Computational framework for streamlining the success of sequential antibiotic therapy

Area of Science:

• Microbiology
• Computational Biology
• Evolutionary Biology

Background:

• Antibiotic resistance is a critical global health threat, leading to treatment failures.
• Emergence of multidrug-resistant bacterial strains necessitates novel therapeutic strategies.
• Understanding collateral sensitivity patterns is key to managing resistance evolution.

Purpose of the Study:

• To develop a mathematical framework for analyzing collateral sensitivity data in bacterial populations.
• To create an open-source computational platform for data-driven antibiotic selection.
• To identify therapeutic regimens that minimize the risk of antibiotic resistance evolution.

Main Methods:

• Systematic characterization of collateral sensitivity patterns using a mathematical framework.
• Implementation of the framework in an open-source, in silico computational platform.
• Application of the platform to analyze data from evolving drug-resistant bacterial populations.

Main Results:

• The computational platform enables rapid identification of optimal antibiotic regimens.
• Demonstrated failure of antibiotic therapy in chronic Pseudomonas aeruginosa infections.
• Identified conditions under which sequential antibiotic therapies are likely to fail.

Conclusions:

• The developed framework provides a scalable strategy for navigating bacterial evolutionary dynamics.
• The tool aids in selecting antibiotics to minimize resistance development.
• Highlights critical factors influencing the success or failure of sequential antibiotic treatments.