Dose-Response Evaluation of the Interaction Between Sertraline and Alprazolam In Vivo
Bibliographic record
Abstract
In vitro data show the inhibition of alprazolam metabolism by sertraline via CYP3A4; therefore, using a randomized, double-blind, placebo-controlled design, the authors conducted this study to assess the potential for similar in vivo inhibition in humans. Ten healthy volunteers participated in two test sessions (placebo/alprazolam 1 mg orally) before the initiation of sertraline treatment. Blood samples were obtained over a 32-hour period and pharmacodynamic measures (sedation, psychomotor performance, memory function) were obtained over an 8-hour period. After a minimum of 2 weeks of daily sertraline self-administration (50, 100, or 150 mg/day), test sessions were repeated. Alprazolam concentrations (N = 6, 4, and 6 at sertraline doses of 50, 100, and 150 mg/day, respectively) showed no significant changes based on peak concentration (Cmax), time to maximum concentration (Tmax), elimination half-life (t1/2[beta]), and area under the concentration-time curve (AUC(0-8)), with the exception of a reduced Cmax in the 50 mg/day group. Similarly, dynamic data showed no significant variations based on peak effect, Tmax, and AUC(0-infinity), with the exception of increased peak impairment in one measure of psychomotor performance. No differences were detected between placebo alone and placebo plus sertraline. These findings suggest that sertraline (50-150 mg/day) does not alter the single-dose kinetics or dynamics of alprazolam; therefore, the combination may be prescribed without an increased risk of alprazolam toxicity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".