siRNA Functionalized Lipid Nanoparticles (LNPs) in Management of Diseases
Bibliographic record
Abstract
RNAi (RNA interference)-based technology is emerging as a versatile tool which has been widely utilized in the treatment of various diseases. siRNA can alter gene expression by binding to the target mRNA and thereby inhibiting its translation. This remarkable potential of siRNA makes it a useful candidate, and it has been successively used in the treatment of diseases, including cancer. However, certain properties of siRNA such as its large size and susceptibility to degradation by RNases are major drawbacks of using this technology at the broader scale. To overcome these challenges, there is a requirement for versatile tools for safe and efficient delivery of siRNA to its target site. Lipid nanoparticles (LNPs) have been extensively explored to this end, and this paper reviews different types of LNPs, namely liposomes, solid lipid NPs, nanostructured lipid carriers, and nanoemulsions, to highlight this delivery mode. The materials and methods of preparation of the LNPs have been described here, and pertinent physicochemical properties such as particle size, surface charge, surface modifications, and PEGylation in enhancing the delivery performance (stability and specificity) have been summarized. We have discussed in detail various challenges facing LNPs and various strategies to overcome biological barriers to undertake the safe delivery of siRNA to a target site. We additionally highlighted representative therapeutic applications of LNP formulations with siRNA that may offer unique therapeutic benefits in such wide areas as acute myeloid leukaemia, breast cancer, liver disease, hepatitis B and COVID-19 as recent examples.
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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 | 0.001 | 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".