Humanized Exposures of a β-Lactam-β-Lactamase Inhibitor, Tazobactam, versus Non-β-Lactam-β-Lactamase Inhibitor, Avibactam, with or without Colistin, against <b><i>Acinetobacter baumannii</i></b> in Murine Thigh and Lung Infection Models
Bibliographic record
Abstract
β-lactam-β-lactamase inhibitors (BLIs) have previously demonstrated antimicrobial activity against Acinetobacter baumannii (AB). Colistin retains the highest susceptibility rate against A. baumannii, and has demonstrated synergy with other antimicrobials, including β-lactam-BLIs. Therefore, we assessed the potential individual activity and synergistic combinations in vivo against carbapenem-susceptible (CS) and multidrug-resistant (MDR) A. baumannii isolates in neutropenic thigh and lung infection models. In vitro, colistin and tazobactam MICs were 1 and 16 µg/mL against AB 25-49 (CS) and 1 and 128 µg/mL against AB 5075 (MDR) respectively. In the lung model, tazobactam alone and in combination with colistin achieved a 1-log reduction in CFU, while colistin alone was not active against AB 25-49. No activity was observed against AB 5075. In the thigh model, tazobactam with and without colistin was bacteriostatic against AB 25-49 but did not demonstrate any activity against AB 5075. Avibactam and colistin alone and in combination were not active against either isolate. No synergy was observed; however, we found tazobactam activity against A. baumannii. This activity was not observed for the non-β-lactam-BLI, avibactam. This suggests that binding to penicillin-binding proteins of the β-lactam molecule is required for tazobactam activity against A. baumannii. These data point to an added role of β-lactam-BLIs beyond their primary purpose of β-lactamase inhibition in the treatment of MDR A. baumannii infections by enhancing the activity of peptide antibiotics, a property that is not shared by the novel non-β-lactam-BLIs. Future studies are needed to define tazobactam and colistin activity in an A. baumannii infection model.
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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) | 0.002 | 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".