Researchers at the University of Pittsburgh have been awarded with a $1.7 million grant from the National Institutes of Health (NIH) to develop better ways of evaluating, and potentially treating, cystic fibrosis (CF).
Specifically, the grant will be used to develop mathematical models of liquid and ion transport in the human lung so as to better treat CF, a complex and inherited condition in which the lungs and digestive system can become clogged with thick, sticky mucus.
The researchers’ ultimate goal is to come up with models that — through a simple examination of a cell culture from a patient’s nose — allow doctors to tailor, or personalize, treatments for CF patients and those with other lung diseases.
The project will be co-led by Robert Parker, with the Swanson School of Engineering, and Timothy Corcoran at the School of Medicine, in close collaboration with Carol Bertrand, Joe Pilewski and Michael Myerburg.
“We know that mucus hydration and clearance are important factors in CF lung disease,” Corcoran said in a news release. “We’ve developed nuclear imaging techniques to measure how mucus and water move in the lungs. This lets us understand the individual lung pathologies of our patients and may allow us to predict what therapies will help them.”
First, the team will gather data from CF patients, biological parents of CF patients carrying the disease-causing genetic mutation, and healthy individuals used as controls. After sampling and culturing human nasal epithelial (HNE) cells (or nasal cells), researchers will use aerosol-based nuclear imaging of the lungs to measure mucus clearance and airway surface liquid dehydration.
Next, they will use mathematical models to determine the association between the cells’ composition and the lungs’ physiology.
“The mathematical models — through a framework of differential equations — describe how basic physiological processes contribute to experimental outcomes,” Parker said. “We can link all of the information we’ve gathered from lab experiments, physiology studies, and clinical studies to better predict how a patient will respond to different therapies. By creating millions of simulations over a broad spectrum of patients, we can identify the underlying biological mechanism and understand how the patients will respond to treatment through the painless, noninvasive sampling of the HNE cells.”
The team hopes to demonstrate that nasal samples and data analysis, via mathematical models, can help in CF treatments, ultimately improving a patient’s quality of life and limiting disease progression.
“We are always going to be limited by the number of patients we can test,” Parker said. “However, we can bridge the gap between the full set of all CF patients and a smaller set of CF patients with similar symptoms who are likely to respond to treatment in a similar way. The mathematical models will help us create those sets and let us predict outcomes and design treatments for individual patients.”
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