In vitro activity of aztreonam/avibactam against isolates of Enterobacterales collected globally from ATLAS in 2019
Bibliographic record
Abstract
: Infections caused by drug-resistant Enterobacterales including those producing metallo-β-lactamases (MBLs) are particularly challenging due to limited therapeutic options. The drug combination aztreonam/avibactam (ATM-AVI) is under clinical development for treating serious infections caused by these strains. This study assessed the in vitro activity of ATM-AVI against Enterobacterales isolates collected globally in the ATLAS surveillance programme in 2019. : Clinical isolates of Enterobacterales (N = 18 713) including Citrobacter freundii, Citrobacter koseri, Enterobacter cloacae complex, Escherichia coli, Klebsiella aerogenes, Klebsiella oxytoca, Klebsiella pneumoniae, Proteus mirabilis, and Serratia marcescens collected from 232 sites in 2019 were analysed. Antimicrobial susceptibility testing was performed by reference broth microdilution. A pharmacokinetic/pharmacodynamic based breakpoint of 8 mg/L was considered for ATM-AVI activity. : ATM-AVI demonstrated potent antimicrobial activity against all Enterobacterales, with 99.9% isolates inhibited at MIC ≤8 mg/L (MIC90, 0.25 mg/L). MICs ≤8 mg/L (>99.0%) were noted for ATM-AVI across regions worldwide. Among other antimicrobials, amikacin, colistin, imipenem, meropenem, and tigecycline were also active (susceptibility >85.0%) against Enterobacterales. Activity of ATM-AVI was sustained against multidrug-resistant, extended-spectrum β-lactamase producing, and carbapenem-resistant isolates (susceptibility >99%; MIC90, 0.25–0.5 mg/L). Importantly, potent activity for ATM-AVI (>99.0%; MIC90, 0.5 mg/L) was noted among MBL-positive isolates and those producing other carbapenemases, such as KPC and OXA-48. : Our results demonstrated that ATM-AVI was highly active against a recent collection of Enterobacterales isolates, including those producing MBLs either alone or in combination with other carbapenemases. Thus, ATM-AVI represents a potential option for treating infections caused by antibiotic-resistant Enterobacterales including MBL-producing strains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".