Monash University research is using data modeling to address Antimicrobial resistance threat

Antimicrobial resistance is a global health threat due to “microorganisms” including fungi, parasites and bacteria changing over time and becoming resistant to medicines.

Study in Australia: Antimicrobial resistance (AMR) is a serious health threat of our time which claimed 1.27 million lives in 2019. The impact is far-reaching and concerning as it also poses threats to many modern medicine practices that include the risk of performing surgery, risk procedures, cancer chemotherapy and conducting caesarean sections. To address this serious issue, Monash Institute of Pharmaceutical Sciences (MIPS) has conducted research using computer modelling to describe and predict when antibiotic resistance will develop during treatment.

Associate Professor Cornelia Landersdorfer, co-lead author from MIPS said, “There’s a pressing need for new ways to predict the emergence of antibiotic resistance during treatment to ensure the dose is right and the drug is working as effectively as possible."

She emphasized that such modelling is not just for now but it is something that will be needed in future too.

“While the development of new antibiotics is occurring, resistance to these drugs typically emerges quickly. This means that unfortunately we can’t rely on these medicines alone, which is where technology steps in. By using modelling to predict when antibiotic resistance will emerge during treatment, we can ideally stay a step ahead of the bugs and, in turn, make sure the dosing regimen is optimised to suppress resistance and increase the likelihood of successful treatment of infection," she added.

QSP Model Details

To predict the AMR during the treatment, the researchers developed the first quantitative systems pharmacology (QSP) model. It focuses on the antibiotic meropenem given for the treatment of intra-abdominal infection, pneumonia, meningitis, and sepsis. The QSP model will describe and predict meropenem resistance. It will use computational models to describe medicine and disease interaction.

The study’s joint first author and MIPS PhD candidate Dominika Fuhs said, “Our QSP model represents a substantial advance compared with the way scientists currently attempt to measure the varying activity of antibiotics over time following administration. Until now, no other models have investigated meropenem regimens in the context of different resistance mechanisms using a panel of bacterial-resistant mutants.”

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