Emulsion and Emulgel-Based Ophthalmic Drug Delivery Systems
Bibliographic record
Abstract
Ophthalmic drug delivery encounters unique challenges due to the anatomical and physiological ocular barriers, necessitating the development of novel drug delivery systems (NDDSs). This review focuses on emerging therapeutic platforms, including nanoemulsions (NEs), microemulsions (MEs), self-emulsifying drug delivery systems (SEDDSs) such as self-nano emulsifying drug delivery systems (SNEDDSs) and self-micro emulsifying drug delivery systems (SMEDDSs), emulgels, and in situ-forming emulgels, as novel strategies for enhancing ocular drug delivery. NEs and MEs, due to their small globule size, excellent drug solubility, stability, and bioavailability, offer promising solutions for effective ocular therapy. SEDDSs further enhance the stability and bioavailability of hydrophobic drugs through self-emulsification in aqueous environments. Emulgels, combining the benefits of emulsions and gels, provide sustained and controlled release of therapeutic agents, improving the ocular retention time and therapeutic efficacy. Additionally, in situ-forming emulgels offer the advantage of liquid-to-gel transition upon contact with ocular surfaces, optimizing drug delivery. The review discusses various ocular diseases, challenges for ocular delivery of conventional formulations, updates on emulsion-based novel drug delivery systems for ophthalmic drug delivery, mechanisms of enhanced ocular permeation, formulation strategies, advantages, and challenges, design-of-experiment considerations for optimization, characterizations, and recent advancements in these systems including patents and clinical trials, highlighting their potential for improving the treatment of various ocular diseases. Furthermore, this review explores marketed ophthalmic emulsions and future prospects for integrating these NDDSs into clinical ophthalmology, emphasizing their ability to overcome ocular barriers and enhance therapeutic efficacy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".