Polynuclear ruthenium complexes are effective antibiotics against <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Abstract There is an urgent need to develop new antibiotics for the treatment of infections caused by drug-resistant Gram-negative bacteria. In particular, new and diverse chemical classes of antibiotics are needed, as most antibiotics in clinical development are derivatives of existing drugs. Despite a history of use as antimicrobials, metals and metal-based compounds have largely been overlooked as a source of new chemical matter for antibacterial drug discovery. In this work, we identify several ruthenium complexes, ruthenium red, Ru265, and Ru360’, that possess potent antibacterial activity against both laboratory and clinical isolates of Pseudomonas aeruginosa . Suppressors with increased resistance were sequenced and found to contain mutations in the mechanosensitive ion channel mscS-1 or the colRS two component system. The antibacterial activity of these compounds translated in vivo to Galleria mellonella larvae and mouse infection models. Finally, we identify strong synergy between these compounds and the antibiotic rifampicin, with a dose-sparing combination therapy showing efficacy in both infection models. Our findings provide clear evidence that these ruthenium complexes are effective antibacterial compounds against a critical priority pathogen and show promise for the development of future therapeutics.
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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) | 0.002 | 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".