Anticancer effect of minor phytocannabinoids in preclinical models of multiple myeloma
Bibliographic record
Abstract
Multiple myeloma (MM) is a blood cancer caused by uncontrolled growth of clonal plasmacells. Bone disease is responsible for the severe complications of MM and is caused by myeloma cells infiltrating the bone marrow and inducing osteoclast activation. To date, no treatment for MM is truly curative since patients relapse and become refractory to all drug classes. Cannabinoids are already used as palliative in cancer patients. Furthermore, their proper anticancer effect was demonstrated in many cancer models in vitro, in vivo, and in clinical trials. Anyway, few information was reported on the effect of cannabinoids on MM and no data has been provided on minor phytocannabinoids such as cannabigerol (CBG), cannabichromene (CBC), cannabinol (CBN), and cannabidivarin (CBDV). Scientific literature also reported cannabinoids beneficial effect against bone disease. Here, we examined the cytotoxic activity of CBG, CBC, CBN, and CBDV in vitro in MM cell lines, their effect in modulating MM cells invasion toward bone cells and the bone resorption. Subsequently, according to the in vitro results, we selected CBN for in vivo study in a MM xenograft mice model. Results showed that the phytocannabinoids inhibited MM cell growth and induced necrotic cell death. Moreover, the phytocannabinoids reduced the invasion of MM cells toward osteoblast cells and bone resorption in vitro. Lastly, CBN reduced in vivo tumor mass. Together, our results suggest that CBG, CBC, CBN, and CBDV can be promising anticancer agents for MM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".