The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development

Abstract

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.

Figure 1. Electronic Health Records support genomic and pharmacogenomic discovery

NLP=Natural Language Processing. PheWAS=Phenome-wide association study; GWAS=Genome-wide association study. Disease clusters adapted from Lingren et al. and Lasko et al.

Figure 2. Mendelian Randomization (MR) vs. Randomized Controlled Trials (RCT)

MI=myocardial infarction. LDL=low density lipoprotein levels. *Allele could be a single SNP or group of SNPs (e.g., genetic risk score).