Polymeric Propranolol Nanoparticles for Intraocular Delivery: Formulation, Characterization, and Vitreous Pharmacokinetics
Bibliographic record
Abstract
Purpose: Recent studies have reported the promising effect of intravitreal propranolol on retinal neovascularization. However, rapid clearance and short half-life of the drug in the vitreous are the main drawbacks of this therapeutic approach. This study investigates the extension of the residence time of propranolol in the vitreous by polymeric nanoparticles (NPs) with the prospect of improving choroidal neovascularization treatment. Methods: The poly (lactic-co-glycolic) acid (PLGA) NPs were fabricated by a modified double emulsion solvent evaporation method and the obtained NPs were characterized for their size, poly dispersity index (PDI), and surface image. The in vitro release, cell cytotoxicity, and uptake of NPs were also evaluated. To investigate the effect of the vitreous pharmacokinetic drug loaded NPs versus that of the free propranolol, they were intravitreally injected into the rabbits’ eyes and the drug vitreous concentrations in defined intervals were analyzed by high performance liquid chromatography (HPLC). Results: The spherical NPs with about 230 nm size, and almost 10% drug loading were obtained. Based on the 3-(4, 5-Dimethylthiazol-2-Yl)-2, 5-Diphenyltetrazolium Bromide (MTT) outcomes, 30 µg/ml of propranolol was considered as the guide dosage in the intravitreal injection. Confocal microscopy images verified the presence of labeled NPs in the posterior segment after five days of receiving the injection. In vivo assay revealed that the vanishing rate of propranolol in rabbits treated with propranolol NPs was reduced at twice the rate as compared to that of the vanishing rate experienced with only the free drug. Conclusion: PLGA NPs can prolong the existence of propranolol in both vitreous and posterior ocular tissues, and thus, may provide an effective approach in treatment of posterior segment neovascularization.
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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) | 0.000 | 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 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".