Recent Advances in Targeted Immunomodulatory Therapies for Chronic Ocular Surface Diseases
Bibliographic record
Abstract
PURPOSE: To review and compare the mechanisms of action, clinical efficacy, and safety profiles of topical immunosuppressive and immunomodulatory agents for the management of chronic ocular surface diseases. METHODS: A comprehensive review of the literature was conducted using PubMed, Scopus, and Web of Science databases. Peer-reviewed clinical trials, observational studies, case series, and meta-analyses from 1960 to 2024 were included. Studies were selected based on their investigation of the efficacy and safety of topical therapies for chronic ocular surface diseases. Systemic therapies and non-ocular conditions were excluded. RESULTS: Corticosteroids remain effective in controlling acute inflammation but are associated with significant adverse effects, particularly with long-term use, including elevated intraocular pressure, cataract formation, and infection risk. In contrast, targeted immunomodulators such as cyclosporine, tacrolimus, lifitegrast, tofacitinib, and reproxalap provide more selective modulation of the immune response. These agents have demonstrated favorable tolerability and efficacy in chronic ocular surface diseases, including dry eye disease, vernal keratoconjunctivitis, and ocular GVHD. Recent advances include IL-1 receptor antagonists, JAK inhibitors, RASP inhibitors, and mitochondrial-targeted antioxidants. CONCLUSIONS: While corticosteroids are indispensable for acute management, targeted immunomodulatory therapies offer a safer and more sustainable approach for long-term treatment of chronic ocular surface inflammation. The emergence of novel topical agents supports a shift toward precision immunotherapy in ophthalmology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".