Smart thrombosis inhibitors without bleeding side effects via charge tunable ligand design
Bibliographic record
Abstract
Current treatments to prevent thrombosis, namely anticoagulants and platelets antagonists, remain complicated by the persistent risk of bleeding. Improved therapeutic strategies that diminish this risk would have a huge clinical impact. Antithrombotic agents that neutralize and inhibit polyphosphate (polyP) can be a powerful approach towards such a goal. Here, we report a design concept towards polyP inhibition, termed macromolecular polyanion inhibitors (MPI), with high binding affinity and specificity. Lead antithrombotic candidates are identified through a library screening of molecules which possess low charge density at physiological pH but which increase their charge upon binding to polyP, providing a smart way to enhance their activity and selectivity. The lead MPI candidates demonstrates antithrombotic activity in mouse models of thrombosis, does not give rise to bleeding, and is well tolerated in mice even at very high doses. The developed inhibitor is anticipated to open avenues in thrombosis prevention without bleeding risk, a challenge not addressed by current therapies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".