# Genomic Approaches for Predicting Drug Response

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2021 · $378,619

## Abstract

PROJECT SUMMARY / ABSTRACT
The field of pharmacogenomics has progressed from the discovery of genetic variants that cause variable
function of drug metabolism enzymes to clinical implementation of gene-guided drug prescribing. However,
only a small number of drugs have clinically valid and actionable genetic associations. One problem with the
current pharmacogenomic approach is a focus on genetic variants with a large effect on drug response among
a small number of genes. For most drugs, the pathways of drug metabolism and response are complex. For
these drugs, the effects of genetic variation on drug disposition and response are also likely to be complex,
including effects of hundreds or thousands of genetic variants with variable effect size. The primary objective of
this project is to quantitate and characterize the influence of variants throughout the genome on drug response
outcomes. Using existing data sets, we will measure the impact of complex polygenic variation on response to
a variety of drugs and drug classes. We will also explore rare genetic variation in fentanyl distribution. Aim 1 is
to analyze the collective effect of all variants genotyped as part of prior genome-wide association studies for
clopidogrel, statins, methotrexate, ACE-inhibitors, antidepressants, and vancomycin, in order to determine the
genetic architecture for response to each drug. Through our analysis, we will use mixed models to quantitate
the amount of variability in drug response can be attributed to all genetic variation captured using genome-wide
genotyping. We will also measure the relative impact of variants with small, moderate, and large effect size.
The findings from completion of Aim 1 will guide future efforts in pharmacogenomics. For drugs where nearly
all genetic effects are mediated by a small number of well-established variants, the focus can shift from variant
discovery to clinical implementation using the current paradigm of targeted genotyping. In contrast, for drugs
with genetic effects due to hundreds of variants with variable effect size, validation of the polygenic models
across diverse populations is the next step. Aim 2 applies the mixed models approach to the commonly used
and highly variable drug, fentanyl. Using data from an ongoing fentanyl pharmacokinetic study, we will define
the genomic architecture of fentanyl disposition in order to create a genomic predictor of fentanyl
pharmacokinetics. The genomic predictor will then be validated in an independent dataset, providing the
opportunity to test the clinical implementation of this genomic predictor in future research. In Aim 3 we will
further explore fentanyl disposition, performing whole genome sequencing in individuals with highly atypical
fentanyl drug concentrations in order to identify novel genes and rare variants driving fentanyl kinetics.
Discovery of these new associations will illuminate biological mechanisms of fentanyl metabolism and
transport. Overall, through a shift ...

## Key facts

- **NIH application ID:** 10139060
- **Project number:** 5R01GM132204-03
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Sara Lynn Van Driest
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $378,619
- **Award type:** 5
- **Project period:** 2019-06-10 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10139060

## Citation

> US National Institutes of Health, RePORTER application 10139060, Genomic Approaches for Predicting Drug Response (5R01GM132204-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10139060. Licensed CC0.

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