Combination therapy with cannabidiol and chemotherapeutics in canine urothelial carcinoma cells
Bibliographic record
Abstract
BACKGROUND: Canine urothelial carcinoma is the most common form of canine bladder cancer. Treatment with chemotherapy has variable response rates leading to most dogs succumbing to their disease within a year. Cannabidiol is an emerging treatment within the field of oncology. In reported in vivo studies, cannabidiol has induced apoptosis, reduced cell migration, and acted as a chemotherapy sensitizer in various human tumor types. The aim of this study was to characterize the effects of cannabidiol on canine urothelial carcinoma cell viability and apoptosis as both a single agent and in combination with chemotherapy in vitro. RESULTS: Cannabidiol reduced cell viability and induced apoptosis in canine urothelial cells as determined by crystal violet viability assay and annexin V/propidium iodide flow cytometry. Furthermore, combinations of cannabidiol with mitoxantrone and vinblastine chemotherapy yielded significantly reduced cell viability and increased apoptosis compared to single agent treatment alone. The drug interactions were deemed synergistic based on combination index calculations. Conversely, the combination of cannabidiol and carboplatin did not result in decreased cell viability and increased apoptosis compared to single agent treatment. Combination index calculations suggested an antagonistic interaction between these drugs. Finally, the combination of the non-steroidal anti-inflammatory drug piroxicam with cannabidiol did not significantly affect cell viability, although, some cell lines demonstrated decreased cell viability when mitoxantrone was combined with piroxicam. CONCLUSIONS: Cannabidiol showed promising results as a single agent or in combination with mitoxantrone and vinblastine for treatment of canine urothelial carcinoma cells. Further studies are justified to investigate whether these results are translatable in vivo.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".