Abstract 1632: Discovery of a novel series of androgen receptor antagonists with potential therapeutic applications in castration-resistant prostate cancer
Bibliographic record
Abstract
Abstract The human androgen receptor (AR) represents a well-established drug target for prostate cancer treatment. All clinically used antiandrogens, such as Bicalutamide and Enzalutamide, possess similar chemical structures and bind to the AR in the androgen binding site (ABS). These AR antagonists are initially effective, but resistance invariably developed in the castration-resistant prostate cancer (CRPC). Even in late-stage patients, the AR activity is still persistent in the progression of the disease. Thus, there is a continuing need for novel chemical classes of AR antagonists that could overcome the drug resistance. In this study, we performed a virtual screening against the AR ABS, and identified novel AR antagonists with chemical structures completely different from existing antiandrogens. A 10-(4-Hydroxybenzylidene)anthracen-9(10H)-one compound was discovered that not only effectively inhibits AR transcription and strongly displaces androgen in the ABS, but induces AR degradation in prostate cancer cells. Starting from the initial hit compound, a series of 10-benzylidene-10H-anthracen-9-ones were synthesized and evaluated by in vitro assays. A close analogue with enhanced potency was identified as a lead compound, which demonstrated strong androgen displacement potency, effective AR transcriptional inhibition, and a profound ability to cause degradation of AR. Notably, it exhibited significant activity against MDV3100-resistant prostate cancer cells. This lead compound was evaluated in both non-castrated and castration-resistant LNCaP xenograft models, and demonstrated significant effect on inhibiting tumor growth and reducing prostate specific antigen (PSA). This series of compounds were predicted to adopt a different binding mode from current antiandrogens, which may circumvent the resistance rendered by identified gain-of-function mutations. Further development of this series of AR antagonists may have great therapeutic potential in CRPC. Citation Format: Huifang Li, Mohamed DH Hassona, Nathan A. Lack, Peter Axerio-Cilies, Eric Leblanc, Emma T. Guns, Paul S. Rennie, Artem Cherkasov. Discovery of a novel series of androgen receptor antagonists with potential therapeutic applications in castration-resistant prostate cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1632. doi:10.1158/1538-7445.AM2014-1632
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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) | 0.003 | 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".