Breaking Barriers in Eye Treatment: Polymeric Nano-Based Drug-Delivery System for Anterior Segment Diseases and Glaucoma
Bibliographic record
Abstract
The eye has anatomical structures that function as robust static and dynamic barriers, limiting the penetration, residence time, and bioavailability of medications administered topically. The development of polymeric nano-based drug-delivery systems (DDS) could be the solution to these challenges: it can pass through ocular barriers, offering higher bioavailability of administered drugs to targeted tissues that are otherwise inaccessible; it can stay in ocular tissues for longer periods of time, requiring fewer drug administrations; and it can be made up of polymers that are biodegradable and nano-sized, minimizing the undesirable effects of the administered molecules. Therefore, therapeutic innovations in polymeric nano-based DDS have been widely explored for ophthalmic drug-delivery applications. In this review, we will give a comprehensive overview of polymeric nano-based drug-delivery systems (DDS) used in the treatment of ocular diseases. We will then examine the current therapeutic challenges of various ocular diseases and analyze how different types of biopolymers can potentially enhance our therapeutic options. A literature review of the preclinical and clinical studies published between 2017 and 2022 was conducted. Thanks to the advances in polymer science, the ocular DDS has rapidly evolved, showing great promise to help clinicians better manage patients.
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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 | 0.001 |
| 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".