Development of a dry powder formulation for pulmonary delivery of azithromycin-loaded nanoparticles
Bibliographic record
Abstract
The COVID-19 pandemic has raised concern regarding respiratory system diseases and oral inhalation stands out as an attractive non-invasive route of administration for pulmonary diseases such as chronic bronchitis, cystic fibrosis, COVID-19 and community-acquired pneumonia. In this context, we encapsulated azithromycin in polycaprolactone nanoparticles functionalized with phospholipids rich in dipalmitoylphosphatidylcholine and further produced a fine powder formulation by spray drying with monohydrated lactose. Nanoparticles obtained by the emulsion/solvent diffusion-evaporation technique exhibited a mean hydrodynamic diameter around 195–228 nm with a narrow monomodal size distribution (PdI < 0.2). Nanoparticle dispersions were spray-dried at different inlet temperatures, atomizing air-flow, aspirator air flow, and feed rate, using lactose as a drying aid, resulting in a maximal process yield of 63% and an encapsulation efficiency of 83%. Excipients and the dry powder formulations were characterized in terms of morphology, chemical structure, thermal analyses and particle size by SEM, FTIR, DSC/TGA and laser light diffraction. The results indicated spherical particles with 90% at 4.06 µm or below, an adequate size for pulmonary delivery. Aerosolization performance in a NGI confirmed good aerodynamic properties. Microbiological assays showed that the formulation preserves AZM antimicrobial effect against Staphylococcus aureus and Streptococcus pneumoniae strains, with halos above 18 mm. In addition, no formulation-related cytotoxicity was observed against the human cell lines BEAS-2B (lung epithelial), HUVEC (endothelial) and HFF1 (fibroblasts). Overall, the approach described here allows the production of AZM-PCL nanoparticles incorporated into inhalable microparticles, enabling more efficient pulmonary therapy of lung 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) | 0.001 | 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".