Evaluation of Angiotensin-Converting Enzyme Inhibitor's Absorption with Retention Data of Micellar Thin-Layer Chromatography and Suitable Molecular Descriptor

Abstract

Twelve angiotensin-converting enzyme (ACE) inhibitors were studied to evaluate correlation between their absorption (ABS) data available in the literature (22-96%) and hydrophobicity parameters (km and Pm/w) obtained in micellar thin-layer chromatography (MTLC) using Brij 35. The theoretical considerations showed that the geometric molecular descriptor-volume value (Vol) should be considered as an independent variable simultaneously with calculated hydrophobicity parameters in multiple linear regression analysis to obtain reliable correlation between ACE inhibitor's absorption and lipophilicity (calculated KOWWINlog P) and that captopril should be excluded from further correlations. The results of MTLC confirmed that between the two hydrophobicity parameters km and Pm/w, for absorption prediction of 11 ACE inhibitors, the micelle-water partition coefficient Pm/w provided higher correlation (R(2) = 0.756), while for the km parameter R(2) = 0.612 was obtained. The micelle-water partition coefficient Pm/w could be considered as analogous to hydrophobicity parameter C0 from reversed-phase thin-layer chromatography. Dissimilar retention behavior of lisinopril indicated its lowest non-polar interaction with micelle, because of its di-acid form. The proposed model which included ACE inhibitors on the opposite site of lipophilicity-lisinopril and fosinopril (KOWWINlog P = -0.96 and KOWWINlog P = 6.61, respectively), both with similar absorption values (25 and 36%, respectively), could indicate that absorption of investigated compounds occurs via two different mechanisms: active and passive transport.

Multiobject Optimization of National Football League Drafts: Comparison of Teams and Experts

“…Due to its flexibility and ease of use, SRD has been used in different fields of science such as eye-tracking [10]; food science [11]; column selection in chromatography [12,13]; variable selection [14]; ordering and grouping octanol-water (logP) partition coefficient determination methods [15,16]; selection of edible insects based on nutritional composition [17]; outlier detection in multivariate calibration [18]; non-parametric ranking of QSAR models [19]; comparison of ensemble learning models [20]; comparison of tea grade identification using electronic tongue data [21]; testing the outer consistency of novel similarity indices [22]; and even ranking of sportsmen [23], just to name a few.…”