Histamine H3 receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects. (24th January 2019)

• Record Type: Journal Article
• Authors: Ghamari, Nakisa; Zarei, Omid; Reiner, David; Dastmalchi, Siavoush; Stark, Holger; Hamzeh‐Mivehroud, Maryam
• Is Part Of: Chemical biology & drug design. Volume 93:Number 5(2019)
• Page Start: 832
• Page End: 843
• Publication Date: 2019-01-24
• Subjects: anti‐H3R agents -- histamine H3 receptor -- molecular docking -- molecular dynamics simulation -- virtual screening; Drugs -- Design -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; 615.19005
• DOI: 10.1111/cbdd.13471
• Languages: English
• ISSNs: 1747-0277

Abstract

Abstract: 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. Abstract : A combination of ligand‐ and structure‐based virtual screening approaches along with molecular modeling methodologies were utilized for identifying novel anti‐H3 R ligands. In vitro binding assays of selected candidate molecules revealed micromolar and submicromolar Ki values for three of the identified molecules. The presented lead candidates may serve as starting points for further medicinal chemistry optimization for development of novel CNS selective H3 R antagonists.