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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".