Tailored Nanocarriers for the Pulmonary Delivery of Levofloxacin against <i>Pseudomonas aeruginosa</i>: A Comparative Study
Bibliographic record
Abstract
Cystic fibrosis (CF) patients are faced with chronic bacterial infections displaying persistent resistance if not eradicated during the first stage of the disease. Nanoantibiotics for pulmonary administration, such as liposomal ciprofloxacin or amikacin, have progressed through clinics thanks to their sustained release, prolonged lung residence time, and low systemic absorption. In this work, we sought a nanoformulation of levofloxacin for the treatment of Pseudomonas aeruginosa. We prepared and compared poly(lactic acid)-grafted-poly(ethylene glycol) nanoparticles, as well as anionic and cationic liposomes for their size, charge, and encapsulation efficiency. Cationic liposomes were unable to encapsulate any drug and were subsequently considered as a control formulation. Regarding the efficiency of the nanocarrier, anionic liposomes exhibited a prolonged release over 72 h and preserved the antibacterial activity of levofloxacin against five strains of P. aeruginosa, whereas polymeric nanoparticles quickly released their entire payload and increased the minimum inhibitory concentration of levofloxacin. Thus, only anionic liposomes were considered for further preclinical development. Anionic liposomes exhibited a suitable colloidal stability in Turbiscan analysis and crossed a layer of artificial mucus in under 1 h in a Transwell setup. Despite their negative surface charge, liposomes still interacted with the P. aeruginosa membrane in a dose-response manner, as demonstrated by flow cytometry. Viability assays confirmed that anionic liposomes, loaded or not, exhibited a good safety profile on A549 epithelial cells, even at high concentrations. Finally, nebulization of anionic liposomes containing levofloxacin did not impact their colloidal stability, and the droplet size distribution was suitable for deep lung deposition, where the P. aeruginosa infection lies. Therefore, levofloxacin-loaded anionic liposomes exhibited suitable properties for the pulmonary treatment of P. aeruginosa in CF. This step-by-step study confirms the promising role of liposomes for lung administration of antibiotics, as recently seen in clinics, and fosters their development for several types of antibiotics.
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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 | 0.001 | 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".