Inhibitory Effects of Propafenone on the Pharmacokinetics of Caffeine in Humans
Bibliographic record
Abstract
CYP1A2 is involved in the metabolism of both caffeine and propafenone, a class Ic antiarrhythmic agent. Despite the widespread consumption of caffeine, drug-drug interactions with this agent are often overlooked. This study investigated effects of propafenone on the pharmacokinetics of caffeine. Eight healthy volunteers were included in our study. A total of 300 mg of caffeine was given on 2 occasions, once alone and once during the coadministration of 300 mg propafenone. Serial blood samples were collected and pharmacokinetic parameters were estimated using a population pharmacokinetic approach. A one-compartment PK model with first-order absorption and elimination described plasma concentration profiles. Concomitant administration of propafenone decreased caffeine oral clearance from 8.3 +/- 0.9 L/h to 5.4 +/- 0.7 L/h (P < 0.05). Elimination half-life of caffeine was also increased 54% by propafenone. One of our volunteers was a poor metabolizer of CYP2D6. Concomitant administration of propafenone to this volunteer caused the greatest increase in caffeine plasma concentrations. These results support the concept of competitive inhibition between propafenone and caffeine. Our results suggest that propafenone causes significant inhibition of CYP1A2 activity leading to a decrease in the clearance of caffeine. Caffeine has intrinsic proarrhythmic effects; thus, its coadministration with an antiarrhythmic agent such as propafenone should be used with caution, especially in patients with poor CYP2D6 activity.
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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) | 0.000 | 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".