Interactions of grapefruit juice and cardiovascular medications: A potential risk of toxicity.
Bibliographic record
Abstract
Recently, drug interactions with grapefruit juice (GFJ) have received considerable attention from basic scientists, physicians, industry and drug regulatory agencies. GFJ has been shown to inhibit cytochrome P-450 3A4 isoenzyme and P-glycoprotein transporters in the intestine and liver. The GFJ-induced inhibitory effects are considered to be responsible for alterations in drug bioavailability, and pharmacokinetic and pharmacodynamic changes when drugs are ingested concurrently with GFJ. However, little or no interaction is observed when GFJ is taken concomitantly with parentally administered drugs. It is well known that risk factors for cardiovascular disease increase with advancing age, while hepatic metabolic activity decreases in elderly individuals. It is, therefore, possible that the combination of GFJ and cardiovascular medications may pose a health risk, especially in elderly patients. A number of studies have shown interactions of GFJ with cardiovascular drugs such as calcium-channel blockers, angiotensin II receptor antagonists, beta-blockers, and statins. Such interactions are likely to change the pharmacokinetics and pharmacodynamics of these drugs, consequently causing undesirable health effects. Therefore, health care professionals and the public need to be advised of the potential risks associated with the concomitant use of GFJ and interacting medications, especially cardiovascular drugs and agents with a narrow therapeutic index. This review focuses on the adverse interactions of GFJ and cardiovascular medications, and the proposed underlying mechanisms of such interactions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".