Idiopathic pulmonary fibrosis, or IPF for short, is an interstitial lung disease that primarily affects the interstitium of the lungs (the tissue around the alveolar space). Overtime this disease causes scar tissue to form in these tissues, making them fibrous and stiff. As the disease progresses the lungs lose their ability to function properly, preventing the adequate intake and distribution of oxygen. There is currently no known cause for IPF and furthermore there are few treatments available. Medications such as pirfenidone and n intedanib can be used to slow the progression of the disease and can be used along with other drugs to mitigate symptoms but there is no cure. With the typical lifespan prognosis being 2-5 years from diagnosis and there being few options for treatment, the development and distribution of new and more effective treatments for IPF is critical.

A group at the Medical University of South Carolina (MUSC) has recently discovered a drug that has promising results for its use in treating IPF. However, the mechanism of this drug is unknown to its developers. Understanding a drugs mechanism can be key in refining its dosage, combination with other drugs, etc. in order to ultimately produce the most effective treatment. With the enormous number of possible molecular targets within cells there are far too many possibilities for the mechanism of this drug to do physical experiments for each cellular signaling molecule. In order to lessen this issue, we have developed a computational model of the cellular signaling pathways in pulmonary fibroblasts (the cells primarily responsible for the creation of the fibrous scar tissue). We particularly focused on those signaling molecules and pathways that have previously been linked to fibrosis both within lung tissue and other bodily tissue.

The resulting pulmonary fibroblast signaling network model contains 111 signaling molecules that participate in 161 individual reactions. Validation procedures show that the model predicts with 74.4% accuracy when compared to literature data. Sensitivity analysis simulations were performed in order to further characterize the model, allowing for the identification of the most sensitive and the most influential nodes in the model. Further simulations were performed in order to make predictions as to the possible target mechanism of the preliminary MUSC IPF drug.