Development and confirmation of humanized plasma and epithelial lining fluid exposures of meropenem, cefiderocol and tobramycin in a standardized neutropenic murine pneumonia model
Bibliographic record
Abstract
BACKGROUND: Murine pneumonia models play a fundamental role in the preclinical development of novel compounds seeking an indication for the treatment of pneumonia. It is vital that plasma exposures in these models are not used as a surrogate for exposure in pulmonary epithelial lining fluid (ELF). Herein, human-simulated regimens (HSRs) in both plasma and ELF of meropenem, cefiderocol and tobramycin are described in the standardized COMBINE murine neutropenic pneumonia model. MATERIALS AND METHODS: HSRs were developed in both plasma and ELF for meropenem and cefiderocol as 2 g q8h 3 h infusions, and tobramycin 7 mg/kg 30 min infusion. Pharmacokinetic studies were performed to confirm plasma and ELF exposures in mice that recapitulated %fT > MIC for meropenem and cefiderocol, and fCmax and fAUC0-24 for tobramycin in humans. RESULTS: Concentration-time profiles and relevant pharmacodynamic exposures for all three compounds were well matched in mice relative to humans. None of the plasma HSRs were able to appropriately humanize the ELF. Thus, modifications of the plasma HSRs were necessary to provide unique HSRs specific to ELF exposure for each compound. CONCLUSIONS: It should not be assumed that lung penetration in mice relative to humans is equivalent. With HSRs confirmed for these three drugs with established clinical use in the treatment of patients with pneumonia, these humanized exposures within the standardized model will allow for back-translation of anticipated efficacy and provide purposeful quantitative benchmarks for cfu/lung assessments for researchers on an international scale.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".