Developing Transient Gene Therapies to Decrease the Stability of Thrombi for Coagulopathy and Thrombosis
Bibliographic record
Abstract
Thrombotic disorders are prevalent and dangerous. Thrombosis involves the formation of unwanted blood clots inside blood vessels, which can impair blood flow and lead to severe cardiovascular events, such as heart attack, stroke, and pulmonary embolism. Current therapies for thrombosis are unsatisfactory in that they require frequent re-administration, and are associated with a significant bleeding risk, a consequence of inhibiting the coagulation cascade upstream of fibrin generation. Coagulation proteins factor XIII (FXIII) and thrombin activatable fibrinolysis inhibitor (TAFI) are enzymes that act to stabilize clots, downstream of fibrin generation and polymerization. Though this makes them ideal targets to reduce the burden of thrombosis while maintaining hemostasis, no inhibitors for either protein are currently available in the clinic. As we have recently published (Strilchuk, et al. Blood. 2020), lipid nanoparticles can be used to deliver siRNA to knock-down FXIII-B and achieve depletion of FXIII-A from circulation in mice and rabbits. FXIII-A depletion causes less antiplasmin to be crosslinked to clots, resulting in clots that are more susceptible to fibrinolysis. In the current abstract, we have expanded on preliminary data showing that while clots are weaker, bleeding is not enhanced in mice or rabbits after minor or major injury. We have also leveraged the same biological tools to knock-down TAFI for days after a single administration. In the short term, these novel RNA agents will be valuable tools in investigating the biology of thrombotic disorders. The ultimate goal is to develop these agents into precise and long-acting prophylactic therapies for thrombotic disorders, that are safer and more effective than current standards of care. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".