Combination Drug Delivery Strategy for the Treatment of Multidrug Resistant Ovarian Cancer
Bibliographic record
Abstract
The onset of multidrug resistance (MDR) in ovarian cancer is one of the main causes of treatment failure and low survival rates. Inadequate drug exposure and treatment-free periods due to intermittent chemotherapy select for cancer cells overexpressing drug efflux transporters, resulting in resistant disease. The present study examines the sustained administration of the chemotherapeutic agent docetaxel (DTX) alone and in combination with cepharanthine (CEP), a potent drug efflux transporter inhibitor. DTX and CEP were delivered via the intraperitoneal route in a sustained manner using an injectable polymer-lipid formulation. In vitro, the combination strategy resulted in significantly (p < 0.05) more apoptosis, greater intracellular accumulation of DTX, and lower DTX efflux in ovarian cancer cells showing the MDR phenotype. In vivo, sustained treatment with DTX and CEP showed significantly greater (p < 0.05) tumor inhibition (91 ± 4%) in a murine model of multidrug resistant ovarian cancer compared to sustained DTX treatment (76 ± 6%) and was more than twice as efficacious as intermittent DTX treatment. Overall findings from these studies highlight the impact of sustained delivery of monotherapy and combination therapy in the management of refractory ovarian cancer displaying the MDR phenotype.
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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) | 0.001 | 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 | 0.001 |
| 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".