Computational Platform for streamlining the success of sequential antibiotic therapy
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The scarcity of antibiotics and the need for swift decision-making present significant challenges for healthcare practitioners. When confronted with such circumstances, practitioners must prioritize their approach based on several key factors. By leveraging the recent biological discovery of collateral sensitivity, we have devised an open-source computational platform. This platform utilizes the drug resistance profile of an evolved strain and initial population conditions to potentially predict the failure of cycling therapy in eradicating or restraining a multi-drug resistant bacterial population. We demonstrate how this framework can anticipate potential failures for a range of antibiotics in chronic pseudomonas aeruginosa infections. This innovative methodology lays the foundation for evolutionary therapies that can facilitate the selection of appropriate treatments, thereby reducing antibiotic resistance.
