Ophthalmic Drops with Nanoparticles Derived from a Natural Product for Treating Age-Related Macular Degeneration
Bibliographic record
Abstract
There is a continuing, urgent need for an ophthalmic (eye) drop for the clinical therapy of age-related macular degeneration (AMD), a leading cause of blindness. Here, we report the first formulation of an eye drop that is effective via autophagy for AMD treatment. This eye drop is based on a single natural product derivative (ACD), which is an amphiphilic molecule containing a 6-aminohexanoate group (H 2 N(CH 2 ) 5 COO−). We demonstrate that this eye drop reverses the abnormal angiogenesis induced in a primate model of AMD that has the pathological characteristics close to that of human AMD. The ACD molecule was self-assembled in an aqueous environment leading to nanoparticles (NPs) about 9.0 nm in diameter. These NPs were encapsulated in calcium alginate hydrogel. The resulting eye drop effectively slowed the release of ACD and displayed extended release periods in both simulated blood (pH 7.4) and inflammatory (pH 5.2) environments. We show that the eye drop penetrated both the corneal and blood–eye barriers and reached the fundus. With low cellular toxicity, the drop targeted S1,25D 3 -membrane-associated rapid response steroid-binding protein (1,25D 3 -MARRS) promoting autophagy in a dose-dependent manner. In addition, the drop inhibited cell migration and tubular formation. On the other hand, when protein 1,25D 3 -MARRS was knocked down, the eye drop did not exhibit such inhibition functionalities. Our study indicates that the 6-aminohexanoate group on self-assembled NPs encapsulated in hydrogel leads to the positive in vivo outcomes. The present formulation offers a promising approach for clinical treatment of human AMD.
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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".