Investigation of the In Vitro and In Vivo efficiency of RM-532-105, a 17β-hydroxysteroid dehydrogenase type 3 inhibitor, in LAPC-4 prostate cancer cell and tumor models
Bibliographic record
Abstract
In the fight against androgen-sensitive prostate cancer, the enzyme 17β-hydroxysteroid dehydrogenase type 3 (17β-HSD3) is an attractive therapeutic target considering its key role in the formation of androgenic steroids. In this study, we attempted to assess the in vivo efficacy of the compound RM-532-105, an androsterone derivative developed as an inhibitor of 17β-HSD3, in the prostate cancer model of androgen-sensitive LAPC-4 cells xenografted in nude mice. RM-532-105 did not inhibit the tumor growth induced by 4-androstene-3,17-dione (4-dione); rather, the levels of the androgens testosterone (T) and dihydrotestosterone (DHT) increased within the tumors. In plasma, however, DHT levels increased but T levels did not. In troubleshooting experiments, the non-androgenic potential of RM-532-105 was confirmed by two different assays (LAPC-4 proliferation and androgen receptor transcriptional activity assays). The enzyme 5α-reductase was also revealed to be the predominant enzyme metabolizing 4-dione in LAPC-4 cells, yielding 5α-androstane-3,17-dione and not T. Other 17β-HSDs than 17β-HSD3 seem responsible in the androgen synthesis. From experiments with LAPC-4 cells, we fortuitously came across the interesting finding that 17β-HSD3 inhibitor RM-532-105 is concentrated inside tumors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".