Development of an In Vitro Blink Model for Ophthalmic Drug Delivery
Bibliographic record
Abstract
Purpose: The purpose of this study was to develop an advanced in vitro blink model that can be used to examine the release of a wide variety of components (for example, topical ophthalmic drugs, comfort-inducing agents) from soft contact lenses. Methods: The model was designed using computer-aided design software and printed using a stereolithography 3D printer. The eyelid and eyeball were synthesized from polyvinyl alcohol and silicone material, respectively. Simulated tear fluid was infused through tubing attached to the eyelid using a syringe pump. With each blink cycle, the eyelid slides and flexes across the eyeball to create an artificial tear film layer. The flow-through fluid was collected using a specialized trough. Two contact lenses, etafilcon A and senofilcon A, were incubated in 2 mL of a water-soluble red dye for 24 h and then placed on the eye model (n = 3). The release of the dye was measured over 24 h using a tear flow rate of 5 µL/min. Results: Approximately 25% of the fluid that flowed over the eye model was lost due to evaporation, nonspecific absorption, and residual dead volume. Senofilcon A absorbed more dye (47.6 ± 2.7 µL) than etafilcon A (22.3 ± 2.0 µL). For etafilcon A, the release of the dye followed a burst-plateau profile in the vial but was sustained in the eye model. For senofilcon A, the release of the dye was sustained in both the vial and the eye model, though more dye was released in the vial (p < 0.05). Overall, the release of the dye from the contact lenses was higher in the vial compared with the eye model (p < 0.05). Conclusion: The blink model developed in this study could be used to measure the release of topical ophthalmic drugs or comfort agents from contact lenses. Simulation of a blink mechanism, an artificial tear film, and nonspecific absorption in an eye model may provide better results than a simple, static vial incubation model.
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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 | 0.000 | 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) | 0.000 | 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".