University of Melbourne develops web-based tool to improve treatment for drug-resistant TB
Tuberculosis (TB) is the leading cause of death globally from a single infectious agent, mycobacterium tuberculosis, that typically affects the lungs, but can also infect other parts of the body. Multiple antibiotics are used to treat TB, however treatment is long, and the emergence of drug-resistant bacteria is increasingly a threat to global health.
According to an announcement by the University of Melbourne, researchers have designed a computer-generated model that will allow clinicians to tailor effective therapies for individual patients with multidrug-resistant tuberculosis (MDRTB), and as a result, reduce drug resistance globally.
Researchers from the Peter Doherty Institute for Infection and Immunity (Doherty Institute) and the University of Melbourne’s Bio21 Molecular Science and Biotechnology Institute (Bio21) used cutting-edge genome sequencing technology to identify an MDRTB mutation in a particular patient.
Armed with this information, University of Melbourne PhD student Malancha Karmakar developed a three-dimensional (3D) computational approach to determine that a drug being used to treat the patient was ineffective due to the mutation. As the standard treatment time for MDRTB can reach two years, this new approach helps to improve treatment and cut the time it takes for patients to receive effective treatment.
Researchers say once the tool is fully developed it will be available through the web, meaning clinicians around the world could enter the resistance mutations into the system to determine effective treatment regimens for their patients.
Associate Professor Justin Denholm, co-author of the study and Medical Director of the Victorian Tuberculosis Program at the Doherty Institute said the new tool was a game-changer. Whereas in the past it could take decades to identify the effectiveness of a TB drug, it could soon be a matter of hours.
Co-author and University of Melbourne Professor David Ascher from Bio21 said by understanding how mutations work within 3D space, the team could identify likely resistant mutations that have never arisen before.
“This is a huge boost to drug-resistant TB programs, which globally have to take a ‘best guess’ approach, where they give the same, standardised therapy to people with MDRTB, with little capacity to adjust that in real-time,” Professor Ascher said.
This study was published in the American Journal of Respiratory and Critical Care Medicine.