Extended release of ciprofloxacin from commercial silicone‐hydrogel and conventional hydrogel contact lenses containing vitamin E diffusion barriers
Bibliographic record
Abstract
SIGNIFICANCE: Vitamin E could be used as a coating with commercial silicone hydrogel lenses to extend the release of various ophthalmic drugs. This concept could provide a promising approach to improve overall ocular therapeutic outcomes for topical ocular drugs. PURPOSE: This study aimed to develop a contact lens-based ocular drug delivery system using vitamin E as a diffusion barrier to extend the release duration of ciprofloxacin. METHODS: Five commercial lenses were soaked for 24 hours in various concentrations of vitamin E dissolved in ethanol (0.0125 to 0.2 g/mL). The lenses were loaded with ciprofloxacin for 24 hours in 3 mL of 3 mg/mL of ciprofloxacin/acetic acid solution. The drug release was evaluated in 3 mL of phosphate-buffered saline solution. At t = 0.5, 1, 2, 4, 6, 8, 12, 16, and 24 hours, the amount of ciprofloxacin released was measured using a UV-VIS spectrophotometer at 270 nm. RESULTS: There was a decrease in ciprofloxacin loading with increasing amounts of vitamin E loaded into the silicone hydrogel lenses. For each lens type, there was an optimal amount of vitamin E loaded that extended the release duration of the drug from 1 hour (without vitamin E) to as long as 16 hours. In contrast, vitamin E loaded into hydrogel lenses had no effect on the amounts of drugs loaded or the release duration. CONCLUSIONS: Vitamin E can be used as a diffusion barrier with commercially available silicone hydrogel lenses to provide sustained release of ciprofloxacin. The results suggest that vitamin E may form blockages in channels within a silicone hydrogel lens material, thereby forcing a longer path for drugs to diffuse into and out of the lens material. There is an optimal amount of vitamin E that needs to be loaded to extend the release duration, and this is lens material dependent.
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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 | 0.000 | 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".