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Updated: May 18, 2026
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Targeted Metabolomics on Rare Primary Cells
Published on: February 23, 2024
Benchmarking untargeted metabolomics data quality with allopurinol-induced perturbations
Terje Vasskog1, Pia J Heinsvig1,2, Ekaterina Sharashova3
1Natural Products and Medicinal Chemistry Research Group, Department of Pharmacy, UiT - The Arctic University of Norway, Hansine Hansens veg 18, 9019, Tromsø, Norway.
Summary
This study introduces a novel test to ensure metabolomics datasets are fit-for-purpose. Known drug effects, like allopurinol
Area of Science:
- Metabolomics
- Quality Control
- Drug-Induced Perturbations
Background:
- Current metabolomics quality control (QC) methods do not assess the biological relevance of detected changes.
- A need exists for methods evaluating the fitness-for-purpose of metabolomics datasets for biological interpretation.
Purpose of the Study:
- To assess if known drug-induced metabolic changes can serve as internal benchmarks for metabolomics dataset quality.
- To validate a targeted exposomics approach for evaluating biological recoverability in metabolomics data.
Main Methods:
- Analyzed 1,000 serum samples using targeted and untargeted metabolomics panels (TROMBOLOME study).
- Classified samples as allopurinol-positive (N=19) based on analytical targets.
- Evaluated endogenous metabolite markers of allopurinol therapy using Mann-Whitney U-tests.
Main Results:
- Confirmed upregulation of xanthine, orotate, and orotidine in allopurinol-positive samples (p < 0.0001).
- Demonstrated reproducibility of well-characterized metabolic perturbations within the dataset.
Conclusions:
- Known drug perturbations can serve as practical benchmarks for assessing metabolomics dataset quality.
- This approach complements traditional QC metrics by evaluating biological recoverability for downstream interpretation.
