Tobramycin-Linked Efflux Pump Inhibitor Conjugates Synergize Fluoroquinolones, Rifampicin and Fosfomycin against Multidrug-Resistant Pseudomonas aeruginosa
Bibliographic record
Abstract
In this study, we examined the in vitro effect of tobramycin-efflux pump inhibitor (TOB-EPI) conjugates in combinations with fluoroquinolones, rifampicin and fosfomycin on the growth of multi-drug resistant (MDR) and extremely-drug resistant (XDR) Pseudomonas aeruginosa. The TOB-EPI conjugates include tobramycin covalently linked to 1-(1-naphthylmethyl)-piperazine (NMP) (1), paroxetine (PAR) (2) and a dibasic peptide analogue of MC-04,124 (DBP) (3). Potent synergism was found for combinations of TOB-NMP (1), TOB-PAR (2) or TOB-DBP (3) with either fluoroquinolones (moxifloxacin, ciprofloxacin), rifampicin or fosfomycin against a panel of multidrug-resistant/extensively drug-resistant (MDR/XDR) P. aeruginosa clinical isolates. In the presence of ≤8 mg/L (6.1–7.2 µM) (≤¼ × MICadjuvant) concentration of the three conjugates, the MIC80 of moxifloxacin, ciprofloxacin, rifampicin and fosfomycin were dramatically reduced. Furthermore, the MIC80 of rifampicin (0.25–0.5 mg/L) and fosfomycin (8–16 mg/L) were reduced below their interpretative susceptibility breakpoints. Our data confirm the ability of TOB-NMP (1), TOB-PAR (2) and TOB-DBP (3) conjugates to strongly synergize with moxifloxacin, ciprofloxacin, rifampicin and fosfomycin against MDR/XDR P. aeruginosa. These synergistic combinations warrant further studies as there is an urgent need to develop new strategies to treat drug-resistant P. aeruginosa infections.
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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".