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Manisha Jain

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Review Open access Aug 2026

FROM EXPOSURE INDICES TO CELLULAR DAMAGE: A REVIEW ON INTEGRATED FRAMEWORK FOR GENOTOXICITY-INFORMED HEAVY METAL RISK ASSESSMENT

The heavy metal contamination of water, food, and soil is a widely recognized global problem.  Yet the strategies commonly used to evaluate the impact of heavy metal contamination on human health typically do not account for the metabolic pathways of metals in living cells. Exposure metrics like Estimated Daily Intake, Hazard Quotient, Hazard Index, and Cancer Risk are simple and inexpensive, and easy to compare to regulatory limits, but they do not provide much information about underlying biological mechanisms: they assume metals are absorbed at a constant rate, do not consider the effects of metal mixtures, and disregard the non-linear cellular damage increasingly shown in in vitro genotoxicity studies. In vitro assays, however, directly demonstrate damage to DNA and chromosomes, and consistently implicate oxidative stress as a common mechanism through which cadmium, arsenic and chromium act; and these assays are seldom connected to actual dietary or drinking water exposures. This review combines the two methods, using contamination data from high-risk environments and data from occupational and environmental biomonitoring studies, which already demonstrate a measurable relationship between exposure and cellular damage. This type of integration is now feasible using newer tools such as physiologically based pharmacokinetic modeling, adverse outcome pathways, and omics-based biomarkers. Building on these, the review proposes a concentration-response approach for assessing heavy metal risk in food. This approach moves beyond a purely statistical exercise and offers a biologically grounded model with real-world implications for the setting and harmonization of international food safety standards. It is based on bioaccessibility-corrected exposure estimates and in vitro-to-in vivo extrapolation.

Manisha Jain, S. Sachdeva, G. Bharat · 0 citations

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