Work

Computational framework for streamlining the success of sequential antibiotic therapy

Year: 2025

Type: article

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.

Source: npj Antimicrobials and Resistance

DOI: 10.1038/s44259-025-00160-w

Authors: Alejandro Anderson, Matthew W. Kinahan, Rodolfo Blanco-Rodriguez, Alejandro H. Gonzalez, Klas Udekwu, Esteban A. Hernandez-Vargas

Institutions: University of Idaho, Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Desarrollo Tecnológico para la Industria Química

Language: English

Topic: Antibiotic Use and Resistance

Subfield: Applied Microbiology and Biotechnology

Field: Immunology and Microbiology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,190