A Targeted-Covalent Inhibitor of 17β-HSD1 Blocks Two Estrogen-Biosynthesis Pathways: In Vitro (Metabolism) and In Vivo (Xenograft) Studies in T-47D Breast Cancer Models
Bibliographic record
Abstract
17β-Hydroxysteroid dehydrogenase type 1 (17β-HSD1) plays an important role in estrogen-dependent breast tumor growth. In addition to being involved in the production of estradiol (E2), the most potent estrogen in women, 17β-HSD1 is also responsible for the production of 5-androsten-3β,17β-diol (5-diol), a weaker estrogen than E2, but whose importance increases after menopause. 17β-HSD1 is therefore a target of choice for the treatment of estrogen-dependent diseases such as breast cancer and endometriosis. After we developed the first targeted-covalent (irreversible) and non-estrogenic inhibitor of 17β-HSD1, a molecule named PBRM, our goal was to demonstrate its therapeutic potential. Enzymatic assays demonstrated that estrone (E1) and dehydroepiandrosterone (DHEA) were transformed into E2 and 5-diol in T-47D human breast cancer cells, and that PBRM was able to block these transformations. Thereafter, we tested PBRM in a mouse tumor model (cell-derived T-47D xenografts). After treatment of ovariectomized (OVX) mice receiving E1 or DHEA, PBRM given orally was able to reduce the tumor growth at the control (OVX) level without any observed toxic effects. Thanks to its irreversible type of inhibition, PBRM retained its anti-tumor growth effect, even after reducing its frequency of administration to only once a week, a clear advantage over reversible inhibitors.
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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) | 0.001 | 0.000 |
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".