Buglak, A.A.; Zherdev, A.V.; Dzantiev, B.B. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules2019, 24, 4537.
Buglak, A.A.; Zherdev, A.V.; Dzantiev, B.B. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules 2019, 24, 4537.
Buglak, A.A.; Zherdev, A.V.; Dzantiev, B.B. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules2019, 24, 4537.
Buglak, A.A.; Zherdev, A.V.; Dzantiev, B.B. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules 2019, 24, 4537.
Abstract
Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure-activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case
Keywords
engineered nanomaterials; safety of nanomaterials; toxicological tests; modeling; descriptors; quasi-qsar
Subject
Chemistry and Materials Science, Nanotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
