Advances in dorzolamide hydrochloride delivery: harnessing nanotechnology for enhanced ocular drug delivery in glaucoma management
Bibliographic record
Abstract
Dorzolamide hydrochloride (DRZ) is a carbonic anhydrase inhibitor utilized in managing elevated intraocular pressure (IOP) associated with glaucoma. However, its clinical effectiveness is hindered by a short half-life, low residence time, and the need for frequent dosing, highlighting the necessity for innovative delivery systems. This work reviews recent advancements in DRZ delivery, particularly focusing on cyclodextrin complexation and nanotechnology applications. It explores the potential of cyclodextrin derivatives to enhance DRZ's bioavailability. DRZ cyclodextrin complexes or nanoparticulate systems maintain high drug concentrations in the eye while minimizing irritation and viscosity-related issues. Nanotechnology introduces nanoparticle-based carriers such as polymeric nanoparticles, solid lipid nanoparticles, liposomes, niosomes, and nanoemulsions. These formulations enable sustained drug release, improved corneal permeation, and enhanced patient compliance. Clinical trials have shown that DRZ nanoparticle eye drops and nanoliposome formulations offer efficacy comparable to conventional therapies, with the potential for better tolerability. Overall, this review highlights significant progress in DRZ delivery systems, suggesting their potential to transform glaucoma treatment by addressing current limitations and improving therapeutic outcomes.
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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) | 0.002 | 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".