Seeing TB in 3D makes finding the right treatment way faster

(Credit: qiagen/Flickr)

A computer-generated model allows clinicians to tailor effective therapies for individual patients with multidrug-resistant tuberculosis (MDR-TB).

While many of us may think of tuberculosis as a historical disease, it actually remains one of the top ten causes of death worldwide. In 2016, around 10.4 million people fell ill with TB globally and 1.7 million lost their lives as a result of the disease.

What would normally take decades could now just take a matter of hours.

Part of the reason that TB remains such a difficult disease to manage is the long time it can take to establish what treatment is best for each patient. Traditional approaches to demonstrating that a specific medication will be effective against a strain of TB involve laboratories culturing the organism and checking whether the drug stops its growth.

And because TB is very slow growing, this process can take weeks or even months; and that is long enough for resistance to develop. The end result is that those suffering may die before the right treatment can be started.

In recent years, genomics has brought advances to TB diagnosis, including the ability to rapidly identify when strains have mutations likely to cause drug resistance.

Even so, when a new mutation is found, understanding whether it can lead to drug resistance means seeing evidence both in laboratory testing and in clinical settings. Clinicians and TB programs, then, may need to treat people while there’s still uncertainty about the meaning of these mutations.

But now innovative technology is providing a game changer for treatment—modeling mutating tuberculosis genes in a matter of hours.

TB in 3D

To speed things up, researchers from the University of Melbourne devised a 3D-computational approach to predicting the impact of mutations in TB.

This work is aimed at supporting global programs that are starting to use new TB therapies, allowing them to tailor treatments to an individual as early as possible while also avoiding the use of ineffective and harmful treatments.

While the research was underway, doctors were treating a patient with multidrug-resistant (MDR) TB who was experiencing significant side effects from the cocktail of medications they required. These problems with side effects are sadly common, as MDR-TB needs treatment for up to two years with medications that can themselves cause deafness, liver and thyroid damage, joint pain, and other issues that add to the difficulty of completing therapy.

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Scientists used this cutting-edge genome sequencing technology to look for drug-resistance mutations to help tailor the patient’s therapy and identified a mutation that had never been reported before.

Suspecting that the mutation might be causing resistance, researchers were able to quickly build a new 3D model to investigate. What they found was that the mutation made one of the drugs completely ineffective. As a result, doctors swapped it for another, more effective drug.

Quicker turn-around

What would normally take decades could now just take a matter of hours.

Right now, in order to use genetic tests to reliably predict whether a drug will be effective, a person must receive a drug therapy, have that therapy fail, and then wait to find out if they have a gene mutation.

And it doesn’t stop there. This is followed up by identifying enough people around the world that have that pattern to inform what treatment you prescribe for that particular mutation. It can take years.

By understanding how mutations work within 3D space, the team can 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, standardized therapy to people with MDRTB, with little capacity to adjust in real-time.

The new modeling can be done quickly, making results available in relatively close to real time. This truly is a “bedside to bench and back” approach, which researchers are now planning to expand to a range of other genes that may cause TB drug resistance.

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More importantly, once the tool is fully developed, it will be available on the web, meaning clinicians around the world can enter the resistance mutations into the system—helping to create and build effective treatments for their patients.

Source: University of Melbourne