A Personalized Medicine Approach to Improve the Prediction of Azathioprine Toxicity

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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.”