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Computational Drug Repositioning: From Data to Therapeutics
Author(s) -
Hurle M R,
Yang L,
Xie Q,
Rajpal D K,
Sanseau P,
Agarwal P
Publication year - 2013
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.2013.1
Subject(s) - drug repositioning , clinical pharmacology , genome wide association study , computational biology , health records , drug discovery , computer science , data science , medicine , drug , bioinformatics , biology , pharmacology , single nucleotide polymorphism , genetics , health care , gene , genotype , economics , economic growth
Traditionally, most drugs have been discovered using phenotypic or target‐based screens. Subsequently, their indications are often expanded on the basis of clinical observations, providing additional benefit to patients. This review highlights computational techniques for systematic analysis of transcriptomics (Connectivity Map, CMap), side effects, and genetics (genome‐wide association study, GWAS) data to generate new hypotheses for additional indications. We also discuss data domains such as electronic health records (EHRs) and phenotypic screening that we consider promising for novel computational repositioning methods. Clinical Pharmacology & Therapeutics (2013); 93 4, 335–341. doi: 10.1038/clpt.2013.1