Effect of lipid nanoparticle siRNA delivery systems on silencing clusterin and progression in enzalutamide resistant prostate cancer in vivo.
Bibliographic record
Abstract
256 Background: Clusterin (CLU) is induced by androgen receptor (AR) pathway inhibition and its overexpression confers treatment resistance. Lipid nanoparticle (LNP) facilitates tumor uptake and intracellular processing through an enhanced permeation and retention effect (EPR), currently with multiple products undergoing clinical evaluation. Gene silencing using small interfering RNA (siRNA) is a promising approach but in vivo delivery remains a major barrier. In our study, we investigated the efficacy siRNA tumor delivery using LNP systems in enzalutamide-resistant (ENZ-R) castration-resistant prostate cancer (CRPC) model. Methods: To validate the effect of LNP siRNA tumor delivery in vivo, gene silencing of a reporter gene, luciferase (LUC), in PC3-M-luc stable cell line was treated with LNP LUC-siRNA and examined for Luc activity by the IVIS imaging system. Next, we investigated the efficacy of LNP CLU-siRNA tumor delivery and LNP CLU-siRNA sensitized AR knockdown activity in ENZ-R CRPC LNCaP in vitro and in vivo models. Results: LNP LUC-siRNA exhibited LUC silencing effects in PC-3M-luc xenograft and metastatic models. LNP CLU-siRNA suppressed PSA and decreased AKT and ERK phosphorylation in ENZ-R LNCaP cells in vitro. LNP CLU-siRNA sensitized AR antisense oligonucleotides (ASO) activity, more potently inhibiting ENZ-R cell growth rates and increased apoptosis when compared to AR-ASO monotherapy. In vivo,combinatory treatment of LNP CLU-siRNA and AR-ASO significantly suppressed tumor growth and serum PSA levels compared to LNP LUC -siRNA (control) and AR-ASO in ENZ-R LNCaP xenografts. Conclusions: The LNP CLU-siRNA systems sensitized AR knockdown resulting in inhibition of ENZ-R LNCaP cell growth, providing pre-clinical proof-of-principle as a promising AR co-targeting approach in ENZ-R CRPC.
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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".