Model-Informed Drug Development in Pulmonary Delivery: Semimechanistic Pharmacokinetic–Pharmacodynamic Modeling for Evaluation of Treatments against Chronic <i>Pseudomonas aeruginosa</i> Lung Infections
Bibliographic record
Abstract
(PA) lung infection resistant to many current antibiotics. Novel treatment strategies are therefore urgently needed. For lung infections, direct delivery of treatments to the site of action in the airway can achieve a higher local concentration with minimal systemic exposure and hence avoid risks of unwanted systemic adverse effects. Previously, a rat preclinical disease model for PA chronic lung infections has been reported. However, the role of this disease model in the development of new treatment has not been thoroughly evaluated. In this study, tobramycin (TOB) was used as a model antibiotic to evaluate the application of this preclinical disease model for PA treatments. The obtained data were used for pharmacokinetic-pharmacodynamic (PKPD) modeling. Plasma samples following pulmonary delivery of TOB via different dosing methods as well as growth and efficacy data from the chronic lung infection disease model following TOB treatments were collected for analysis and modeling. The developed PKPD model incorporates a semimechanistic description on biofilm development in chronic infections to allow the evaluation of drug action on bacteria in different states (i.e., planktonic, biofilm, and latent) and describes the available data from the efficacy study. The PKPD model can be used to support the application of the preclinical lung infection disease model by providing a quantitative description of the drug exposure-response relationship and a mechanistic platform to integrate all available PK and PKPD data with predictive capacity. With the support of appropriate experimental designs, the model can be further extended for other applications to, for instance, study the transition of bacteria between states and describe drug actions on biofilms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".