Characterizing drug-related adverse events by joint analysis of biomedical and genomic data: A case study of drug-induced pulmonary fibrosis

Spontaneous reporting systems such as the FDA’s adverse event reporting system (FAERS) present a great resource to mine for and analyze real-world medication usage. Our study is based on a central premise that FAERS captures unsuspected drug-related adverse events (AEs). Since drug-related AEs result for several reasons, no single approach will be able to predict the entire gamut of AEs. A fundamental premise of systems biology is that a full understanding of a biological process or phenotype (e.g., drug-related AE) requires that all the individual elements be studied in conjunction with one another. We therefore hypothesize that integrative analysis of FAERS-based drug-related AEs with the transcriptional signatures from disease models and drug treatments can lead to the generation of unbiased hypotheses for drug-induced AE-modulating mechanisms of action as well as drug combinations that may target those mechanisms. We test this hypothesis using drug-induced pulmonary fibrosis (DIPF) as a proof-of-concept study