Development and clinical applications of siRNA-encapsulated lipid nanoparticles in cancer
Bibliographic record
Abstract
Efficient delivery of siRNA to cancer cells after systemic administration poses a significant challenge. While many methods of nucleic acid delivery have been described, encapsulation of siRNA in lipid nanoparticles (LNPs) represents the most clinically advanced delivery approach. Currently, there are two siRNA-LNP-based treatments (ALN-VSP and TKM-PLK1) in clinical trials targeting solid tumors, with additional studies ongoing for noncancer diseases. The consensus from these clinical studies is that siRNA-LNP represents safe and potent silencing systems. Improvements in LNP technology through development of more potent and biocompatible ionizable cationic lipids along with targeting lipids to mediate delivery of LNPs specifically to cancer cells are on the horizon. In combination with genomic screening, it is possible that within the next 5 years the pathogenic drivers of individual cancers will be identified and siRNA-based personalized medicines will be formulated to achieve successful treatment of cancer and other genetic diseases.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".