Sanja O. Podunavac Kuzmanović

Chemometric estimation of the RP TLC retention behaviour of some estrane derivatives by using multivariate regression methods

AbstractQuantitative structure-retention relationship (QSRR) was developed for a series of estrane derivatives, on the basis of their retention data, obtained in reversed-phase thin-layer chromatography (RP TLC), and in silico molecular descriptors. Physicochemical and topological descriptors, as well as molecular bulkiness descriptors, were calculated from the optimized molecular structures. Full geometry optimization was achieved by using Austin Model 1 (AM1) semi-empirical molecular orbital method. In the present study, QSRR analysis was based on principal component analysis (PCA), multiple linear regression (MLR) and partial least squares (PLS) method. PCA was applied in order to reveal similarities or dissimilarities between analytes, and MLR and PLS regression methods were carried out in order to identify the most important in silico molecular descriptors and quantify their influence on the retention behaviour of studied compounds. Physically meaningful and statistically significant structure-retention relationships were established.

Assessment of Chromatographic Lipophilicity of Some Anhydro-D-Aldose Derivatives on Different Stationary Phases by QSRR Approach

This study is based on the analysis of the retention behavior of some anhydro-D-aldose derivatives in reversed-phase thin-layer chromatography (RP-TLC). Chromatographic separations were achieved applying a methanol/water mobile phase and three different stationary phases: C18-bonded silica gel, silica gel impregnated with paraffin oil, and silica gel impregnated with squalane. Retention behavior of the analyzed molecules was defined by constant and correlated with in silico molecular descriptors. According to the statistical validation parameters, obtained results showed that the established multiple-linear quantitative structure-retention relationship models (MLR-QSRR) can successfully predict the chromatographic lipophilicity of structurally similar compounds. In the present study the influence of some functional groups on the total lipophilicity of studied derivatives was determined as well. Principal component analysis (PCA) was applied in order to obtain an overview of similarity or dissimilarity among the analyzed compounds and hierarchical cluster analysis (HCA) was applied in order to compare separation characteristics of the applied stationary phases.

Chemometric estimation of post-mortem interval based on Na+ and K+ concentrations from human vitreous humour by linear least squares and artificial neural networks modelling

The subject of this paper is to determine the post-mortem interval (PMI) using the data obtained by potentiometric measurements of the electrolyte concentrations (potassium and sodium) in human vitreous humour. The data were processed by linear least squares (LLS) and artificial neural network (ANN) procedures. The LLS mathematical models have been developed as calibration models for prediction of the PMI. The quality of the models was validated by the leave one out (LOO) technique and by using an external data set. High agreement between experimental and predicted PMI values indicated the good quality of the derived models. Additionally, we analysed the influence of various factors (the cause of death, sex, differences between electrolyte concentrations in left and right eye) on the accuracy and reliability of obtained PMI. The ANN method was based on 174 forensic cases with different causes of death and known PMI ranging from 3.1–24.1 hours. The external data sets corresponding to 40 selected forensic cases were tested. Excellent correlation between experimental PMI and PMI predicted by ANN was obtained with a coefficient of correlation r2=0.9611. In comparison to the LLS regression method applied on the complete available data, the prediction of PMI with ANN was improved by1.66 hours.

Structure-Retention Analysis of Some 1,6-anhydrohexose and D-aldopentose Derivatives by Linear Multivariate Approach

Quantitative structure-retention relationships (QSRR) was developed for eleven 1,6-anhydrohexose and D-aldopentose derivatives using their retention data (RM0 values), obtained by normal phase (NP) thin-layer chromatography (TLC) and partition coefficients for n-octanol/water bi-phase system–logP. In order to select molecular descriptors that best describe the retention behavior of the investigated molecules, principal component analysis (PCA) was carried out, followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR). The best QSRR models were validated by leave-one-out technique and by the calculation of statistical parameters. Established mathematical models are meaningful and statistically significant and can successfully predict retention behavior of investigated 1,6-anhydrohexose and D-aldopentose derivatives.

Chemometric Approach to Prediction of Antibacterial Agent Production by Streptomyces hygroscopicus

The nutritional requirements for antimicrobial agent production using Streptomyces hygroscopicus were analyzed in shake flask experiments. Antimicrobial activity was tested against Staphylococcus aureus and Bacillus cereus. The mathematical models have been generated with relative high complexity in order to give an adequate fit to the data. All the results suggest a high dependence of produced antimicrobial agent quantities on the amount of carbon, nitrogen, and phosphorus in cultivation medium. The statistical results of the generated models reflect the high predictive ability. The derived models were validated using leave-one-out cross-validation technique, and from statistical point of view, they have significantly high values of the cross-validation parameters.

Lipophilicity estimation and characterization of selected steroid derivatives of biomedical importance applying RP HPLC.

HIGHLIGHTSChromatographic lipophilicity of newly synthesized steroid derivatives was defined using RP HPLC combined with two chromatographic columns.Computational modeling of structures of investigated steroids was carried out.Molecular features influencing the chromatographic lipophilicity were determined and defined.Novel linear and multiple linear QSRR models were generated.Novel classification of investigated steroids was done. ABSTRACT The present paper deals with chromatographic lipophilicity determination of twenty‐nine selected steroid derivatives using reversed‐phase high‐performance liquid chromatography (RP HPLC) combined with two mobile phase, acetonitrile‐water and methanol‐water. Chromatographic behavior of four groups (triazole and tetrazole, toluenesulfonylhydrazide, nitrile and dinitrile and dione) of selected steroid derivatives was studied. Investigated compounds were grouped using principal component analysis (PCA) according to their logk values for both mobile phases. Grouping was in the very good accordance with the polarity and lipophilicity of the investigated compounds. QSRR (quantitative structure‐retention relationship) approach was used to model chromatographic lipophilicity behavior using molecular descriptors. Modeling was performed using linear regression (LR) and multiple linear regression (MLR) methods. The most influential molecular descriptors were lipophilicity descriptors that are important for molecules ability to pass through biological membranes and geometrical descriptors. All established LR‐QSRR and MLR‐QSRR models were statistically validated by standards, cross‐ and external validation parameters as well as with two graphical methods. According to all these assessments, MLR models were better for chromatographic lipophilicity prediction. It was shown that chromatographic systems with methanol‐water were better for modeling of logk than systems with acetonitrile‐water, as well as the systems that contained lower volume fractions of organic component in mobile phase. Modeling was performed in order to obtain lipophilicity profiles of investigated compounds as future drug candidates of biomedical importance.

Lipophilicity Estimation of Some Carbohydrate Derivatives in TLC with Benzene as a Diluent

Quantitative structure–retention relationship analysis has been performed in order to correlate the retention of carboxydrate derivatives with their calculated lipophilicity, using normal-phase thin-layer chromatography. Principal component analysis followed by hierarchical cluster analysis and multiple linear regression was performed. Statistically significant and physically meaningful structure–retention relationships were obtained. Very good predictive ability of the established mathematical models allows us to estimate the retention behavior of structurally similar compounds. Sum of ranking differences was applied for comparing values obtained using different chromatographic systems and for comparing calculated lipophilicity descriptors of investigated derivatives.

Structure-Retention Relationship Study of 2,4-dioxotetrahydro- 1,3-thiazole Derivatives

In this paper correlations between molecular lipophilicity of a twelve 2,4-dioxotetrahydro-1,3-thiazole derivatives and their retention characteristics obtained by reversed-phase thin-layer chromatography are presented. The regression models were defined applying two chemometric approaches: linear regression and multiple linear regression. The studied thizole derivatives were described with molecular lipophilicity descriptors that were used for prediction of their retention characteristic RM. Statistically significant and physically meaningful structure–retention relationships were obtained and cross-validated. The predicted results are very well correlated with the experimental data. Very good predictive ability of the established mathematical models allows us to estimate the retention behavior of structurally similar compounds.