Combination therapy with androgen receptor N‐terminal domain antagonist EPI‐7170 and enzalutamide yields synergistic activity in AR‐V7‐positive prostate cancer
Bibliographic record
Abstract
Resistance of castration-resistant prostate cancer (CRPC) to enzalutamide and abiraterone involves the expression of constitutively active, truncated androgen receptor (AR) splice variants (AR-Vs) that lack a C-terminal ligand-binding domain (LBD). Both full-length AR and truncated AR-Vs require a functional N-terminal domain (NTD) for transcriptional activity thereby providing rationale for the development of ralaniten (EPI-002) as a first-in-class antagonist of the AR-NTD. Here, we evaluated the antitumor effect of a next-generation analog of ralaniten (EPI-7170) as a monotherapy or in combination with enzalutamide in prostate cancer cells that express AR-V7 that were resistant to enzalutamide. EPI-7170 had 8-9 times improved potency compared to ralaniten. Enzalutamide increased levels of AR-V7 and expression of its target genes. Knockdown of AR-V7 restored sensitivity to enzalutamide, indicating a role for AR-V7 in the mechanism of resistance. EPI-7170 inhibited expression of genes transcriptionally regulated by full-length AR and AR-V7. A combination of EPI-7170 and enzalutamide resulted in synergistic inhibition of proliferation of enzalutamide-resistant cells that was consistent with results from cell cycle and clonogenic assays. In addition, this drug enhanced the antitumor effect of enzalutamide in enzalutamide-resistant CRPC preclinical models. Thus, a combination therapy targeting both the NTD and LBD of AR, and thereby blocking both full-length AR and AR-Vs, has potential for the treatment of enzalutamide-resistant CRPC.
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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) | 0.001 | 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) | 0.001 | 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".