Invariant sets for switched systems to tackle bacterial resistance
The limited availability of antibiotics and the need for prompt decision-making present significant challenges for healthcare practitioners. When faced with this situation, practitioners must prioritize their approach based on several key factors. By leveraging the emergent understanding of collateral sensitivity among antibiotic-exposed pathogens, we demonstrate the utility of control invariant sets to predict treatment failure when antibiotic cycling is applied as a therapeutic strategy aiming to eradicate or prevent emergence of multi-drug resistant pathogens. Our results here pave the way for point-of-care diagnostic technologies to identify infections and select appropriate treatments quickly, reducing unnecessary antibiotic use.
bioinformatics↗