Towards better understanding of lipophilicity: Assessment of in silico and chromatographic logP measures for pharmaceutically important compounds by nonparametric rankings

Abstract

Lipophilicity is one of the most frequently used physicochemical properties that affects compound solubility, determines its passive transport through biological membranes, influences biodistribution, metabolism and pharmacokinetics. We compared, ranked and grouped chromatographic lipophilicity indices and computationally estimated logP-s by sensitive and robust non-parametric approaches: sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM). Chromatographic indices of fourteen neurotoxins and twenty one 1,2,4-triazole compounds have been derived from typical reversed-phase thin-layer chromatography and micellar chromatography. They were compared with in silico estimated logP-s. Under typical reversed-phase conditions, octadecyl-, octyl-, and cyanopropyl-modified silica have clear advantage over ethyl-, aminopropyl, and diol-modified beds, i.e., the preferable choice of the stationary phase follows this order: octadecyl gt octyl gt cyanopropyl gt ethyl gt octadecyl wettable gt aminopropyl gt diol. Many of these indices outperform the majority of computationally estimated logP-s. Clear distinction can be made based on cross-validation and statistical tests. Oppositely, micellar chromatography may not be successfully used for the lipophilicity assessment, since retention parameters obtained from the typical reversed-phase conditions outperform the parameters obtained by micellar chromatography. Both ranking approaches, SRD and GPCM, although based on different background, provide highly similar variable ordering and grouping leading to the same, above mentioned conclusions. However, GPCM results in more degeneracy, i.e., in some cases it cannot distinguish the lipophilicity parameters whereas SRD and its cross-validated version can. On the other hand GPCM produces a more characteristic grouping. Both methods can be successfully used for selection of the most and least appropriate lipophilicity measures.