Incorporating More Science Into Cancer Dose-Response Extrapolations
Dose-response models can be an important part of the quantitative cancer risk assessments supporting risk management and regulatory decision making. Current dose-response models have limited biological rationale and tend to greatly over-simplify the carcinogenic process. While complete understanding of carcinogenic mechanisms and all-encompassing models are still beyond our grasp, new approaches to dose-response modeling can provide opportunities for these models to incorporate more scientific information. A new family of dose-response models called the Individualized Response Models is introduced and shown to provide such opportunities. The models allow for the use of more biologically relevant dose scales such as those provided by physiologically-based pharmacokinetic models of the absorption, delivery, metabolism, and elimination of a chemical as well as research on cell turnover rates, repair mechanisms, and immune system responses even if these phenomena are age-dependent. Since different individuals may have different physiological behavior and different background exposures, allowances are made for modeling populations with distributions of individual susceptibilities and background doses. The inclusion of time in the Individualized Response Models allows them to incorporate dose levels, susceptibilities, and background exposures which are not constant over time and also reflect age-dependent changes in the number of target cells and the effects of cell proliferation on the number of intermediate cells in a multistage process. Since the improved dose scales and other modeling extensions are related to physical phenomena, their nature, interrelationship, and functional dependence on dose, time, and age can frequently be investigated through independent scientific research prior to the fitting of the chronic dose-response data.
KeywordsIndividual variation susceptibility dose-response modeling biologically effective dose physiologically-based pharmacokinetic models biological models
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