Mechanistic understanding enables the rational design of salicylanilide combination therapies for Gram-negative infections
Bibliographic record
Abstract
Abstract One avenue to combat multidrug-resistant Gram-negative bacteria is the co-administration of multiple drugs (combination therapy), which can be particularly promising if drugs synergize. The identification of synergistic drug combinations, however, is challenging. Detailed understanding of antibiotic mechanisms can address this issue by facilitating the rational design of improved combination therapies. Here, using diverse biochemical and genetic assays, we reveal the molecular mechanisms of niclosamide, a clinically-approved salicylanilide compound, and demonstrate its potential for Gram-negative combination therapies. We discovered that Gram-negative bacteria possess two innate resistance mechanisms that reduce their niclosamide susceptibility: a primary mechanism mediated by multidrug efflux pumps and a secondary mechanism of nitroreduction. When efflux was compromised, niclosamide became a potent antibiotic, dissipating the proton motive force (PMF), increasing oxidative stress and reducing ATP production to cause cell death. These insights guided the identification of diverse compounds that synergized with salicylanilides when co-administered (efflux inhibitors, membrane permeabilizers, and antibiotics that are expelled by PMF-dependent efflux), thus suggesting that salicylanilide compounds may have broad utility in combination therapies. We validate these findings in vivo using a murine abscess model, where we show that niclosamide synergizes with the membrane permeabilizing antibiotic colistin against high-density infections of multidrug-resistant Gram-negative clinical isolates. We further demonstrate that enhanced nitroreductase activity is a potential route to adaptive niclosamide resistance but show that this causes collateral susceptibility to clinical nitro-prodrug antibiotics. Thus, we highlight how mechanistic understanding of mode of action, innate/adaptive resistance, and synergy can rationally guide the discovery, development and stewardship of novel combination therapies. Importance There is a critical need for more effective treatments to combat multidrug-resistant Gram-negative infections. Combination therapies are a promising strategy, especially when these enable existing clinical drugs to be repurposed as antibiotics. We reveal the mechanisms of action and basis of innate Gram-negative resistance for the anthelmintic drug niclosamide, and subsequently exploit this information to demonstrate that niclosamide and analogs kill Gram-negative bacteria when combined with antibiotics that inhibit drug efflux or permeabilize membranes. We confirm the synergistic potential of niclosamide in vitro against a diverse range of recalcitrant Gram-negative clinical isolates, and in vivo in a mouse abscess model. We also demonstrate that nitroreductases can confer resistance to niclosamide, but show that evolution of these enzymes for enhanced niclosamide resistance confers a collateral sensitivity to other clinical antibiotics. Our results highlight how detailed mechanistic understanding can accelerate the evaluation and implementation of new combination therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".