Abstract
This paper proposes a new cancer risk assessment strategy and methodology that optimizes population-based responses by yielding the lowest disease/tumor incidence across the entire dose continuum. The authors argue that the optimization can be achieved by integrating two seemingly conflicting models; i.e., the linear no-threshold (LNT) and hormetic dose-response models. The integration would yield the optimized response at a risk of 10 with the LNT model. The integrative functionality of the LNT and hormetic dose response models provides an improved estimation of tumor incidence through model uncertainty analysis and major reductions in cancer incidence via hormetic model estimates. This novel approach to cancer risk assessment offers significant improvements over current risk assessment approaches by revealing a regulatory sweet spot that maximizes public health benefits while incorporating practical approaches for model validation.
Published Version
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