Quantitative performance of humanized plasma and epithelial lining fluid exposures of meropenem, cefiderocol and tobramycin against a challenge set of <i>Klebsiella pneumoniae</i> and <i>Pseudomonas aeruginosa</i> in a standardized neutropenic murine pneumonia model
Bibliographic record
Abstract
BACKGROUND: The COMBINE murine neutropenic pneumonia model looks to standardize an important element of preclinical development and provide interlaboratory uniformity. Herein we provide quantitative bacterial density in lung benchmark efficacy data of humanized exposures of meropenem, cefiderocol and tobramycin in plasma and epithelial lining fluid (ELF) against a collection of Klebsiella pneumoniae and Pseudomonas aeruginosa. METHODS: In accordance with the COMBINE protocol, human-simulated regimens (HSRs) based on both plasma and ELF exposures of meropenem, cefiderocol (both as 2 g q8h as 3 h infusions) and tobramycin 7 mg/kg as 30 min infusions were tested against K. pneumoniae and P. aeruginosa isolates. The 24 h change in cfu/lung for each HSR was calculated. Each isolate was tested in duplicate against both the plasma and ELF HSRs on separate experiment days. RESULTS: Meropenem HSRs demonstrated >1 log10 kill against all P. aeruginosa isolates with MICs of ≤16 mg/L, but only against K. pneumoniae isolates with MICs of ≤2 mg/L as isolates with MICs of >2 mg/L generally harboured carbapenemases. Cefiderocol HSRs uniformly achieved >1 log10 kill against both species at MICs of ≤8 mg/L, with net growth and extensive variability in P. aeruginosa isolates with MICs of 16 mg/L. All tobramycin-susceptible isolates demonstrated >1 log10 kill, while non-susceptible isolates did not. Differences in cfu/lung magnitude between the plasma and ELF HSRs were most pronounced around the clinical breakpoints. CONCLUSIONS: In the COMBINE pneumonia model, administration of plasma and ELF HSRs of meropenem, cefiderocol and tobramycin demonstrated 24 h cfu/lung within reason of expectation given known PK/PD properties and existing clinical breakpoints.
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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) | 0.000 | 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) | 0.001 | 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".