Anticancer properties of cannabidiol and Δ9-tetrahydrocannabinol and synergistic effects with gemcitabine and cisplatin in bladder cancer cell lines
Bibliographic record
Abstract
Abstract Introduction With the legalization of cannabis in multiple jurisdictions throughout the world, a larger proportion of the population consumes cannabis. Several studies have demonstrated anti-tumor effects of components present in cannabis in different models. Unfortunately, little is known about the potential anti-tumoral effects of cannabinoids in bladder cancer and how cannabinoids could potentially synergize with chemotherapeutic agents. Our study aims to identify whether a combination of cannabinoids, like cannabidiol and Δ 9 -tetrahydrocannabinol, with agents commonly used to treat bladder cancer, such as gemcitabine and cisplatin, can produce desirable synergistic effects. We also evaluated if co-treatment with different cannabinoids resulted in synergistic effects. Methods We generated concentration curves with several drugs, including several cannabinoids, to identify the range at which they could exert anti-tumor effects in bladder cancer cell lines. We tested the cytotoxic effects of gemcitabine (up to 100 nM), cisplatin (up to 100 μM), and cannabinoids (up to 10 μM) in T24 and TCCSUP cells. We also evaluated the activation of the apoptotic cascade and whether cannabinoids have the ability to reduce invasion in T24 cells. Results Cannabidiol, Δ 9 -tetrahydrocannabinol, cannabichromene, and cannabivarin reduce cell viability of bladder cancer cell lines, and their combination with gemcitabine or cisplatin may induce differential responses, from antagonistic to additive and synergistic effects, depending on the concentrations used. Cannabidiol and Δ 9 -tetrahydrocannabinol were also shown to induce apoptosis via caspase-3 cleavage and reduce invasion in a Matrigel assay. Cannabidiol and Δ 9 -tetrahydrocannabinol also display synergistic properties with other cannabinoids like cannabichromene or cannabivarin, although individual cannabinoids may be sufficient to reduce cell viability of bladder cancer cell lines. Discussion Our results indicate that cannabinoids can reduce human bladder transitional cell carcinoma cell viability, and that they can potentially exert synergistic effects when combined with other agents. Our in vitro results will form the basis for future studies in vivo and in clinical trials for the development of new therapies that could be beneficial for the treatment of bladder cancer in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".