Nontoxic polyphosphate inhibitors reduce thrombosis while sparing hemostasis
Bibliographic record
Abstract
Polyphosphate (polyP) is secreted by activated platelets and has been shown to contribute to thrombosis, suggesting that it could be a novel antithrombotic target. Previously reported polyP inhibitors based on polycationic substances, such as polyethylenimine, polyamidoamine dendrimers, and polymyxin B, although they attenuate thrombosis, all have significant toxicity in vivo, likely due to the presence of multiple primary amines responsible for their polyP binding ability. In this study, we examined a novel class of nontoxic polycationic compounds initially designed as universal heparin reversal agents (UHRAs) to determine their ability to block polyP procoagulant activity and also to determine their utility as antithrombotic treatments. Several UHRA compounds strongly inhibited polyP procoagulant activity in vitro, and 4 were selected for further examination in mouse models of thrombosis and hemostasis. Compounds UHRA-9 and UHRA-10 significantly reduced arterial thrombosis in mice. In mouse tail bleeding tests, administration of UHRA-9 or UHRA-10 was associated with significantly less bleeding compared with therapeutically equivalent doses of heparin. Thus, these compounds offer a new platform for developing novel antithrombotic agents that target procoagulant anionic polymers such as polyP with reduced toxicity and bleeding side effects.
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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) | 0.001 | 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".