New Antidepressant Development in the Treatment of Depression.

Major depressive disorder (MDD) continues to pose a major therapeutic challenge due to its clinical heterogeneity. This chapter looks at the development of antidepressant treatments, starting with early interventions such as electroconvulsive therapy (ECT), monoamine oxidase inhibitors (MAOIs) and tricyclic antidepressants (TCAs). Although these treatments targeted the monoaminergic system, they had significant limitations in terms of safety and efficacy. The introduction of selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) improved tolerability but left unmet needs, particularly in terms of treatment resistance and side effects. In response, research has expanded beyond monoamines and focused on new mechanisms. A breakthrough came with N-methyl-D-aspartate (NMDA) receptor antagonists such as ketamine and esketamine, which achieved fast-acting effects and shifted the focus to glutamatergic modulation. Other developments in this area include modulators such as partial agonists, positive allosteric modulators (PAMs), and negative allosteric modulators (NAMs). In addition, gamma-aminobutyric acid (GABA) modulation has gained attention, with neurosteroids such as zurolone (approved for postpartum depression [PPD]) representing a new therapeutic approach. Other new strategies target the opioid system, particularly kappa-opioid receptor (KOR) antagonism, whose role in the treatment of anhedonia and depression is being investigated. Psychedelics, including psilocybin, have come back into focus as potential treatments due to their ability to elicit rapid and sustained antidepressant effects via agonism of the serotonin 2A receptor (5-HT2A), although their efficacy and safety require further research. In addition, innovative treatments targeting orexin, trace amine-associated receptor 1 (TAAR1) and members of the Q subfamily of voltage-gated potassium channels (KCNQ) are also in development. Despite these advances, some challenges remain. These include diagnostic heterogeneity, incomplete understanding of neurobiological mechanisms, limitations of preclinical models, lack of reliable biomarkers, and economic obstacles. Future advances could be driven by artificial intelligence (AI), which has the potential to revolutionize drug discovery, optimize clinical trials, and personalize treatments for patients.