Recent Patents on New Steroid Agents Targeting the Steroidogenesis for Endocrine Cancer Treatments
Bibliographic record
Abstract
Cancer is a leading cause of death in the population and despite the significant technological advances that have been made over the last years, there is a great need for new and better treatments with fewer side effects. Among the various types, hormone-dependent cancers are stimulated by the presence of certain steroidal hormones such as androgens and estrogens, which act through a nuclear receptor. The use of small molecules to block the biosynthesis (steroidogenesis) or the action of hormones (androgens or estrogens) is a therapeutic approach that has yielded interesting results and whose development continues. This review article emphasizes the patents and patent applications published over the last five years. It deals exclusively with steroid compounds developed as inhibitors of key enzymes (17α-hydroxylase/17,20-lyase, steroid sulfatase, 5α-reductases, aromatase and 17β-hydroxysteroid dehydrogenases) involved in the steroidogenesis and identified as therapeutic targets. Such inhibitors could be used as a drug to reduce the concentration of androgens or estrogens and, consequently, for treating hormone-dependent diseases such as prostate cancer, breast cancer and endometriosis.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".