Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: 17beta-Hydroxysteroid dehydrogenases (17beta-HSDs) mainly catalyze the reduction of C17-ketosteroids to their corresponding hydroxylated forms as well as the reverse reaction (oxidation). Able to convert inactive or less active steroid hormones into more potent ones and vice versa, certain 17beta-HSDs play a key role, especially in the regulation of estrogen and androgen levels. The therapeutic potential of this enzyme family, especially for the treatment of breast cancer, prostate cancer, acne and osteoporosis, then stimulated the development of inhibitors of 17beta-HSDs and important progress was achieved over the last years. AREAS COVERED IN THIS REVIEW: This review article reports all patent applications related to the inhibitors of 17beta-HSDs, including some articles needed to complement the information presented. WHAT THE READER WILL GAIN: Readers will be informed about the role and function of 17beta-HSDs in the first section and about the history of inhibitor development in the second section. Furthermore, in the third and main section, the readers will learn about the structures of patented inhibitors originating from different companies and academic groups. TAKE HOME MESSAGE: The increase in the number of 17beta-HSD inhibitors reported in the last years augurs well for the future. The challenge is now to translate these results into clinical studies to allow determination of the therapeutic usefulness of 17beta-HSD inhibitors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".