Use of the Proton Pump Inhibitor Pantoprazole to Modify the Distribution and Activity of Doxorubicin: A Potential Strategy to Improve the Therapy of Solid Tumors
Bibliographic record
Abstract
PURPOSE: Limited drug distribution within solid tumors is an important cause of drug resistance. Basic drugs (e.g., doxorubicin) may be sequestered in acidic organelles, thereby limiting drug distribution to distal cells and diverting drugs from their target DNA. Here we investigate the effects of pantoprazole, a proton pump inhibitor, on doxorubicin uptake, and doxorubicin distribution and activity using in vitro and murine models. EXPERIMENTAL DESIGN: Murine EMT-6 and human MCF-7 cells were treated with pantoprazole to evaluate changes in endosomal pH using fluorescence spectroscopy, and uptake of doxorubicin using flow cytometry. Effects of pantoprazole on tissue penetration of doxorubicin were evaluated in multilayered cell cultures (MCC), and in solid tumors using immunohistochemistry. Effects of pantoprazole to influence tumor growth delay and toxicity because of doxorubicin were evaluated in mice. RESULTS: Pantoprazole (>200 μmol/L) increased endosomal pH in cells, and also increased nuclear uptake of doxorubicin. Pretreatment with pantoprazole increased tissue penetration of doxorubicin in MCCs. Pantoprazole improved doxorubicin distribution from blood vessels in solid tumors. Pantoprazole given before doxorubicin led to increased growth delay when given as single or multiple doses to mice bearing MCF7 xenografts. CONCLUSIONS: Use of pantoprazole to enhance the distribution and cytotoxicity of anticancer drugs in solid tumors might be a novel treatment strategy to improve their therapeutic index.
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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 | 0.000 |
| 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".