Clinical efficacy, safety and pharmacokinetics of novel β-lactam/β-lactamase inhibitor combinations: a systematic review
Bibliographic record
Abstract
Background: Antimicrobial resistance is a global public health threat that requires urgent solutions. One strategy to decrease resistance of Gram-negative bacteria (GNB) to β-lactam antibiotics (BL) is their combination with β-lactamase inhibitors (BLI). Objectives: This systematic review analyses the outcomes, safety and pharmacokinetics (PK) of recently approved or under clinical development BLI and BL/BLI combinations. Methods: The systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. PubMed, Embase, and Cochrane electronic databases were used to search for articles from January 2010 to November 2024. The studies were retrieved and screened on the basis of predefined exclusion and inclusion criteria. A quality assessment of the included studies was conducted following the New Castle-Ottawa Scale. Results: A total of 191 articles addressing clinical research regarding the efficacy, safety, tolerability, and PK of new BL/BLI combinations with avibactam, durlobactam, enmetazobactam, nacubactam, relebactam, taniborbactam, tazobactam, vaborbactam and zidebactam were included. According to the published literature, clinical research supports the novel BL/BLI combinations for the treatment of complicated urinary tract infections, complicated intra-abdominal infections, and hospital-acquired and ventilator-associated pneumonia (HAP/VAP) caused by GNB. In spite of that, the development of new BLI effective for class B metallo-β-lactamases (MBL) is still challenging, being aztreonam/avibactam the only approved combination active against MBL-producing bacteria. Conclusions: Although there has been extensive research to develop new BLI and BL/BLI combinations, only a few have reached the market. More evidence of its usefulness in the real world is still needed.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".