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.

     

Using the Isalos platform to develop a (Q)SAR model that predicts metal oxide toxicity utilizing facet-based electronic,image analysis-based,and periodic table derived properties as descriptors

Structural Chemistry - Engineered nanoparticles (NPs) are being studied for their potential to harm humans and the environment. Biological activity, toxicity, physicochemical properties, fate, and...     

Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of micro-electro-mechanical systems

Micro-electro-mechanical systems (MEMS) are involved in various fields of nanotechnology. MEMS are characterized by complex and unclear molecular architecture. However, in most cases information about chemical composition and conditions of synthesis is available. One-variable models for thermal conductivity of MEMS are suggested. These models are based on the representation of MEMS by their chemical composition and technological attributes. We have examined three random splits of available data into the training set and validation set. The average statistical characteristics of these models are quite good. Development of suggested here models is carried out without information on molecular architecture of MEMS.     

Global versus local QSPR models for persistent organic pollutants: balancing between predictivity and economy

Experimentally determined data on the key physicochemical parameters for halogenated congeners of persistent organic pollutants (POPs) are available only for a limited number of compounds. In the absence of experimental data, a range of computational methods can be applied to characterize those species for which experimental data is not available. One of the techniques widely used in this context is quantitative structure–property relationships (QSPR) approach. There are two ways to develop the QSPR models: using a more complex global model or fitting a simple local model that covers a specific class of chemically related compounds. The essence of the study was to investigate, if local models have significantly better explanatory and predictive ability than global models with wider applicability domains. Based on the obtained results, we concluded that whenever global models fulfill all quality recommendations by OECD, they would be applied in practice as more efficient ones in state of more time consuming procedure of modeling the particular groups of POPs one-by-one. On the contrary, local models are applicable to solve specific problems (i.e., related to only one group of POPs), when high-quality experimental data are available for a sufficient number of training and validation compounds.     

Inflow of polonium, uranium and plutonium radionuclides in Odra River catchment area assessment by environmetric expertise

The study deals with the application of cluster analysis (CA) and non-parametric tests (Shapiro–Wilk, Kruskal–Wallis, Dunn, U Mann–Whitney) to classify and interpret of a monitoring data set for Odra River water quality assessment based on concentration values of radiochemical parameters. The data set represents results for 3 alpha emitters (²¹⁰Po, ²³⁸U and ^239+240Pu) measured in surface water samples collected at 13 different sampling locations (5 in major Odra stream while 8 in Odra tributaries) within four seasons: winter, spring, summer and autumn, in the framework of 1 year-term quality monitoring research. The correlation analysis of polonium, uranium and plutonium data indicates that significant values of Spearman’s correlation coefficient appears between ²¹⁰Po and ^239+240Pu (r = 0.55 in autumn and 0.77 in winter as well as 0.49 in all year), while statistical significant correlation between uranium and plutonium as well as uranium and polonium were not found. In the Odra drainage basin, the biggest differences were observed in the case of ²³⁸U. The hypothesis about possible geographic and seasonal differences between concentration of ²¹⁰Po, ²³⁸U and ^239+240Pu in the Odra River catchment area was verified by cluster analysis (CA). Finally, to asses if there are statistically significant differences in mean concentration value of ²¹⁰Po, ²³⁸U and ^239+240Pu for Vistula and Odra Rivers drainage basins were obtained by used of the non-parametric tests. Comparing to Vistula catchment area, statistically different concentration of ²¹⁰Po and ^239+240Pu in all year was observed for river samples collected on Odra drainage basin.