Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: A mechanistic QSTR approach

Kar, Supratik; Gajewicz, Agnieszka; Puzyn, Tomasz; Roy, Kunal; Leszczyński, Jerzy

doi:10.1016/j.ecoenv.2014.05.026

Cited by 107 publications

(68 citation statements)

References 38 publications

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“…The structure-cytotoxicity relationship for the same dataset of 17 metal oxide NPs was further investigated in a succession of papers [18,[26][27][28][29][30][31][32]. Density functional theory (DFT)-based descriptors (energy gap, hardness, softness, electronegativity, and electrophilicity index), in conjunction with the MLR statistical method, were used to find a high correlation between experimental and predicted activity values [27].…”

Section: Metal Oxidesmentioning

confidence: 99%

“…In a model by Kar et al, electronegativity (χ) and charge of the metal cation were used as molecular descriptors to build QSAR models for the prediction of cytotoxicity of metal oxide NPs (Table 1). They hypothesized that small particles of metal oxides release an electron much easier than the same particles in the crystal structure; small fragments initiate formation of reactive oxygen species, which invoke the oxidative stress condition to bacteria [28]. A simple QSAR model with high predictive ability (R 2 = 0.87) was built based on two descriptors: Absolute electronegativity of metal and electronegativity of metal oxide [32].…”

Section: Metal Oxidesmentioning

confidence: 99%

“…The model by Pan et al used the same dataset, a simplified molecular input line entry system (SMILES)-based optimal descriptor and the MLR method, and showed the highest predictive ability towards both training (R 2 = 0.89-0.98) and test set (R 2 (test) = 0.82-0.87) [18]. Other works [20,27,28,32,33] also used the MLR method. Classification models were developed using Monte Carlo modeling [26], random forest (RF) [29,39], the ensemble learning approach [30], the read-across method [31], support vector machines (SVMs) [40], and counter propagation artificial neural networks [38].…”

Section: Metal Oxidesmentioning

confidence: 99%

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

2019

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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.

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Section: Metal Oxidesmentioning

confidence: 99%

Section: Metal Oxidesmentioning

confidence: 99%

Section: Metal Oxidesmentioning

confidence: 99%

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

2019

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“…The concept of QSAR for nanoparticles (nano-QSAR) was already proved [8][9][10][11][12][13][14][15][16][17].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling

Ambure

Aher

Gajewicz

et al. 2015

Chemometrics and Intelligent Laboratory Systems

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152

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“…127,131 There are a large number of molecular, chemical and physical descriptors of those MONMs available in databases. Currently, the simplified molecular inputline entry system (SMILES), 134,135,140,150 "liquid drop" model (LDM)-derived descriptors, 136 molecular operating environment (MOE), 127 and optimal descriptors 139 are the most frequently used databases for descriptor selection. Many descriptors can be obtained readily based on the molecular structure and atomic or group contributions, e.g., molecular weight, van der Waals, surface area, and size.…”

Section: Correlation Between Nano-bio-eco Interactions and Nanotoxicomentioning

confidence: 99%

Toxicity of engineered metal oxide nanomaterials mediated by nano–bio–eco–interactions: a review and perspective

Aker

et al. 2015

Environ. Sci.: Nano

101

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Along with the expanding use of engineered metal oxide nanomaterials (MONMs), there is a growing concern over their unintentional adverse toxicological effects on human health and the environment upon release and exposure. It is inevitable that biota will be exposed to nanomaterials, through intentional administration or inadvertent contact under such circumstances. Therefore, a thorough investigation of the potential nanotoxicity of MONMs at the nano-bio-eco interface is urgently needed. In general, nanomaterials interact with their surrounding environments, biotic and abiotic, immediately upon introduction into the environment. The behavior and fate of MONMs are influenced by the dynamics of the environment. Thus, understanding the interactions at the nano-bio-eco interface is necessary for selecting and designing MONMs with minimum adverse impacts. Despite the limitations of currently available techniques, careful characterization of nanomaterials and the choosing of methodologies that promote further risk assessment promise more reliable and accurate data output. Conventional toxicological analysis techniques lack the power to handle the large datasets generated from in vitro/in vivo observations. This paper provides a comprehensive review of the recent experimental and theoretical studies on the toxicity of MONMs mediated by two-way or three-way interactions. In the Perspectives, we also call for more open collaborations between industry, academia, and research labs to facilitate nanotoxicological studies focused specifically on interactions at the nano-bio-eco interface, leading to safe and effective nanotechnology for commercial, environmental, and medicinal use. Environ. Sci.: Nano This journal isThe behavior and fate of metal oxide nanomaterials (MONMs) are influenced by the dynamic interactions among the different compartments in natural environments. Thus, understanding the interactions at the nano-bio-eco interface is necessary for selecting and designing MONMs with minimum adverse impacts. This paper provides a comprehensive review of the recent experimental and theoretical studies on the toxicity of MONMs mediated by two-way or three-way interactions. In the Perspectives, we also call for more open collaborations between industry, academia, and research labs to facilitate nanotoxicological studies focused specifically on interactions at the nano-bio-eco interface, leading to safe and effective nanotechnology for commercial, environmental, and medicinal use.

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Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: A mechanistic QSTR approach

Cited by 107 publications

References 38 publications

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

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

“NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling

Toxicity of engineered metal oxide nanomaterials mediated by nano–bio–eco–interactions: a review and perspective

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