Recent advances in the development of 17beta-hydroxysteroid dehydrogenase inhibitors
Bibliographic record
Abstract
• 17beta-hydroxysteroid dehydrogenases (17β-HSDs) carry out key reduction and oxidation reactions. • Inhibitors of 17β-HSD enzymes could be used to treat estrogen/androgen-dependent diseases. • 17β-HSD inhibitors are the subject of significant work to develop new drugs. • New inhibitors of 17β-HSDs (types 1, 2, 3, 5, 7, 10, 12, 13 and 14) are discussed. • This review article focusses on inhibitors of 17β-HSDs not covered in recent years. The family of 17β-hydroxysteroid dehydrogenases (17β-HSDs) occupies a prominent place due to its number of isoforms, which carry out a bidirectional transformation (reduction of a steroid carbonyl to alcohol and oxidation of a steroid alcohol to ketone) depending on the nature of the cofactor present. Involved in the activation or inactivation of key estrogens and androgens, 17β-HSDs are therefore therapeutic targets whose selective inhibition would make it possible to be considered for the treatment of several diseases, such as breast cancer, prostate cancer, endometriosis, Alzheimer’s disease and osteoporosis. This review article is a continuation of those having reported the great diversity of inhibitors developed over the last years but focusses on inhibitors recently developed. Work to obtain more effective inhibitors that target the first known isoforms (types 1, 2, 3, 5 and 7) has continued, among others, but new inhibitors that target the isoforms more recently reported in the literature (types 10, 12, 13 and 14) are now being reported. Dual inhibitors of two enzymes (17β-HSD1 and steroid sulfatase) were also reported. These inhibitors were grouped according to the 17β-HSD type inhibited and their backbone (steroidal or non-steroidal) when necessary. They were also reported in chronological order and according to the research group.
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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 | 0.001 | 0.001 |
| 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) | 0.006 | 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".