<i>In vitro</i> killing of drug susceptible and multidrug resistant bacteria by amikacin considering pulmonary drug concentrations based on an inhaled formulation
Bibliographic record
Abstract
Nosocomial infections with drug resistant bacteria impact morbidity and mortality, length of therapy and stay and the overall cost of treatment. Key pathogens with ventilator associated pneumonia may be drug-susceptible or multi-drug resistant and inhaled amikacin has been investigated as an adjunctive therapy option. High pulmonary drug concentrations (epithelial lining fluid [ELF]) along with minimal systemic toxicity is seen as an advantage to inhaled formulations. In vitro killing of bacteria using clinically relevant drug concentrations provide insight on bug-drug interactions. The aim of this study was to measure killing of clinical isolates of Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus and methicillin-susceptible S. aureus using the minimum inhibitory concentration (MIC), mutant prevention concentration (MPC) and median (976 µg/ml) ELF drug concentration for amikacin. Overall killing took longer at the MIC drug concentration and was inconsistent amongst the pathogens tested with the percentage of bacteria killed following 180 min of drug exposure ranging from growth in the presence of the drug to 95% kill. At the MPC drug concentrations, killing ranged from 55-88% for all pathogens following 30 min of drug exposure and increased to 99-100% following 180 min of drug exposure. At the ELF amikacin tested, killing was 81-100% following 20 min and 94-100% by 30 min of drug exposure. Rapid killing against MDR respiratory pathogens by amikacin ELF drug concentrations is encouraging.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".