PI-53Effects of propafenone on the pharmacokinetics of caffeine
Bibliographic record
Abstract
BACKGROUND/AIMS 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. METHODS Eight healthy volunteers were included in a two-phase study. Caffeine (300 mg) was given on two occasions; once alone and once during the coadministration of propafenone (300 mg). Serial blood samples were collected and pharmacokinetic parameters were estimated using a population pharmacokinetic approach. RESULTS A one-compartment PK model with first order absorption and elimination described caffeine data. Clearance of caffeine was significantly altered by the coadministration of propafenone; a decrease from 8.3±0.9 L/h to 5.4±0.7 L/h was observed for the oral clearance. Elimination half-life of caffeine was also increased 55% by propafenone. A greater increase in plasma levels of caffeine was observed during coadministration of propafenone in a poor metabolizer of CYP2D6. These results support the concept of competitive inhibition between caffeine and propafenone. CONCLUSIONS Propafenone causes significant inhibition of CYP1A2 activity. Caffeine is associated with supraventricular tachycardia; thus, its co-administration with an antiarrhythmic agent such as propafenone should be used with caution especially in patients with poor CYP2D6 activity. Clinical Pharmacology & Therapeutics (2005) 79, P21–P21; doi: 10.1016/j.clpt.2005.12.074
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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) | 0.003 | 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".