Release of Moxifloxacin from Contact Lenses Using an In Vitro Eye Model: Impact of Artificial Tear Fluid Composition and Mechanical Rubbing
Bibliographic record
Abstract
Purpose: The aim of this study was to evaluate and compare the release of moxifloxacin from a variety of daily disposable (DD) contact lenses (CLs) under various conditions using a novel in vitro eye model. Methods: Four commercially available DD conventional hydrogel (CH) CLs (nelfilcon A, omafilcon A, etafilcon A, and ocufilcon B) and three silicone hydrogel (SH) CLs (somofilcon A, narafilcon A, and delefilcon A) were evaluated. These lenses were incubated in moxifloxacin for 24 hours. The release of the drug was measured using a novel in vitro model in three experimental conditions: (1) phosphate buffered saline (PBS); (2) artificial tear solution (ATS) containing a variety of proteins and lipids; and (3) ATS with mechanical rubbing produced by the device. Results: Overall, CH CLs had a higher drug release than SH CLs (P < 0.05) under all conditions. Typically, a higher drug release was observed in PBS than ATS (P < 0.05). For CH, drug release was found to be higher in ATS with rubbing than PBS or ATS (P < 0.05). For most lens types, ATS with rubbing produced higher drug release than ATS alone (P < 0.05). Generally, the release kinetics for all conditions were sustained over the 24-hour testing period, and no burst release was observed (P < 0.05). Conclusions: Moxifloxacin release from a CL into ATS is lower when compared to release into PBS. When mechanical rubbing is introduced, the amount of drugs released is increased. Translational Relevance: Results suggest that sophisticated in vitro models are necessary to adequately model on-eye drug release from CL materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".