Exploring Antibiotic-Potentiating Effects of Tobramycin–Deferiprone Conjugates in Pseudomonas aeruginosa
Bibliographic record
Abstract
Metal ions, including Fe3+, affect the target site binding of some antibiotics and control the porin- and siderophore-mediated uptake of antibiotics. Amphiphilic tobramycins are an emerging class of antibiotic potentiators capable of synergizing with multiple classes of antibiotics against Gram-negative bacteria, including Pseudomonas aeruginosa. To study how the antibiotic-potentiating effect of amphiphilic tobramycins is affected by the presence of intermolecular iron chelators, we conjugated the FDA-approved iron chelator deferiprone (DEF) to tobramycin (TOB). Three TOB-DEF conjugates differing in the length of the carbon tether were prepared and tested for antibacterial activity and synergistic relationships with a panel of antibiotics against clinical isolates of P. aeruginosa. While all TOB-DEF conjugates were inactive against P. aeruginosa, the TOB-DEF conjugates strongly synergized with outer-membrane-impermeable antibiotics, such as novobiocin and rifampicin. Among the three TOB-DEF conjugates, 1c containing a C12 tether showed a remarkable and selective potentiating effect to improve the susceptibility of multidrug-resistant P. aeruginosa isolates to tetracyclines when compared with other antibiotics. However, the antibacterial activity and antibiotic-potentiating effect of the optimized conjugate was not enhanced under iron-depleted conditions, indicating that the function of the antibiotic potentiator is not affected by the Fe3+ concentration.
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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) | 0.000 | 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 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".