Metabolomics in chemical risk analysis

2021 
Abstract Exposure to chemical hazards is a growing concern in today’s society, and it is of utmost interest to know the levels of exposure to chemicals and the risks associated with such exposure in order to implement effective health prevention strategies. Chemical risk analysis represents a complex and laborious task due to the large number of known substances, but also unknown compounds and emerging risks that must be addressed. In this challenging scenario, the study of metabolic perturbations induced by exposure to a given chemical hazard has recently emerged as an interesting alternative approach to apply in chemical risk analysis. Specifically, the biomarkers of effect identified by metabolomics are expected to reveal the adverse effects of chemicals and further link exposure to disease development. In this context, analytical chemistry has become an essential part of the strategy to highlight such biomarkers. The corollary is that the relevance of the discovered biomarkers will largely depend on both the quality of the analytical approaches implemented and the part of the metabolome covered by the analytical technique used. This review focuses on describing significant applications of metabolomics in the field of chemical risk analysis. The different risk assessment steps, including hazard identification, dose-response assessment and exposure assessment, and risk management are addressed through various examples to illustrate that such an approach is fit-for-purpose and meets the expectations and requirements of chemical risk analysis. It can be considered as an innovative tool for predicting the probable occurrence and nature of risks, while addressing the current challenges of chemical risk analysis (e.g. replacement, reduction and refinement (3R) of animal testing, effects of exposure to chemical mixtures at low doses, etc.), and with the aim of responding to chemical exposures concerns in a holistic manner and anticipating human health problems.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    197
    References
    1
    Citations
    NaN
    KQI
    []