NEW APPROACHES IN UNDERSTANDING DRUG METABOLISM

Abstract

Limitations in technology, such as DNA sequencing and appropriate model systems, have made it difficult to understand the genetic and non-genetic factors that influence the liver's role in metabolizing drugs. New approaches are required to overcome these limitations. In this Dissertation, we evaluate 3 such new approaches. Our first new approach relates to the field of pharmacogenetics: using genetics to predict how a patient will respond to medication based on their genetic code. We looked for polymorphisms in a novel target gene, Cytochrome P450 Oxidoreductase (POR). Our results show a mutation in P450 reductase (L577P) that associates with decreased metabolism for 8 of 10 major drug metabolizing enzymes. However, even though we found a statistical association between POR polymorphism and drug metabolism, a wide range of variation in POR activity was still observed among the samples with the L577/ P577 genotype, making predicting POR activity solely on the basis of L577P genotype difficult. POR represents only a single gene amongst the tens of thousands present in the human genome. To investigate the relationship between how genes and their products interact, a systems approach is necessary. Therefore, in our second new approach, we will characterize the transcriptome of our model system, the HepaRG cell line. We found that HepaRG cells globally transcribe genes at the levels more similar to human primary hepatocytes and human liver than HepG2 cells, particularly in genes encoding drug processing proteins. Finally, I describe the third new approach: the use of next-generation DNA sequencing to understand hepatic drug response. This section contains two parts. First, we introduce methods that significantly decrease the false discovery rate of genotyping from RNA-Seq data. With these high fidelity SNPs, we were able to perform a genome-wide pharmacogenomic analysis on HepaRG cells. Second, we introduce a new program, called PRUNE, to more accurately quantify gene expression, and compare its performance to that of established programs.