Contact lenses for antifungal ocular drug delivery: a review
Bibliographic record
Abstract
INTRODUCTION: Fungal keratitis, a potentially blinding disease, has been difficult to treat due to the limited number of approved antifungal drugs and the taxing dosing regimen. Thus, the development of a contact lens (CL) as an antifungal drug delivery platform has the potential to improve the treatment of fungal keratitis. A CL can serve as a drug reservoir to continuously release drugs to the cornea, while limiting drug loss through tears, blinking, drainage and non-specific absorption. AREAS COVERED: This review will provide a summary of currently available methods for delivering antifungal drugs from commercial and model CLs, including vitamin E coating, impregnated drug films, cyclodextrin-functionalized hydrogels, polyelectrolyte hydrogels and molecular imprinting. This review will also highlight some of the main factors that influence antifungal drug delivery with CLs. EXPERT OPINION: Several novel CL materials have been developed, capable of extended drug release profiles with a wide range of antifungal drugs lasting from 8 h to as long as 21 days. However, there are factors, such as first-order release kinetics, effectiveness of continuous drug release, microbial resistance, ocular toxicity and potential complications from inserting a CL in an infected eye, that still need to be addressed before commercial applications can be realized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".