Strategies for ocular siRNA delivery: Potential and limitations of non-viral nanocarriers
Bibliographic record
Abstract
Controlling gene expression via small interfering RNA (siRNA) has opened the doors to a plethora of therapeutic possibilities, with many currently in the pipelines of drug development for various ocular diseases. Despite the potential of siRNA technologies, barriers to intracellular delivery significantly limit their clinical efficacy. However, recent progress in the field of drug delivery strongly suggests that targeted manipulation of gene expression via siRNA delivered through nanocarriers can have an enormous impact on improving therapeutic outcomes for ophthalmic applications. Particularly, synthetic nanocarriers have demonstrated their suitability as a customizable multifunctional platform for the targeted intracellular delivery of siRNA and other hydrophilic and hydrophobic drugs in ocular applications. We predict that synthetic nanocarriers will simultaneously increase drug bioavailability, while reducing side effects and the need for repeated intraocular injections. This review will discuss the recent advances in ocular siRNA delivery via non-viral nanocarriers and the potential and limitations of various strategies for the development of a 'universal' siRNA delivery system for clinical applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".