Design and Evaluation of New Quinazolin-4(3<i>H</i>)-one Derived PqsR Antagonists as Quorum Sensing Quenchers in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
P. aeruginosa (PA) continues to pose a threat to global public health due to its high levels of antimicrobial resistance (AMR).The ongoing AMR crisis has led to an alarming shortage of effective treatments for resistant microbes, and hence there is a pressing demand for the development of novel antimicrobial interventions.The potential use of antivirulence therapeutics to tackle bacterial infections has attracted considerable attention over the past decades as they hamper the pathogenicity of target microbes with reduced selective pressure, minimizing the emergence of resistance.One such approach is to interfere with the PA pqs quorum sensing system which upon the interaction of PqsR, a Lys-R type transcriptional regulator, with its cognate signal molecules 4-hydroxy-2-heptylquinoline (HHQ) and 2-heptyl-3-hydroxy-4-quinolone (PQS), governs multiple virulence traits and host-microbe interactions.In this study, we report the hit identification and optimization of PqsR antagonists using virtual screening coupled with whole cell assay validation.The optimized hit compound 61 ((R)-2-(4-(3-(6-chloro-4-oxoquinazolin-3(4H)-yl)-2hydroxypropoxy)phenyl)acetonitrile) was found to inhibit the expression of the PA P pqsA promoter controlled by PqsR with an IC 50 of 1 μM.Using isothermal titration calorimetry, a K d of 10 nM for the PqsR ligand binding domain (PqsR LBD ) was determined for 61.Furthermore, the crystal structure of 61 with PqsR LBD was attained with a resolution of 2.65 Å. Compound 61 significantly reduced levels of pyocyanin, PQS, and HHQ in PAO1-L, PA14 lab strains and PAK6085 clinical isolate.Furthermore, this compound potentiated the effect of ciprofloxacin in early stages of biofilm treatment and in Galleria mellonella infected with PA.Altogether, this data shows 61 as a potent PqsR inhibitor with potential for hit to lead optimization toward the identification of a PA QS inhibitor which can be advanced into preclinical development.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".