The Current Landscape of Phage–Antibiotic Synergistic (PAS) Interactions
Bibliographic record
Abstract
Background: In response to the urgent need for new antibiotics targeting high-priority MDR pathogens, bacteriophages (phages) have emerged as promising non-traditional antimicrobial agents. Phages are viruses that infect bacteria and induce cell lysis through mechanisms distinct from those of antibiotics, making them largely unaffected by most antibiotic resistance mechanisms. Importantly, phages have been shown to work cooperatively with an array of clinically useful antibiotics, and phage–antibiotic synergy (PAS) represents a sophisticated strategy that may improve treatment outcomes. However, the interactions between phages and antibiotics are diverse, ranging from synergistic to antagonistic, and understanding the mechanisms underlying these interactions is crucial for developing effective PAS treatments. In this review, we summarize the potential evolutionary and molecular mechanisms that drive PAS and the current landscape of phage–antibiotic interactions. Conclusions: Towards the development of robust PAS strategies, we review in vitro methods for assessing PAS and considerations for choosing and employing candidate phage–antibiotic combinations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".