Proton pump inhibitors and clopidogrel: Evidence of a Pharmacological interaction of great clinical impact
Bibliographic record
Abstract
Summary Clopidogrel, a thienopyridine, is an antiplatelet drug which currently represents the cornerstone for the treatment of acute coronary events. It is a pro-drug that must be converted at hepatic level into its active form by the CYP2C19 isoenzyme. Recently the interaction between proton pump inhibitors and clopidogrel has been widely brought to attention. The competitive inhibition that these generate on the hepatic enzymatic system probably activates clopidogrel, thus causing a decrease in its effectiveness as an antithrombotic. Nevertheless, evidence is contradictory, and until now no clinical trials have been performed to clear up doubts. If a PPI is to be used on patients who take clopidogrel, the use of Pantoprazole or Esomeprazole is recommended because they exert minimal inhibition on CYP2C19. Key words Proton pump inhibitors, clopidogrel, P450, thrombosis INTRODUCTION Th e proton pump inhibitors (PPIs) are one of the most commonly prescribed medications in the world, with more than 12.4 million prescriptions in 2004 in Canada (1). Clopidogrel (CPDG) has been approved as a fi rst line treatment for reducing major cardiovascular events such as death from cardiovascular origin, stent thrombosis, acute coronary syndrome, and recurrent revascularization (2). It is usually prescribed together with a proton pump inhibitor which reduces the risk of gastrointestinal bleeding (GIB) (3). CPDG is a second gene ration thienopyridine prodrug. Its effi ciency is similar to ticlopidine but it is tolerated bet-ter (2 and 4). In order to turn it into an active metabolite and inhibit platelet aggregation it must be bioactivated by cytochrome P450 at a hepatic level (4). It blocks platelet aggregation by irreversibly inhibiting the P2Y12 adeno-sine diphosphate (ADP) receptor (4).
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".