Next Generation of Cancer Drug Repurposing: Therapeutic Combination of Aspirin and Oseltamivir Phosphate Potentiates Gemcitabine to Disable Key Survival Pathways Critical for Pancreatic Cancer Progression
Bibliographic record
Abstract
Resistance to chemotherapeutics and high metastatic rates contribute to the abysmal survival rate in patients with pancreatic cancer. An alternate approach for treating human pancreatic cancer involves repurposing the anti-inflammatory drug, aspirin (ASA), with oseltamivir phosphate (OP) in combination with the standard chemotherapeutic agent, gemcitabine (GEM). The question is whether treatment with ASA and OP can sensitize cancer cells to the cytotoxicity induced by GEM and limit the development of chemoresistance. To assess the key survival pathways critical for pancreatic cancer progression, we used the AlamarBlue cytotoxicity assay to determine the cell viability and combination index for the drug combinations, flow cytometric analysis of annexin V apoptosis assay to detect apoptotic and necrotic cells, fluorometric QCM™ chemotaxis migration assay to assess cellular migration, fluorometric extracellular matrix (ECM) cell adhesion array kit to assess the expression of the ECM proteins, scratch wound assay using the 96-well WoundMaker™, and the methylcellulose clonogenic assay to assess clonogenic potential. The combination of ASA and OP with GEM significantly upended MiaPaCa-2 and PANC-1 pancreatic cancer cell viability, clonogenic potential, expression of critical extracellular matrix proteins, migration, and promoted apoptosis. ASA in combination with OP significantly improves the effectiveness of GEM in the treatment of pancreatic cancer and disables key survival pathways critical to disease progression.
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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".