ABSTRACTIn view of “excitotoxic” effects of glutamate, wherein excessive excitatory input causes increase in intracellular Ca2+ and ultimately cell death, NMDA receptor has emerged as an important target for treatment and prevention of several neurological disorders, like Alzheimer disease. Prompted by the successful application of in-silico pharmacophore-based virtual screening in lead identification, we have made an effort to implement in-silico protocols to identify novel NMDA receptor antagonist. A series of novel benzo[b]quinolizinium cations as NMDA receptor antagonists have been used as a starting point to develop prognostic pharmacophore models. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, one hydrogen bond acceptor, one hydrophobic and two ring aromatic, showed a correlation (r) of 0.89, root mean square of 0.259, and the cost difference of 43.01 bits between null and fixed cost. The model was thoroughly validated and subjected to a chemical database search, which lead to the identification of 400 hits from NCI and Maybridge databases which were checked for Lipinski’s violation and predictive potency.This reduced the list to 10 compounds, out of which, two most potent compounds were subjected to molecular docking using Libdock software and interestingly, all the docked conformations showed hydrogen bond interactions with important amino acids Tyr214, His88, Thr174, Val169 and Arg121. In summary, through our validated pharmacophore-based virtual screening protocol, we have identified two potent, structurally diverse, druggable and novel NMDA receptor antagonist which might be of great help to address the unmet medical need of Alzheimer disease.
Identification of novel and structurally diverse N-Methyl-D-Aspartate Receptor Antagonists: Successful Application of Pharmacophore Modeling, Virtual Screening and Molecular Docking
