Danuta Leszczynska

Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles

It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

Advancing risk assessment of engineered nanomaterials: Application of computational approaches

Nanotechnology that develops novel materials at size of 100nm or less has become one of the most promising areas of human endeavor. Because of their intrinsic properties, nanoparticles are commonly employed in electronics, photovoltaic, catalysis, environmental and space engineering, cosmetic industry and - finally - in medicine and pharmacy. In that sense, nanotechnology creates great opportunities for the progress of modern medicine. However, recent studies have shown evident toxicity of some nanoparticles to living organisms (toxicity), and their potentially negative impact on environmental ecosystems (ecotoxicity). Lack of available data and low adequacy of experimental protocols prevent comprehensive risk assessment. The purpose of this review is to present the current state of knowledge related to the risks of the engineered nanoparticles and to assess the potential of efficient expansion and development of new approaches, which are offered by application of theoretical and computational methods, applicable for evaluation of nanomaterials.

CORAL: Quantitative structure–activity relationship models for estimating toxicity of organic compounds in rats

For six random splits, one‐variable models of rat toxicity (minus decimal logarithm of the 50% lethal dose [pLD50], oral exposure) have been calculated with CORAL software (http://www.insilico.eu/coral/). The total number of considered compounds is 689. New additional global attributes of the simplified molecular input line entry system (SMILES) have been examined for improvement of the optimal SMILES‐based descriptors. These global SMILES attributes are representing the presence of some chemical elements and different kinds of chemical bonds (double, triple, and stereochemical). The “classic” scheme of building up quantitative structure–property/activity relationships and the balance of correlations (BC) with the ideal slopes were compared. For all six random splits, best prediction takes place if the aforementioned BC along with the global SMILES attributes are included in the modeling process. The average statistical characteristics for the external test set are the following: n = 119 ± 6.4, R2 = 0.7371 ± 0.013, and root mean square error = 0.360 ± 0.037.

Prediction of rate constants for radical degradation of aromatic pollutants in water matrix: A QSAR study

We present the results of the QSAR/QSPR study on the degradation rate constants of 78 aromatic compounds by the hydroxyl radicals in water. A genetic algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. Additionally to DRAGON descriptors, the parameters from quantum-chemical calculations at semiempirical and at density functional theory level (B3LYP/6-31G(d,p)) were applied. The most predictive model is a four-variable model that had a good ratio of the number of variables and the predictive ability to avoid overfitting. As it was expected, the main contribution to the degradation rate was given by the E(HOMO) parameter. Additionally, a number of topological descriptors in selected models showed an importance of polarizability term regarding the degradation rate of compounds. Overall, the applied GA-MLRA approach with the use of quantum-chemical and DRAGON generated descriptors showed good results in this study. The obtained statistically robust structure-degradation rate model can be used for future studies of the presence of organic compounds in the environment, and especially their degradation by hydroxyl radicals as a part of a water/wastewater treatment.

Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies

Abstract The production of nanomaterials increases every year exponentially and therefore the probability these novel materials that they could cause adverse outcomes for human health and the environment also expands rapidly. We proposed two types of mechanisms of toxic action that are collectively applied in a nano-QSAR model, which provides governance over the toxicity of metal oxide nanoparticles to the human keratinocyte cell line (HaCaT). The combined experimental–theoretical studies allowed the development of an interpretative nano-QSAR model describing the toxicity of 18 nano-metal oxides to the HaCaT cell line, which is a common in vitro model for keratinocyte response during toxic dermal exposure. The comparison of the toxicity of metal oxide nanoparticles to bacteria Escherichia coli (prokaryotic system) and a human keratinocyte cell line (eukaryotic system), resulted in the hypothesis that different modes of toxic action occur between prokaryotic and eukaryotic systems.

Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli.

Convenient to apply and available on the Internet software CORAL (http://www.insilico.eu/CORAL) has been used to build up quantitative structure-activity relationships (QSAR) for prediction of cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of concentration for 50% effect pEC50). In this study six random splits of the data into the training and test set were examined. It has been shown that the CORAL provides a reliable tool that could be used to build up a QSAR of the pEC50.

Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling

Nowadays nanotechnology is one of the most promising areas of science. The number and quantity of synthesized nanomaterials increase exponentially, therefore it is reasonable to expect that comprehensive risk assessment based only on empirical testing of all novel engineered nanoparticles (NPs) will very soon become impossible. Hence, the development of computational methods complementary to experimentation is very important. Quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models widely used in pharmaceutical chemistry and environmental science can also be modified and adopted for nanotechnology to predict physico-chemical properties and toxicity of empirically untested nanomaterials. All QSPR/QSAR modelling activities are based on experimentally derived data. It is important that, within a given data set, all values should be consistent, of high quality and measured according to a standardized protocol. Unfortunately, the amount of such data available for engineered nanoparticles in various data sources (i.e. databases and the literature) is very limited and seldom measured with a standardized protocol. Therefore, we have proposed a framework for collecting and evaluating the existing data, with the focus on possible applications for computational evaluation of properties and biological activities of nanomaterials.

The interaction of nitrobenzene with the hydrate basal surface of montmorillonite: an ab initio study

The results of an ab initio Hartree–Fock study of the cluster molecular model mimicking the ditrigonal cavity of montmorillonite are reported. Full optimization of the geometry has been applied for the Si6Al6O30H18 cluster using the HF/6-31G(d) basis set. In order to obtain a model of the isomorphic substitution in the alumino oxygen layer of clay minerals, a magnesium atom has replaced one of the aluminium atoms of the alumino–oxygen layer in the cluster. To compensate for the negative charge which inevitably appears in this type of substitution, the Na+ exchangeable cation has been placed in the area with the most negative electrostatic potential in the basal plane of the cluster model. Finally, we have calculated the geometry and interaction energy for the adsorption complex which is formed by the interaction of nitrobenzene (the simplest nitroaromatic compound, having the chemical formula C6H5NO2) with the hydrated surface of clay minerals. The predicted BSSE corrected energy of the interaction is of the same order (11.8 kcal mol−1 at the B3LYP/6-31G(d)//HF/6-31G(d) level) as has been estimated for the adsorption of nitro-compounds interacting with full molecular contacts. We have concluded that this type of adsorption of nitro-compounds should be at least as effective as the adsorbtion which includes the donor–acceptor type of interactions with a nonhydrated silicon–oxygen surface.

InChI-based optimal descriptors: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors by correlation balance.

The International Chemical Identifier (InChI) has been used to construct InChI-based optimal descriptors to model the binding affinity for fullerene[C60]-based inhibitors of human immunodeficiency virus type 1 aspartic protease (HIV-1 PR). Statistical characteristics of the one-variable model obtained by the balance of correlations are as follows: n=8, r(2)=0.9769, q(2)(LOO)=0.9646, s=0.099, F=254 (subtraining set); n=7, r(2)=0.7616, s=0.681, F=16 (calibration set); n=5, r(2)=0.9724, s=0.271, F=106, R(m)(2)=0.9495 (test set). Predictability of this approach has been checked with three random splits of the data: into the subtraining set, calibration set, and test set.

Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

The rate constants (KOH) of reactions between 78 organic aromatic pollutants and hydroxyl radical were examined. Simplified molecular input line entry system was used as representation of the molecular structure of the pollutants. Quantitative structure–property relationships was developed using CORAL software (http://www.insilico.eu/CORAL) for four random splits of the data into the subtraining, calibration, and test sets. The obtained results reveal good predictive potential of the applied approach: correlation coefficients (r2) for the test sets of the four random splits are 0.75, 0.91, 0.84, and 0.80. Using the Monte Carlo method CORAL software generated the optimal descriptors for one‐variable models. The reproducibility of each model was tested performing three runs of the Monte Carlo optimization. The current data were compared to previous results and discussed.