Repurposed Antimicrobial Combination Therapy: Tobramycin-Ciprofloxacin Hybrid Augments Activity of the Anticancer Drug Mitomycin C Against Multidrug-Resistant Gram-Negative Bacteria
Bibliographic record
Abstract
The lack of therapeutic options to treat infections caused by recalcitrant multidrug-resistant pathogens, especially Gram-negative bacteria, is apparent. Therefore, it is imperative to develop new strategies to address the problem of antimicrobial resistance. Repurposing non-antibiotic commercial drugs for antimicrobial therapy presents a viable option. We screened six anticancer drugs for their potential use in antimicrobial therapy. Here, we provide in vitro evidence that suggests feasibility to repurpose the anticancer drug mitomycin C against multidrug-resistant Gram-negative bacteria. We also demonstrated that mitomycin C, etoposide and doxorubicin were affected by drug efflux in Pseudomonas aeruginosa. In combination with a tobramycin-ciprofloxacin antibiotic hybrid (TOB-CIP), the antibacterial activity of mitomycin C was enhanced against multidrug-resistant clinical isolates of P. aeruginosa, Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae and Enterobacter cloacae. In fact, 4 µg/mL (3 µM) TOB-CIP reduced the minimum inhibitory concentration of mitomycin C to ≤1 µg/mL against multidrug-resistant Gram-negative bacteria, except A. baumannii. We showed that synergy was inherent to TOB-CIP and that neither tobramycin nor ciprofloxacin individually synergized with mitomycin C. Our finding supports identifying adjuvant partners for mitomycin C, such as TOB-CIP, to enhance suitability for antimicrobial therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".