Karolina Jagiello

Direct QSPR: the most efficient way of predicting organic carbon/water partition coefficient (log K OC) for polyhalogenated POPs

The organic carbon/water partition coefficient (KOC) is one of the most important parameters describing partitioning of chemicals in soil/water system and measuring their relative potential mobility in soils. Because of a large number of possible compounds entering the environment, the experimental measurements of the soil sorption coefficient for all of them are virtually impossible. The alternative methods, such as quantitative structure–property relationship (QSPR techniques) have been applied to predict this important physical/chemical parameter. Most available QSPR models have been based on correlations with the n-octanol/water partition coefficient (KOW), which enforces the requirement to conduct experiments for obtaining the KOW values. In our study, we have developed a QSPR model that allows predicting logarithmic values of the organic carbon/water partition coefficient (log KOC) for 1,436 chlorinated and brominated congeners of persistent organic pollutants based on the computationally calculated descriptors. Appling such approach not only reduces time, cost, and the amount of waste but also allows obtaining more realistic results.

Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure–Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure–Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.

Towards modelling of the environmental fate of pharmaceuticals using the QSPR-MM scheme

Pharmaceuticals are considered as a new, important group of pollutants. These compounds can enter the environment via several routes and can disturb the natural balance of ecosystems. Factors affecting the environmental fate of medical substances can be determined with computational modelling. The routine application of the modelling methodology in the environmental risk assessment for newly designed pharmaceuticals would enable prediction of their important physical/chemical properties and forecasting their long-range transport and fate. In this contribution, we present the existing state-of-the-art and review the currently available modelling tools of two groups: Quantitative Structure-Property Relationship techniques and Multimedia Mass-balance models. We discuss the current research needs in the context of extending the applicability of the existing tools onto pharmaceuticals, being a more structurally diversified group of contaminants than persistence organic pollutants, for which the majority of the existing models have been originally developed. Multimedia fate models applied for pharmaceuticals are reviewed.QSPR approach to predict physical/chemical properties of pharmaceuticals is reviewed.QSPR-MM combined model is suggested to predict Pov and LRTP of chemical pollutants.

Spectral density distribution moments as novel descriptors for QSAR/QSPR

We propose spectral density distribution moments as molecular descriptors. We apply the new descriptors for developing a QSPR model that predicts the logarithmic values of subcooled liquid vapor pressure. We consider the infrared spectra of chloronaphthalenes.

Size-dependent electronic properties of nanomaterials: How this novel class of nanodescriptors supposed to be calculated?

In this study, the influence of the size on the electronic properties (e.g. electronic energy) of three nanometal oxides: ZnO, TiO2, and Al2O3 were investigated. The wurtzite, rutile and corundum type of clusters were selected to represent ZnO, TiO2, and Al2O3, respectively. To study the effect of the size on the property, we have build several molecular cluster models with different number of atoms and performed for those clusters quantum–mechanical calculations. For small clusters, up to 40 atoms, the calculations at different levels of theory, including: density functional theory (DFT), Hartree–Fock method, and the semi-empirical PM6 method were carried out. The results from ab initio and DFT calculations were utilized to validate the less time-consuming PM6 approach. The PM6 method was then employed for larger clusters. Linear regression models were developed to describe the relationships between size (number of atoms in cluster) and the electronic properties. The developed and validated methodology is transferable and could be applied for other type of nanosized clusters to calculate properties that are considered as potential nanodescriptors for nano-QSAR modelling.

Addressing a bottle neck for regulation of nanomaterials: quantitative read-across (Nano-QRA) algorithm for cases when only limited data is available

The number and variety of engineered nanoparticles have been growing exponentially. Since the experimental evaluation of nanoparticles causing public health concerns is expensive and time consuming, efficient computational tools are amongst the most suitable approaches to identifying potential negative impacts, to the human health and the environment, of new nanomaterials before their production. However, developing computational models complimentary to experiments is impossible without incorporating consistent and high quality experimental data. Although there are limited available data in the literature, one may apply read-across techniques that seem to be an attractive and pragmatic alternative way of predicting missing physico-chemical or toxicological data. Unfortunately, the existing methods of read-across are strongly dependent on the experts knowledge. In consequence, the results of estimations may vary dependently on personal experience of expert conducting the study and as such cannot guarantee the reproducibility of their results. Therefore, it is essential to develop novel read-across algorithm(s) that will provide reliable predictions of the missing data without the need to for additional experiments. We proposed a novel quantitative read-across approach for nanomaterials (Nano-QRA) that addresses and overcomes a basic limitation of existing methods. It is based on: one-point-slope, two-point formula, or the equation of a plane passing through three points. The proposed Nano-QRA approach is a simple and effective algorithm for filling data gaps in quantitative manner providing reliable predictions of the missing data.

A new metric for long-range transport potential of chemicals.

We propose a new metric for long-range transport potential (LRTP), GIF, based on source-receptor analyses and evaluate the LRTP and persistence of a wide variety of chlorinated and brominated organic compounds using GIF and overall persistence (POV), respectively. We calculated GIF and POV using our global 3D dynamic multimedia model (FATE). Physicochemical properties were obtained from quantitative structure-property relationship (QSPR) models. The FATE-QSPR combined model enabled us to systematically investigate the LRTP and persistence of a wide variety of chemical substances. On average, the estimated GIF and POV for chlorinated compounds were larger than those for their brominated counterparts, with the largest and smallest values found for polychlorinated biphenyls and polybrominated dibenzodioxins, respectively. We also compared GIF with four differently defined LRTP metrics and two LRTP metrics obtained from a simple model. The results of our analyses indicate that the LRTP ranks can differ considerably among LRTP metrics, the differences being dependent on the governing environmental processes, relevant physicochemical properties, and multimedia model.

Geometry optimization of steroid sulfatase inhibitors - the influence on the free binding energy with STS

In the paper we review the application of two techniques (molecular mechanics and quantum mechanics) to study the influence of geometry optimization of the steroid sulfatase inhibitors on the values of descriptors coded their chemical structure and their free binding energy with the STS protein. We selected 22 STS-inhibitors and compared their structures optimized with MM+, PM7 and DFT B3LYP/6–31++G* approaches considering separately the bond lengths, angles, dihedral angles and total energies. We proved that different minimum energy conformers could be generated depending on the choice of the optimization method. However, the results indicated that selection of the geometry optimization method did not affect the optimal STS inhibitor coordinates, and hence the values of molecular descriptors which describe the 3D structure of the molecule. To study the interaction pattern of the STS inhibitors (optimized using different methods) with the target receptor we applied two strategies: AutoDock and PathDock. The docking studies point out that selection of software to docking simulation is one of the crucial factors determining the binding mode of STS inhibitors with their molecular target. Other factor is related to the ligand orientation in the binding pocket. Finally, obtained results indicate that MM+ and PM7 methods (faster and less expensive) could be successfully employed to geometry optimization of the STS inhibitors before their docking procedure as well as for molecular descriptors calculations.

Chemometric outlook on correlations between retention parameters of polar and semipolar HPLC columns and physicochemical characteristics of ampholytic substances of biological and pharmaceutical relevance

The Quantitative Property-Retention Relation (QPRR) approach was applied to analyze the correlations between the retention parameters of ampholytic, biologically active substances and their physicochemical (predicted/spectral) characteristics. The retention parameters were obtained for polar and semipolar HPLC columns at various compositions of mobile phases and pH conditions. These values are a unique collection of chromatographic parameters that are a measure of lipophilicity and, consequently, can be very helpful in assessing pharmacological potency of the compounds investigated. Three QPRR models that meet the predictive capability criteria were developed. The relationships can be used to gain pharmacologically interesting information on the biologically active ampholytic substances.

Molecular features of thymidine analogues governing the activity of human thymidine kinase

Modified uridines seem to be able to work as hypoxic tumour cell radiosensitisers. Before they sensitise cells to ionising radiation, they have to be incorporated into the genomic DNA and the latter process has to be preceded by the phosphorylation of the modified uridine, which in human cells is executed by human thymidine kinase 1 (hTK1). In the current study, we present the quantitative structure-activity relationship (QSAR) model allowing to identify and understand the molecular features of nucleoside derivatives governing the hTK1 kinase activity. The developed model meets all requirements of a reliable QSAR model and is based on only two molecular properties: the shape of the nucleoside determined by atom substitutions and the ability of the molecule to intermolecular interactions with the enzyme. These results have important implications for the rational designing of new hTK1 substrates and should significantly reduce the time and cost of studies on new radiosensitisers.