In silico profiling of systemic effects of drugs to predict unexpected interactions

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Identifying unexpected drug interactions is an essential step in drug development. Most studies focus on predicting whether a drug pair interacts or is effective on a certain disease without considering the mechanism of action (MoA). Here, we introduce a novel method to infer effects and interactions of drug pairs with MoA based on the profiling of systemic effects of drugs. By investigating propagated drug effects from the molecular and phenotypic networks, we constructed profiles of 5,441 approved and investigational drugs for 3,833 phenotypes. Our analysis indicates that highly connected phenotypes between drug profiles represent the potential effects of drug pairs and the drug pairs with strong potential effects are more likely to interact. When applied to drug interactions with verified effects, both therapeutic and adverse effects have been successfully identified with high specificity and sensitivity. Finally, tracing drug interactions in molecular and phenotypic networks allows us to understand the MoA.
Publisher
NATURE PUBLISHING GROUP
Issue Date
2018-01
Language
English
Article Type
Article
Keywords

DATABASE; COMBINATION; SIMILARITY; DISCOVERY; UPDATE; IRBESARTAN; RAMIPRIL; THERAPY; WALKING; NETWORK

Citation

SCIENTIFIC REPORTS, v.8

ISSN
2045-2322
DOI
10.1038/s41598-018-19614-5
URI
http://hdl.handle.net/10203/240223
Appears in Collection
BiS-Journal Papers(저널논문)
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