Androgen receptor signaling inhibitors: post-chemotherapy, pre-chemotherapy and now in castration-sensitive prostate cancer
Bibliographic record
Abstract
Based on pioneering work by Huggins, Hodges and others, hormonal therapies have been established as an effective approach for advanced prostate cancer (PC) for the past eight decades. However, it quickly became evident that androgen deprivation therapy (ADT) via surgical or medical castration accomplishes inadequate inhibition of the androgen receptor (AR) axis, with clinical resistance inevitably emerging due to adrenal and intratumoral sources of androgens and other mechanisms. Early efforts to augment ADT by adding adrenal-targeting agents (aminoglutethimide, ketoconazole) or AR antagonists (flutamide, bicalutamide, nilutamide, cyproterone) failed to achieve overall survival (OS) benefits, although they did exhibit some evidence of limited clinical activity. More recently, four new androgen receptor signaling inhibitors (ARSIs) successfully entered clinical practice. Specifically, the CYP17 inhibitor abiraterone acetate and the second generation AR antagonists (enzalutamide, apalutamide and darolutamide) achieved OS benefits for PC patients, confirmed the importance of reactivated AR signaling in castration-resistant PC and validated important concepts that had been proposed in the field several decades ago but had remained so far unproven, including adrenal-targeted therapy and combined androgen blockade. The past decade has seen steady advances toward more comprehensive AR axis targeting. Now the question is raised whether we have accomplished the maximum AR axis inhibition possible or there is still room for improvement. This review, marking the 80-year anniversary of ADT and 10-year anniversary of successful ARSIs, examines their current clinical use and discusses future directions, in particular combination regimens, to maximize their efficacy, delay emergence of resistance and improve patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".