Addressing the Challenge: Current and Future Directions in Ovarian Cancer Therapy
Bibliographic record
Abstract
Numerous ovarian gene therapy strategies are in clinical phases based on concepts of replacement/ knock out of deregulated gene, suicide gene strategies, strengthening of the immune response against a tumor, inhibition of tumor angiogenesis and growth factors. Non-viral delivery systems have potential advantages over currently widely used viral vectors and other classical vectors for delivering therapeutic gene of interest. The present review provides a comprehensive overview of potential of various delivery systems currently in use. Non-viral formulations used in ovarian gene therapy include injecting naked DNA, liposomes, polyplexes, lipopolyplexes, nanoparticles, gene gun and ultrasound/microbubble mediated gene delivery. In addition to improving vector delivery, the DNA constructs need to be optimised for both efficient and long-term transgene expression. Minicircles using minimal immunological defined gene expression (MIDGE) technology, are a promising future alternative to plasmid for use in non-viral ovarian gene therapy in terms of biosafety, improved gene transfer, potential bioavailability, minimal size and little immune reaction. The review explores the best route of administration for ovarian cancer gene therapy given its peritoneal dissemination which poses a major challenge in treating ovarian cancer patients. Enhancement of therapeutic index can be further achieved by overcoming barriers both at cellular and nuclear levels. Selective tumor targeting with minimal toxicity using folate modified, incorporating nuclear localization signal and PEGylated stealth liposome's represents a popular approach and needs to be exploited in ovarian gene therapy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".