Quantitative performance of humanized serum and epithelial lining fluid exposures of tigecycline and levofloxacin 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: Lack of uniformity in infection models complicates preclinical development. The COMBINE protocol has standardized the murine neutropenic pneumonia model. Herein we provide benchmark efficacy data of humanized exposures of tigecycline and levofloxacin in plasma and epithelial lining fluid (ELF) against a collection of Klebsiella pneumoniae and Pseudomonas aeruginosa. METHODS: Following the COMBINE protocol, plasma and ELF human-simulated regimens (HSRs) of tigecycline 100 mg followed by 50 mg q12h and levofloxacin 750 mg once daily were developed and confirmed in the murine neutropenic pneumonia model. Tigecycline HSRs were tested against seven K. pneumoniae isolates. Levofloxacin HSRs were assessed against 10 K. pneumoniae and 9 P. aeruginosa. The change in cfu/lung over 24 h for each treatment was calculated. Each isolate was tested in duplicate against both the plasma and ELF HSRs on separate experiment days. RESULTS: Tigecycline 1.8 and 3 mg/kg q12h achieved humanized exposures of serum and ELF, respectively. Levofloxacin 120 and 90 mg/kg q8h led to fAUC exposures in plasma and ELF similar to in humans. Both tigecycline regimens were ineffective across the MIC range. Levofloxacin regimens achieved multilog kill against susceptible isolates, and no appreciable cfu/lung reductions in isolates with an MIC of ≥32 mg/L. Differences in cfu/lung were evident between the levofloxacin plasma and ELF HSRs against isolates with MICs of 4 and 8 mg/L. CONCLUSIONS: Administering HSRs of tigecycline and levofloxacin based on both serum/plasma and ELF in the COMBINE pneumonia model resulted in cfu/lung values reasonably aligned with MIC. These data serve as translational benchmarks for future investigations with novel compounds.
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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".