Carbidopa abrogates <scp>L</scp>‐dopa decarboxylase coactivation of the androgen receptor and delays prostate tumor progression
Bibliographic record
Abstract
The androgen receptor (AR) plays a central role in prostate cancer progression to the castration-resistant (CR) lethal state. L-Dopa decarboxylase (DDC) is an AR coactivator that increases in expression with disease progression and is coexpressed with the receptor in prostate adenocarcinoma cells, where it may enhance AR activity. Here, we hypothesize that the DDC enzymatic inhibitor, carbidopa, can suppress DDC-coactivation of AR and retard prostate tumor growth. Treating LNCaP prostate cancer cells with carbidopa in transcriptional assays suppressed the enhanced AR transactivation seen with DDC overexpression and decreased prostate-specific antigen (PSA) mRNA levels. Carbidopa dose-dependently inhibited cell growth and decreased survival in LNCaP cell proliferation and apoptosis assays. The inhibitory effect of carbidopa on DDC-coactivation of AR and cell growth/survival was also observed in PC3 prostate cancer cells (stably expressing AR). In vivo studies demonstrated that serum PSA velocity and tumor growth rates elevated ∼2-fold in LNCaP xenografts, inducibly overexpressing DDC, were reverted to control levels with carbidopa administration. In castrated mice, treating LNCaP tumors, expressing endogenous DDC, with carbidopa delayed progression to the CR state from 6 to 10 weeks, while serum PSA and tumor growth decreased 4.3-fold and 5.4-fold, respectively. Our study is a first time demonstration that carbidopa can abrogate DDC-coactivation of AR in prostate cancer cells and tumors, decrease serum PSA, reduce tumor growth and delay CR progression. Since carbidopa is clinically approved, it may be readily used as a novel therapeutic strategy to suppress aberrant AR activity and delay prostate cancer progression.
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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 | 0.001 |
| 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".