Computational framework for streamlining the success of sequential antibiotic therapy

Abstract

Antibiotic resistance represents a growing global health crisis, diminishing the effectiveness ofexisting treatments and accelerating the emergence of multidrug-resistant bacterial strains. In thisstudy, we present a mathematical framework for systematically characterizing data sets of collateralsensitivity patterns in evolving drug-resistant bacterial populations. This formalization is implementedin an open-source computational platform providing an intuitive and accessible in silico tool for datadrivenantibiotic selection. By leveraging this approach, we can rapidly identify a therapeutic regimenthat minimizes the risk of resistance evolution. The utility of this framework is demonstrated byhighlighting the failure of antibiotic therapy in chronic Pseudomonas aeruginosa infections. Ourapproach offers a scalable strategy for navigating bacterial evolutionary landscapes and delineateskey conditions under which sequential antibiotic therapies are prone to failure.