J. Planar Chromatogr. 35, 265-272 (2022). HPTLC of newly synthesized azaisocytosine-containing congeners on RP-CN with buffered (pH = 7.4) 0.1 M sodium dodecyl sulphate with 20 % of tetrahydrofuran. Detection under UV light at 254 nm. The micellar chromatographic parameters measured using the TLC technique were proposed as lipophilicity descriptors, and, together with the molecular weight and polarizability, the descriptors were applied in the QSARs methodology to predict the compounds’ blood‒brain distribution. 

Food Chem. 334, 127496 (2021). TLC of 36 compounds of 11 classes of bioactives including polyphenols, flavonoids, phytosterols, saponin and saccharides on silica gel. The relationship between structure and color of the bioactives was studied using a broad-spectrum staining solution: p-anisaldehyde - acetic acid - 95 % ethanol - sulfuric acid 920:375:338:125, followed by heating at 250 °C for 10-20 s. Color formation was recorded by a mobile phone camera. Videos were disassembled into frames employing the functions of Matlab. 

Chemosphere. 81, 299-305 (2010). HPTLC of eleven mono- and poly-substituted phenolic
compounds on RP-18 with methanol - water (with increasing methanol content from 40-70 %) (1), acetonitrile - water (40-70 %) (2), and acetone - water (45-75 %) (3) and on cyano phase with methanol - water (30-60 %) (4) and acetonitrile - water (30-55 %) (5). In all instances, the content of organic modifier was increased in steps of 5 %. Detection under UV light at 254 nm. Chromatographic descriptors were statistically established to determine the soil–water partition coefficient for the compounds.

J. Liq. Chromatogr. Relat. Technol. 43, 925-933 (2020). HPTLC of 21 cycloalkylspiro-5-hydantoins on RP-18 with mixtures of water and organic solvent (acetone: 0.5-0.8 v/v, 1,4-dioxane: 0.5-0.74 v/v, tetrahydrofuran: 0.4-0.7 v/v, acetonitrile: 0.5-0.7 v/v). Detection at UV 254 nm. The logarithm of partition coefficient, log P, for the investigated compounds was calculated. Multivariate data analysis (hierarchical cluster analysis (HCA) and principal component analysis (PCA) was performed.

J. Liq. Chromatogr. Relat. Technol. 43, 852-862 (2020). HPTLC of diphenylacetamide derivatives on RP-18 with binary mixtures of organic modifier (ethanol, n-propanol, tetrahydrofuran and acetonitrile 36–52 % with 4 % increments). Detection at UV 254 nm. Lipophilicity of examined derivatives was determined. Two multivariate methods, Cluster Analysis, (CA) and Principal Component Analysis, (PCA) were performed to study the compliance of the chromatographic parameters, the standard measure of lipophilicity, the pharmacokinetic predictors and the toxicity parameters of the substances.

J. Liq. Chromatogr. Relat. Technol. 43, 580-588 (2020). Retardation factor (hRF) of 42 amino acids in two different eluents (acetonitrile - sodium azide and 1,2 dioxane-sodium azide solutions) were predicted by different quantitative structure-retention relationship (QSRR) methods. The method analyzed the effect of sum of geometrical distances between N and O in separation of amino acids in RP-TLC.

J. Planar Chromatogr. 33, 245-253 (2020). HPTLC of twenty-one chalcones on RP-18 with three binary combinations (acetonitrile - water, ethanol - water and acetone - water) in the following compositions: 1:1, 3:2, 7:3 and 4:1. Detection at UV 254 nm. Acetonitrile - water was found to be the most suitable system for lipophilicity prediction of tested compounds using calculated logP values. Quantitative structure-retention relationship (QSRR) analysis was performed to identify the most important structural and physico-chemical properties that influence retention behavior of tested compounds.

J. Liq. Chromatogr. Relat. Technol. 43, 328-335 (2020). HPTLC of verapamil, diltiazem, ivabradine, and its related analogs on silica gel with acetonitrile - methanol (acidified with 25 % formic acid) with the following volume fraction of methanol: 0.25, 0.20, 0.15, 0.13 and 0.10. Detection under UV 254 nm. Retention behavior was investigated and quantitative structure retention relationship (QSRR) modeling was performed with the use of the stepwise multiple linear regression.