A New Era in Ocular Therapeutics: Advanced Drug Delivery Systems for Uveitis and Neuro-Ophthalmologic Conditions
Bibliographic record
Abstract
The eye's intricate anatomical barriers pose significant challenges to the penetration, residence time, and bioavailability of topically applied medications, particularly in managing uveitis and neuro-ophthalmologic conditions. Addressing this issue, polymeric nano-based drug delivery systems (DDS) have surfaced as a promising solution. These systems enhance drug bioavailability in hard-to-reach target tissues, extend residence time within ocular tissues, and utilize biodegradable and nanosized polymers to reduce undesirable side effects. Thus, they have stimulated substantial interest in crafting innovative treatments for uveitis and neuro-ophthalmologic diseases. This review provides a comprehensive exploration of polymeric nano-based DDS used for managing these conditions. We discuss the present therapeutic hurdles posed by these diseases and explore the potential role of various biopolymers in broadening our treatment repertoire. Our study incorporates a detailed literature review of preclinical and clinical studies from 2017 to 2023. Owing to advancements in polymer science, ocular DDS has made rapid strides, showing tremendous potential to revolutionize the treatment of patients with uveitis and neuro-ophthalmologic disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".