Presently available antidepressant drugs have unpleasant side effects, addictive properties, or can induce schizophrenia. Developing fast-onset antidepressants without these drawbacks is thus an important neuropharmacological goal.
Scientists have opened the door for a new class of fast-onset antidepressants.
According to a recent study, a new small-molecule compound that regulates the firing of serotonergic neurons has a fast-acting antidepressant effect. The results pave the way for the development of a new class of treatments for major depressive disorder (MDD) and other difficult-to-treat mood disorders. MDD is one of the most common mental disorders, affecting hundreds of millions of individuals globally.
The majority of today’s antidepressants target the serotonin transporter (SERT). These drugs, however, are limited. SERT-targeted antidepressants not only take up to 4 weeks to take effect, but they may also have serious side effects, including suicide, and only a percentage of individuals who take them recover from depression following treatment. While ketamine has been considered as an alternative, its potentially addictive properties as well as the danger of schizophrenia have aroused concerns.
As a result, there is a need for new, fast-acting antidepressant targets and compounds without these serious drawbacks. Here, Nan Sun and colleagues present one such solution. Sun and his team designed a fast-onset antidepressant that works by disrupting the interaction between SERT and neuronal nitric oxide synthase (nNOS).
The authors found that disassociating SERT from nNOS in the brains of mice reduced intercellular serotonin in a brain region called the dorsal raphe nucleus. This enhanced serotonergic neuron activity in this area and dramatically increased serotonin release into the medial prefrontal cortex. According to the findings, this resulted in a fast-acting antidepressant effect in a mouse model of MDD.
Reference: “Design of fast-onset antidepressant by dissociating SERT from nNOS in the DRN” by Nan Sun, Ya-Juan Qin, Chu Xu, Tian Xia, Zi-Wei Du, Li-Ping Zheng, An-an Li, Fan Meng, Yu Zhang, Jing Zhang, Xiao Liu, Ting-You Li, Dong-Ya Zhu and Qi-Gang Zhou, 27 October 2022, Science.
DOI: 10.1126/science.abo3566
Source: SciTechDaily
