Nano-QSAR modeling for ecosafe design of heterogeneous TiO2-based nano-photocatalysts
Alicja Mikołajczyk , Agnieszka Gajewicz , Ewa Mulkiewicz , Bakhtiyor Rasulev , Martyna Marchelek , Magdalena Diak , Seishiro Hirano , Adriana Zaleska-Medynska , Tomasz Puzyn
AbstractThe human health and environmental risk assessment of engineered nanomaterials (NPs) is nowadays of high interest. It is important to assess and predict the biological activity, toxicity, physicochemical proper- ties, fate and transport of NPs. In this work, a combined experimental and computational study is performed in order to estimate the toxicity and develop a predictive model for heterogeneous NPs ( i.e. modified NPs, created from more than one type of NPs). Quantitative structure – activity relationship (QSAR) methods have not been yet adopted for predicting the toxicity/physicochemical properties of modified heterogeneous nanoparticles (so-called heterogeneous NPs). Since the main problem for nano-QSAR/ nano-QSPR modeling of heterogeneous NPs was a lack of appropriate descriptors that are able to ex- press the specific characteristics of 2nd generation NPs, we developed here a novel approach. The novel approach to encode heterogeneous NPs is based on the idea of additive descriptors for mixture systems previously applied only to mixtures of organic/inorganic compounds. Thus, based on the proposed novel approach, we have performed experimental and theoretical studies to develop nano-QSAR models de- scribing the cytotoxicity of 34 TiO 2 -based NPs modified by (poly)metallic clusters (Au, Ag, Pt) to the Chi- nese hamster ovary cell line. The models showed a good predictive ability and robustness. This approach can be used as an efficient tool for assessing the toxicity as well as physicochemical properties of unexplored heterogeneous NPs.
|Journal series||Environmental Science-Nano, ISSN 2051-8153|
|Publication size in sheets||0.5|
|Score|| = 40.0, 11-04-2018, ArticleFromJournal|
= 40.0, 11-04-2018, ArticleFromJournal
|Publication indicators||: 2016 = 6.047 (2) - 2016=6.056 (5)|
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