Researchers from the University of California, Los Angeles, along with scientists from the Universitat Rovira i Virgili and the University of Bremen, have developed a new screening technique to rapidly assess the safety of metal-oxide nanomaterials in large batches.
The research team discovered that semiconducting metal-oxide nanomaterials demonstrated an electron-transfer effect during their contact with human cells containing electronically active molecules, causing oxidation–reduction reactions and producing oxygen radicals. These oxygen radicals are highly reactive oxygen molecules, which can cause damage to the cells and result in acute inflammation in lungs of animals and humans exposed to the nanomaterials.
The research team hypothesized that the electron energy level or band-gap energy of metal-oxide nanomaterials must be similar to that of electronically active molecules found in the body, thus causing harmful electron transfer and resulting in oxidative damage. Based on this hypothesis, the team monitored two dozen metal-oxide nanoparticles to detect which one is harmful under actual exposure.
The team performed a high-throughput screening assay by using an automated image-capture microscope and robotic equipment to detect the toxicity of the 24 nanomaterials on different cell types within few hours. During the assay, 6 of the 24 nanomaterials were found to cause oxidative damage in cells. The assay results were in line with the team’s prediction based on the band-gap energy. The team then carried out well-planned animal studies on the two dozen materials and their results were also as predicted by the band-gap hypothesis.
The research team believes that this new safety-assessment technique is an ideal replacement to time-consuming conventional technique and can rapidly evaluate the safety of large numbers of new nanomaterials. Since this predictive approach and screening technology is based on the evaluation of the properties of nanomaterials, one can lower the toxicity level of these materials by identifying and redesigning their properties. The use of high-throughput screening may trigger the advancement of computer tools for use in prediction-making.
Source: http://www.ucla.edu/