Novel Computational Methods to Discover Genes Linked to Drug Response

Metformin is used first line for treatment of type 2 diabetes (T2D) and is one of the most frequently prescribed drugs worldwide. As the global incidence of T2D rapidly increases, the low cost of metformin makes this treatment option particularly attractive in developing nations. Understanding metformin’s efficacy in different patient populations with diverse genetic backgrounds will be critical in managing this deleterious metabolic disorder. The major goal of this dissertation research was to use novel, quantitative approaches to elucidate genetic and non-genetic components that predict metformin disposition and glycemic response. As a first goal, the role of transcription factor variants on metformin pharmacokinetics and pharmacodynamics was investigated. From this analysis, five variants in SP1 were significantly associated with changes in treatment HbA1c (p < 0.01) and metformin secretory clearance (p < 0.05). Genetic variants in transcription factors PPAR-alpha and HNF4-alpha were significantly associated with HbA1c change only, but were not significantly associated with pharmacokinetics. A plausible biological mechanism by which genetic variants affected the pharmacological variation of metformin was determined using gene expression levels linked to genetic variants (eQTLs). The focus was on transporter expression. From this study, we discovered that genomic regions proximal to metformin transporters were linked to expression levels of SLC47A1, SLC22A3, and SLC22A2, with a potential transcription factor-binding hypothesis for SP1. We also found variants in transcription factor HNF4-alpha were the most influential trans-eQTLs, accounting for expression level variation in both SLC47A1 and SLC22A1. Finally, we developed a mathematical model to quantify disease progression on metformin therapy using HbA1c data with the goal of explaining long-term HbA1c variability through the investigation of genetic, demographic, and clinical factors. From this analysis, we found two SNPs in CSMD1 (rs2617102, rs2954625) and one SNP in SLC22A2 (rs316009) as significantly influencing the long-term variance in HbA1c. Overall, this dissertation research enhances our current knowledge of the pharmacogenetic landscape by expanding the set of pharmacologically relevant genes and providing a pharmacokinetic and biological basis for some of these genes. Future research will continue to focus on replication and uncovering the mechanism driving the pharmacological genes highlighted here