In vitro activity of aztreonam-avibactam and comparators against Metallo-β-Lactamase-producing Enterobacterales from ATLAS Global Surveillance Program, 2016–2020
Bibliographic record
Abstract
OBJECTIVES: Metallo-β-lactamase (MBL)-producing Enterobacterales are a major challenge worldwide due to limited treatment options. Aztreonam-avibactam (ATM-AVI), which is under clinical development, has shown activity against MBL-positive isolates. This study evaluated the prevalence of MBL producers and the nature of enzymes among a global collection of clinical isolates of Enterobacterales from the Antimicrobial Testing Leadership and Surveillance program (ATLAS) surveillance program (2016-2020), and the antimicrobial activity of ATM-AVI and comparators against this collection. METHODS: Non-duplicate clinical isolates of Enterobacterales (N = 106 686) collected across 63 countries were analysed. Antimicrobial susceptibility was performed using broth microdilution. Minimum inhibitory concentrations (MICs) were interpreted using Clinical and Laboratory Standards Institute and European Committee on Antimicrobial Susceptibility Testing breakpoints. Provisional pharmacokinetic/pharmacodynamic breakpoint of ≤8 mg/L was considered for ATM-AVI. β-lactamase genes were characterized by polymerase chain reaction and sequencing. The Cochran Armitage Trend test was used to determine significant trends in percentage of isolates over time. RESULTS: , 1 mg/L). Consistent activity was also noted across different regions. Potent activity was demonstrated against different NDM variants and MBL-positive isolates co-carrying other carbapenemases (98.1% and 99.7% isolates inhibited at ≤8 mg/L, respectively). About 0.6% MBL-positive isolates (10/1707) had MICs >8 mg/L for ATM-AVI. CONCLUSION: ATM-AVI demonstrated potent activity against MBL-positive isolates, including NDM variants and MBL-positive isolates co-carrying other carbapenemases, and may represent a good option for treating infections caused by MBL-positive Enterobacterales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".