Addressing Pitfalls of Metabolomics for Toxicology: A Call for Standardization, Reproducibility and Data Sharing
Min Nian, Xing Chen, Mingliang Fang
Abstract
Metabolomics has emerged as a pivotal tool in toxicology, providing unique insights into biochemical and molecular disruptions upon toxicant exposure. However, its application faces challenges such as metabolite misannotation, insufficient quality assurance and quality control (QA/QC), and limitations in dose-response and time-response studies. Pathway enrichment analysis is often hindered by incomplete databases and irrelevant background metabolites, leading to false positives or missed key pathways, while the lack of robust validation mechanisms can blur distinctions between general stress responses and toxicant-specific mechanisms. Addressing these pitfalls requires standardized protocols for sample preparation, analytical workflows, and data processing to ensure reproducibility. Rigorous QA/QC practices are essential to minimize batch effects, while cross-validation with transcriptomics and proteomics strengthens mechanistic insights. Comprehensive data sharing through public repositories enhances transparency and supports secondary analysis for novel discoveries. By adopting these strategies, metabolomics can achieve greater reliability and advance toxicological research by identifying early biomarkers, elucidating toxicant mechanisms, and improving environmental health assessments.