Articlenpj antimicrobials and resistance2025
Computational framework for streamlining the success of sequential antibiotic therapy.
Article in npj antimicrobials and resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
6 authors.
Funding
Abstract
Antibiotic resistance represents a growing global health crisis, diminishing the effectiveness of existing treatments and accelerating the emergence of multidrug-resistant bacterial strains. In this study, we present a mathematical framework for systematically characterizing data sets of collateral sensitivity patterns in evolving drug-resistant bacterial populations. This formalization is implemented in an open-source computational platform providing an intuitive and accessible in silico tool for data-driven antibiotic selection. By leveraging this approach, we can rapidly identify a therapeutic regimen that minimizes the risk of resistance evolution. The utility of this framework is demonstrated by highlighting the failure of antibiotic therapy in chronic Pseudomonas aeruginosa infections. Our approach offers a scalable strategy for navigating bacterial evolutionary landscapes and delineates key conditions under which sequential antibiotic therapies are prone to failure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.