P.021 Nonclinical studies of abuse potential with dual orexin-receptor antagonists: concordance with real-world use
Bibliographic record
Abstract
Background: Traditional insomnia drugs enhance gamma-aminobutyric acid and are associated with abuse/dependence. Dual orexin-receptor antagonists (DORAs) represent an alternate mechanism promoting wakefulness, rather than inhibition. Nonclinical studies indicate DORAs do not demonstrate abuse potential. Nonetheless, based on human abuse potential (HAP) studies and lack of postmarketing data at approval, DORAs are Schedule 4 controlled substances. However, HAP studies may not predict real-world abuse-potential risk. Methods: Adverse events with preferred terms (PTs) of drug-withdrawal-syndrome, drug-abuse, and drug-dependence were evaluated from Eisai’s ongoing global postmarketing safety surveillance system in the US, Canada, and Japan (20/Dec/2019–30/Sep/2023) and the FDA Adverse Event Reporting System (FAERS; 01/Jan/2015–30/Jun/2023). In FAERS, reports of those PTs from DORAs (lemborexant/suvorexant/daridorexant) were compared with zolpidem and with benzodiazepines approved for patients with insomnia (estazolam/temazepam/triazolam). Results: Since lemborexant’s approval, few of the 3 PTs were reported in Eisai’s surveillance system (~0.15 cases per million patient-days of global exposure). Reports in FAERS for PTs of drug-withdrawal-syndrome, drug-abuse, and drug-dependence for DORAs (10,202 reports) were <0.1%/<0.1%/0.1%, respectively. Reports for benzodiazepines (5534 reports) were 0.8%/12.9%/3.7%, respectively, and 1.0%/9.1%/5.3% for zolpidem (18,330 reports), respectively. Conclusions: Abuse potential may be better represented by nonclinical studies and national surveillance systems, suggesting DORAs may not pose meaningful abuse potential and related risks.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".