Cefepime–taniborbactam and ceftibuten–ledaborbactam maintain activity against KPC variants that lead to ceftazidime–avibactam resistance
Bibliographic record
Abstract
ABSTRACT Klebsiella pneumoniae carbapenemases (KPCs) are widespread β-lactamases that are a major cause of clinical non-susceptibility of Gram-negative bacteria to carbapenems and other β-lactam antibiotics. Ceftazidime combined with the β-lactamase inhibitor avibactam (CAZ–AVI) has been effective in treating infections by KPC-producing bacteria, but emerging KPC variants confer resistance to the combination. Taniborbactam and ledaborbactam are bicyclic boronate β-lactamase inhibitors currently under development with cefepime and ceftibuten, respectively, to treat carbapenem-resistant bacterial infections. Here, we assessed the effects of clinically important KPC-2 and KPC-3 variants (V240G, D179Y, and D179Y/T243M) on the antibacterial activity of cefepime–taniborbactam (FEP–TAN) and ceftibuten–ledaborbactam (CTB–LED) and examined catalytic activity and inhibition of these variants. Unlike CAZ–AVI, FEP–TAN and CTB–LED were highly active against Escherichia coli strains expressing these KPC variants. Experiments with purified enzymes showed that FEP and CTB were poorly hydrolyzed by the KPC variants and had weak affinity for variants containing D179Y. In addition, the D179Y substitution in KPC-2 reduced inhibition by TAN and LED, but inactivation efficiencies ( k 2 / K ) for these inhibitors were significantly higher than those for AVI. K 2 / K was less affected for D179Y-containing KPC-3 variants, and robust inhibition was observed by TAN, LED, and AVI. Together, the findings illustrate a biochemical basis for FEP–TAN and CTB–LED efficacy in KPC variant-mediated CAZ–AVI resistance backgrounds, whereby the boronate inhibitors have sufficient inhibitory activity, while FEP and CTB are poor substrates and bind to the variant enzymes with reduced affinity.
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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 | 0.001 |
| 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".