Antimicrobial Peptide Novicidin Synergizes with Rifampin, Ceftriaxone, and Ceftazidime against Antibiotic-Resistant Enterobacteriaceae <i>In Vitro</i>
Bibliographic record
Abstract
The spread of antibiotic resistance among Gram-negative bacteria is a serious clinical threat, and infections with these organisms are a leading cause of mortality worldwide. Traditional novel drug development inevitably leads to the emergence of new resistant strains, rendering the new drugs ineffective. Therefore, reviving the therapeutic potentials of existing antibiotics represents an attractive novel strategy. Novicidin, a novel cationic antimicrobial peptide, is effective against Gram-negative bacteria. Here, we investigated novicidin as a possible antibiotic enhancer. The actions of novicidin in combination with rifampin, ceftriaxone, or ceftazidime were investigated against 94 antibiotic-resistant clinical Gram-negative isolates and 7 strains expressing New Delhi metallo-β-lactamase-1. Using the checkerboard method, novicidin combined with rifampin showed synergy with >70% of the strains, reducing the MICs significantly. The combination of novicidin with ceftriaxone or ceftazidime was synergistic against 89.7% of the ceftriaxone-resistant strains and 94.1% of the ceftazidime-resistant strains. Synergistic interactions were confirmed using time-kill studies with multiple strains. Furthermore, novicidin increased the postantibiotic effect when combined with rifampin or ceftriaxone. Membrane depolarization assays revealed that novicidin alters the cytoplasmic membrane potential of Gram-negative bacteria. In vitro toxicology tests showed novicidin to have low hemolytic activity and no detrimental effect on cell cultures. We demonstrated that novicidin strongly rejuvenates the therapeutic potencies of ceftriaxone or ceftazidime against resistant Gram-negative bacteria in vitro. In addition, novicidin boosted the activity of rifampin. This strategy can have major clinical implications in our fight against antibiotic-resistant bacterial infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".