Mechanistic Understanding Enables the Rational Design of Salicylanilide Combination Therapies for Gram-Negative Infections
Bibliographic record
Abstract
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 examined the mechanisms of action and basis of innate Gram-negative resistance for the anthelmintic drug niclosamide and subsequently exploited 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) | 0.001 | 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 | 0.001 |
| 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".