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The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development
Author(s) -
Denny Joshua C.,
Driest Sara L.,
Wei WeiQi,
Roden Dan M.
Publication year - 2018
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 - 10.1002/cpt.951
Subject(s) - pharmacogenomics , biobank , repurposing , drug repositioning , precision medicine , identification (biology) , drug development , data science , drug discovery , medicine , informatics , big data , personalized medicine , mendelian randomization , drug , computer science , bioinformatics , pharmacology , biology , genetic variants , data mining , engineering , ecology , botany , pathology , genotype , gene , electrical engineering , biochemistry
Drug development continues to be costly and slow, with medications failing due to lack of efficacy or presence of toxicity. The promise of pharmacogenomic discovery includes tailoring therapeutics based on an individual's genetic makeup, rational drug development, and repurposing medications. Rapid growth of large research cohorts, linked to electronic health record (EHR) data, fuels discovery of new genetic variants predicting drug action, supports Mendelian randomization experiments to show drug efficacy, and suggests new indications for existing medications. New biomedical informatics and machine‐learning approaches advance the ability to interpret clinical information, enabling identification of complex phenotypes and subpopulations of patients. We review the recent history of use of “big data” from EHR‐based cohorts and biobanks supporting these activities. Future studies using EHR data, other information sources, and new methods will promote a foundation for discovery to more rapidly advance precision medicine.