The 3D computational approach was developed to help determine if a drug being used to treat the patient is ineffective due to the mutation. The new approach helps to improve treatment and cut the time it takes for patients to receive effective treatment.
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.
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