Preparation and physicochemical characterization of Eudragit® RL100 Nanosuspension with potential for Ocular Delivery of Sulfacetamide
Bibliographic record
Abstract
Purpose: Polymeric nanosuspension was prepared from an inert polymer resin (Eudragit® RL100) with the aim of improving the availability of sulfacetamide at the intraocular level to combat bacterial infections. Methods: Nanosuspensions were prepared by the solvent displacement method using acetone and Pluronic® F108 solution. Drug to polymer ratio was selected as formulation variable. Characterization of the nanosupension was performed by measuring particle size, zeta potential, Fourier Transform infrared spectra (FTIR), Differential Scanning Calorimetry (DSC), Powder X-Ray Diffraction (PXRD), drug entrapment efficiency and in vitro release. In addition, freeze drying, redispersibility and short term stability study at room temperature and at 40C were performed. Results: Spherical, uniform particles (size below 500 nm) with positive zeta potential were obtained. No significant chemical interactions between drug and polymer were observed in the solid state characterization of the freeze dried nanosuspension (FDN). Drug entrapment efficiency of the selected batch was increased by changing the pH of the external phase and addition of polymethyl methacrylate in the formulation. The prepared nanosuspension exhibited good stability after storage at room temperature and at 40C. Sucrose and Mannitol were used as cryoprotectants and exhibited good water redispersibility of the FDN. Conclusion: The results indicate that the formulation of sulfacetamide in Eudragit® RL100 nanosuspension could be utilized as potential delivery system for treating ocular bacterial infections.
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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".