Influence of the proton pump inhibitor lansoprazole on distribution and activity of doxorubicin in solid tumors
Bibliographic record
Abstract
Cellular causes of resistance and limited drug distribution within solid tumors limit therapeutic efficacy of anticancer drugs. Acidic endosomes in cancer cells mediate autophagy, which facilitates survival of stressed cells, and may contribute to drug resistance. Basic drugs (e.g. doxorubicin) are sequestered in acidic endosomes, thereby diverting drugs from their target DNA and decreasing penetration to distal cells. Proton pump inhibitors (PPIs) may raise endosomal pH, with potential to improve drug efficacy and distribution in solid tumors. We determined the effects of the PPI lansoprazole to modify the activity of doxorubicin. To gain insight into its mechanisms, we studied the effects of lansoprazole on endosomal pH, and on the spatial distribution of doxorubicin, and of biomarkers reflecting its activity, using in vitro and murine models. Lansoprazole showed concentration-dependent effects to raise endosomal pH and to inhibit endosomal sequestration of doxorubicin in cultured tumor cells. Lansoprazole was not toxic to cancer cells but potentiated the cytotoxicity of doxorubicin and enhanced its penetration through multilayered cell cultures. In solid tumors, lansoprazole improved the distribution of doxorubicin but also increased expression of biomarkers of drug activity throughout the tumor. Combined treatment with lansoprazole and doxorubicin was more effective in delaying tumor growth as compared to either agent alone. Together, lansoprazole enhances the therapeutic effects of doxorubicin both by improving its distribution and increasing its activity in solid tumors. Use of PPIs to improve drug distribution and to inhibit autophagy represents a promising strategy to enhance the effectiveness of anticancer drugs in solid tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".