Characterization of a New Class of Androgen Receptor Antagonists with Potential Therapeutic Application in Advanced Prostate Cancer
Bibliographic record
Abstract
The human androgen receptor plays a major role in the development and progression of prostate cancer and represents a well-established drug target. All clinically approved androgen receptor antagonists possess similar chemical structures and exhibit the same mode of action on the androgen receptor. Although initially effective, resistance to these androgen receptor antagonists usually develops and the cancer quickly progresses to castration-resistant and metastatic states. Yet even in these late-stage patients, the androgen receptor is critical for the progression of the disease. Thus, there is a continuing need for novel chemical classes of androgen receptor antagonists that could help overcome the problem of resistance. In this study, we implemented and used the synergetic combination of virtual and experimental screening to discover a number of new 10-benzylidene-10H-anthracen-9-ones that not only effectively inhibit androgen receptor transcriptional activity, but also induce almost complete degradation of the androgen receptor. Of these 10-benzylidene-10H-anthracen-9-one analogues, a lead compound (VPC-3033) was identified that showed strong androgen displacement potency, effectively inhibited androgen receptor transcriptional activity, and possesses a profound ability to cause degradation of androgen receptor. Notably, VPC-3033 exhibited significant activity against prostate cancer cells that have already developed resistance to the second-generation antiandrogen enzalutamide (formerly known as MDV3100). VPC-3033 also showed strong antiandrogen receptor activity in the LNCaP in vivo xenograft model. These results provide a foundation for the development of a new class of androgen receptor antagonists that can help address the problem of antiandrogen resistance in prostate cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".