PROTACs in Ovarian Cancer: Current Advancements and Future Perspectives
Bibliographic record
Abstract
Ovarian cancer is the deadliest gynecologic malignancy. The majority of patients diagnosed with advanced ovarian cancer will relapse, at which point additional therapies can be administered but, for the most part, these are not curative. As such, a need exists for the development of novel therapeutic options for ovarian cancer patients. Research in the field of targeted protein degradation (TPD) through the use of proteolysis-targeting chimeras (PROTACs) has significantly increased in recent years. The ability of PROTACs to target proteins of interest (POI) for degradation, overcoming limitations such as the incomplete inhibition of POI function and the development of resistance seen with other inhibitors, is of particular interest in cancer research, including ovarian cancer research. This review provides a synopsis of PROTACs tested in ovarian cancer models and highlights PROTACs characterized in other types of cancers with potential high utility in ovarian cancer. Finally, we discuss methods that will help to enable the selective delivery of PROTACs to ovarian cancer and improve the pharmacodynamic properties of these agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".