Selectively Targeting Prostate Cancer with Antiandrogen Equipped Histone Deacetylase Inhibitors
Bibliographic record
Abstract
Diverse cellular processes relevant to cancer progression are regulated by the acetylation status of proteins. Among such processes is chromatin remodeling via histone proteins, controlled by opposing histone deacetylase (HDAC) and histone acetyltransferase (HAT) enzymes. Histone deacetylase inhibitors (HDACi) show great promise in preclinical cancer models, but clinical trials treating solid tumors have failed to improve patient survival. This is due in part to an inability of HDACi to effectively accumulate in cancerous cells. To address this problem we designed HDACi with secondary pharmacophores to facilitate selective accumulation in malignant cells. We present the first example of HDACi compounds targeted to prostate tumors by equipping them with the additional ability to bind the androgen receptor (AR) with nonsteroidal antiandrogen moieties. Leads among these new dual-acting molecules bind to the AR and halt AR transcriptional activity at lower concentrations than clinical antiandrogens. They inhibit key isoforms of HDAC with low nanomolar potency. Fluorescent microscopy reveals varying degrees of AR nuclear localization in response to these compounds that correlates with their HDAC activity. These biological properties translate into potent anticancer activity against hormone-dependent (AR+) LNCaP and to a lesser extent against hormone-independent (AR-) DU145 prostate cancer, while having greatly reduced toxicity in noncancerous cells. This illustrates that engaging multiple biological targets with a single chemical probe can achieve both potent and cell-type-selective responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".