Addressing Pitfalls of Metabolomics for Toxicology: A Call for Standardization, Reproducibility and Data Sharing

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.