On the Optimal Treatment of Metallo-Beta-Lactamase–Producing Enterobacterales Infections Using Aztreonam and Avibactam
Bibliographic record
Abstract
To the editor—Thank you for the opportunity to respond to Tamma and colleagues’ commentary on the discussion of the REVISIT (Revisiting serious bacterial infection with innovation) study. First, to clarify, we agree with the 2024 Infectious Diseases Society of America guidance that in the absence of US Food and Drug Administration (FDA)–approved beta-lactam/beta-lactamase inhibitors with activity against metallo-beta-lactamase(MBL)-producing Enterobacterales, the preferred antibiotic options for New-Delhi metallo-beta-lactamase (NDM)-producing Enterobacterales (or other MBLs) include ceftazidime-avibactam plus aztreonam [1]. Fortunately, aztreonam/avibactam (ATM/AVI), which was developed through public–private partnership, has recently been approved by both the FDA and the European Medicines Agency for the treatment of infections caused by aerobic gram-negative bacteria where treatment options are limited. The pharmacokinetics–pharmacodynamics of ATM/AVI were evaluated in multiple in vivo and in vitro studies, and data are available from phase 1–3 studies and modeling [2]. The dosing regimen was optimized. This dosing regimen differs from that suggested for ceftazidime/avibactam + aztreonam by the hollow-fiber model and a Monte Carlo simulation [3, 4]. The latter studies advise the administration of 2 g aztreonam with ceftazidime/avibactam. Notably, the 2-gram dosing by prolonged or continuous infusion was found to be associated with elevations in liver enzymes, in some cases severe [5]. In contrast, the 1.5-g dosing of aztreonam in the ATM/AVI combination was not. Moreover, ATM/AVI dose regimens achieved high joint probability of target attainment (PTA) across renal function groups. In contrast, joint PTA with proposed ceftazidime/avibactam + aztreonam dose regimens for normal renal function was suboptimal (<85%) because of insufficient avibactam exposures when given 3 times daily, regardless of the ATM dose (2 g every 6 hours or every 8 hours) [6].
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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 | 0.001 |
| 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 | 0.001 | 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".