Herb-drug Pharmacokinetic Interaction between Carica Papaya Extract and Amiodarone in Rats
Bibliographic record
Abstract
PURPOSE: Carica papaya has been traditionally used worldwide in folk medicine to treat a wide range of ailments in humans, including the management of obesity and digestive disorders. However, scientific information about its potential to interact with conventional drugs is lacking. Thus, this work aimed to investigate the interference of a standardized C. papaya extract (GMP certificate) on the systemic exposure to amiodarone (a narrow therapeutic index drug) in rats. METHODS: In the first pharmacokinetic study, rats were simultaneously co-administered with a single-dose of C. papaya (1230 mg/kg, p.o.) and amiodarone (50 mg/kg, p.o.); in the second study, rats were pre-treated for 14 days with C. papaya (1230 mg/kg/day, p.o.) and received amiodarone (50 mg/kg, p.o.) on the 15th day. Rats of the control groups received the herbal extract vehicle. Blood samples were collected before dosing and at 0.25, 0.5, 1, 2, 4, 6, 8 and 12 h following amiodarone administration; in addition, at 24 h post-dose, blood and tissues (heart, liver, kidneys and lungs) were also harvested. Thereafter, the concentrations of amiodarone and its major metabolite (mono-N-desethylamiodarone) were determined in plasma and tissue samples employing a high-performance liquid chromatography-diode array detection method previously developed and validated. RESULTS: In both studies was observed a delay in attaining the maximum plasma concentrations of amiodarone (tmax) in the rats treated with the extract. Nevertheless, it must be highlighted the marked increase (60-70%) of the extent of amiodarone systemic exposure (as assessed by AUC0-t and AUC0-∞) in the rats pre-treated with C. papaya comparatively with the control (vehicle) group. CONCLUSIONS: The results herein found suggest an herb-drug interaction between C. papaya extract and amiodarone, which clearly increase the drug bioavailability. To reliably assess the clinical impact of these findings appropriate human studies should be conducted.
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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 | 0.001 |
| 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".