A Comparison of the Pharmacokinetics of Oral and Sublingual Cyproheptadine
Bibliographic record
Abstract
BACKGROUND: Cyproheptadine is reported to be effective in treating serotonin syndrome. It is only available as an oral preparation and administration after SSRI overdose treated with activated charcoal is problematic. Sublingual administration may circumvent this problem. The pharmacokinetics of sublingual cyproheptadine are not characterized. This study compares the pharmacokinetics of cyproheptadine following oral and sublingual administration. METHODS: Cross-over, non-blinded, volunteer study using five healthy males. Eight milligrams of oral and sublingual cyproheptadine were administered on separate occasions with a one-week washout period. Sublingual arm subjects were pretreated with 50 g of oral activated charcoal 30 min prior to cyproheptadine, to prevent any gut absorption. Serum cyproheptadine concentration was measured at baseline, 30 min, and 1, 2, 3, 4, 6, 8, and 10 h by liquid chromatography and mass spectroscopy. RESULTS: Mean C(max) for oral and sublingual were 30.0 microg/L and 4.0 microg/L respectively: mean T(max) were 4 h and 9.6 h; mean AUC were 209 and 25 microg x hr/L. Mean +/- SEM within-subject difference between oral and sublingual C(max) was 25.9 +/- 4.1 (p = 0.003) and AUC was 184 +/- 31 (p = 0.004). CONCLUSIONS: Serum concentrations after sublingual cyproheptadine are significantly less than after oral administration. At these concentrations, the sublingual route is unlikely to be effective in treating serotonin syndrome.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".