[10]-Gingerol improves doxorubicin anticancer activity and decreases its side effects in triple negative breast cancer models
Bibliographic record
Abstract
PURPOSE: Although doxorubicin is widely used to treat cancer, severe side effects limit its clinical use. Combination of standard chemotherapy with natural products can increase the efficacy and attenuate the side effects of current therapies. Here we studied the anticancer effects of a combined regimen comprising doxorubicin and [10]-gingerol against triple-negative breast cancer, which does not respond to hormonal or targeted therapies. METHODS: Cytotoxicity was evaluated by MTT assay, cell cycle progression and apoptosis were analyzed by flow cytometry and signaling pathways were analyzed by Western blotting in human and murine triple negative breast cancer cell systems. The anticancer/antimetastatic and toxic effects of the combined regimen was evaluated using syngeneic and xenograft orthotopic models. RESULTS: The combination of doxorubicin and [10]-gingerol significantly increased the number of apoptotic cells, compared to each compound alone. In 4T1Br4 cells, the combined regimen was the only condition able to increase the levels of active caspase 3 and γH2AX and to decrease the level of Cdk-6 cyclin. In vivo, doxorubicin (3 mg/Kg, D3) and [10]-gingerol (10 mg/Kg, G10) resulted in a significant reduction in the volume of primary tumors and a decrease in the number of circulating tumor cells (CTCs). Interestingly, only the combined regimen led to decreased tumor burdens to distant organs (i.e., metastasis) and reduced chemotherapy-induced weight loss and hepatotoxicity in tumor-bearing animals. Likewise, in a xenograft model, only the combined regimen was effective in significantly reducing the primary tumor volume and the prevalence of CTCs. CONCLUSIONS: Our data indicate that [10]-gingerol has potential to be used as a neoadjuvant or in combined therapy with doxorubicin, to improve its anticancer activity.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".