In silico profiling of systemic effects of drugs to predict unexpected Interactions약물의 전신 효과 프로파일링을 통한 약물 상호작용 예측

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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.
Advisors
Lee, Kwang-Hyungresearcher이광형researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2018.8,[iii, 77 p. :]

Keywords

drug interaction▼anetwork analysis▼abio network▼adrug efficacy▼amechanism of actions; 약물 상호작용▼a네트워크 분석▼a바이오 네트워크▼a약물 효능▼a약물 작용기작

URI
http://hdl.handle.net/10203/264695
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=827885&flag=dissertation
Appears in Collection
BiS-Theses_Ph.D.(박사논문)
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