Multi-targeting of virulence factors of <i>P. aeruginosa</i> by β-lactam antibiotics to combat antimicrobial resistance
Bibliographic record
Abstract
Antimicrobial resistance poses a significant challenge to public health, especially in developing countries, due to a substantial rise in bacterial resistance. This situation has become so concerning that we are now at risk of losing the effectiveness of antibiotics altogether. Recent research has firmly established that bacteria engage in a process called quorum sensing (QS). QS regulates various functions, including nutrient scavenging, immune response suppression, increased virulence, biofilm formation and mobility. Pseudomonas aeruginosa, an opportunistic bacterial pathogen, plays a significant role in various medical conditions such as chronic wounds, corneal infections, burn wounds and cystic fibrosis. While antibiotics are effective in killing bacteria, only a few antibiotics, particularly those from the β-lactam group, have been studied for their impact on the quorum sensing of P. aeruginosa. Given the lack of concentrated efforts in this area, we have investigated the role of β-lactam antibiotics on various potential targets of P. aeruginosa. Based on their toxicological profiles and the average binding energy obtained through molecular docking, azlocillin and moxalactam have emerged as lead antibiotics. The binding energy for the docking of azlocillin and moxalactam with LasA was determined to be −8.2 and −8.6 kcal/mol, respectively. Molecular simulation analysis has confirmed the stable interaction of both these ligands with all three target proteins (LasI, LasA and PqsR) under physiological conditions. The results of this research underscore the effectiveness of azlocillin and moxalactam. These two antibiotics may be repurposed to target the quorum sensing of P. aeruginosa.Communicated by Ramaswamy H. Sarma
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".