What’s New in Ocular Drug Delivery: Advances in Suprachoroidal Injection since 2023
Bibliographic record
Abstract
Despite significant advancements in ocular drug delivery, challenges persist in treating posterior segment diseases like macular edema (ME) and age-related macular degeneration (AMD). Suprachoroidal (SC) injections are a promising new method for targeted drug delivery to the posterior segment of the eye, providing direct access to the choroid and retina while minimizing systemic exposure and side effects. This review examines the anatomical and physiological foundations of the SC space; evaluates delivery devices such as microcatheters, hypodermic needles, and microneedles; and discusses pharmacokinetic principles. Additionally, advancements in gene delivery through SC injections are explored, emphasizing their potential to transform ocular disease management. This review also highlights clinical applications in treating macular edema, diabetic macular edema, age-related macular degeneration, choroidal melanoma, and glaucoma. Overall, SC injections are emerging as a promising novel route for administering ophthalmic treatments, with high bioavailability, reduced systemic exposure, and favorable safety profiles. Key therapeutic agents such as triamcinolone acetonide, dexamethasone, AAV-based gene therapy, and axitinib have shown promise. The field of suprachoroidal injection is progressing rapidly, and this review article, while attempting to encapsulate most of the published preclinical and clinical studies, mainly focuses on those that are published within 2023 and 2024.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".