Pulmonary fibrosis is the final common pathway of a number of adult and pediatric diseases that are associated with fibroblast invasion and excessive deposition of the extracellular matrix (ECM) in the lung. In adults, fibrosis occurs in both the idiopathic pneumonias and in the systemic connective-tissue diseases. Idiopathic Pulmonary Fibrosis (IPF) is perhaps the most pernicious and enigmatic form of lung fibrosis and recent evidence indicates that the prevalence the disease is increasing in the U.S., and around the world. Pirfenidone and nintedanib are two recently FDA-approved therapies that slow the rate of decline in lung function in patients with IPF, but do not reverse fibrosis. Thus, newer effective therapies with significant advantages over these current agents to inhibit or halt the progression of fibrosis are needed. In this proposal, we combine our informatics expertise in systems biology with biomedical expertise in IPF relevant assays to develop and apply integrative omics-based approaches for novel therapeutic discovery in IPF. Our proposal is based on: (i) our ability to develop and apply big data analytic techniques to genomic and biomedical data to identify and rank drug targets and drug repositioning candidates for rare disorders; and (ii) our ability to validate IPF candidate therapeutics using in vitro screening assays and human IPF biopsies. We propose to leverage publicly available gene-expression signatures from IPF patients and IPF model systems to interrogate the 1.4 million L1000 perturbagen profiles from the NIH?s Library of Integrated Network-based Cellular Signatures (LINCS). Novel anti-fibrotic candidate therapeutics will be identified by gene signature-based strategy complemented with systems biology-based approaches encompassing cellular networks and signaling pathways (Aim 1). Computationally identified and prioritized anti-IPF candidate therapeutics will be validated using in vitro screening assays and human IPF biopsies (Aim 2). The multidisciplinary approach of our study will be facilitated by a team with expertise in all aspects of lung pathology in IPF, as well as advanced data computing methods. Our approach is innovative by utilizing available genomic and patient data to identify candidate pathways and targets for clinical intervention of IPF. The proposed research is significant in that completion of this study will identify mechanisms in the pathogenesis of IPF and possible combinatorial designs, and synergistic strategies that more completely arrest or reverse fibrosis in IPF.
Idiopathic pulmonary fibrosis (IPF) is an incurable, disabling, and often fatal disease characterized by the distribution of fibrotic lesions predominantly in the peripheral areas of the lung. Currently approved therapies for IPF delay the rate of decline in lung function, but do not halt the progression of fibrosis. This proposal seeks to integrate computational ?big data? approaches with experimental validations to identify new pre-clinical therapies for IPF.
