Due to their unique physicochemical properties, nanomaterials have the potential to interact with living organisms in novel ways. Nanomaterial variants are too numerous to be screened for toxicity individually by traditional animal testing. Existing data on the toxicity of inhaled nanomaterials in animal models are sparse in comparison to the number of potential factors that may affect toxicity. This paper presents meta-analysis-based risk models developed with the machine-learning technique, random forests (RFs), to determine the relative contribution of different physical and chemical attributes on observed toxicity. The findings from this analysis indicate that carbon nanotube (CNT) impurities explain at most 30% of the variance in pulmonary toxicity as measured by polymorphonuclear neutrophils (PMNs) count. Titanium dioxide nanoparticle size and aggregation affected the observed toxic response by less than 10%. Differences in observed effects for a group of metal oxide nanoparticles associated with differences in Gibbs free energy on lactate dehydrogenase (LDH) concentrations amount to only 4% to the total variance.
|Original language||English (US)|
|Journal||ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering|
|State||Published - Jun 2016|
All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Safety Research
- Mechanical Engineering