Prolonged Ocular Retention of Mucoadhesive Nanoparticle Eye Drop Formulation Enables Treatment of Eye Diseases Using Significantly Reduced Dosage
Bibliographic record
Abstract
Eye diseases, such as dry eye syndrome, are commonly treated with eye drop formulations. However, eye drop formulations require frequent dosing with high drug concentrations due to poor ocular surface retention, which leads to poor patient compliance and high risks of side effects. We developed a mucoadhesive nanoparticle eye drop delivery platform to prolong the ocular retention of topical drugs, thus enabling treatment of eye diseases using reduced dosage. Using fluorescent imaging on rabbit eyes, we showed ocular retention of the fluorescent dye delivered through these nanoparticles beyond 24 h while free dyes were mostly cleared from the ocular surface within 3 h after administration. Utilizing the prolonged retention of the nanoparticles, we demonstrated effective treatment of experimentally induced dry eye in mice by delivering cyclosporin A (CsA) bound to this delivery system. The once a week dosing of 0.005 to 0.01% CsA in NP eye drop formulation demonstrated both the elimination of the inflammation signs and the recovery of ocular surface goblet cells after a month. Thrice daily administration of RESTASIS on mice only showed elimination without recovering the ocular surface goblet cells. The mucoadhesive nanoparticle eye drop platform demonstrated prolonged ocular surface retention and effective treatment of dry eye conditions with up to 50- to 100-fold reduction in overall dosage of CsA compared to RESTASIS, which may significantly reduce side effects and, by extending the interdosing interval, improve patient compliance.
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 | 0.000 | 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".