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TSRC, Tob. Sci. Res. Conf., 2016, 70, abstr. 73

Quantitative risk assessment of tobacco products in substantial equivalence evaluations

GENTRY R.(1); GUPTA M.S.(2); MARANO K.M.(2)
(1) Ramboll Environ, Monroe, LA, USA; (2) RAI Services Company, Winston-Salem, NC, USA

Risk assessment is an important tool used by regulatory agencies to inform decision-making by describing potential impact to public health using the available scientific evidence. In terms of tobacco product regulation, quantitative risk assessment (QRA) provides a useful, practical, and efficient approach to address questions that might arise regarding human health risk and potential influence on public health. Specifically, QRA is informative in substantial equivalence (SE) evaluations, one of the regulatory pathways for tobacco products in the US. In SE reporting, when differences in product characteristics necessitate the determination of whether the new product raises different questions of public health, the results from QRA are a valuable metric. An approach for QRA in this context is modelled after the methodology for risk assessment of chemical mixtures in the US Environmental Protection Agency Risk Assessment Guidelines for Superfund (USEPA RAGS). Given the intent in both cases is an assessment of the public health impact resulting from the totality of exposure to a mixture of chemicals, the applicability is appropriate for products in which exposure to multiple chemicals occurs. The QRA process for both environmental or product exposure includes the four steps associated with any risk assessment: hazard identification, toxicity assessment, exposure assessment, and risk characterization. Additionally, in the risk characterization step, additivity of the risk of individual chemicals is assumed in order to characterize the totality of chemical exposure/potential risk; ultimately, a comparison of risk estimates is made. Although some uncertainties in the information incorporated may exist, relying on the most appropriate of the available data increases the confidence and decreases the uncertainty in the risk characterization and the overall QRA using this data-driven methodology.