Trimeric Tobramycin/Nebramine Synergizes β-Lactam Antibiotics against <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide β-Lactam antibiotics remain one of the most effective therapeutics to treat infections caused by Gram-negative bacteria (GNB). However, since ancient times, bacteria have developed multiple resistance mechanisms toward this class of antibiotics including overexpression of β-lactamases, suppression of porins, outer membrane impermeability, overexpression of efflux pumps, and target modifications. To cope with these challenges and to extend the lifetime of existing β-lactam antibiotics, β-lactamase inhibitors are combined with β-lactam antibiotics to prevent antibiotic inactivation by β-lactamases. The combination therapy of an outer membrane permeabilizer with β-lactam antibiotics is an alternative approach to overcoming bacterial resistance of β-lactams in GNB. This approach is of particular interest for pathogens with highly impermeable outer membranes like Pseudomonas aeruginosa . Previous studies have shown that outer membrane permeabilizers can be designed by linking tobramycin and nebramine units together in the form of dimers or chimeras. In this study, we developed trimeric tobramycin and nebramine-based outer membrane permeabilizers presented on a central 1,3,5-triazine framework. The resultant trimers are capable of potentiating outer membrane-impermeable antibiotics but also β-lactams and β-lactam/β-lactamase inhibitor combinations against resistant P. aeruginosa isolates. Furthermore, the microbiological susceptibility breakpoints of ceftazidime, aztreonam, and imipenem were reached by a triple combination consisting of an outer-membrane permeabilizer/β-lactam/β-lactamase inhibitor in β-lactam-resistant P. aeruginosa isolates . Overall, our results indicate that trimeric tobramycins/nebramines can rescue clinically approved β-lactams and β-lactam/β-lactamase inhibitor combinations from resistance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".