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    Home > Biochemistry News > Biotechnology News > eLife: A model that can predict the impact of drug interactions on antibiotic resistance

    eLife: A model that can predict the impact of drug interactions on antibiotic resistance

    • Last Update: 2021-08-04
    • Source: Internet
    • Author: User
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    This research can help doctors optimize the choice, timing, dosage and sequence of antibiotics used to treat common infections, and help curb the growing threat of antibiotic resistance to modern medicine


    Erida Gjini, a researcher at the Department of Mathematics at the Institute of Advanced Technology of the University of Lisbon, Portugal, explained: “Drug combinations are a particularly promising method to reduce resistance, but the evolutionary impact of combination therapies is still difficult to predict, especially in clinical settings.


    In their research, Gjini and Kevin Wood of the University of Michigan tried to simplify things


    This scaling model shows that the choice of drug resistance traits is determined by drug interaction and cross-resistance (cells are resistant to one drug while they are resistant to a second drug)


    After establishing the basic model, the research team added the effect of genetic mutations on drug resistance


    In addition to being able to include mutations in the model, the research team also tested whether they can predict the impact of the timing and sequence of different antibiotic treatments


    "We have built a model that combines drug interactions and cross-resistance to predict how microorganisms adapt over time in a way that can be measured experimentally," concludes co-author Wood, who is from Michigan Associate professor of the University's Department of Biophysics and Physics



    Journal Reference :

    1. Erida Gjini, Kevin B Wood.



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