Development of spray-dried phage-aztreonam microparticles for inhalation therapy of Pseudomonas aeruginosa lung infections
Bibliographic record
Abstract
Despite considerable efforts in antibiotic therapy, pulmonary infections caused by Pseudomonas aeruginosa remain a major challenge, particularly in diseases such as cystic fibrosis. Bacteriophage-antibiotic combinations have recently emerged as a potential alternative due to synergistic effects and the ability to overcome resistance. However, phage instability and sensitivity continue to hinder clinical translation. In this study, we developed an inhalable spray-dried microparticle formulation co-encapsulating bacteriophages against P. aeruginosa and the β-lactam antibiotic aztreonam using mannitol, leucine, and trehalose as excipients. The particles exhibited favorable aerosol properties for deep lung delivery (geometric diameter: 1.39 ± 0.11 µm, mass median aerodynamic diameter: 2.42 ± 0.14 µm) and a burst release profile enabling immediate antimicrobial activity. Aztreonam was efficiently encapsulated (97.87 ± 0.91 %), while phage viability could be maintained through processing, remaining stable for 28 days at 4 and -20 °C. Therapeutic efficacy was confirmed by treatment of P. aeruginosa biofilms. A reduction in bacterial load by 99.90 % was achieved within 24 h. Microparticle biocompatibility was demonstrated in vitro using human lung epithelial cells. The spray-dried phage-aztreonam microparticles enabled phage viability and stability in a formulation and highlight the feasibility of phage-antibiotic co-delivery through dry powder inhalation therapy for treating pulmonary P. aeruginosa 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".