Preparation and characterization of sustained release pirfenidone loaded microparticles for pulmonary drug delivery: Spray drying approach
Bibliographic record
Abstract
Pirfenidone (PFD) is a drug of choice for the treatment of idiopathic pulmonary fibrosis. For the preparation of sustained release microparticles of PFD, Ethyl cellulose (EC 300) with Eudragit RS 100 in combination was used as encapsulating agents. The 3-level 2-factorial design was employed for the design of experiments (DoE). The spray dried microparticles were studied for their particle size distribution, surface topography, drug entrapment, in-vitro drug release, and aerodynamic performance. Compatibility between drug and excipients were evaluated by Fourier Transform Infrared (FTIR) Spectroscopy and Differential Scanning Calorimetry (DSC). Particle morphology and size distribution were performed using Field Emission Scanning Electron Microscopy (FESEM) and Dynamic light scattering (DLS). The average particle size of the optimized PFD loaded formulation was found to be 3.99 μm. The surface topography study of the optimized formulation showed that the microparticles are nearly spherical with a smooth surface. In addition, the in-vitro aerosol performance was studied by Anderson cascade impactor and developed microparticles showed favorable aerodynamic performance (MMAD 4.25 μm) with narrow particle diameter distribution (GSD 1.52), therefore developed microparticles can be used as a dry powder for inhalation (DPI) for the targeted delivery to the lungs. In conclusion, sustained release microparticles of PFD were successfully prepared by the spray drying technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".