Inhibitory effect of resveratrol on the pharmacokinetics of ticagrelor in vivo and in vitro
Bibliographic record
Abstract
This study was to evaluate the effect of resveratrol on the pharmacokinetics of ticagrelor in rats and the metabolism of ticagrelor in human cytochrome P450 (CYP) 3A4 (CYP3A4) and liver microsomes. Eighteen Sprague-Dawley rats were randomly divided into three groups: group A (control group), group B (50 mg/kg resveratrol), and group C (150 mg/kg resveratrol). After 30 min administration of resveratrol, a single dose of ticagrelor (18 mg/kg) was administered orally. The in vitro experiment was performed to examine the influence of resveratrol on ticagrelor metabolism in CYP3A4*1, human, and rat liver microsomes. Serial biological samples were assayed by validated ultra high-performance liquid chromatography - tandem mass spectrometer methods. For the in vivo study, the area under the concentration-time curve and mean peak plasma concentrations of ticagrelor in group B and C appeared to be significantly higher than the control group, while volume of distribution in terminal phase and apparent clearance of ticagrelor in group B and C were significantly decreased. For the in vitro study, resveratrol exhibited an inhibitory effect on CYP3A4*1, human and rat liver microsomes. The half-maximal inhibitory concentration values of resveratrol were 56.75 μM, 69.07 μM, and 14.22 μM, respectively. Our results indicated that resveratrol had an inhibitory effect on the metabolism of ticagrelor in vitro and in vivo. Further research should focus on the clinical combination of resveratrol with ticagrelor, and ticagrelor plasma concentration should be monitored to avoid the occurrence of adverse reaction.
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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".