Parametric Drug Release Optimization of Anti-Inflammatory Drugs by Gold Nanoparticles for Topically Applied Ocular Therapy
Bibliographic record
Abstract
Eye drops represent 90% of all currently used ophthalmic treatments. Only 0.02% of therapeutic molecules contained in eye drops reach the eye anterior chamber despite their high concentration. The tear film efficiently protects the cornea, reducing access to the target. Thereby, the increase in the drug bioavailability and efficiency must come from the mucoadhesion optimization of the drug delivery system. The gold nanoparticles, used as a drug delivery system in this study, already showcased ultrastable and mucoadhesive properties. The goal was to study the gold nanoparticles' ability to release two specific ophthalmic drugs, flurbiprofen and ketorolac. The parameters of interest were those involving the loading conditions, the gold nanoparticles properties, and the release experimental conditions. The drug release was measured using an in vitro model based on dialysis bags coupled with UV-visible spectroscopy. Gold nanoparticles showed an ability to release different molecules, whether hydrophobic or hydrophilic, in passive or active drug release environments. Based on these preliminary results, gold nanoparticles could represent a promising drug delivery system for ketorolac and flurbiprofen when topically applied through eye drops.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".