Targeting the N-Terminal Domain of the Androgen Receptor: A New Approach for the Treatment of Advanced Prostate Cancer
Bibliographic record
Abstract
: Despite the recent approval and widespread use of abiraterone acetate and enzalutamide for the treatment of castration-resistant prostate cancer (CRPC), this disease still poses significant management challenges because of various tumor escape mechanisms, including those that allow androgen receptor (AR) signaling to remain active. These AR-related resistance mechanisms include AR gene amplification or overexpression, constitutively active ligand-independent AR splice variants, and gain-of-function mutations involving the AR ligand-binding domain (LBD), among others. Therefore, the development of AR-targeted therapies that function independently of the LBD represents an unmet medical need and has the potential to overcome many of these resistance mechanisms. This article discusses N-terminal domain (NTD) inhibition as a novel concept in the field of AR-directed therapies for prostate cancer. AR NTD-targeting agents have the potential to overcome shortcomings of current hormonal therapies by inhibiting all forms of AR-mediated transcriptional activity, and as a result, may affect a broader AR population including mutational and splice variant ARs. Indeed, the first clinical trial of an AR NTD inhibitor is now underway. IMPLICATIONS FOR PRACTICE: Because of emerging resistance mechanisms that involve the ligand-binding domain of the androgen receptor (AR), there is currently no effective treatment addressing tumor escape mechanisms related to current AR-targeted therapies. Many patients still demonstrate limited clinical response to current hormonal agents, and castration-resistant prostate cancer remains a lethal disease. Intense research efforts are under way to develop therapies to target resistance mechanisms, including those directed at other parts of the AR molecule. A novel small-molecule agent, EPI-506, represents a new pharmaceutical class, AR N-terminal domain inhibitors, and shows preclinical promise to overcome many known resistance mechanisms related to novel hormonal therapies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".