Estimating <i>In Vivo</i> Airway Surface Liquid Concentration in Trials of Inhaled Antibiotics
Why this work is in the frame
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Bibliographic record
Abstract
Antibiotic drugs exhibit concentration dependence in their efficacy. Therefore, ensuring appropriate concentration of these drugs in the relevant body fluid is important for obtaining the desired therapeutic and physiological action. Until recently there had been no suitable method available to measure or estimate concentration of drugs in the human airways resulting from inhaled aerosols or to determine the amount of inhaled antibiotics required to ensure minimum inhibitory concentration of a drug in the airway surface liquid (ASL). In this paper a numerical method is used for estimating local concentration of inhaled pharmaceutical aerosols in different generations of the human tracheobronchial airways. The method utilizes a mathematical lung deposition model to estimate amounts of aerosols depositing in different lung generations, and a recent ASL model along with deposition results to assess the concentration of deposited drugs immediately following inhalation. Examples of concentration estimates for two case studies: one for the antibiotic tobramycin against Pseudomonas aeruginosa, and another for taurolidine against Burkholderia cepacia are presented. The aerosol characteristics, breathing pattern and properties of nebulized solutions were adopted from two recent clinical studies on efficacy of these drugs in cystic fibrosis (CF) patients and from other sources in the literature. While the clinically effective tobramycin showed a concentration higher than the required in vivo concentration, that for the ineffective taurolidine was found to be below the speculated required in vivo concentration. Results of this study thus show that the mathematical ASL model combined with the lung deposition model can be an effective tool for helping decide the optimum dosage of inhaled antibiotic drugs delivered during human clinical trials.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it