Blockade of protease-activated receptor-4 (PAR4) provides robust antithrombotic activity with low bleeding
Bibliographic record
Abstract
Antiplatelet agents are proven efficacious treatments for cardiovascular and cerebrovascular diseases. However, the existing drugs are compromised by unwanted and sometimes life-threatening bleeding that limits drug usage or dosage. There is a substantial unmet medical need for an antiplatelet drug with strong efficacy and low bleeding risk. Thrombin is a potent platelet agonist that directly induces platelet activation via the G protein (heterotrimeric guanine nucleotide-binding protein)-coupled protease-activated receptors PAR1 and PAR4. A PAR1 antagonist is approved for clinical use, but its use is limited by a substantial bleeding risk. Conversely, the potential of PAR4 as an antiplatelet target has not been well characterized. Using anti-PAR4 antibodies, we demonstrated a low bleeding risk and an effective antithrombotic profile with PAR4 inhibition in guinea pigs. Subsequently, high-throughput screening and an extensive medicinal chemistry effort resulted in the discovery of BMS-986120, an orally active, selective, and reversible PAR4 antagonist. In a cynomolgus monkey arterial thrombosis model, BMS-986120 demonstrated potent and highly efficacious antithrombotic activity. BMS-986120 also exhibited a low bleeding liability and a markedly wider therapeutic window compared to the standard antiplatelet agent clopidogrel tested in the same nonhuman primate model. These preclinical findings define the biological role of PAR4 in mediating platelet aggregation. In addition, they indicate that targeting PAR4 is an attractive antiplatelet strategy with the potential to treat patients at a high risk of atherothrombosis with superior safety compared with the current standard of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".