Advances in Development of Inhibitors of 17β-Hydroxysteroid Dehydrogenases
Bibliographic record
Abstract
The 17beta-hydroxysteroid dehydrogenases (17beta-HSDs) are involved in the regulation of estrogens and androgens by catalyzing the reduction of 17-ketosteroids or the oxidation of 17beta hydroxysteroids. The enzyme activities associated with the different 17beta-HSD isoforms are widespread in human tissues, not only in classic steroidogenic tissues but also in a large series of peripheral intracrine tissues. Being involved at the end of steroidogenesis, the numerous members of 17beta-HSD family constitute interesting therapeutic targets for controlling the concentration of estrogens and androgens. Thus, inhibitors of reductive 17beta-HSD isoforms are attractive to block the formation of hydroxysteroids that stimulate estrogeno-sensitive pathologies (breast, ovarian, and endometrium cancers) and androgeno-sensitive pathologies (prostate cancer, benign prostatic hyperplasia, acne, and hirsutism). The inhibitors could be used to block the degradation of estradiol, an attractive strategy for treating osteoporosis and Alzheimer's disease. In addition to their classical use as anti-cancer agents and therapeutic agents, inhibitors of 17beta-HSDs are also useful tools to elucidate the role of these enzymes in particular biological systems. The present review article gives a description of novel inhibitors of 17beta-HSDs that were published in 2003-2006.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".