The Autophagy Inhibitor Verteporfin Moderately Enhances the Antitumor Activity of Gemcitabine in a Pancreatic Ductal Adenocarcinoma Model
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is highly resistant to chemotherapy. It has been described as requiring elevated autophagy for growth and inhibiting autophagy has been proposed as a treatment strategy. To date, all preclinical reports and clinical trials investigating pharmacological inhibition of autophagy have used chloroquine or hydroxychloroquine, which interfere with lysosomal function and block autophagy at a late stage. Verteporfin is a newly discovered autophagy inhibitor that blocks autophagy at an early stage by inhibiting autophagosome formation. Here we report that PDAC cell lines show variable sensitivity to verteporfin in vitro, suggesting cell-line specific autophagy dependence. Using image-based and molecular analyses, we show that verteporfin inhibits autophagy stimulated by gemcitabine, the current standard treatment for PDAC. Pharmacokinetic and efficacy studies in a BxPC-3 xenograft mouse model demonstrated that verteporfin accumulated in tumors at autophagy-inhibiting levels and inhibited autophagy in vivo, but did not reduce tumor volume or increase survival as a single agent. In combination with gemcitabine verteporfin moderately reduced tumor growth and enhanced survival compared to gemcitabine alone. While our results do not uphold the premise that autophagy inhibition might be widely effective against PDAC as a single-modality treatment, they do support autophagy inhibition as an approach to sensitize PDAC to gemcitabine.
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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".