Characterization of Niphatenones that Inhibit Androgen Receptor N-Terminal Domain
Bibliographic record
Abstract
Androgen ablation therapy causes a temporary reduction in tumor burden in patients with advanced prostate cancer. Unfortunately the malignancy will return to form lethal castration-recurrent prostate cancer (CRPC). The androgen receptor (AR) remains transcriptionally active in CRPC in spite of castrate levels of androgens in the blood. AR transcriptional activity resides in its N-terminal domain (NTD). Possible mechanisms of continued AR transcriptional activity may include, at least in part, expression of constitutively active splice variants of AR that lack the C-terminal ligand-binding domain (LBD). Current therapies that target the AR LBD, would not be effective against these AR variants. Currently no drugs are clinically available that target the AR NTD which should be effective against these AR variants as well as full-length AR. Niphatenones were originally isolated and identified in active extracts from Niphates digitalis marine sponge. Here we begin to characterize the mechanism of niphatenones in blocking AR transcriptional activity. Both enantiomers had similar IC50 values of 6 µM for inhibiting the full-length AR in a functional transcriptional assay. However, (S)-niphatenone had significantly better activity against the AR NTD compared to (R)-niphatenone. Consistent with niphatenones binding to and inhibiting transactivation of AR NTD, niphatenones inhibited AR splice variant. Niphatenone did not affect the transcriptional activity of the related progesterone receptor, but slightly decreased glucocorticoid receptor (GR) activity and covalently bound to GR activation function-1 (AF-1) region. Niphatenone blocked N/C interactions of AR without altering either AR protein levels or its intracellular localization in response to androgen. Alkylation with glutathione suggests that niphatenones are not a feasible scaffold for further drug development.
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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) | 0.002 | 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".