Antitumour activity of a potent MEK inhibitor RDEA119/BAY 869766 combined with rapamycin in human orthotopic primary pancreatic cancer xenografts
Bibliographic record
Abstract
BACKGROUND: Combining MEK inhibitors with other signalling pathway inhibitors or conventional cytotoxic drugs represents a promising new strategy against cancer. RDEA119/BAY 869766 is a highly potent and selective MEK1/2 inhibitor undergoing phase I human clinical trials. The effects of RDEA119/BAY 869766 as a single agent and in combination with rapamycin were studied in 3 early passage primary pancreatic cancer xenografts, OCIP19, 21, and 23, grown orthotopically. METHODS: Anti-cancer effects were determined in separate groups following chronic drug exposure. Effects on cell cycle and downstream signalling were examined by flow cytometry and western blot, respectively. Plasma RDEA119 concentrations were measured to monitor the drug accumulation in vivo. RESULTS: RDEA119/BAY 869766 alone or in combination with rapamycin showed significant growth inhibition in all the 3 models, with a significant decrease in the percentage of cells in S-phase, accompanied by a large decrease in bromodeoxyuridine labelling and cell cycle arrest predominantly in G1. The S6 ribosomal protein was inhibited to a greater extent with combination treatment in all the three models. Blood plasma pharmacokinetic analyses indicated that RDEA119 levels achieved in vivo are similar to those that produce target inhibition and cell cycle arrest in vitro. CONCLUSIONS: Agents targeting the ERK and mTOR pathway have anticancer activity in primary xenografts, and these results support testing this combination in pancreatic cancer patients.
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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 | 0.001 |
| 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".