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



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.



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 (▵Hme+) 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.



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.
a State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences , Beijing , China .
Yunsong
Y
Mu
a State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences , Beijing , China .
Fengchang
F
Wu
b Institute of Applied Ecology, Chinese Academy of Sciences , Shenyang , China .
Qing
Q
Zhao
c State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University , Nanjing , China .
Rong
R
Ji
a State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences , Beijing , China .
Yu
Y
Qie
c State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University , Nanjing , China .
Yue
Y
Zhou
a State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences , Beijing , China .
Yan
Y
Hu
d Department of Environmental Sciences , Informatics and Statistics, University Ca' Foscari of Venice , Venice , Italy .
Chengfang
C
Pang
d Department of Environmental Sciences , Informatics and Statistics, University Ca' Foscari of Venice , Venice , Italy .
Danail
D
Hristozov
e Toxicology Centre, University of Saskatchewan , Saskatoon , Canada , and.
John P
JP
Giesy
f Stockbridge School of Agriculture, University of Massachusetts , Amherst , USA.
Baoshan
B
Xing
D003198:Computer Simulation; D004926:Escherichia coli / Q000187:drug effects; D006801:Humans; D053768:Metal Nanoparticles / Q000737:chemistry* / Q000633:toxicity*; D008962:Models, Theoretical; D053758:Nanoparticles / Q000737:chemistry* / Q000633:toxicity*; D010087:Oxides; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D014018:Tissue Distribution
D010087:Oxides
Cytotoxicity; in silico modeling; metal oxide nanoparticles; nano-QSARs