# A Personalized Medicine Approach to Improve the Prediction of Azathioprine Toxicity

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2020 · $410,067

## Abstract

Abstract
Azathioprine is an immunosuppressive drug widely used for the treatment of rheumatic and other inflammatory
conditions. However, it has a narrow therapeutic index, and the frequency of clinically significant side effects
associated with its use is approximately 50%. Based on clinical importance and differences in mechanisms, this
project focuses on two of the most serious adverse effects of AZA: myelosuppression and pancreatitis. Currently,
clinicians are limited to thiopurine methyltransferase (TPMT) testing to predict patients' risk for azathioprine
toxicity. Despite their usefulness, TPMT polymorphisms explain only one in four cases of myelosuppression
associated with azathioprine, and they do not predict pancreatitis. Recent evidence suggests other genetic
variants have important roles in azathioprine-related side effects. For example, NUDT15 and the HLA-
DQA1*02:01–HLA-DRB1*07:01 haplotype are genetic determinants of myelosuppression and pancreatitis,
respectively. Nevertheless, their usefulness in routine clinical practice and their combined ability to predict side
effects of AZA remains unclear. The overarching hypothesis of this proposal is that genetic risk scores
can identify patients who develop azathioprine toxicity. Using state of the art and novel techniques and
resources, we will conduct genetic and gene expression association analyses, leveraging two large practice-
based biobanks: (1) Vanderbilt's BioVU, one of the largest practice-based biobanks in the U.S., and (2) the
Million Veteran Program (MVP), currently enrolling, collecting clinical data from, and genotyping U.S. Veterans.
In Aim 1, we will conduct genetic association analyses to discover novel genetic predictors of myelosuppression
and pancreatitis in patients taking azathioprine. In Aim 2, we will test the hypothesis that novel genetic variants,
identified by gene expression association analyses, predict AZA-related myelosuppression and pancreatitis. We
will predict gene expression by utilizing the Genotype Tissue-Expression (GTEx) database. In Aim 3, we will
combine all variants identified from Aims 1 and 2 to generate two genetic risk scores (i.e., myelosuppression risk
and pancreatitis risk) for patients in the BioVU cohort. We will further validate the genetic risk scores in the
independent MVP cohort.
This project aims to further the goals of the Precision Medicine Initiative by constructing two genetic models that
will predict serious and frequent side effects of azathioprine. Better prediction capacity will offer better treatment
options for patients and advance personalized medicine, which seeks to deliver “the right drug, at the right dose,
to the right patient.”

## Key facts

- **NIH application ID:** 9976543
- **Project number:** 5R01GM126535-03
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Cecilia Pilar Chung
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $410,067
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9976543

## Citation

> US National Institutes of Health, RePORTER application 9976543, A Personalized Medicine Approach to Improve the Prediction of Azathioprine Toxicity (5R01GM126535-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9976543. Licensed CC0.

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