Parallel Solid-Phase Synthesis using a New Diethylsilylacetylenic Linker and Leading to Mestranol Derivatives with Potent Antiproliferative Activities on Multiple Cancer Cell Lines
Bibliographic record
Abstract
BACKGROUND: RM-133 belongs to a new family of aminosteroid derivatives demonstrating interesting anticancer properties, as confirmed in vivo in four mouse cancer xenograft models. However, the metabolic stability of RM-133 needs to be improved. After investigation, the replacement of its androstane scaffold by a more stable estrane scaffold led to the development of the mestranol derivative RM-581. METHODS: Using solid-phase strategy involving five steps, we quickly synthesized a series of RM-581 analogs using the recently-developed diethylsilylacetylenic linker. To establish structure-activity relationships, we then investigated their antiproliferative potency on a panel of cancer cell lines from various cancers (breast, prostate, ovarian and pancreatic). RESULTS: Some of the mestranol derivatives have shown in vitro anticancer activities that are close to, or better than, those observed for RM-581. Compound 23, a mestranol derivative having a ((3,5-dimethylbenzoyl)- L-prolyl)piperazine side chain at position C2, was found to be active as an antiproliferative agent (IC50 = 0.38 ± 0.34 to 3.17 ± 0.10 µM) and to be twice as active as RM-581 on LNCaP, PC-3, MCF-7, PANC-1 and OVCAR-3 cancer cells (IC50 = 0.56 ± 0.30, 0.89 ± 0.63, 1.36 ± 0.31, 2.47 ± 0.91 and 3.17 ± 0.10 µM, respectively). CONCLUSION: Easily synthesized in good yields by both solid-phase organic synthesis and classic solution-phase chemistry, promising compound 23 could be used as an antiproliferative agent on a variety of cancers, notably pancreatic and ovarian cancers, both having very bad prognoses.
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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) | 0.001 | 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".