Creating Designer Engineered Extracellular Vesicles for Diverse Ligand Display, Target Recognition, and Controlled Protein Loading and Delivery
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".