Evaluating Viscosity and Tear Breakup Time of Contemporary Commercial Ocular Lubricants on an In Vitro Eye Model
Bibliographic record
Abstract
Purpose: To evaluate the link between the viscosity of ophthalmic formulation and tear film stability using a novel in vitro eye model. Methods: The viscosities and noninvasive tear breakup time (NIKBUT) of 13 commercial ocular lubricants were measured to evaluate the correlation between viscosity and NIKBUT. The complex viscosity of each lubricant was measured three times for each angular frequency (ranging from 0.1 to 100 rad/s) using the Discovery HR-2 hybrid rheometer. The NIKBUT measurements were performed eight times for each lubricant using an advanced eye model mounted on the OCULUS Keratograph 5M. A contact lens (CL; ACUVUE OASYS [etafilcon A]) or a collagen shield (CS) was used as the simulated corneal surface. Phosphate-buffered saline was used as a simulated fluid. Results: The results showed a positive correlation between viscosity and NIKBUT at high shear rates (at 10 rad/s, r = 0.67) but not at low shear. This correlation was even better for viscosities between 0 and 100 mPa*s (r = 0.85). Most of the lubricants tested in this study also had shear-thinning properties. OPTASE INTENSE, I-DROP PUR GEL, I DROP MGD, OASIS TEARS PLUS, and I-DROP PUR had higher viscosity in comparison to other lubricants (P < 0.05). All of the formulations had a higher NIKBUT than the control (2.7 ± 1.2 seconds for CS and 5.4 ± 0.9 seconds for CL) without any lubricant (P < 0.05). I-DROP PUR GEL, OASIS TEARS PLUS, I-DROP MGD, REFRESH OPTIVE ADVANCED, and OPTASE INTENSE had the highest NIKBUT using this eye model. Conclusions: The results show that the viscosity is correlated with NIKBUT, but further work is necessary to determine the underlying mechanisms. Translational Relevance: The viscosity of ocular lubricants can affect NIKBUT and tear film stability, so it is an important property to consider when formulating ocular lubricants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".