Simple TLC-spectrodensitometric method for studying lipophilicity and quantitative analysis of hypoglycemic drugs in their binary mixture

Insights into the lipophilicity of four commonly prescribed antidiabetic drugs and their simultaneous analysis using a simple TLC-spectrodensitometric method: Application to fixed-dose combination tablets and human plasma

The retention and lipophilicity characteristics of four oral antidiabetic drugs namely; Metformin (MET), Linagliptin (LIN), Empagliflozin (EMP), and Dapagliflozin (DAP) were evaluated by a facile TLC-spectrodensitometric method. The developed method was validated and employed for simultaneous determination of the investigated drugs in their synthetic quaternary mixture, single- and multi-component tablets, and human plasma. The separation of the cited drugs was achieved using silica gel G 60F₂₅₄-TLC plates and a mobile system consisting of n-butanol: water: glacial acetic acid (7: 3: 1, v/v/v). After scanning at 234 nm, good linearities (10.0-2000.0 ng/band for each drug) and correlation coefficients (r = 0.99882-0.99972) with lower limits of detection and quantitation (2.17-3.58 and 6.57-10.85 ng/band, respectively) were statistically calculated. The obtained recoveries (98.35-101.38%) proved the wide applicability of the established method for concurrent estimation of the studied antidiabetics in fixed-dose combination tablets and human plasma. Besides, the present work was extended to estimate the lipophilicity parameters of the targeted drugs. Molecular lipophilicity (R_M), relative lipophilicity (R_M0), and lipophilic descriptor (C₀) were calculated for MET, LIN, EMP, and DAP. Good correlations (r = 0.8729-0.9933) between the chromatographic retention data and molecular descriptors of the studied drugs were attained. The obtained results confirmed the poor lipophilicity of MET and LIN compared to EMP and DAP. Lastly, understanding the lipophilicity of the cited drugs may be promising for the future design of safer and more effective formulations for diabetes mellitus, cancer, and Alzheimer's disease. Over and above, this work may be further applied to QSAR studies.