Volume 10 Supplement 2
Translational toxicology and exposomics for food safety risk management
© Wu; licensee BioMed Central Ltd. 2012
Published: 17 October 2012
Materials and methods
Traditionally, risk assessment is based on deterministic endpoints, i.e., use of the no observed (adverse) effect level (NO(A)EL) and the mean or high level of exposure. In the 21st century, exposure science has increasingly embraced deterministic models to predict levels of diverse exposures based on categorical data and on measured levels of pollutants in biological fluids and tissues. Increasingly, more probabilistic and distributional methods are included, to characterize the hazard(s) as well as the exposure(s). Investigations of total personal exposure initially employed external measurements of chemicals that can enter the body, which provide the more probabilistic and distributional methods. These approaches allow for more description of variability in the population as well as uncertainty in the risk estimates. Moreover, additional risk assessment outcomes are being reported, such as the margin of exposure (MOE), which gives a relative indication of the level of health concern with actually quantifying the risk [3–5].
The manner in which health reference guide values (HBGVs) such as the acceptable, tolerance, and reference dose (RfD) are estimated usually generates deterministic values in that they imply a demarcation between what is a “safe” level of exposure (i.e., exposures below the value) versus a “non-safe” level (i.e., exposures above the value). In many instances over the years, these deterministic values have been used as a common “bright line” approach to managing risk. Decision makers and competent authorities use these reference values to set standards and regulations for what are appropriate exposures. If uncertainty and variability be kept in mind, probabilistic modeling (e.g., with distributions around the values) provides risk managers more detailed dose response modeling with greater transparency of the uncertainty surrounding many of these values. To aid the decision, the risk assessment should provide information on the nature and magnitude of uncertainties in both the toxicological and exposure data that make up the inputs to the distributions being modeled.
Diet Exposure for Inorganic Arsenic (iAS) in Rice for various Cluster Diet (g/Kg.bw per day)*
Rice Consumption (g)
Average iAs Intake
P90 iAs Intake
P99 iAs Intake
One can imagine a future in which individuals’ exposomes are contrasted between diseased and healthy populations for molecular epidemiology. In either case, the goal would be to discover causes of ill health and to generate hypotheses regarding identification and elimination or reduction of harmful exposures. These expansions of risk assessment tools and information provided require additional risk management approaches, included in the platform to translational toxicology and exposomics.
The paper was funded by the National Basic Research Program of China (973 Program 2012CB720804), National Key Technology R&D Program (2006BAK02A01 & 2012BAK01B01), and China Ministry of Health (grant number 200902009).
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