Affinity-based design of a synthetic universal reversal agent for heparin anticoagulants
Bibliographic record
Abstract
Heparin-based anticoagulant drugs have been widely used for the prevention of blood clotting during surgical procedures and for the treatment of thromboembolic events. However, bleeding risks associated with these anticoagulants demand continuous monitoring and neutralization with suitable antidotes. Protamine, the only clinically approved antidote to heparin, has shown adverse effects and ineffectiveness against low-molecular weight heparins and fondaparinux, a heparin-related medication. Alternative approaches based on cationic molecules and recombinant proteins have several drawbacks including limited efficacy, toxicity, immunogenicity, and high cost. Thus, there is an unmet clinical need for safer, rapid, predictable, and cost-effective anticoagulant-reversal agents for all clinically used heparins. We report a design strategy for a fully synthetic dendritic polymer-based universal heparin reversal agent (UHRA) that makes use of multivalent presentation of branched cationic heparin binding groups (HBGs). Optimization of the UHRA design was aided by isothermal titration calorimetry studies, biocompatibility evaluation, and heparin neutralization analysis. By controlling the scaffold's molecular weight, the nature of the protective shell, and the presentation of HBGs on the polymer scaffold, we arrived at lead UHRA molecules that completely neutralized the activity of all clinically used heparins. The optimized UHRA molecules demonstrated superior efficacy and safety profiles and mitigated heparin-induced bleeding in animal models. This new polymer therapeutic may benefit patients undergoing high-risk surgical procedures and has potential for the treatment of anticoagulant-related bleeding problems.
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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".