Design of a Mestranol 2‐<i>N</i>‐Piperazino‐Substituted Derivative Showing Potent and Selective in vitro and in vivo Activities in MCF‐7 Breast Cancer Models
Bibliographic record
Abstract
Anticancer structure-activity relationship studies on aminosteroid (5α-androstane) derivatives have emerged with a promising lead candidate: RM-133 (2β-[1-(quinoline-2-carbonyl)pyrrolidine-2-carbonyl]-N-piperazine-5α-androstane-3α,17β-diol), which possesses high in vitro and in vivo activities against several cancer cells, and selectivity over normal cells. However, the relatively weak metabolic stability of RM-133 has been a drawback to its progression toward clinical trials. We investigated the replacement of the androstane backbone by a more stable mestranol moiety. The resulting compound, called RM-581 ({4-[17α-ethynyl-17β-hydroxy-3-methoxyestra-1,3,5(10)-trien-2-yl]piperazin-1-yl}[(2S)-1-(quinolin-2-ylcarbonyl)pyrrolidin-2-yl]methanone), was synthesized efficiently in only five steps from commercially available estrone. In comparison with RM-133, RM-581 was found to be twice as metabolically stable, retains potent cytotoxic activity in breast cancer MCF-7 cell culture, and fully blocks tumor growth in a mouse xenograft model of breast cancer. Advantageously, the selectivity over normal cells has been increased with this estrane version of RM-133. In fact, RM-581 showed a better selectivity index (15.3 vs. 3.0) for breast cancer MCF-7 cells over normal breast MCF-10A cells, and was found to be nontoxic toward primary human kidney proximal tubule cells at doses reaching 50 μm.
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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) | 0.002 | 0.001 |
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".