In the United States, more than 2 million cases of prescription drug-related adverse events (AE) occur annually, including 100,000 deaths. With more than 30% of the approved drugs implicated, drug-induced pulmonary disease (DIPD) is an underdiagnosed major safety concern. As many as 10% of patients who receive chemotherapeutic agents develop a lung- related AE. DIPD may present in a variety of clinical syndromes such as asthma, pulmonary fibrosis, pulmonary edema and interstitial pneumonia, and several of these manifestations are often fatal and irremediable. Currently, no alternatives exist for patients subjected to DIPD because the cause of DIPD is the primary medication and mitigating DIPD necessitates the immediate cessation of the suspected medication, which is either unlikely or not practical in most therapeutic regimens (e.g., bleomycin use in chemotherapy). On the other hand, treatment of complex multifactorial disorders may lead to inadvertent polypharmacy predisposing patients to drug-interaction induced pulmonary disease. Thus, understanding the molecular mechanisms underlying DIPD and finding safer and effective therapeutic regimens is of paramount importance. Building on our earlier and ongoing work in post-marketing surveillance of approved drugs, we will use the drug-related adverse events (AE) data from the United States Food and Drug Administration's Adverse Events Reporting Systems (FAERS) and the compound screening data from the NIH's Library of Integrated Network-based Cellular Signatures (LINCS) database as the basis for computational models that integrate network analyses with systems biology approaches to elucidate the mechanistic relationships of DIPD and guide the therapeutic interventions. Systematic deep mining of FAERS should allow us to find not only DIPD- predisposing drugs or drug combinations, but also beneficial approved drugs or drug combinations which can mitigate DIPD. Our two specific aims are to: (i) Find novel drug combinations that aggravate or mitigate DIPD and (ii) Delineate and characterize the DIPD- mitigating effects of drug combinatorials. Successful completion of this study can help generate testable hypotheses underlying DIPD mechanism, intervention, therapeutics choice, and drug repositioning for DIPD. Computational models generated from this study can help guide the design of in vitro assays, focused animal studies, and ultimately improved methods for DIPD prevention and intervention.
We will use powerful data-driven approaches on publicly collected drug-related adverse events from the FDA to identify drugs that are either causative of or protect from drug-induced pulmonary adverse events. Candidate drugs or drug combinations inversely correlated with drug-induced pulmonary diseases (DIPD) may have a protective/beneficial role in DIPD. We will further analyze these candidates through systems biology-based approaches using large scale publicly-available drug-gene expression data to generate mechanism of action and drug repositioning hypotheses.
| Jiang, Alex; Jegga, Anil G (2018) Characterizing drug-related adverse events by joint analysis of biomedical and genomic data: A case study of drug-induced pulmonary fibrosis. AMIA Jt Summits Transl Sci Proc 2017:91-97 |
| Wang, Yunguan; Yella, Jaswanth; Chen, Jing et al. (2017) Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers. BMC Pulm Med 17:133 |
