Antiproliferative effects of a series of novel synthetic sulfonate esters on human breast cancer cell line MCF-7.
Bibliographic record
Abstract
BACKGROUND: It has been well documented that some organosulfur compounds (OACs) show promise as anticancer agents. MATERIALS AND METHODS: The growth inhibitory effects of six novel different synthetic sulfonate esters was evaluated on cancerous (MCF-7) and non-cancerous (MCF-10A) human breast epithelial cells. RESULTS: We found that the most active compounds against MCF-7 breast cancer cells had a common structure of p-methoxyphenyl p-toluenesulfonate with the methoxy substituent shifted from position 4 (22) to 2 (22o) or to 3 (22m). 3-Methoxyphenyl p-toluenesulfonate (22m) showed the lowest IC50 value (89.83 microM) on breast cancer cells but was also very active on non-cancerous MCF-10A cells (IC50 value of 53.96 microM). We found that compound 22 caused a greater degree of cell cycle arrest and induced apoptosis in cancerous MCF-7 cells compared with normal breast epithelial MCF-10A cells. However, compound 22m, was less selective by significantly arresting normal cells at G2/M-phase followed by a weak induction of apoptosis. CONCLUSION: P-methoxyphenyl p-toluenesulfonate (22) appeared to be a more selective inhibitor of the growth of human breast cancer cells. Taken together, these results show that synthetic OSC compounds evaluated in this study can be effective antineoplastic agents and are worthy of further investigation.
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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) | 0.001 | 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".