Microchemical Journal 158 (2020): 105236.
Nowadays screening methods for assessment of biological properties of drug candidates are highly desired. Among naturally occurring membranes, one of the most important is the blood-brain barrier (BBB). BBB plays crucial role for central nervous system active drug candidates, since each molecule targeting a receptor in the brain must pass through the BBB first. This work assesses the possibility to apply micellar electrokinetic chromatography (MEKC) with cetrimonium bromide (CTAB) as surfactant in order to predict logBB. A model set of 45 marketed drugs, with known logBB values, varies in terms of chemical structures and pharmacological activities was used in this study. The established models were additionally supported by P_VSA-like descriptors molecular descriptors calculated by Dragon software. Two regression methods, multiple linear regression and support vector machine were evaluated and compared in terms of effectives of prediction of logBB. Both models showed similar prediction power, evidenced by similar values of root-mean-squared error of cross-validation 0.310 and 0.314 respectively. Proposed models met the Tropsha et al. criteria R2 > 0.6 and Q2 > 0.5 These results indicate that obtained model can be useful to predict BBB permeability of drug candidates and are attractive alternatives of time-consuming and demanding direct methods for log BB measurement.
