Selectively Targeting the DNA-binding Domain of the Androgen Receptor as a Prospective Therapy for Prostate Cancer
Bibliographic record
Abstract
The androgen receptor (AR) is a transcription factor that has a pivotal role in the occurrence and progression of prostate cancer. The AR is activated by androgens that bind to its ligand-binding domain (LBD), causing the transcription factor to enter the nucleus and interact with genes via its conserved DNA-binding domain (DBD). Treatment for prostate cancer involves reducing androgen production or using anti-androgen drugs to block the interaction of hormones with the AR-LBD. Eventually the disease changes into a castration-resistant form of PCa where LBD mutations render anti-androgens ineffective or where constitutively active AR splice variants, lacking the LBD, become overexpressed. Recently, we identified a surfaced exposed pocket on the AR-DBD as an alternative drug-target site for AR inhibition. Here, we demonstrate that small molecules designed to selectively bind the pocket effectively block transcriptional activity of full-length and splice variant AR forms at low to sub-micromolar concentrations. The inhibition is lost when residues involved in drug interactions are mutated. Furthermore, the compounds did not impede nuclear localization of the AR and blocked interactions with chromatin, indicating the interference of DNA binding with the nuclear form of the transcription factor. Finally, we demonstrate the inhibition of gene expression and tumor volume in mouse xenografts. Our results indicate that the AR-DBD has a surface site that can be targeted to inhibit all forms of the AR, including enzalutamide-resistant and constitutively active splice variants and thus may serve as a potential avenue for the treatment of recurrent and metastatic prostate cancer.
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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".