Aptamer-functionalized liposomes for drug delivery
Bibliographic record
Abstract
Among the various targeting ligands for drug delivery, aptamers have attracted much interest in recent years because of their smaller size compared to antibodies, ease of modification, and better batch-to-batch consistency. In addition, aptamers can be selected to target both known and even unknown cell surface biomarkers. For drug loading, liposomes are the most successful vehicle and many FDA-approved formulations are based on liposomes. In this paper, aptamer-functionalized liposomes for targeted drug delivery are reviewed. We begin with the description of related aptamers selection, followed by methods to conjugate aptamers to liposomes and the fate of such conjugates in vivo. Then a few examples of applications are reviewed. In addition to intravenous injection for systemic delivery and hoping to achieve accumulation at target sites, for certain applications, it is also possible to have aptamer/liposome conjugates applied directly at the target tissue such as intratumor injection and dropping on the surface of the eye by adhering to the cornea. While previous reviews have focused on cancer therapy, the current review mainly covers other applications in the last four years. Finally, this article discusses potential issues of aptamer targeting and some future research opportunities.
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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) | 0.001 | 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) | 0.003 | 0.003 |
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".