Schizophrenia is a severe mental illness that affects ~1% of the world's population. It is clinically characterized by positive, negative, and cognitive symptoms. Currently available antipsychotic medications are relatively ineffective in improving negative and cognitive deficits, which are related to a patient's functional outcomes and quality of life. Negative symptoms and cognitive deficits are unmet by the antipsychotic medications developed to date. In recent decades, compelling animal and clinical studies have supported the NMDA receptor (NMDAR) hypofunction hypothesis of schizophrenia and have suggested some promising therapeutic agents. Notably, several NMDAR-enhancing agents, especially those that function through the glycine modulatory site (GMS) of NMDAR, cause significant reduction in psychotic and cognitive symptoms in patients with schizophrenia. Given that the NMDAR-mediated signaling pathway has been implicated in cognitive/social functions and that GMS is a potential therapeutic target for enhancing the activation of NMDARs, there is great interest in investigating the effects of direct and indirect GMS modulators and their therapeutic potential. In this review, we focus on describing preclinical and clinical studies of direct and indirect GMS modulators in the treatment of schizophrenia, including glycine, D-cycloserine, D-serine, glycine transporter 1 (GlyT1) inhibitors, and D-amino acid oxidase (DAO or DAAO) inhibitors. We highlight some of the most promising recently developed pharmacological compounds designed to either directly or indirectly target GMS and thus augment NMDAR function to treat the cognitive and negative symptoms of schizophrenia. Overall, the current findings suggest that indirectly targeting of GMS appears to be more beneficial and leads to less adverse effects than direct targeting of GMS to modulate NMDAR functions. Indirect GMS modulators, especially GlyT1 inhibitors and DAO inhibitors, open new avenues for the treatment of unmet medical needs for patients with schizophrenia.
Read full abstract