Milica Ž. Karadžić

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.

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.

Retention Data from Normal-Phase Thin-Layer Chromatography in Characterization of Some 1,6-anhydrohexose and D-aldopentose Derivatives by QSRR Method

The relationship between retention behavior of eleven 1,6-anhydrohexose and D-aldopentose derivatives and their molecular characteristics was studied using quantitative structure–retention relationships (QSRR) approach. Retention parameters RM0, obtained by normal-phase thin-layer chromatography, were correlated with molecular and in silico absorption, distribution, metabolism and excretion (ADME) descriptors. For describing the retention behavior of investigated molecules and determining the similarities between molecules, principal component analysis (PCA) was performed, followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR). For both sets of descriptors, PCA resulted in a model with the two significant principal components (PCs). HCA was conducted to confirm the grouping of the compounds already obtained by PCA. MLR equations were established for both sets of descriptors and statistical quality of the generated models was determined by standard statistical measures and cross-validation parameters. According to statistical validation, two very good models with molecular and one with in silico ADME descriptors were obtained. Very good predictive ability of the established mathematical models allows us to estimate retention behavior of structurally similar compounds and to understand their behavior in similar chromatographic systems.

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.

Binding affinity toward human prion protein of some anti-prion compounds — Assessment based on QSAR modeling, molecular docking and non-parametric ranking

ABSTRACT The present study is based on the quantitative structure‐activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrPC) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non‐linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Graphical abstract Figure. No Caption available.

A comparative study of chromatographic behavior and lipophilicity of selected natural styryl lactones, their derivatives and analogues

ABSTRACT This study is based on the analyses of the retention behavior of selected natural styryl lactones and their synthetic analogues in reversed‐phase high‐performance liquid chromatography. Chromatographic separations were achieved applying ZORBAX SB‐C18 column and two different mobile phases: methanol‐water and acetonitrile‐water. Chromatographic lipophilicity of the analyzed compounds was defined by logk0 constant and correlated with in silico molecular descriptors. According to the statistical validation parameters, obtained results indicate that the presented linear and multiple quantitative structure‐retention relationship models can successfully predict the chromatographic lipophilicity of structurally similar compounds. Hierarchical cluster analyses (HCA) was applied in order to group similar compounds according to their chromatographic and in silico lipophilicity. It can be concluded that chromatographic systems with methanol‐water were better for modelling of logk0. Modelling was performed in order to characterize compounds regarding their lipophilicity profiles as future drug candidates. Graphical abstract Figure. No Caption available.

Chemometrics approach based on chromatographic behavior, in silico characterization and molecular docking study of steroid analogs with biomedical importance

ABSTRACT Physicochemical characterization of steroid analogs (triazole, tetrazole, toluenesulfonylhydrazide, nitrile, dinitrile and dione) is considered to be a very important step in further drug selection. This study applies to the determination of lipophilicity of previously synthesized steroid derivatives using reversed‐phase high‐performance liquid chromatography (RP HPLC). Chemometric aspect of chromatographic lipophilicity is given throughout multiple linear regression (MLR) quantitative structure‐retention relationships (QSRR) approach. Minimal inhibitory concentration (MIC) is determined for two steroid derivatives possessing antimicrobial activity against Staphylococcus aureus. Molecular docking study was performed in order to identify the compound with the most promising potential as human cytochrome P450 CYP17A1inhibitor. Identified 3&bgr;‐hydroxyandrost‐5‐eno[16,17‐d]‐1,2,3‐triazole (I.2.) could be recommended for further trials for anticancer drugs and subjected to the absorption, distribution, metabolism, excretion and toxicity (ADMET) evaluation. Graphical Abstract Figure. No Caption available.