Improvements in Topical Ocular Drug Delivery Systems: Hydrogels and Contact Lenses
Bibliographic record
Abstract
PURPOSE: Conventional ophthalmic systems present very low corneal systemic bioavailability due to the nasolacrimal drainage and the difficulty to deliver the drug in the posterior segment of ocular tissue. For these reasons, recent advances have focused on the development of new ophthalmic drug delivery systems. This review provides an insight into the various constraints associated with ocular drug delivery, summarizes recent findings in soft contact lenses (SCL) and the applications of novel pharmaceutical systems for ocular drug delivery. Among the new therapeutic approaches in ophthalmology, SCL are novel continuous-delivery systems, providing high and sustained levels of drugs to the cornea. The tendency of research in ophthalmic drug delivery systems development are directed towards a combination of several technologies (bio-inspired and molecular imprinting techniques) and materials (cyclodextrins, surfactants, specific monomers). There is a tendency to develop systems which not only prolong the contact time of the vehicle at the ocular surface, but also at the same time slow down the clearance of the drug. Different materials can be applied during the development of contact lenses and can be combined with natural inspired strategies of drug immobilization and release, providing successful tools for ocular drug delivery systems.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".