Melatonin Interaction Resulting in Severe Sedation
Bibliographic record
Abstract
PURPOSE: Natural health products (NHPs), including melatonin, are widely used products. Despite the widespread assumption that all NHPs are safe, they contain pharmacologically active substances and can therefore have adverse effects and/or interact with pharmaceuticals. OBJECTIVE: To investigate the mechanism underlying NHP interactions identified through the Pharmacy SONAR active surveillance study. METHODS: Active surveillance was undertaken in community pharmacies to identify adverse events in patients who had recently taken NHPs together with conventional pharmaceuticals. For suspected NHP-pharmaceutical interactions, the possible mechanism of action was explored by in vitro analysis of samples of different products to identify cytochrome P450 enzyme (CYP) inhibition potential. RESULTS: Active surveillance identified a 19-year-old male taking citalopram, nortriptyline and oxycodone concomitantly and who experienced severe sedation when melatonin was added to this regimen. In vitro analysis involving several melatonin products showed product-dependent inhibition of CYP1A2, CYP2C19 and CYP3A7. CONCLUSION: The adverse event was likely due to a primary pharmacokinetic interaction between melatonin and citalopram; although mechanistically, interactions affecting cytochrome P450-mediated metabolism may have occurred with all of these health products. A pharmacodynamic interaction may also be possible, but beyond the capacity of this study to establish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".