How a virtual monkey lung could advance treatment for a centuries-old global threat
A team of researchers say they’ve made a breakthrough that could speed up the search for more effective treatments and vaccines for the world’s No. 1 infectious killer.
Using mathematics, the team of University of Michigan researchers and their partners around the globe have developed a whole lung simulation capable of reproducing activity in the lungs, lymph nodes, and blood vessels during a pulmonary Tuberculosis (TB) infection.
The first-of-its-kind model will create opportunities for researchers to conduct virtual clinical trials of new drug regimens and prospective vaccines. Not only can the model speed up the process that would typically begin with mouse trials and require significant trial and error, but it could quickly rule out treatments that won’t work as well.
“It’s not just ‘let’s try this, let’s try that,’” said Denise Kirschner, a mathematical biologist at the University of Michigan. “Because we have the bulk of mathematics and computational science at our fingertips and statistics, we can do all sorts of analyses to actually make very accurate predictions about what might be the optimal solution here.
“We can make very accurate predictions about what’s the next best thing or next four best things to try, and we can say don’t try those other five because they definitely won’t work.”
Tuberculosis, also known as TB, has been spreading around the world for thousands of years. Not only is it the world’s No. 1 infectious killer -- responsible for 1.5 million deaths per year worldwide -- but it’s estimated that roughly 23% of the world’s population is believed to be infected with the bacteria that causes TB.
In as many as 90% of those infections, the disease is latent and causing no symptoms while it sits in the lungs. It can stay that way for a person’s lifetime, making it difficult to diagnose, yet easy to spread.
However, when an infected person has their immune system compromised due to old age or another illness, their TB can cause serious illness. For example, TB is the leading cause of death for people with HIV.
Like respiratory viruses, TB is spread through respiratory droplets that people release when they talk, cough, or sneeze. It typically takes prolonged exposure to become infected.
Current treatment for TB has its flaws. It typically requires a series of four pills taken daily for about nine months. When patients misuse or stop taking the medication prematurely, it can lead to the bacteria becoming drug resistant.
That’s where Kirschner hopes her team’s work over the last two decades can help speed up progress on TB treatment and prevention. Given the available funding, she estimated that advancements could be available within the next couple of years.
“That’s the idea, to have much faster turnaround times where this is going to really shorten that timeline between the experimentation and the clinic,” she said. “That’s what we’re hoping for and we think this is all possible now.”
Kirschner’s model was developed in partnership with her University of Michigan colleague Jenifer Linderman, University of Pittsburgh Professor JoAnne Flynn, and Rutgers University Professor Veronique Dartois. Their paper was recently featured by the Society for Industrial and Applied Mathematics (SIAM).
At the center of the project was understanding the workings of spherical clusters of immune cells known as granulomas that develop around the invading bacteria in the lung as part of the body’s immune system. After accurately developing a model for the granulomas, the research team was then able to expand to a whole lung simulator using a digitized monkey lung.
In the future, Kirschner hopes to develop a full body model using the same technique. “This multiscale approach to biology and modeling is incredibly important,” she said.
The U.S. has seen a significant decrease in reported TB cases over the last 30 years. The incidence rate was about 2.2 cases per 100,000 people in 2020 -- compared to 10.4 cases per 100,000 people in 1992.
However, health officials don’t yet know the effects the coronavirus pandemic had on TB transmission, testing and reporting. Because symptoms of TB are similar to that of COVID-19, it’s possible cases were missed, which could allow for further spread.
Cases of TB could also have declined over the last two years due to the increase in prevention methods like the wearing of face coverings and avoiding of crowds, especially when a person is feeling ill, according to CDC officials.
“Delayed or missed tuberculosis disease diagnoses are threatening the health of people with TB disease and the communities where they live,” said Dr. Philip LoBue, the CDC’s director of the tuberculosis elimination division. “A delayed or missed TB diagnosis leads to TB disease progression and can result in hospitalization or death – and the risk of transmitting TB to others.”