Computational modeling design of novel NMDAR agonist for the treatment of Schizophrenia

Schizophrenia (SZ) is a complex, chronic mental disorder characterized by positive symptoms (such as delusions and hallucinations), negative symptoms (including anhedonia, alogia, avolition, and social withdrawal), and cognitive deficits (affecting attention, processing speed, verbal and visuospatial learning, problem-solving, working memory, and mental flexibility). Extensive animal and clinical studies have emphasized the NMDAR hypofunction hypothesis of SZ. Glycine plays a crucial role as an agonist of NMDAR, enhancing the receptor's affinity for glutamate and supporting normal synaptic function and plasticity, that is, signal transmission between neurons. In the absence of glycine or any other co-agonists (serine and D-cycloserine), NMDAR responsiveness to glutamate decreases, reducing its likelihood to open and allow ion flow, which leads to impaired synaptic plasticity and neurotransmission. Current antipsychotic treatments are severely limited, as they only address positive symptoms, can lead to significant neurological and metabolic side effects such as sexual dysfunction, and are effective in only about half of SZ patients. Similarly, direct glycine-site modulators have shown considerable side-effects due to high-dose usage, such as nausea, nephrotoxicity, anxiety, depression, and hyperexcitability resulting from the external administration of glycine, serine, and D-cycloserine. To this effect, the current study considers glycine-like compounds with improved BBB permeability directly targeting the Glycine modulatory site (GMS). A thorough evaluation encompassing ADMET analysis, virtual screening, and molecular dynamics was used to screen the glycine-like library. Data collected revealed Compound_8, Compound_15, and Compound_945 as promising agonists. Further experimental validation is needed to confirm their preclinical relevance as SZ treatment.