Tomasz Puzyn

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.

Calculation of Quantum-Mechanical Descriptors for QSPR at the DFT Level : Is It Necessary?

Most of the recently published quantitative structure-property relationship (QSPR) models, which can be used to predict environmentally relevant physicochemical data for persistent organic pollutants (e.g., polychlorinated dibenzo- p-dioxins, dibenzofurans, and biphenyls), employ molecular descriptors obtained by means of relatively costly calculations at the density functional theory (DFT) level. However, new semiempirical methods, PM6 and RM1, have recently been developed by J. J. P. Stewarts group. In this study, we compared various QSPR models based on DFT (B3LYP functional) descriptors with the same models based on semiempirical (PM6 and RM1) descriptors. We recalibrated 10 previously published models (for different properties and groups of congeneric compounds) employing PM6 and RM1 descriptors instead of B3LYP ones. We demonstrated that by applying RM1 and PM6 descriptors, we could obtain QSPR models with quality similar to that of models based on B3LYP descriptors. This level of accuracy was out of reach for the models employing AM1- and PM3-based descriptors.

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.

Recent Advances in QSAR Studies

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Concentrations of Mercury in Wild Growing Higher Fungi and underlying Substrate near Lake Wdzydze, Poland

Fourteen species of wild growing mushrooms and surface (0–10 cm) soils were collected near Lake Wdzydze in the northern part of Poland in 1996–1997 to understand the status of mercury pollution. Concentrations of mercury in mushrooms varied between 100±30 and 2400±1900 ng g-1 dry matter in caps and 60±1 and 1300±1500 ng g-1 dry matter in stalks. Concentrations of mercury in underlying soil were between 30±1 and 140±120 ng g-1 dry matter (between 36±18 and 63±100 ng g-1 depending on the soil type). Bioconcentration factors (BCF: concentrations in mushroom/concentrationin soil) of total mercury were between 2.3±1.1 and 90±110 for caps, and between 2.1±1.0 and 53±56 for stalks. Scaly tooth (Sarcodon imbricatum) contained the greatest concentrations of mercury in the flesh. However, there was no significant relationship (p >: 0.05) between mercury content in the fruiting bodies of this speciesto soil mercury concentrations. A significant (p < 0.01) positive relationship between mercury content in caps to underlying soil was noted for European cow bolete (Suillus bovinus), while a negative relationship between mercury content in caps and stalks to underlying soil was observed for Sandy knight-cap (Tricholomaflavovirens), Shaggy scale-head (Pholiota squaroso-adiposa),Gypsy mushroom (Rozites caperata) and Pine spike cap (Chroogomphus rutilus).

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.

Nano-quantitative structure–activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells

As experimental evaluation of the safety of nanoparticles (NPs) is expensive and time-consuming, computational approaches have been found to be an efficient alternative for predicting the potential toxicity of new NPs before mass production. In this background, we have developed here a regression-based nano quantitative structure-activity relationship (nano-QSAR) model to establish statistically significant relationships between the measured cellular uptakes of 109 magnetofluorescent NPs in pancreatic cancer cells with their physical, chemical, and structural properties encoded within easily computable, interpretable and reproducible descriptors. The developed model was rigorously validated internally as well as externally with the application of the principles of Organization for Economic Cooperation and Development (OECD). The test for domain of applicability was also carried out for checking reliability of the predictions. Important fragments contributing to higher/lower cellular uptake of NPs were identified through critical analysis and interpretation of the developed model. Considering all these identified structural attributes, one can choose or design safe, economical and suitable surface modifiers for NPs. The presented approach provides rich information in the context of virtual screening of relevant NP libraries.

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

Nanotechnology has evolved as a frontrunner in the development of modern science. Current studies have established toxicity of some nanoparticles to human and environment. Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs). In the present work, metal electronegativity (χ), the charge of the metal cation corresponding to a given oxide (χox), atomic number and valence electron number of the metal have been used as simple molecular descriptors to build up quantitative structure-toxicity relationship (QSTR) models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli. These descriptors can be easily obtained from molecular formula and information acquired from periodic table in no time. It has been shown that a simple molecular descriptor χox can efficiently encode cytotoxicity of metal oxides leading to models with high statistical quality as well as interpretability. Based on this model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. Moreover, the required information for descriptor calculation is independent of size range of NPs, nullifying a significant problem that various physical properties of NPs change for different size ranges.

QSPR Modeling of Partition Coefficients and Henry’s Law Constants for 75 Chloronaphthalene Congeners by Means of Six Chemometric Approaches—A Comparative Study

n-octanol/water and n-octanol/air partition coefficients were calculated for 75 chloronaphthalenes (CNs) by means of quantitative structure-property relationship (QSPR) strategy to fill significant lacks in the empirical data. The QSPR models based on quantum-chemical descriptors computed on the level of density functional theory using B3LYP functional and 6-311++G** basis set. For each property, six models were identified using chemometric approaches such as: multiple regression method, principal component regression, partial least square regression, partial least square regression with initial elimination of the uninformative variables, partial least square regression with variable selection by a genetic algorithm (GA-PLS), and neural networks with variable selection by a genetic algorithm (GA-NN). They were calibrated and validated using the experimentally measured values of logKOW available for 16 congeners and the values of logKOA existing for 43 congeners. The models were compared regarding to their...