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.