In vitro synergy of β-lactam combinations against KPC-producing Klebsiella pneumoniae strains
Bibliographic record
Abstract
BACKGROUND: Double carbapenem therapy has been promoted as an alternative treatment for infections due to carbapenemase-producing Enterobacteriaceae where carbapenemase inhibitors are unavailable or when other agents have demonstrated toxicity with equally limited evidence. The capacity of other β-lactams and β-lactamase inhibitors to provide synergistic activity with carbapenems is unclear. OBJECTIVES: This study sought to investigate the in vitro synergistic potential of other β-lactam/β-lactamase combinations with meropenem against KPC producers. METHODS: Time-kill assays were performed on 24 unique strains of KPC-producing Klebsiella pneumoniae. Combinations evaluated included meropenem or imipenem with one of the following: ertapenem, piperacillin/tazobactam or ceftolozane/tazobactam. Concentrations used for each drug were those considered physiologically attainable in patients with a time above the concentration exceeding 40%-50% of the dose interval. Combinations were considered to be synergistic when they reduced bacterial cfu/mL by ≥2 log10 at 24 h as compared with the single most active agent. RESULTS: The combination of piperacillin/tazobactam with meropenem was found to be synergistic against 70.8% of the isolates, followed by ertapenem with meropenem (58.3%) and ceftolozane/tazobactam with meropenem (41.7%). The piperacillin/tazobactam combination was found to be more bactericidal than the other combinations, with 58.3% of isolates demonstrating a ≥4 log10 cfu/mL reduction at 24 h, as compared with 37.5% for ertapenem and 20.8% for ceftolozane/tazobactam combinations. CONCLUSIONS: The combination of piperacillin/tazobactam with meropenem may be a potential therapy against KPC-producing K. pneumoniae when other therapies are unavailable or prohibitively toxic.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".