Identification of H3 Ligands through Hybrid Virtual Screening, Docking, Molecular Dynamics Simulations, and Investigation of Their Biological Effects

Abstract

Introduction: Since the discovery of the histamine H3 receptor (H3R) in 1983, tremendous advances in the pharmacological aspects of H3 receptor antagonists/inverse agonists have been accomplished in preclinical studies. Histamine H3 receptors (H3R) are responsible for modulating the release of histamine and 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. Aims: In the current study, we aimed to identify novel H3R ligands using in silico virtual screening methods. Materials and 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 H3R. 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-H3R ligands as promising candidate molecules based on known parent lead compound. Moreover, the antagonistic activity combined with anti-cholinesterase properties of the two more potent lead compounds were also tested. Furthermore, molecular docking and binding free energy calculations were also conducted for prediction of binding mode and affinity of studied ligands towards H3R and cholinesterase enzymes. Results: In vitro binding assays of selected molecules demonstrated three of them being active within the micromolar and submicromolar Ki range. In addition, biological evaluations revealed inhibitory activity of studied compounds in nanomolar and micromolar values for H3R antagonizing and cholinesterase inhibition, respectively. Disccusion: The results can be used for lead optimization where dual inhibitory activity on H3R and cholinesterase enzymes is needed. Conclusion: Collectively, the current integrated computational and experimental methods used in this work can provide new general insights for systematic hit identification for novel anti-H3R and anti cholineseterase agents from large compound libraries.