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AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:91-97. eCollection 2018.

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

Jiang A1,2, Jegga AG1,3,4.

Author information

1
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
2
Comell University, Ithaca, New York, USA.
3
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
4
Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, Ohio, USA.

Abstract

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.

PMID:
29888048
PMCID:
PMC5961825

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