Study seeks to more accurately predict antibiotic resistance before it occurs in patients
Scientists have embarked on a study to enable them to more accurately predict resistance to new antibiotics before it occurs in patients.
The work, being led by researchers at Nottingham Trent University, will be instrumental in improving the development of diagnostic tests for antimicrobial resistance.
Antimicrobial resistance is a serious global issue which leads to failure of antimicrobial treatment of infections. It was attributed to 1.27 million deaths in 2019.
Microbes in bacterial infections develop methods that protect them from antimicrobials, and this leads to resistance.
The new study, funded by the Royal Society, aims to limit this resistance by enabling the selection of antibiotics which will successfully treat an infection.
Diagnostic tests currently predict antimicrobial resistance through the detection of segments of DNA, which produce enzymes that result in antibiotic resistance.
Changes, or mutations, in the genes associated with resistance to antibiotics, however, can result in a mismatch between predicted and actual antibiotic resistance, limiting the usefulness of these tests.
The research will involve identifying and characterising mutations which change the function of resistance genes.
The team will initially study resistance to the new antibiotic meropenem-vaborbactam, which has recently been introduced for the treatment of complicated urinary tract infections, of which E.coli is the primary cause.
They will use a targeted approach to produce mutations resulting in resistance to the antibiotic, which will then enable them to determine the effect of these mutations on growth, enzyme activity and resistance to other antibiotics.
“Bacterial infections are becoming increasingly hard to treat due to resistance to at least one antibiotic,” said lead researcher Dr Alasdair Hubbard, an evolutionary microbiologist in Nottingham Trent University’s School of Science and Technology.
He said: “We need to get better at understanding and detecting antimicrobial resistance to enable clinicians to choose the right treatment options. This study will help us identify mutations which cause resistance to new antibiotic combinations before they arise in the clinic so that antimicrobial resistance can be detected and bacterial infections treated successfully.
“This will improve the development of diagnostic tests to predict antibiotic resistance and antibiotic selection, helping to limit the emergence of resistance.”