Effects of the potassium-sparing diuretic amiloride on chemotherapy response in canine osteosarcoma cells
Bibliographic record
Abstract
BACKGROUND: Osteosarcoma (OSA) is a common bone tumor of mesenchymal origin in dogs. Chemotherapy delays metastasis, yet most dogs die of this disease within 1 year of diagnosis. The high metabolic demand of cancer cells promotes proton pump upregulation, leading to acidification of the tumor microenvironment and chemoresistance. The potassium-sparing diuretic amiloride is among a class of proton pump inhibitors prescribed for refractory heart failure treatment in dogs. OBJECTIVE: We hypothesized that amiloride treatment improves chemotherapy response by reducing acidification in canine OSA cells. Our objective was to assess the in vitro effects of amiloride on cell viability, apoptosis, and metabolism. METHODS: In vitro study. Assessments of cell viability and apoptosis were performed after single agent or combination treatment, along with calculations of pharmacological synergism using the combination index. Protein signaling during apoptosis was evaluated by Western blotting. Metabolic profiling was performed using a Seahorse bioanalyzer. RESULTS: Amiloride strongly synergized with doxorubicin in combination treatment and exhibited additive or antagonistic effects with carboplatin in canine OSA cells. Combination treatment with doxorubicin significantly upregulated p53-mitochondrial signaling to activate apoptosis and downregulate Akt phosphorylation. Amiloride-treated cells further exhibited metabolic switching with reductions in glycolytic capacity and maximal respiration. CONCLUSION AND CLINICAL IMPORTANCE: Amiloride synergized with doxorubicin to potentiate apoptosis in canine OSA cells. These results justify further investigation into repurposing of amiloride as an oncology drug for the treatment of OSA in dogs.
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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".