Pharmacophore elucidation and 3D-QSAR analysis of a new class of highly potent inhibitors of acid ceramidase based on maximum common substructure and field fit alignment methods

Abstract

Comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) studies have been carried on a series of 2,4-dioxopyrimidine-1-carboxamides as acid ceramidase inhibitors. Two alignment rules for the compounds were defined using maximum common substructure and field fit. The best orientation was then searched by all-orientation search strategy, to minimize the effect of the initial orientation of the structures. The Kennard Stone algorithm was used to divide the entire set into training (25 compounds) and test (7 compounds) sets. Pharmacophore model identification was also performed using DISCOtech algorithm and refinement was carried out using GASP, to highlight important structural features that could be responsible for the inhibitory activity. All constructed models showed appropriate statistical parameters in terms of q2 and rpred2. Based upon the information obtained from CoMFA, CoMSIA, and developed pharmacophore pattern, some key features that may be used to design new inhibitors for acid ceramidase have been identified.