Ceftolozane/tazobactam disrupts <i>Pseudomonas aeruginosa</i> biofilms under static and dynamic conditions
Bibliographic record
Abstract
BACKGROUND: Pseudomonas aeruginosa biofilms limit the efficacy of currently available antibacterial therapies and pose significant clinical challenges. Pseudomonal biofilms are complicated further when other markers of persistence such as mucoid and hypermutable phenotypes are present. There is currently a paucity of data regarding the activity of the newer β-lactam/β-lactamase inhibitor combination ceftolozane/tazobactam against P. aeruginosa biofilms. METHODS: We evaluated the efficacy of ceftolozane/tazobactam against clinical P. aeruginosa isolates, the laboratory isolate PAO1 and its isogenic mutS-deficient hypermutator derivative (PAOMS) grown under static and dynamic biofilm conditions. The clinical isolate collection included strains with mucoid and hypermutable phenotypes. RESULTS: Ceftolozane/tazobactam exposure led to a bactericidal (≥3 log cfu/cm2) biofilm reduction in 15/18 (83%) clinical isolates grown under static conditions, irrespective of carbapenem susceptibility or mucoid phenotype, with greater activity compared with colistin (P < 0.05). Dynamically grown biofilms were less susceptible to ceftolozane/tazobactam with active biofilm reduction (≥1 log cfu/cm2) observed in 2/3 isolates. Hypermutability did not affect the antibiofilm efficacy of ceftolozane/tazobactam in either static or dynamic conditions when comparing PAO1 and PAOMS. Consistent with the activity of ceftolozane/tazobactam as a potent inhibitor of PBP3, dramatic impacts on P. aeruginosa morphology were observed. CONCLUSIONS: Our data demonstrate that ceftolozane/tazobactam has encouraging properties in the treatment of P. aeruginosa biofilm infections, and its activity is not diminished against mucoid or hypermutable variants at the timepoints examined.
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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 | 0.000 | 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) | 0.001 | 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".