal ions could collectively induce DNA damage and cell apoptosis.

Significance: These findings may provide an alternative method for prioritizing current and future MeONPs for potential in vivo testing, virtual prescreening and for designing environmentally benign nanomaterials.

Keywords:

• Cytotoxicity
• in silico modeling
• metal oxide nanoparticles
• nano-QSARs

Declaration of interest

The authors declare no competing financial interest.

The authors acknowledge support from the National Natural Science Foundation of China (No. 41130743, 41521003 and 21507120).

Supplementary material available online