Recent advances in drug delivery strategies for treatment of ovarian cancer
Bibliographic record
Abstract
INTRODUCTION: Ovarian cancer is associated with the highest mortality rate of all gynecological malignancies, due in part to inadequate treatment strategies and the asymptomatic nature of the disease. Current standard of care includes surgery and systemic chemotherapy. However, this approach can result in toxicities and eventual disease relapse, due to the emergence of multidrug resistance. Drug delivery systems (DDS) have shown promise in overcoming many of the limitations facing conventional treatment regimens. AREAS COVERED: This review provides an overview of recent advances in DDS strategies for the treatment ovarian cancers. Nano-sized systems, including nanoparticles, micelles, liposomes and drug conjugates; microspheres; implants and injectable depots are discussed. The advantages, limitations and clinical potential of these strategies are also outlined. EXPERT OPINION: Nano-sized DDS enable passive targeting to tumors due to their size, and further improvements in tumor localization can be made using targeting moieties. Microspheres, implants and injectable depots have been investigated for peritoneal localized and sustained therapy. Overall, the benefits of using DDS for ovarian cancer therapy include higher drug levels at the diseased site, circumvention of drug resistance mechanisms, minimization of non-specific toxicities, improvements in solubility of poorly soluble drugs and elimination of toxicities associated with conventionally used pharmaceutical excipients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".