Cell‐ <scp>ELISA</scp> ‐Based High‐Throughput Screening Leads to the Discovery of Androgen Receptor Degraders to Conquer Castration‐Resistant Prostate Cancer
Bibliographic record
Abstract
Androgen receptor (AR) antagonists play a pivotal role in the treatment of castration-resistant prostate cancer (CRPC). However, the reactivation of AR signaling during antiandrogen therapy remains a major factor contributing to resistance against currently used clinical antagonists. As a result, strategies aimed at degrading the AR protein have garnered substantial attention for CRPC therapy. In this study, we first established a high-throughput screening (HTS) model for AR degraders based on Cell-ELISA technology. Using this model to screen our in-house chemical database, we identified a novel AR degrader, ZC9. Functional evaluations demonstrated that ZC9 exhibits significant inhibitory activity against CRPC cell proliferation and effectively downregulates AR protein levels. Mechanistic studies revealed that ZC9 directly binds to AR and inhibits dihydrotestosterone (DHT)-induced nuclear translocation of AR. Furthermore, ZC9 promotes AR degradation via the ubiquitin-proteasome system (UPS) and suppresses AR transcriptional activity. Collectively, these findings highlight ZC9 as a promising lead compound for the treatment of CRPC.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".