siRNA Lipid Nanoparticle Potently Silences Clusterin and Delays Progression When Combined with Androgen Receptor Cotargeting in Enzalutamide-Resistant Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Lipid nanoparticle (LNP) formulations facilitate tumor uptake and intracellular processing through an enhanced permeation and retention effect (EPR), and currently multiple products are undergoing clinical evaluation. Clusterin (CLU) is a cytoprotective chaperone induced by androgen receptor (AR) pathway inhibition to facilitate adaptive survival pathway signaling and treatment resistance. In our study, we investigated the efficacy of siRNA tumor delivery using LNP systems in an enzalutamide-resistant (ENZ-R) castration-resistant prostate cancer (CRPC) model. EXPERIMENTAL DESIGN: Gene silencing of a luciferase reporter gene in the PC-3M-luc stable cell line was first assessed in subcutaneous and metastatic PC-3 xenograft tumors. Upon validation, the effect of LNP siRNA targeting CLU in combination with AR antisense oligonucleotides (ASO) was assessed in ENZ-R CRPC LNCaP in vitro and in vivo models. RESULTS: LNP LUC-siRNA silenced luciferase expression in PC-3M-luc subcutaneous xenograft and metastatic models. LNP CLU-siRNA potently suppressed CLU and AR ASO-induced CLU and AKT and ERK phosphorylation in ENZ-R LNCaP cells in vitro, more potently inhibiting ENZ-R cell growth rates and increased apoptosis when compared with AR-ASO monotherapy. In subcutaneous ENZ-R LNCaP xenografts, combinatory treatment of LNP CLU-siRNA plus AR-ASO significantly suppressed tumor growth and serum PSA levels compared with LNP LUC-siRNA (control) and AR-ASO. CONCLUSIONS: LNP siRNA can silence target genes in vivo and enable inhibition of traditionally non-druggable genes like CLU and other promising cotargeting approaches in ENZ-R CRPC therapeutics.
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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".