Computational Approaches and Pharmacogenomics Data Resources for Drug Repositioning

Abstract

Large-scale data sets of cancer patients have been being generated due to the significantly reduced cost of sequencing full genome of individual patients using the Next Generation Sequencing (NGS) technology. Comprehensive genomics data analysis revealed the diverse dysfunctional biomarkers of individual cancer patients, which are believed to be responsible for heterogeneous drug response. Thus precision medicine is becoming popular that aims to find the optimal treatments for individual patients based on their genomics profiling data. However, it is challenging to interpret the complicated and distinct genome mutation and variation patterns, and associate them to optimal treatments. Though a set of approaches and data resources have been reported to reposition FDA approved drugs and investigational drugs for specific diseases, novel and sophisticated computational approaches are needed urgently to reposition drugs for cancer subtypes or individual patients. In this study, some widely used computational approaches and pharmacogenomics data resources for repositioning optimal drugs are introduced and discussed, which aims to provide a general overview of the genomic data-driven drug repositioning, and help readers understand the topic conveniently.

Key words: Precision medicine, Personalized medicine, Drug repositioning, Drug combination, Genetic medicine