Lipid nanoparticle siRNA systems for silencing the androgen receptor in human prostate cancer <i>in vivo</i>
Bibliographic record
Abstract
The androgen receptor (AR) plays a critical role in the progression of prostate cancer. Silencing this protein using short-hairpin RNA (shRNA) has been correlated with tumor growth inhibition and decreases in serum prostate specific antigen (PSA). In our study, we have investigated the ability of lipid nanoparticle (LNP) formulations of small-interfering RNA (siRNA) to silence AR in human prostate tumor cell lines in vitro and in LNCaP xenograft tumors following intravenous (i.v.) injection. In vitro screening studies using a panel of cationic lipids showed that LNPs containing the ionizable cationic lipid 2,2-dilinoleyl-4-(2-dimethylaminoethyl)-[1,3]-dioxolane (DLin-KC2-DMA) exhibited the most potent AR silencing effects in LNCaP cells. This is attributed to an optimized ability of DLin-KC2-DMA-containing LNP to be taken up into cells and to release the siRNA into the cell cytoplasm following endocytotic uptake. DLin-KC2-DMA LNPs were also effective in silencing the AR in a wild-type AR expressing cell line, LAPC-4, and a variant AR expressing cell line, CWR22Rv1. Importantly, it is demonstrated that LNP AR-siRNA systems containing DLin-KC2-DMA can silence AR gene expression in distal LNCaP xenograft tumors and decrease serum PSA levels following i.v. injection. To our knowledge, this is the first report demonstrating the feasibility of LNP delivery of siRNA for silencing AR gene expression in vivo.
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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".