Lipid-based nanoparticles: innovations in ocular drug delivery
Bibliographic record
Abstract
Ocular drug delivery presents significant challenges due to intricate anatomy and the various barriers (corneal, tear, conjunctival, blood-aqueous, blood-retinal, and degradative enzymes) within the eye. Lipid-based nanoparticles (LNPs) have emerged as promising carriers for ocular drug delivery due to their ability to enhance drug solubility, improve bioavailability, and provide sustained release. LNPs, particularly solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs), and cationic nanostructured lipid carriers (CNLCs), have emerged as promising solutions for enhancing ocular drug delivery. This review provides a comprehensive summary of lipid nanoparticle-based drug delivery systems, emphasizing their biocompatibility and efficiency in ocular applications. We evaluated research and review articles sourced from databases such as Google Scholar, TandFonline, SpringerLink, and ScienceDirect, focusing on studies published between 2013 and 2023. The review discusses the materials and methodologies employed in the preparation of SLNs, NLCs, and CNLCs, focusing on their application as proficient carriers for ocular drug delivery. CNLCs, in particular, demonstrate superior effectiveness attributed due to their electrostatic bioadhesion to ocular tissues, enhancing drug delivery. However, continued research efforts are essential to further optimize CNLC formulations and validate their clinical utility, ensuring advancements in ocular drug delivery technology for improved patient outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".