Targeting Capabilities of Native and Bioengineered Extracellular Vesicles for Drug Delivery
Bibliographic record
Abstract
Extracellular vesicles (EVs) are highly promising as drug delivery vehicles due to their nanoscale size, stability and biocompatibility. EVs possess natural targeting abilities and are known to traverse long distances to reach their target cells. This long-range organotropism and the ability to penetrate hard-to-reach tissues, including the brain, have sparked interest in using EVs for the targeted delivery of pharmaceuticals. In addition, EVs can be readily harvested from an individual's biofluids, making them especially suitable for personalized medicine applications. However, the targeting abilities of unmodified EVs have proven to be insufficient for clinical applications. Multiple attempts have been made to bioengineer EVs to fine-tune their on-target binding. Here, we summarize the current state of knowledge on the natural targeting abilities of native EVs. We also critically discuss the strategies to functionalize EV surfaces for superior long-distance targeting of specific tissues and cells. Finally, we review the challenges in achieving specific on-target binding of EV nanocarriers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".