Novel Histamine H₃ Receptor Ligands by Integrated Ligand- and Structure-Based Computational Modeling

The histamine H₃ receptor (H₃R) is an attractive central nervous system target due to its regulatory role in histaminergic neurotransmission and its ability to modulate the release of several other neurotransmitters implicated in cognition, arousal, and neurodegenerative disorders. In this study, an integrated chemometric workflow was developed to identify novel H₃R ligands by combining structure-based and ligand-based pharmacophore modeling, QSAR analysis, molecular docking, and experimental validation. A structure-based pharmacophore model derived from the H₃R crystal structure and a ligand-based pharmacophore model generated from known H₃R inhibitors were validated by ROC analysis. The top-performing pharmacophore models showed excellent discrimination between actives and decoys. The QSAR model demonstrated good predictive performance (r² = 0.75, q² = 0.78, predicted r² = 0.67), supporting its application in hit prioritization. The validated pharmacophore models were subsequently used to virtually screen the ChemDiv CNS BBB library of 22 790 compounds. Docking, consensus scoring, and QSAR-guided filtering identified 20 top-ranked hits for experimental evaluation in a [³H]-N^α-methylhistamine displacement assay. One hit, D466-0370 (60), exhibited submicromolar affinity for H₃R (K_i = 641 nM), while seven additional compounds showed low micromolar binding affinities. In silico ADMET profiling for the most active hits further supported their compatibility with CNS-relevant chemical space. Overall, this integrated workflow efficiently identified structurally novel H3R ligands with promising biological activities.