Ratio-Dependent Synergism of a Doxorubicin and Olaparib Combination in 2D and Spheroid Models of Ovarian Cancer
Bibliographic record
Abstract
Ovarian cancer is the fourth leading cause of death in women in developed countries. Even though patients with the most lethal form of the disease (HGSOC; high grade serous ovarian cancer) respond well to initial treatment, they often relapse with progressively resistant disease. Inhibitors of the poly(ADP-ribose) polymerase (PARP) enzymes are a relatively new class of molecularly targeted small molecule drugs that show promise in overcoming resistance. The present study explores the combination of a DNA damaging agent, doxorubicin (DOX), with the PARP inhibitor, olaparib (OLP), in order to achieve optimal synergy of both drugs in serous ovarian cancer. This drug combination was evaluated and optimized in 2D monolayers and 3D multicellular tumor spheroids (MCTS) using a genetically and histologically characterized panel of nine OC cell lines with or without BRCA1 or BRCA2 mutations. Combination index (CI) values of DOX and OLP were determined using the Chou and Talalay method. The potency of this drug combination was found to rely heavily on the molar ratios at which the two drugs are combined. In general, MCTS growth inhibition was reflective of the patterns predicted by the CI values obtained in monolayers. Promising combination ratios identified in this study warrant further preclinical and clinical investigation.
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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".