US scientists at the University of California San Diego have developed a new tool that can predict if pairs of antibiotics will help or harm a patient, reducing risks and improving outcomes. The new tool provides a prediction of “resistance evolution” when using two antibiotics, put simply, the likelihood that the bacteria in the body will mutate and stop the drugs from working. The tool could help improve clinical prescribing and reduce the dangerous growth of antibiotic-resistant bacteria.
Since their discovery, antibiotics have saved millions of lives, but over-prescribing has reduced their effectiveness. In the battle against evolving bacteria, clinicians are increasingly prescribing pairs of antibiotics to reduce the risk of bacteria developing resistance, but such treatments can be risky.
“The problem with using multiple drugs to treat bacteria is that we just don’t know which mutations are available to bacteria,” said co-author Sergey Kryazhimskiy. “In many situations, bacteria can have access to mutations that make them resistant to both drugs as well as to mutations that make them resistant to the first drug but susceptible to the second one.”
To help clinicians improve prescribing, Kryazhimskiy and his colleague Sarah M Ardell developed a mathematical model to calculate the risk of resistance evolution for antibiotic pairs. The researchers characterised the known mutations available to bacteria, identifying them as either stopping or starting multidrug resistance. Known as joint distribution of fitness effects (of new mutations), or JDFEs, the model can help better predict resistance outcomes, say the researchers.
“The crux of our result is that we can predict the probability of evolving collateral resistance,” said Kryazhimskiy. The researchers hope that the model could have practical implications for antibiotic prescribing improving patient outcomes. “Our work may eventually help clinicians choose drugs that minimize the evolution of multidrug resistance,” Kryazhimskiy said.
The model uses the latest data, but like everything, it’s not foolproof. Bacterial evolution can be random, but that shouldn’t detract from the incredible value of the model. “It’s not perfect but it’s preferable to having no idea what will happen at all. If we choose the drug pairs carefully, we can minimize the probability of collateral resistance,” Kryazhimskiy said.
At Biocentaur, we’re pioneering personalised medical testing that can provide insights into the effectiveness of treatment with antibiotics, anti-inflammatories, and anticoagulants. Our SNPs test analyses a patient’s DNA to characterise how they would respond to treatment, including the effectiveness of standard antibiotics. We create a personalised profile that provides essential information when prescribing the most effective drugs and medicines for specific conditions.
You can read the full paper, The population genetics of collateral resistance and sensitivity, here.
