Doxorubicin nanoformulations on therapy against cancer: An overview from the last 10 years
Bibliographic record
Abstract
Doxorubicin (DOX) is a natural antibiotic with antineoplastic activity. It has been used for over 40 years and remains one of the most used drugs in chemotherapy for a variety of cancers. However, cardiotoxicity limits its use for long periods. To overcome this limitation, encapsulation in smart drug delivery systems (DDS) brings advantages in comparison with free drug administration (i.e., conventional anticancer drug therapy). In this review, we present the most relevant nanostructures used for DOX encapsulation over the last 10 years, such as liposomes, micelles and polymeric vesicles (i.e., polymersomes), micro/nanoemulsions, different types of polymeric nanoparticles and hydrogel nanoparticles, as well as novel approaches for DOX encapsulation. The studies highlighted here show these nanoformulations achieved higher solubility, improved tumor cytotoxicity, prolonged DOX release, as well as reduced side effects, among other interesting advantages.
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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 | 0.001 |
| 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".