Fruit juices inhibit organic anion transporting polypeptide–mediated drug uptake to decrease the oral availability of fexofenadine
Bibliographic record
Abstract
OBJECTIVES: Our objective was to examine the effect of different fruits and their constituents on P-glycoprotein and organic anion transporting polypeptide (OATP) activities in vitro and on drug disposition in humans. METHODS: P-glycoprotein-mediated digoxin or vinblastine efflux was determined in polarized epithelial cell monolayers. OATP-mediated fexofenadine uptake was measured in a transfected cell line. The oral pharmacokinetics of 120 mg fexofenadine was assessed with water, 25%-strength grapefruit juice, or normal-strength grapefruit, orange, or apple juices (1.2 L over 3 hours) in a randomized 5-way crossover study in 10 healthy subjects. RESULTS: Grapefruit juice and segments and apple juice at 5% of normal strength did not alter P-glycoprotein activity. Grapefruit extract reduced transport. 6',7'-Dihydroxybergamottin had modest inhibitory activity (50% inhibitory concentration [IC(50)], 33 micromol/L). In contrast, grapefruit, orange, and apple juices at 5% of normal strength markedly reduced human OATP and rat oatp activity. 6',7'-Dihydroxybergamottin potently inhibited rat oatp3 and oatp1 (IC(50), 0.28 micromol/L). Other furanocoumarins and bioflavonoids also reduced rat oatp3 activity. Grapefruit, orange, and apple juices decreased the fexofenadine area under the plasma concentration-time curve (AUC), the peak plasma drug concentration (C(max)), and the urinary excretion values to 30% to 40% of those with water, with no change in the time to reach C(max), elimination half-life, renal clearance, or urine volume in humans. Change in fexofenadine AUC with juice was variable among individuals and inversely dependent on value with water. CONCLUSIONS: Fruit juices and constituents are more potent inhibitors of OATPs than P-glycoprotein activities, which can reduce oral drug bioavailability. Results support a new model of intestinal drug absorption and mechanism of food-drug interaction.
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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) | 0.002 | 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".