Model-Based Drug Development in Pulmonary Delivery: Pharmacokinetic Analysis of Novel Drug Candidates for Treatment of Pseudomonas aeruginosa Lung Infection
Bibliographic record
Abstract
Antibiotic resistance is a major public health threat worldwide. In particular, about 80% of cystic fibrosis patients have chronic Pseudomonas aeruginosa (PA) lung infection resistant to many current antibiotics. We are therefore developing a novel class of antivirulence agents, quorum sensing inhibitors (QSIs), which inhibit biofilm formation and sensitize PA to antibiotic treatments. For respiratory conditions, targeted delivery to the lung could achieve higher local concentrations with reduced risk of adverse systemic events. In this study, we report the pharmacokinetics of 3 prototype QSIs after pulmonary delivery, and the simultaneous analysis of the drug concentration-time profiles from bronchoalveolar lavage, lung homogenate and plasma samples, using a pharmacometric modeling approach. In addition to facilitating the direct comparison and selection of drug candidates, the developed model was used for dosing simulation studies to predict in vivo exposure following different dosing scenarios. The results show that systemic clearance has limited impact on local drug exposure in the lung after pulmonary delivery. Therefore, we suggest that novel QSIs designed for pulmonary delivery as targeted treatments for respiratory conditions should ideally have a long residence time in the lung for local efficacy with rapid clearance after systemic absorption for reduced risk of systemic adverse events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".