Utilizing Non-β-Lactam Antibiotics to Combat Antimicrobial Resistance by Targeting Multiple Virulence Factors of <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
In pre-antibiotic times, various highly contagious diseases like cholera, smallpox and tuberculosis were widespread worldwide. Penicillin discovery in the late 1920s was a groundbreaking moment in medical history, saving countless lives. However, over the next few decades, microbes developed antibiotic resistance, leading to a global public health threat known as antimicrobial resistance (AMR). Pseudomonas aeruginosa is a major contributor to hospital-acquired infections, affecting millions of patients and causing numerous deaths annually. Several non-[Formula: see text]-lactam antibiotics combat these infections effectively, while their effect on P. aeruginosa quorum sensing (QS) has been insufficiently explored. We have undertaken comprehensive research to understand the effect of non-[Formula: see text]-lactam antibiotics on various targets of P. aeruginosa. Using molecular simulations, we scrutinize these antibiotics” dynamic behavior and stability. Based on toxicity, binding energy and binding site, platensimycin and sulfasalazine were identified as promising candidates against various targets of P. aeruginosa. The binding energies for sulfasalazine and platensimycin with LasA were found to be −8.1 and −8.6 kcal/mol, respectively. Both of these leading antibiotics were interacting at the active sites of all tested proteins (LasA, LasI and PqsR). The examination of molecular dynamics confirmed the stable complex formation of the lead non-[Formula: see text]-lactam antibiotics with all selected target proteins under normal physiological conditions. These findings emphasize the potential efficacy of platensimycin and sulfasalazine. They could potentially be repurposed for targeting the QS of P. aeruginosa.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".