A stimulus-responsive, in situ-forming, nanoparticle-laden hydrogel for ocular drug delivery
Bibliographic record
Abstract
Most medications targeting optic neuropathies are administered as eye drops. However, their corneal penetration efficiencies are typically < 5%. There is a clear, unmet need for novel transcorneal drug delivery vehicles. To this end, we have developed a stimulus-responsive, in situ-forming, nanoparticle-laden hydrogel for controlled release of poorly bioavailable drugs into the aqueous humor of the eye. The hydrogel is formulated as a composite of hyaluronic acid (HA) and methylcellulose (MC). The amphiphilic nanoparticles are composed of poly(ethylene oxide) (PEO) and poly(lactic acid) (PLA). Experimental design aided the identification of hydrogel composition and nanoparticle content in the formulation, and the formulation reliably switched between thixotropy and temperature-dependent rheopexy when it was tested in a rheometer under conditions that simulate the ocular surface, including blinking. These properties should ensure that the formulation coats the cornea through blinking of the eyelid and facilitate application of the medication as an eye drop immediately prior to the patient's bedtime. We subsequently tested the efficacy of our formulation in whole-eye experiments by loading the nanoparticles with cannabigerolic acid (CBGA). Our formulation exhibits over a 300% increase in transcorneal penetration over control formulations. This work paves the way for the introduction of novel products targeting ocular diseases to the market.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".