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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".