Bergamottin contribution to the grapefruit juice?felodipine interaction and disposition in humans
Bibliographic record
Abstract
OBJECTIVES: Our objectives were to evaluate the contribution of bergamottin to the grapefruit juice-felodipine interaction and to characterize bergamottin disposition. METHODS: In this study 250 mL grapefruit juice; 2-, 6-, or 12-mg capsules of bergamottin plus water; or water was administered with 5 mg extended-release felodipine to 11 volunteers in a partially randomized, 5-way crossover study. Plasma concentrations of felodipine, its primary metabolite (dehydrofelodipine), bergamottin, and 6',7'-dihydroxybergamottin were determined. RESULTS: Grapefruit juice (containing 1.7 mg bergamottin) increased peak plasma concentration (C max ) and area under the plasma concentration-time curve (AUC) of felodipine by 89% (P < .025) and 54% (P < .025), respectively, compared with water. With 2 mg bergamottin, felodipine C max increased by 33% (P < .05). The increase by bergamottin was markedly variable among individuals (range, -33% to 125%). With 6 mg bergamottin, felodipine C max was enhanced by 35% (P < .025), and with 12 mg bergamottin, felodipine C max increased by 40% (P < .05) and AUC increased by 37% (P < .05) compared with water. Bergamottin measured in plasma after administration of 6 and 12 mg produced C max values of 2.1 and 5.9 ng/mL, respectively, and times to reach C max of 0.8 and 1.1 hours, respectively. The bergamottin metabolite 6',7'-dihydroxybergamottin was detected in plasma of some subjects after bergamottin administration. CONCLUSIONS: Bergamottin enhanced the oral bioavailability of felodipine and may cause a clinically relevant drug interaction in susceptible individuals. Grapefruit juice-drug interactions likely also involve other furanocoumarins, possibly acting in combination by additive or synergistic mechanisms. Bergamottin has systemic availability and is metabolized in vivo to 6',7'-dihydroxybergamottin.
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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) | 0.001 | 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 | 0.001 | 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".