Testing drug release from medicated contact lenses: The missing link to predict in vivo performance
Bibliographic record
Abstract
Contact lenses (CLs) offer a wide variety of advantages as ocular drug-releasing platforms, but the feasibility of medicated CL development is constrained by numerous scientific, technological, and regulatory challenges. One main difficulty is the setting of release rate specifications for each drug, since at present there are no standardized in vitro release models that can appropriately predict the performance of drug-eluting CLs once placed onto the eye. CL-adapted release tests may provide knowledge on how the drug release pattern should perform in vivo to trigger and maintain the therapeutic effects for both anterior and posterior ocular tissues. Moreover, in vitro release tests are valuable tools for quality assessment during production and to investigate the effect of a change in composition or process variables. This review aims to shed light on biorelevant ways of evaluating in vitro drug release from CLs and the feasibility of establishing in vitro-in vivo correlations (IVIVC) to predict in vivo performance. First, general guidelines and Pharmacopeia release tests for topical ophthalmic formulations as well as in vitro release tests implemented for drug-CLs in the last two decades are analyzed. Then, development of an appropriate method to investigate IVIVC is attempted from the few papers simultaneously reporting in vitro release profiles and either in vivo release or therapeutic response. Finally, key points to be considered for in vitro testing drug release from a medicated CL are suggested to pave the way to the clinical arena.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".