Pharmacokinetic and Pharmacodynamic Evaluation of the Inhibition of Alprazolam by Citalopram and Fluoxetine
Bibliographic record
Abstract
The selective serotonin reuptake inhibitor antidepressant fluoxetine inhibits alprazolam metabolism in vivo by inhibition of the cytochrome P450 3A4 enzyme. Citalopram is a selective serotonin reuptake inhibitor antidepressant that has not yet been fully evaluated with respect to its potential for cytochrome P450 3A4-mediated drug interactions in vivo. Building on the existing in vitro and in vivo evidence that suggest a minimal effect of citalopram on cytochrome P450 3A4, we hypothesized that therapeutic doses of citalopram (20 mg/d), as compared with fluoxetine (20 mg/d), would cause less impairment in the metabolism of the probe drug alprazolam (1 mg) through inhibition of the cytochrome P450 3A4 isozyme as measured by pharmacokinetic and pharmacodynamic parameters in vivo. We found that fluoxetine prolonged the half-life of alprazolam by 16% and increased the area under the curve 0-infinity of alprazolam by 32%, while citalopram did not affect these parameters, although the time of maximum concentration of alprazolam was prolonged by 30 minutes after citalopram administration. Neither selective serotonin reuptake inhibitor significantly affected the pharmacodynamic profile of alprazolam. This experiment suggests differential effects by citalopram and fluoxetine on alprazolam kinetics.
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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 | 0.002 |
| 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 | 0.001 |
| 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".