A research collaboration between the University of Pittsburgh’s schools of Engineering and Medicine has been awarded a $1.7 million grant by the National Institutes of Health (NIH) to develop new mathematical models that can evaluate personalized approaches in treating cystic fibrosis (CF) and other lung diseases.

Robert Parker with the university’s Swanson School of Engineering and Tim Corcoran at the School of Medicine will lead the team, joined by Carol Bertrand (Pediatrics), Joe Pilewski and Mike Myweburg (Division of Pulmonary, Allergy and Critical Care Medicine).

The researchers expect that these models — of liquid and ion transport in the lung — will help doctors to administer the most effective, patient-targeted treatments by analyzing a cell culture from a person’s nose.

“We know that mucus hydration and clearance are important factors in CF lung disease,” Corcoran said in a press 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. The techniques we are using were actually developed here, and we’re pretty much the only ones using them.”

First, researchers will collect data from CF patients, parents of CF patients who carry the CF gene mutation, and healthy controls. Then, participants’ human nasal epithelial (HNE) cells will be sampled and cultured. Researchers will use nuclear imaging based on aerosol to measure patients’ mucus clearance and airway surface liquid dehydration in the lungs — the goal is to use computational techniques to find correlations between the physiology of the patients’ nasal cells and that of the lungs.

Parker will lead efforts to translate the collected data into multi-scale mathematical models, which might be capable of offering data on the patients’ physiology at the cell and organ level.

The team expects that their investigations will demonstrate how nasal cell sampling, together with these mathematical models, can help develop personalized approaches for CF treatment, which could start as soon as a patient is born. If successful, this approach could have significant impact on a patient’s quality of life and limit disease progression.

“The mathematical models 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.

“We are always going to be limited by the number of patients we can test,” he added. “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.”