2β-(<i>N</i>-Substituted Piperazino)-5α-Androstane-3α,17β-Diols: Parallel Solid-Phase Synthesis and Antiproliferative Activity on Human Leukemia HL-60 Cells
Bibliographic record
Abstract
Leukemia is the most common cancer affecting children. A steroid possessing a methylpiperazine nucleus was recently reported to inhibit the proliferation of HL-60 leukemia cells. To speed up the development of this promising potential new drug, we generated libraries of analogues using parallel solid-phase organic synthesis (SPOS). A 6-step sequence of reactions, starting from dihydrotestosterone, afforded a steroidal 2,3alpha-epoxide, which was selectively opened to give, after N-Fmoc protection, a diol with suitable stereochemistry. The difference of reactivity between 3alpha-OH and 17beta-OH was then used to allow the regioselective coupling of 17beta-OH to chloro-activated butyldiethylsilane polystyrene. We next generated three libraries of 2beta-piperazinyl-5alpha-androstane-3alpha,17beta-diol N-derivatives with 1, 2, or 3 levels of molecular diversity in acceptable yields and purities for our biological screening assay. Several members of these libraries were more potent than the lead compound, especially five members with a proline as the first level of diversity and a cyclohexylcarbonyl, methylbutyryl, cyclohexylacetyl, cyclopentylpropionyl, or hexanoyl as the second level of diversity. They efficiently inhibited HL-60 cell proliferation with IC50 values of 0.58, 0.66, 1.78, 1.98, and 2.57 microM, respectively. The present work demonstrates the potential of our SPOS approach for the optimization of a new class of cytotoxic agents.
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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".