Targeted liposomal drug delivery: a nanoscience and biophysical perspective
Bibliographic record
Abstract
Liposomes are a unique platform for drug delivery, and a number of liposomal formulations have already been commercialized. Doxil is a representative example, which uses PEGylated liposomes to load doxorubicin for cancer therapy. Its delivery relies on the enhanced permeability and retention (EPR) effect or passive targeting. Drug loading can be achieved using both standard liposomes and also those containing a solid core such as mesoporous silica and poly(lactide-co-glycolide) (PLGA). Developments have also been made on active targeted delivery using bioaffinity ligands such as small molecules, antibodies, peptides and aptamers. Compared to other types of nanoparticles, the surface of liposomes is fluid, allowing dynamic organization of targeting ligands to achieve optimal binding to cell surface receptors. This review article summarizes development of liposomal targeted drug delivery systems, with an emphasis on the biophysical properties of lipids. In both passive and active targeting, the effects of liposome size, charge, fluidity, rigidity, head-group chemistry and PEGylation are discussed along with recent examples. Most of the examples are focused on targeting tumors or cancer cells. Finally, a few examples of commercialized formulations are described, and some future research opportunities are discussed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".