Cancer risk assessment: Optimizing human health through linear dose–response models

• Published in: Food and chemical toxicology Vol. 81; pp. 137 - 140
• Main Authors: Calabrese, Edward J., Shamoun, Dima Yazji, Hanekamp, Jaap C.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-07-2015
• Subjects: Animals; Biological Assay; Carcinogens - toxicity; Databases, Factual; Disease Models, Animal; Dose–response; Hormesis; Humans; Linear Models; Linearity; LNT; Models, Biological; Neoplasms - prevention & control; Precautionary principle; Public Health - statistics & numerical data; Risk Assessment; Toxicity Tests; Dose–response; Hormesis; LNT; Precautionary principle; Risk assessment; Linearity
• ISSN: 0278-6915; 1873-6351
• DOI: 10.1016/j.fct.2015.04.023

•This paper provides the first attempt to reconcile and integrate the concepts of LNT and hormesis for cancer risk assessment.•The paper identifies a population based exposure optima which results in the derivation of the regulatory “sweet spot”.•If the proposed integrated model is adopted, it would profoundly change cancer risk assessment worldwide.
This paper proposes that generic cancer risk assessments be based on the integration of the Linear Non-Threshold (LNT) and hormetic dose–responses since optimal hormetic beneficial responses are estimated to occur at the dose associated with a 10−4 risk level based on the use of a LNT model as applied to animal cancer studies. The adoption of the 10−4 risk estimate provides a theoretical and practical integration of two competing risk assessment models whose predictions cannot be validated in human population studies or with standard chronic animal bioassay data. This model-integration reveals both substantial protection of the population from cancer effects (i.e. functional utility of the LNT model) while offering the possibility of significant reductions in cancer incidence should the hormetic dose–response model predictions be correct. The dose yielding the 10−4 cancer risk therefore yields the optimized toxicologically based “regulatory sweet spot”.