Computational framework for streamlining the success of sequential antibiotic therapy.

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 infection

SUBMITTER: Anderson A 

PROVIDER: S-EPMC12583666 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature