Lipid nanoparticles and siRNA targeting plasminogen provide lasting inhibition of fibrinolysis in mouse and dog models of hemophilia A
Bibliographic record
Abstract
Antifibrinolytic drugs are used extensively for on-demand treatment of severe acute bleeding. Controlling fibrinolysis may also be an effective strategy to prevent or lessen chronic recurring bleeding in bleeding disorders such as hemophilia A (HA), but current antifibrinolytics have unfavorable pharmacokinetic profiles. Here, we developed a long-lasting antifibrinolytic using small interfering RNA (siRNA) targeting plasminogen packaged in clinically used lipid nanoparticles (LNPs) and tested it to determine whether reducing plasmin activity in animal models of HA could decrease bleeding frequency and severity. Treatment with the siRNA-carrying LNPs reduced circulating plasminogen and suppressed fibrinolysis in wild-type and HA mice and dogs. In HA mice, hemostatic efficacy depended on the injury model; plasminogen knockdown improved hemostasis after a saphenous vein injury but not tail vein transection injury, suggesting that saphenous vein injury is a murine bleeding model sensitive to the contribution of fibrinolysis. In dogs with HA, LNPs carrying siRNA targeting plasminogen were as effective at stabilizing clots as tranexamic acid, a clinical antifibrinolytic, and in a pilot study of two dogs with HA, the incidence of spontaneous or excess bleeding was reduced during 4 months of prolonged knockdown. Collectively, these data demonstrate that long-acting antifibrinolytic therapy can be achieved and that it provides hemostatic benefit in animal models of HA.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".