Pulmonary Safety Profile of Esc Peptides and Esc-Peptide-Loaded Poly(lactide-co-glycolide) Nanoparticles: A Promising Therapeutic Approach for Local Treatment of Lung Infectious Diseases
Bibliographic record
Abstract
In recent years, we have discovered Esc(1-21) and its diastereomer (Esc peptides) as valuable candidates for the treatment of Pseudomonas lung infection, especially in patients with cystic fibrosis (CF). Furthermore, engineered poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) were revealed to be a promising pulmonary delivery system of antimicrobial peptides. However, the "ad hoc" development of novel therapeutics requires consideration of their stability, tolerability, and safety. Hence, by means of electrophysiology experiments and preclinical studies on healthy mice, we demonstrated that neither Esc peptides or Esc-peptide-loaded PLGA NPs significantly affect the integrity of the lung epithelium, nor change the global gene expression profile of lungs of treated animals compared to those of vehicle-treated animals. Noteworthy, the Esc diastereomer endowed with the highest antimicrobial activity did not provoke any pulmonary pro-inflammatory response, even at a concentration 15-fold higher than the efficacy dosage 24 h after administration in the free or encapsulated form. The therapeutic index was ≥70, and the peptide was found to remain available in the bronchoalveolar lavage of mice, after two days of incubation. Overall, these studies should open an avenue for a new up-and-coming pharmacological approach, likely based on inhalable peptide-loaded NPs, to address CF lung disease.
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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".