Creating Designer Engineered Extracellular Vesicles for Diverse Ligand Display, Target Recognition, and Controlled Protein Loading and Delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Efficient and targeted delivery of therapeutic agents remains a bottleneck in modern medicine. Here, biochemical engineering approaches to advance the repurposing of extracellular vesicles (EVs) as drug delivery vehicles are explored. Targeting ligands such as the sugar GalNAc are displayed on the surface of EVs using a HaloTag-fused to a protein anchor that is enriched on engineered EVs. These EVs are successfully targeted to human primary hepatocytes. In addition, the authors are able to decorate EVs with an antibody that recognizes a GLP1 cell surface receptor by using an Fc and Fab region binding moiety fused to an anchor protein, and they show that this improves EV targeting to cells that overexpress the receptor. The authors also use two different protein-engineering approaches to improve the loading of Cre recombinase into the EV lumen and demonstrate that functional Cre protein is delivered into cells in the presence of chloroquine, an endosomal escape enhancer. Lastly, engineered EVs are well tolerated upon intravenous injection into mice without detectable signs of liver toxicity. Collectively, the data show that EVs can be engineered to improve cargo loading and specific cell targeting, which will aid their transformation into tailored drug delivery vehicles.
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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 | 0.001 |
| 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) | 0.000 | 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 it