Efficacy and safety profiles of FDA-approved dual orexin receptor antagonists in depression: A systematic review of pre-clinical and clinical studies
Bibliographic record
Abstract
BACKGROUND: Sleep disturbances, such as insomnia, are a group of prevalent and debilitating symptoms of Major Depressive Disorder (MDD). Dual Orexin Receptor Antagonists (DORAs) have emerged as potential treatments for insomnia and comorbid psychiatric conditions, such as MDD. We sought to comprehensively examine the efficacy, safety, and pharmacology of DORAs in the context of MDD and insomnia. METHODS: We performed a systematic review of primary research on daridorexant, lemborexant, and suvorexant retrieved from PubMed and OVID databases up to January 2025. Included studies were limited to randomized controlled trials and other studies investigating DORAs that examined the efficacy and safety of these agents on sleep architecture and depressive symptoms in individuals with MDD, Insomnia Disorder (ID), and healthy controls. RESULTS: A total of 19 studies were included in this review. DORAs demonstrated significant improvements in sleep onset latency, wake after sleep onset, and total sleep time, which were consistent across individuals with insomnia and comorbid MDD. The impact across DORAs on depressive symptoms was modest. The safety profiles were generally favourable, with most adverse events being mild to moderate. LIMITATIONS: The studies reviewed were predominantly short-term and there was substantial variability in sample composition. Further research is needed to assess DORAs in larger, long-term clinical trials within the MDD population. CONCLUSION: DORAs show potential in improving sleep parameters in individuals with MDD. While their effects on depressive symptoms in this review are modest, further research is needed to assess long-term efficacy, safety, and their impact on depressive symptoms, as well as to explore potential combination therapies.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".