Antimicrobial activity of peptoids against Metallo-β-lactamase-producing <i>Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa</i>, and other WHO priority pathogens, including <i>Candida auris</i>
Bibliographic record
Abstract
AIMS: The World Health Organization has identified ESKAPE bacteria and Candida auris as priority pathogens, emphasizing an urgent need for novel antimicrobials to combat them. This study aimed to explore the therapeutic potential of antimicrobial peptidomimetics, specifically peptoids with sequence-specific N-substituted glycines, against ESKAPEE pathogens, including metallo-β-lactamase (MBL) producers, as well as C. auris strains. METHODS AND RESULTS: This study evaluated activity of the peptoids against the multidrug-resistant priority pathogens. The peptoid TM8 (with an N-decyl alkyl chain) demonstrated a geometric mean minimum inhibitory concentration (MIC) of 7.8 μg ml-1 against MBL-producing bacteria, and 5.5 μg ml-1 against C. auris. TM8 showed synergy with ciprofloxacin, enhancing its effectiveness 4-fold against NDM-1-producing Klebsiella pneumoniae. No antagonism was seen when TM8 was used with either conventional antibiotics or antifungals. Peptoids that had therapeutic indices below 3 were generally more hydrophobic, due to either alkyl chains or bromine. Scanning electron microscopy and live-dead staining assay on peptoid-treated C. auris confirmed morphological changes and killing activity, respectively. Furthermore, the peptoid could effectively inhibit biofilm formation by C. auris. CONCLUSION: Peptoids demonstrated antibacterial activity against ESKAPEE, particularly against MBL-producing Gram-negative bacteria. Additionally, they exhibited antifungal and anti-biofilm activities against C. auris strains.
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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 | 0.000 |
| 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".