Comparison of reversal activity and mechanism of action of UHRA, andexanet, and PER977 on heparin and oral FXa inhibitors
Bibliographic record
Abstract
Anticoagulants such as unfractionated heparin (UFH), low-molecular-weight heparins (LMWHs), fondaparinux, and direct oral anticoagulants (DOACs) targeting thrombin (IIa) or factor Xa (FXa) are widely used in prevention and treatment of thromboembolic disorders. However, anticoagulant-associated bleeding is a concern that demands monitoring and neutralization. Protamine, the UFH antidote, has limitations, while there is no antidote available for certain direct FXa inhibitors. Improved antidotes in development include UHRA (Universal Heparin Reversal Agent) for all heparin anticoagulants; andexanet alfa (andexanet), a recombinant antidote for both direct FXa inhibitors and LMWHs; and ciraparantag (PER977), a small-molecule antidote for UFH, LMWHs, and certain DOACs. The binding affinities of these antidotes for their presumed anticoagulant targets have not been compared. Here, isothermal titration calorimetry (ITC) was used to determine the affinity of each antidote for its putative targets. Clotting and chromogenic FXa assays were used to characterize neutralization activity, and electron microscopy was used to visualize the effect of each antidote on clot morphology in the absence or presence of anticoagulant. ITC confirmed binding of UHRA to all heparins, and binding of andexanet to edoxaban and rivaroxaban, and to the antithrombin-enoxaparin complex. PER977 was found to bind heparins weakly, but not the direct FXa inhibitors studied. For UHRA and andexanet, an affinity at or below the micromolar level was found to correlate with neutralization activity, while no reversal activity was observed for the PER977/anticoagulant systems. Standard metrics of clot structure were found to correlate weakly with PER977's activity. This is the first study comparing 3 antidotes in development, with each exerting activity through a distinct mechanism.
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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".