Seltorexant for major depressive disorder
Bibliographic record
Abstract
INTRODUCTION: Preclinical and clinical pharmacologic evidence indicates that orexin systems are relevant to sleep-wake cycle regulation and dimensions of reward and cognition, providing the basis for hypothesizing that they may be effective as therapeutics in mental disorders. Due to the limited efficacy and tolerability profiles of existing treatments for Major Depressive Disorder (MDD), investigational compounds in novel treatment classes are needed; seltorexant, an orexin receptor antagonist, is a potential new treatment currently under investigation. AREAS COVERED: Mechanisms implicated in MDD, including reward and sleep, are first overviewed. Then, the safety, tolerability, and efficacy profiles of seltorexant and the wider context of orexin receptor antagonism for depression are discussed in focus. Preclinical and clinical data are also discussed. PubMed, Medline, Cochrane Library, Embase, Scopus, and Web of Science were systematically searched from inception to 10 October 2024, in accordance with PRISMA guidelines. EXPERT OPINION: Early clinical evidence suggests that seltorexant is effective in treating MDD, both in individuals diagnosed with insomnia and those not, although greater antidepressant effects are observed in individuals with severe sleep disturbance. Results from large phase III clinical trials are needed to confirm efficacy and safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".