Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles

Abstract

It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

Frameworks and tools for risk assessment of manufactured nanomaterials

“…Quantitative Nano-structure Activity Relationships (QNAR) (Burello and Worth, 2011a, Burello and Worth, 2011b, Puzyn et al, 2011, Puzyn et al, 2009a, Puzyn et al, 2009b, Toropov et al, 2007, Toropov and Leszczynski, 2006, Liu et al, 2013b, Liu et al, 2014, Liu et al, 2013a, Gómez et al, 2013 In silico tools for hazard assessment (Liu et al, 2014, Liu et al, 2013a, Liu et al, 2013b In silico tools for hazard assessment Some of these tools are capable of assessing uncertainties. The Precautionary Matrix for Synthetic Nanomaterials uses a "specific framework conditions" criterion that represents uncertainties resulting from knowledge gaps with respect to the origin of the MNs, their characteristics and uses.…”

“…Such ITS would combine testing and non-testing methods to generate data for RA. In this context the development and application of in silico tools has become prominent and a number of such tools have been proposed (Burello and Worth, 2011a, Burello and Worth, 2011b, Puzyn et al, 2011, Puzyn et al, 2009a, KAR et al, 2014, Richarz et al, 2015, Toropova and Toropov, 2015. Statistical analysis and machine learning methods (e.g.…”

Assessing nanoparticle toxicity in cell-based assays: influence of cell culture parameters and optimized models for bridging the in vitro–in vivo gap

“…Coli based on the effect of 17 different metal oxide NP 138 . However, many obstacles still need to be overcome before the first QSAR can be implemented that allows the prediction of the adverse effects of any NP towards human health.…”

Titanium dioxide nanomaterials cause endothelial cell leakiness by disrupting the homophilic interaction of VE–cadherin

“…Species like vascular endothelial growth factor (VEGF), thrombin, angiopoietin-like 4 protein 4 and histamine 5 can perturb this tightly regulated intercellular and intracellular homeostasis and result in ECL. Toxic metal oxide NM are known to be endocytosed and induced oxidative stress 6,7 ; indirectly leading to ECL 8 . However, we postulate a novel mechanism where ECL can occur without uptake of NM but by virtue of their small dimensions, NM can migrate into and disrupt the adherens junction between endothelial cells.…”