Effects of vorapaxar on platelet reactivity and biomarker expression in non-ST-elevation acute coronary syndromes
Bibliographic record
Abstract
Vorapaxar is an antagonist of the protease activated receptor-1 (PAR-1), the principal platelet thrombin receptor. The Thrombin Receptor Antagonist for Clinical Event Reduction (TRACER) trial evaluated vorapaxar compared to placebo in non-ST-elevation (NSTE)-acute coronary syndrome (ACS) patients. It was the study's objective to assess the pharmacodynamic effects of vorapaxar versus placebo that included aspirin or a thienopyridine or, frequently, a combination of both agents in NSTE-ACS patients. In a substudy involving 249 patients, platelet aggregation was assessed by light transmittance aggregometry (LTA) in 85 subjects (41 placebo, 44 vorapaxar) using the agonists thrombin receptor activating peptide (TRAP, 15 μM), adenosine diphosphate (ADP, 20 μM), and the combination of collagen-related peptide (2.5 μg/ml) + ADP (5 μM) + TRAP (15 μM) (CAT). VerifyNow® IIb/IIIa and vasodilator-stimulated phosphoprotein (VASP) phosphorylation assays were performed, and platelet PAR-1 expression, plasma platelet/endothelial and inflammatory biomarkers were determined before and during treatment. LTA responses to TRAP and CAT and VerifyNow results were markedly inhibited by vorapaxar. Maximal LTA response to TRAP (median, interquartile range) 2 hours post loading dose: placebo 68% (53-75%) and vorapaxar 3% (2-6%), p<0.0001. ADP inhibition was greater in the vorapaxar group at 4 hours and one month (p<0.01). In contrast to the placebo group, PAR-1 receptor number in the vorapaxar group at one month was significantly lower than the baseline (179 vs 225; p=0.004). There were significant changes in selected biomarker levels between the two treatment groups. In conclusion, vorapaxar caused a potent inhibition of PAR-1-mediated platelet aggregation. Further studies are needed to explore vorapaxar effect on P2Y12 inhibition, PAR-1 expression and biomarkers and its contribution to clinical outcomes.
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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 | 0.001 |
| 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 | 0.000 | 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".