Histamine H3 receptors (H3 R), belonging to G-protein coupled receptors (GPCR) class A superfamily, are responsible for modulating the release of histamine as well as of other neurotransmitters by a negative feedback mechanism mainly in the central nervous system (CNS). These receptors have gained increased attention as therapeutic target for several CNS related neurological diseases. In the current study, we aimed to identify novel H3 R ligands using in silico virtual screening methods. To this end, a combination of ligand- and structure-based approaches was utilized for screening of ZINC database on the homology model of human H3 R. Structural similarity- and pharmacophore-based approaches were employed to generate compound libraries. Various molecular modeling methodologies such as molecular docking and dynamics simulation along with different drug likeness filtering criteria were applied to select anti-H3 R ligands as promising candidate molecules based on different known parent lead compounds. In vitro binding assays of the selected molecules demonstrated three of them being active within the micromolar and submicromolar Ki range. The current integrated computational and experimental methods used in this work can provide new general insights for systematic hit identification for novel anti-H3 R agents from large compound libraries.
Keywords: anti-H3R agents; histamine H3 receptor; molecular docking; molecular dynamics simulation; virtual screening.
© 2018 John Wiley & Sons A/S.