Exploration of BAY 11-7082 as a Potential Antibiotic
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Exposure of the Gram-negative pathogen Pseudomonas aeruginosa to subinhibitory concentrations of antibiotics increases the formation of biofilms. We exploited this phenotype to identify molecules with potential antimicrobial activity in a biofilm-based high-throughput screen. The anti-inflammatory compound BAY 11-7082 induced dose-dependent biofilm stimulation, indicative of antibacterial activity. We confirmed that BAY 11-7082 inhibits the growth of P. aeruginosa and other priority pathogens, including methicillin-resistant Staphylococcus aureus (MRSA). We synthesized 27 structural analogues, including a series based on the related scaffold 3-(phenylsulfonyl)-2-pyrazinecarbonitrile (PSPC), 10 of which displayed increased anti- Staphylococcal activity. Because the parent molecule inhibits the NLR Family Pyrin Domain Containing 3 (NLRP3) inflammasome, we measured the ability of select analogues to reduce interleukin-1β (IL-1β) production in mammalian macrophages, identifying minor differences in the structure–activity relationship for the anti-inflammatory and antibacterial properties of this scaffold. Although we could evolve stably resistant MRSA mutants with cross-resistance to BAY 11-7082 and PSPC, their lack of shared mutations suggested that the two molecules could have multiple targets. Finally, we showed that BAY 11-7082 and its analogues synergize with penicillin G against MRSA, suggesting that this scaffold may serve as an interesting starting point for the development of antibiotic adjuvants.
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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".