Thrombolytic therapy based on lyophilized platelet-derived nanocarriers for ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Intravenous administration of fibrinolytic drugs, such as recombinant tissue plasminogen activator (rtPA) is the standard treatment of acute thrombotic diseases. However, current fibrinolytics exhibit limited clinical efficacy because of their short plasma half-lives and risk of hemorrhagic transformations. Platelet membrane-based nanocarriers have received increasing attention for ischemic stroke therapies, as they have natural thrombus-targeting activity, can prolong half-life of the fibrinolytic therapy, and reduce side effects. In this study we have gone further in developing platelet-derived nanocarriers (defined as cellsomes) to encapsulate and protect rtPA from degradation. Following lyophilization and characterization, their formulation properties, biocompatibility, therapeutic effect, and risk of hemorrhages were later investigated in a thromboembolic model of stroke in mice. RESULTS: Cellsomes of 200 nm size and loaded with rtPA were generated from membrane fragments of human platelets. The lyophilization process did not influence the nanocarrier size distribution, morphology, and colloidal stability conferring particle preservation and long-term storage. Encapsulated rtPA in cellsomes and administered as a single bolus showed to be as effective as a continuous clinical perfusion of free rtPA at equal concentration, without increasing the risk of hemorrhagic transformations or provoking an inflammatory response. CONCLUSIONS: This study provides evidence for the safe and effective use of lyophilized biomimetic platelet-derived nanomedicine for precise thrombolytic treatment of acute ischemic stroke. In addition, this new nanoformulation could simplify the clinical use of rtPA as a single bolus, being easier and less time-consuming in an emergency setting than a treatment perfusion, particularly in stroke patients. We have successfully addressed one of the main barriers to drug application and commercialization, the long-term storage of nanomedicines, overcoming the potential chemical and physical instabilities of nanomedicines when stored in an aqueous buffer.
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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".