%0 Journal Article %A Yunguan Wang %A Jaswanth K. Yella %A Sudhir Ghandikota %A Tejaswini C. Cherukuri %A Satish K. Madala %A Anil G. Jegga %T Pan-transcriptome-based Candidate Therapeutic Discovery for Idiopathic Pulmonary Fibrosis %D 2019 %R 10.1101/824367 %J bioRxiv %P 824367 %X Rationale Although the advent of two FDA-approved therapies for idiopathic pulmonary fibrosis (IPF) has energized the field, their effects are largely suppressive than pulmonary fibrosis remission- or reversion-inducing. Hence, the pursuit for newer IPF therapeutics continues. Recent studies showed that joint analysis of systems biology level information with drug-disease connectivity are effective in discovery of biologically-relevant candidate therapeutics.Objectives To identify novel candidate therapeutics for IPFMethods and Measurements Publicly available gene expression signatures from IPF patients are used to query large scale perturbagen signature libraries to identify compounds that can potentially reverse IPF. Two methods are used to calculate IPF-compound connectivity: gene expression-based and feature-based connectivity. Identified compounds are further prioritized based on shared compound mechanisms of action.Results We identified 77 compounds as potential candidate therapeutics for IPF. Of these 39 compounds are either FDA-approved for other diseases or are currently phase 2/3 trial drugs suggesting their repurposing potential for IPF. Among these compounds are multiple receptor kinase inhibitors (e.g., nintedanib, currently approved for IPF, and sunitinib), aurora kinase inhibitor (barasertib), EGFR inhibitors (erlotinib, gefitinib), calcium channel blocker (verapamil), phosphodiesterase inhibitors (roflumilast, sildenafil), PPAR agonists (pioglitazone), HDAC inhibitors (entinostat), and opioid receptor antagonists (nalbuphine).Conclusion As almost half of the candidates we have discovered in this study are either FDA-approved or are currently in clinical trials for other diseases, rapid translation of these compounds is potentially feasible. The generalizable, integrative connectivity analysis framework in this study can be readily adapted in early phase drug discovery for other diseases. %U https://www.biorxiv.org/content/biorxiv/early/2019/10/30/824367.full.pdf