Sequential Application of Ligand and Structure Based Modeling Approaches to Index Chemicals for Their hH.sub.4R Antagonism

The human histamine H.sub.4 receptor (hH.sub.4 R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH.sub.4 R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH.sub.4 R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH.sub.4 R bioactivity. An application of the prediction model on external test set composed of more than 160 hH.sub.4 R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ~4000 chemicals highly indexed as H.sub.4 R antagonists' candidates. Next, a series of 3D models of hH.sub.4 R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH.sub.4 R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.