The comparative study of micellar TLC and RP-TLC as potential tools for lipophilicity assessment based on QSRR approach

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Graphical abstractHighlightsApplication of micellar TLC retention parameters to lipophilicity assessment.Comparison of cationic (CTAB) and anionic (SDS) surfactants as potential modifier of TLC mobile phases.Insights into molecular mechanism of retention in micellar TLC using QSRR approach.Lipophilicity of compound is well known as vital physicochemical property of a molecule, which determines its biological activity. Nonetheless, the assessment of a lipophilicity is still problematic and focuses attention of scientists. Although, the shake-flask method is still considered as a gold standard for experimental determination of lipophilicity, currently the chromatographic approach is mostly used. Among chromatographic methods used for lipophilicity assessment, thin layer chromatography (TLC) is still one of the most popular tools. The main goal of this study was to compare classical reversed-phase thin layer chromatography (RP-TLC) and micellar TLC as potential tools for lipophilicity assessment. Micellar liquid chromatography has significantly lover environment impact than classical reversed-phase liquid chromatography. Additionally comparison of cationic and anionic surfactants (CTAB and SDS), which have different chemical properties, as modifiers of mobile phase in micellar TLC were investigated. The Quantitative Structure–Retention Relationships (QSRR) approach was used in order to present molecular mechanisms of retention in investigated chromatographic systems. The study was based on chemically diverse model set compounds, with a proved therapeutic or toxic potential. According to obtained results the micellar TLC with CTAB as surfactant can be recommended to logP prediction. The obtained QSRR models indicated that adsorption of CTAB monomers on CN modified surface of silica gel and the silanol–quaternary ammonium interactions are possible. Consequently, the reduction of interaction between molecules and free silanol, contributes to the improvement of logP predictions. These result were confirmed by regression and classification methods.

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