A Multivalent Peptoid Conjugate Modulates Androgen Receptor Transcriptional Activity to Inhibit Therapy-resistant Prostate Cancer
Bibliographic record
Abstract
Prostate cancers adapt to androgen receptor (AR) pathway inhibitors and progress to castration resistance due to ongoing AR expression and function. To counter this, we developed a new approach to modulate the AR and inhibit castration-resistant prostate cancer (CRPC) using multivalent peptoid conjugates (MPC) that contain multiple copies of the AR-targeting ligand ethisterone attached to a peptidomimetic scaffold. Here, we investigated the antitumor effects of compound MPC309, a trivalent display of ethisterone conjugated to a peptoid oligomer backbone that binds to the AR with nanomolar affinity. MPC309 exhibited potent antiproliferative effects on various enzalutamide-resistant prostate cancer models, including those with AR splice variants, ligand-binding mutations, and noncanonical AR gene expression programs, as well as mouse prostate organoids harboring defined genetic alterations that mimic lethal human prostate cancer subtypes. MPC309 is taken up by cells through macropinocytosis, an endocytic process more prevalent in cancer cells than in normal ones, thus providing an opportunity to target tumors selectively. MPC309 triggers a distinct AR transcriptome compared with DHT and enzalutamide, a clinically used antiandrogen. Specifically, MPC309 enhances the expression of differentiation genes while reducing the expression of genes needed for cell division and metabolism. Mechanistically, MPC309 increases AR chromatin occupancy and alters AR interactions with coregulatory proteins in a pattern distinct from DHT. In xenograft studies, MPC309 produced significantly greater tumor suppression than enzalutamide. Altogether, MPC309 represents a promising new AR modulator that can combat resistant disease by promoting an AR antiproliferative gene expression program.
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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".