Antitumor Activity of Abnormal Cannabidiol and Its Analog O-1602 in Taxol-Resistant Preclinical Models of Breast Cancer
Bibliographic record
Abstract
Cannabinoids exhibit anti-inflammatory and anti-tumorigenic properties. Contrary to most cannabinoids present in the Cannabis plant, some, such as O-1602 and abnormal cannabidiol, have no or only little affinity to the CB1 or CB2 cannabinoid receptors and instead exert their effects through other receptors. Here, we investigated whether the synthetic regioisomers of cannabidiol, abnormal cannabidiol, and a closely related compound, O-1602, display anti-tumorigenic effects in cellular models of breast cancer and whether it could reduce tumorigenesis in vivo. Several studies have shown the effects of cannabinoids on chemotherapy-sensitive breast cancer cell lines, but less is known about the anti-tumorigenic effects of cannabinoids in chemotherapy-resistant cell lines. Paclitaxel-resistant MDA-MB-231 and MCF-7 breast cancer cell lines were used to study the effect of O-1602 and abnormal cannabidiol on viability, apoptosis and migration. The effects of O-1602 and abnormal cannabidiol on cell viability could be completely blocked by the combination of GPR55 and GPR18-specific siRNAs. Both O-1602 and abnormal cannabidiol decreased viability in Paclitaxel-resistant breast cancer cells in a concentration-dependent manner through induction of apoptosis. The effect of these cannabinoids on tumor growth in vivo was studied in a zebrafish xenograft model. In this model, treatment with O-1602 and abnormal cannabidiol (2M) significantly reduced tumor growth. Our results suggest that atypical cannabinoids, like O-1602 and abnormal cannabidiol, exert anti-tumorigenic effects on Paclitaxel-resistant breast cancer cells. Due to their lack of central sedation and psychoactive effects, these atypical cannabinoids could represent new leads for the development of additional anti-cancer treatments when resistance to conventional chemotherapy occurs during the treatment of breast and possibly other cancers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".