Using Extracellular Vesicles for Brain Delivery of Therapeutic Proteins
Bibliographic record
Abstract
Increasing evidence suggests that extracellular vesicles (EVs) are promising natural nanocarriers that can be used for delivery of various types of therapeutics. We reported earlier engineered EV-based formulations for treatment of neurodegenerative diseases and cancer. Herein, we investigated the use of EVs for brain delivery of different therapeutic proteins, including a soluble lysosomal enzyme tripeptidyl peptidase-1, TPP1; a potent antioxidant, catalase, and glial cellderived neurotrophic factor, GDNF. The therapeutic proteins were loaded into EVs using two methods: (i) transfection of EV-producing cells, macrophages, with drug-encoding plasmid DNA, or (ii) incorporation of the therapeutic protein into naive empty EVs. The second approach utilized sonication, or extrusion, or freeze-thaw cycles, or permeabilization of EVs membranes with saponin to achieve high loading efficiency. The utilized methods provided effective incorporation of functional therapeutic proteins into EVs. Notably, along with the enzyme, EVs released by pre-transfected macrophages contained drug-encoding pDNA. EVs significantly increased stability of the proteins against protease degradation and provided extraordinary drug delivery to target cells in in vitro and in vivo models of neurodegeneration. A robust accumulation of EVs carriers was detected in the inflamed brain. Finally, systemic administration of drug loaded EVs significantly increased neuronal survival and decreased neuroinflammation. We hypothesized that EV-based formulations have a potential to be a versatile strategy to treat different neurodegenerative disorders.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".