Human exposure to chemical mixtures: Challenges for the integration of toxicology with epidemiology data in risk assessment

- Evaluation of chemical mixture effects requires an integrated and systematic approach.
- Epidemiology can support the relation between human's low-dose exposures and diseases.
- Human epidemiology studies may play a key role in the new vision of toxicity testing.
- Cumulative risk assessment (CRA) remains a biggest regulatory challenge.
- In vivo, mechanistic, epidemiological and exposure data can be integrated for CRA.

Little is known about the potential adverse effects from longterm exposure to complex mixtures at low doses, close to health-based reference values. Traditional chemical-specific risk assessment based on animal testing may be insufficient and the lack of toxicological studies on chemical mixtures remains a major regulatory challenge. Hence, new methodologies on cumulative risk assessment are being developed but still present major limitations. Evaluation of chemical mixture effects requires an integrated and systematic approach and close collaboration across different scientific fields, particularly toxicology, epidemiology, exposure science, risk assessment and statistics for a proper integration of data from all these disciplines. Well designed and conducted epidemiological studies can take advantage of this new paradigm and can provide insight to support the correlation between humans low-dose exposures and diseases, thus avoiding the uncertainty associated with extrapolation across species. In this regard, human epidemiology studies may play a significant role in the new vision of toxicity testing. However, this type of information has not been fully considered in risk assessment, mainly due to the inherent limitations of epidemiologic studies. An integrated approach of in vivo, in vitro and in silico data, together with systematic reviews or meta-analysis of high quality epidemiological studies will improve the robustness of risk assessment of chemical mixtures and will provide a stronger basis for regulatory decisions. The ultimate goal is that experimental and mechanistic data can lend support and biological plausibility to the human epidemiological observations.