Anti-cancer potential of cannabis terpenes in a Taxol-resistant model of breast cancer
Bibliographic record
Abstract
Chemotherapeutic resistance can limit breast cancer outcomes; therefore, the exploration of novel therapeutic options is warranted. Isolated compounds found in cannabis have previously been shown to exhibit anti-cancer effects, but little is known about their effects in resistant breast cancer. Our study aimed to evaluate the effects of terpenes found in cannabis in in vitro chemotherapy-resistant model of breast cancer. We aimed to identify whether five terpenes found in cannabis produced anti-cancer effects, and whether their effects were improved upon co-treatment with cannabinoids and flavonoids also found in cannabis. Nerolidol and β-caryophyllene produced the greatest cytotoxic effects, activated the apoptotic cascade, and reduced cellular invasion. Combinations with the flavonoid kaempferol potentiated the cytotoxic effects of ocimene, terpinolene, and β-myrcene. Combinations of nerolidol and Δ9-tetrahydrocannabinol or cannabidiol produced variable responses ranging from antagonism and additivity to synergy, depending on concentrations used. Our results indicate that cannabis terpenes, alone or combined with cannabinoids and flavonoids, produced anti-cancer effects in chemotherapy-resistant breast cancer cell lines. This study is a first step in the identification of compounds that could have therapeutic potential in the treatment of resistant breast cancer.
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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 | 0.001 | 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".