Combatting Pseudomonas aeruginosa with β-Lactam Antibiotics: A Revived Weapon?
Bibliographic record
Abstract
Pseudomonas aeruginosa is a significant threat to public health as an aggressive, opportunistic pathogen. The use of β-lactam antibiotics such as penicillins, cephalosporins, monobactams, and carbapenems remains a front-line treatment against P. aeruginosa. However, the widespread use of β-lactams has led to the emergence of β-lactam-resistant isolates that significantly increase the economic burden and risk of mortality in patients. With the declining productivity of the antibiotic discovery pipeline, research has investigated synergistic agents to revive the use of β-lactam antibiotics against β-lactam-resistant P. aeruginosa. In this review, we summarize the mechanism of β-lactam antibiotics and provide an overview of major mechanisms associated with β-lactam resistance in P. aeruginosa. We then describe the background and use of three promising classes of agents that have shown extensive beneficial effects with β-lactam antibiotics against P. aeruginosa, namely β-lactamase inhibitors, bacteriophages, and antimicrobial peptides. The current understanding of the mechanisms of these synergistic agents is discussed. Lastly, we provide an overview of the current barriers impeding antibiotic development, and offer a glimpse into recent advances of artificial intelligence-based discovery that may serve as a new foundation for antimicrobial discovery and treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".