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Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia
Physical Faculty, St. Petersburg State University, 7/9 Universitetskaya Naberezhnaya, 199034 St. Petersburg, Russia
Institute of Physiologically Active Compounds, Russian Academy of Sciences, Severny Proezd 1, 142432 Chernogolovka, Moscow Region, Russia
Author to whom correspondence should be addressed.
Academic Editor: Marjan Vračko
Molecules 2019, 24(24), 4537;
Received: 30 October 2019 / Revised: 24 November 2019 / Accepted: 10 December 2019 / Published: 11 December 2019
(This article belongs to the Special Issue Integrated QSAR)
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. View Full-Text
Keywords: engineered nanomaterials; safety of nanomaterials; toxicological tests; modeling; descriptors; quasi-QSAR engineered nanomaterials; safety of nanomaterials; toxicological tests; modeling; descriptors; quasi-QSAR
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MDPI and ACS Style

Buglak, A.A.; Zherdev, A.V.; Dzantiev, B.B. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules 2019, 24, 4537.

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