A Meta-Analysis of In Vitro Release of Hydrophilic Therapeutics from Contact Lenses Using Mathematical Modeling
Bibliographic record
Abstract
Background/Objectives: A meta-analysis was conducted to study the in vitro release of hydrophilic therapeutics from contact lenses, loaded using the soaking method. Fifty-three experiments were studied that measure the cumulative release of therapeutics from (mostly) commercial contact lenses placed in a vial. Methods: A mathematical model and a parameter-fitting algorithm are presented to estimate the diffusion coefficient (D) and 50% therapeutic release time (T50) of all the experimental lens–therapeutic combinations. Statistical methods were used to analyze the relationships between lens materials, therapeutic properties, and predicted parameter values (D and T50). Results: The mathematical framework was validated against previous studies. It was found that lens water content directly and moderately influences the estimated diffusion coefficient. More specifically, the median diffusivity of silicone hydrogel (SH) contact lenses was statistically different from that of conventional hydrogel (CH) lenses. The dependencies of other lens and therapeutic properties on diffusivity were complex, with special cases studied to elicit dependencies. A predictive tool was constructed to estimate the logarithm of 50% therapeutic release time (log(T50)), given the lens water content and the therapeutic molecular volume and density. Conclusions: The conducted meta-analysis found that the kinetic release of therapeutics from contact lenses depends on the properties of both the contact lens and therapeutics. The statistical model explained 64% of the variability of the log(T50) and can be used in the preliminary stages of contact lens drug delivery development.
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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 | 0.038 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.056 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".