title: Some implications for quantitative risk assessment if hormesis exists.
abstract: The existence of hormesis should impact quantitative risk assessment in at least seven fundamental ways. (1) The dose-response models for bioassay and epidemiological data should have greater flexibility to fit the observed shape of the dose-response data and no longer be forced to always be linearly increasing at low doses. (2) Experimental designs should be altered to provide greater opportunity to identify the hormetic component of a dose-response relationship. (3) Rather than a lifetime average daily dose or its analog for shorter time periods, dose scales or metrics should be used that reflect the age or time dependence of the dose level. (4) Low-dose risk characterization should include the likelihood of beneficial effects and the likelihood that a dose level has reasonable certainty of no appreciable adverse health effects. (5) Exposure assessments should make greater efforts to characterize the distribution of actual doses from exposure rather than just upper bounds. (6) Uncertainty characterizations should be expanded to include both upper and lower bounds, and there should be an increased explicit use of expert judgement and weight-of-evidence based distributional analyses reflecting more of the available relevant dose-response information and alternative risk characterizations. (7) Risk should be characterized in terms of the net effect of a dose on health rather than a dose's effect on a single factor affecting health - for example, risk would be better expressed in terms of mortality from all causes combined rather than a specific type of fatal disease.
authors.affiliation: Sielken, Inc., Bryan, Texas, USA.
authors.forename: R L
authors.initials: RL
authors.lastname: Sielken
authors.forename: D E
authors.initials: DE
authors.lastname: Stevenson
mesh_terms: D000818:Animals; D004305:Dose-Response Relationship, Drug; D006801:Humans; D012107:Research Design; D018570:Risk Assessment; D014116:Toxicology / Q000379:methods* / Q000706:statistics & numerical data