siRNA Targeting Mcl-1 Potentiates the Anticancer Activity of Andrographolide Nanosuspensions via Apoptosis in Breast Cancer Cells
Bibliographic record
Abstract
Breast cancer is the second leading cause of cancer-related death in the US. However, recurrence is frequently found despite adjuvant therapy being available. Combination therapy with cytotoxic drugs and gene therapy is being developed to be a new promising cancer treatment strategy. Introducing substituted dithiocarbamate moieties at the C12 position of andrographolide (3nAG) could improve its anticancer selectivity in the MCF-7 breast cancer cell line. However, its hydrophobicity is one of its main drawbacks. This work successfully prepared 3nAG nanosuspension stabilized with the chitosan derivative NSC (3nAGN-NSC) to increase solubility and pharmacological effectiveness. siRNAs have emerged as a promising therapeutic alternative for interfering with particular mRNA. The 3nAGN-NSC had also induced Mcl-1 mRNA expression in MCF-7 human breast cancer cells at 8, 12, and 24 h. This indicates that, in addition to Mcl-1 silencing by siRNA (siMcl-1) in MCF-7 with substantial Mcl-1 reliance, rationally devised combination treatment may cause the death of cancer cells in breast cancer. The Fa-CI analysis showed that the combination of 3nAGN-NSC and siMcl-1 had a synergistic effect with a combination index (CI) value of 0.75 (CI < 1 indicating synergistic effects) at the fractional inhibition of Fa 0.7. The synergistic effect was validated by flow cytometry, with the induction of apoptosis as the mechanism of reduced cell viability. Our findings suggested the rational use of 3nAGN-NSC in combination with siMcl-1 to kill breast cancer cells.
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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 | 0.000 |
| 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 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".