In vitro activity of cefiderocol against Gram-negative pathogens isolated from people with cystic fibrosis and bronchiectasis
Bibliographic record
Abstract
Gram-negative pathogens causing respiratory infection in people with cystic fibrosis and bronchiectasis are becoming progressively more resistant to conventional antibiotics. Although cefiderocol is licensed for the treatment of infections due to Gram-negative organisms, there is limited data on the activity of cefiderocol against pathogens associated with chronic respiratory diseases. The aim of this study was to determine the susceptibility of Gram-negative pathogens from cystic fibrosis and bronchiectasis to cefiderocol and comparator antibiotics. Minimal inhibitory concentrations (MICs) of cefiderocol and 15 comparator antibiotics were determined by broth microdilution against 300 respiratory isolates: Burkholderia spp., Stenotrophomonas spp., Achromobacter spp., Ralstonia spp. and Pandoraea spp. and used to calculate the MIC of each antibiotic required to inhibit 50% (MIC50) and 90% (MIC90) of isolates. The MIC50 and MIC90 of cefiderocol for all 300 isolates tested was 0.25 and 32 mg/L with 232 (77.3%) isolates having an MIC value ≤2 mg/L. In addition, cefiderocol demonstrated excellent activity against Stenotrophomonas spp. and Achromobacter spp. isolates with 86.7% and 87.2%, respectively, exhibiting an MIC of 2 mg/L. Tigecycline also demonstrated good activity against all isolates with an MIC50 of <0.5 mg/L. These in vitro data demonstrated that cefiderocol had greater activity than most comparator antibiotics and could be an alternative treatment option for respiratory infection caused by these pathogens that has not responded to first line therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".