Preclinical Synergistic Combination Therapy of Lurbinectedin with Irinotecan and 5-Fluorouracil in Pancreatic Cancer
Bibliographic record
Abstract
Pancreatic cancer is a devastating disease with a poor prognosis. Novel chemotherapeutics in pancreatic cancer have shown limited success, illustrating the urgent need for new treatments. Lurbinectedin (PM01183; LY-01017) received FDA approval in 2020 for metastatic small cell lung cancer on or after platinum-based chemotherapy and is currently undergoing clinical trials in a variety of tumor types. Lurbinectedin stalls and degrades RNA Polymerase II and introduces breaks in DNA, causing subsequent apoptosis. We now demonstrate lurbinectedin's highly efficient killing of human-derived pancreatic tumor cell lines PANC-1, BxPC-3, and HPAF-II as a single agent. We further demonstrate that a combination of lurbinectedin and irinotecan, a topoisomerase I inhibitor with FDA approval for advanced pancreatic cancer, results in the synergistic killing of pancreatic tumor cells. Western blot analysis of combination therapy indicates an upregulation of γH2AX, a DNA damage marker, and the Chk1/ATR pathway, which is involved in replicative stress and DNA damage response. We further demonstrate that the triple combination between lurbinectedin, irinotecan, and 5-fluorouracil (5-FU) results in a highly efficient killing of tumor cells. Our results are developing insights regarding molecular mechanisms underlying the therapeutic efficacy of a novel combination drug treatment for pancreatic 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".