New Strategies for Prediction of Human Cancer Risk. J. E. Korte and I. Hertz-Picciotto, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill NC 27599; and L. Ball, Department of Environmental Science and Engineering, University of North Carolina at Chapel Hill, Chapel Hill NC 27599
Results from animal dose-response studies have been used to predict low-dose carcinogenic effects in humans, and to set appropriate regulations; however, the use of animal data as a proxy for human data is often problematic. In this study, methods were developed to directly compare animal and human low-dose carcinogenic risk predictions. Using human data, we estimated the liver angiosarcoma carcinogenic potency (ß) of vinyl chloride (VC). We calculated several measures typically used in animal-based predictions: the unit risk, and Tumorigenic Doses (TD1, TD0.1, and TD0.01) estimated to result in specified excess mortality. We constructed multiple decrement lifetables to calculate the background lifetime cancer mortality risks, and to identify the target lifetime risks for TD1, TD0.1, and TD0.01. In order to estimate the doses resulting in these lifetime risks, we constructed lifetables in which a new cancer death rate was calculated for each age group by adding (ß*dose) to the observed cancer death rate, reflecting the linear additive potency model. The unit risk was estimated to be 118 per million for males, and 169 per million for females. The TD1, TD0.1, and TD0.01 were estimated to be 85.3, 8.5, and 0.8 µmg/m3, respectively, for males; and 59.6, 5.9, and 0.6 µ+mg/m3, respectively, for females. This technique is applicable to any human dose-response carcinogen data, and allows direct comparisons between animal-based and human-based low-dose predictions. By improving our understanding of the relationship between animal-based and human-based predictions, we will be able to increase the quality of animal-based risk assessment methodology, and expand our ability to predict low-dose cancer risks in humans based on animal data.
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