Development of an intravaginal ring for the topical delivery of Aurora kinase A inhibitor, MLN8237
Bibliographic record
Abstract
Human papilloma virus (HPV) is the main culprit in cervical cancers. Although the HPV vaccine is now available, the slow and gradual process for HPV cancers to form means little will change, even for vaccinated individuals. This warrants the development of new therapeutic strategies in both the newly diagnosed and recurrent patients. We have previously shown that Alisertib (MLN8237), an Aurora A kinase inhibitor, potently and selectively kills HPV-positive cervical cancer cells. However, Alisertib is known for its unfavorable side effects when administered systemically. A targeted delivery approach is therefore warranted. The topical delivery of drugs to the cervix for the treatment of cervical cancer is an underexplored area of research that has the potential to significantly improve therapeutic outcome. Here, we design a novel topical drug delivery system for localized delivery in the vaginal tract using intravaginal silicone rings loaded with Alisertib. We assessed the suitability of the drug for the application and delivery method and develop a high-performance liquid chromatography method, then show that the vaginal rings were effective at releasing Alisertib over an extended period of time. Furthermore, we showed that Alisertib-loaded vaginal rings did not induce overt inflammation in the mouse vaginal tract. Our work has major translational implications for the future development of vaginal ring devices for the topical treatment of cervical cancer.
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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) | 0.000 | 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) | 0.001 | 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 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".