Pharmacogenetic Analysis of TPMT and NUDT15 in a European Pediatric Cohort with IBD and Autoimmune Diseases: Frequency Data and Clinical Relevance
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Updated: Jan 10, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Anna Pau1, Ilaria Galliano1,2, Alice Ponte3
1Department of Public Health and Pediatrics, School of Medicine, University of Turin, 10126 Turin, Italy.
Background/Objectives:
Thiopurines remain a cornerstone in the management of inflammatory bowel disease (IBD) and gastrointestinal immune diseases but are associated with significant interindividual variability in efficacy and toxicity, mainly influenced by polymorphisms in Thiopurine S-methyltransferase TPMT and Nudix Hydrolase 15 NUDT15. This study aimed to assess the frequency of TPMT and NUDT15 variants in a pediatric cohort and evaluate their clinical impact to support a pharmacogenetic-guided approach to thiopurine therapy.
Methods:
Eighty-three pediatric patients with IBD and other autoimmune diseases were genotyped for clinically relevant TPMT and NUDT15 variants using two HRM-PCR assays and were confirmed with sequencing. Variant frequencies were compared to expected population data, and clinical records were reviewed to assess thiopurine dosing, tolerance, and adverse events.
Results:
Among the cohort, six carried heterozygous TPMT variants *1/*3A, while 2 carried the NUDT15 *1/*9 diplotype, with frequencies higher than expected. Among patients with TPMT variant alleles, some needed dose reductions or treatment discontinuation due to adverse effects, while others tolerated standard dosing without significant issues. Notably, no significant differences in adverse reactions were observed between NUDT15 *1/*9 carriers and wild-type patients.
Conclusions:
Our results confirm the clinical relevance of TPMT and NUDT15 genotyping to personalize thiopurine therapy in pediatric IBD. Routine implementation of rapid genetic testing, combined with therapeutic drug monitoring and a structured management algorithm, may optimize treatment outcomes and minimize preventable toxicity.
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