Diphenhydramine Alters the Disposition of Venlafaxine Through Inhibition of CYP2D6 Activity in Humans
Bibliographic record
Abstract
CYP2D6 is the major enzyme involved in the metabolism of venlafaxine. Subjects with a low CYP2D6 activity have increased plasma concentrations of venlafaxine that may predispose them to cardiovascular side effects. In vitro and in vivo studies showed that diphenhydramine, a nonprescription antihistamine, can inhibit CYP2D6 activity. Therefore, the authors investigated in this study a potential drug interaction between diphenhydramine and venlafaxine. Fifteen male volunteers, nine with the extensive metabolizer (EM) and six with the poor metabolizer (PM) phenotype of CYP2D6, received venlafaxine hydrochloride 18.75 mg orally every 12 hours for 48 hours on two occasions (1 week apart): once alone and once during the concomitant administration of diphenhydramine hydrochloride (50 mg every 12 hours). Blood and urine samples were collected for 12 hours under steady-state conditions. In EMs, diphenhydramine decreased venlafaxine oral clearance from 104+/-60 L/hr to 43+/-23 L/hr (mean +/- SD; p < 0.05) without any effect on renal clearance (4+/-1 L/hr during venlafaxine alone and 4+/-2 L/hr during venlafaxine plus diphenhydramine). In PMs, coadministration of diphenhydramine did not cause significant changes in oral clearance and partial metabolic clearances of venlafaxine to its various metabolites. Diphenhydramine disposition was only slightly affected by genetically determined low CYP2D6 activity or concomitant administration of venlafaxine. In conclusion, diphenhydramine, at therapeutic doses, inhibits CYP2D6-mediated metabolism of venlafaxine in humans. Clinically significant interactions could be encountered during the concomitant administration of diphenhydramine and other antidepressant or antipsychotic drugs that are substrates of CYP2D6.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".