Canagliflozin Is a Novel Androgen Receptor Pathway Inhibitor in Castrate-Sensitive and Castrate-Resistant Prostate Cancer
Bibliographic record
Abstract
Androgen receptor (AR) pathway inhibitors (ARPIs) improve outcomes in advanced prostate cancer (PC) in combination with androgen deprivation therapy (ADT). However, PC rapidly develops ARPI resistance, frequently through expression of truncated AR variants (AR-Vs), like AR-V7, highlighting a need for more effective therapies. The sodium-glucose co-transporter 2 inhibitor (SGLT2i) canagliflozin, an approved diabetes drug, also suppresses PC growth and inhibits AR-related gene expression. Therefore, we hypothesized that canagliflozin may directly inhibit AR. Cellular and tumor models of PC were subjected to proliferation, clonogenic, and xenograft studies. RNA-seq and siRNA knockdown approaches defined molecular mechanisms. Molecular docking, thermal shift, and surface plasmon resonance assays assessed drug-target interactions. Stable sh-AR full-length (sh-AR-FL) and sh-AR-V7 cell lines were generated to interrogate the transcriptomic impact of AR and prognostic analysis was performed using clinical datasets. We found that canagliflozin suppresses PC growth through AR. It interacts with the AR ligand binding domain (LBD) with estimated affinity comparable to ARPIs and blocks AR signaling. Canagliflozin reduces the transcript and protein levels of the HSP70 chaperone and suppresses the cytoplasmic and nuclear levels of AR-FL and AR-Vs through proteasomal degradation. It mediates substantial reprogramming of PC transcriptional activity, including inhibition of AR pathway, cell-cycle, E2F and MYC hallmark targets. Its gene expression profile overlaps with silencing AR-FL or AR-V7 is associated with improved prognosis in clinical datasets. The results of this study demonstrate the potential for canagliflozin to function as a clinically useful ARPI and support prospective clinical investigation of this drug in PC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".