@article{4eea3de1918b45729cc6924c307905ea,
title = "Linear low-dose extrapolation for noncancer heath effects is the exception, not the rule",
abstract = "The nature of the exposure-response relationship has a profound influence on risk analyses. Several arguments have been proffered as to why all exposure-response relationships for both cancer and noncarcinogenic endpoints should be assumed to be linear at low doses. We focused on three arguments that have been put forth for noncarcinogens. First, the general {"}additivity-to- background{"} argument proposes that if an agent enhances an already existing disease-causing process, then even small exposures increase disease incidence in a linear manner. This only holds if it is related to a specific mode of action that has nonuniversal propertiesproperties that would not be expected for most noncancer effects. Second, the {"}heterogeneity in the population{"} argument states that variations in sensitivity among members of the target population tend to {"}flatten out and linearize{"} the exposure-response curve, but this actually only tends to broaden, not linearize, the dose-response relationship. Third, it has been argued that a review of epidemiological evidence shows linear or no-threshold effects at low exposures in humans, despite nonlinear exposure-response in the experimental dose range in animal testing for similar endpoints. It is more likely that this is attributable to exposure measurement error rather than a true nonthreshold association. Assuming that every chemical is toxic at high exposures and linear at low exposures does not comport to modern-day scientific knowledge of biology. There is no compelling evidence-based justification for a general low-exposure linearity; rather, case-specific mechanistic arguments are needed.",
keywords = "Additivity to background, dose-response, exposure measurement error, linear, nonlinear, population heterogeneity, threshold",
author = "Rhomberg, {Lorenz R.} and Goodman, {Julie E.} and Haber, {Lynne T.} and Michael Dourson and Andersen, {Melvin E.} and Klaunig, {James E.} and Bette Meek and Price, {Paul S.} and McClellan, {Roger O.} and Cohen, {Samuel M.}",
note = "Funding Information: This paper was prepared with financial support provided by the American Chemistry Council to Gradco LLC d/b/a Gradient. Gradient, the employer of Lorenz R. Rhomberg and Julie E. Goodman, is a private consulting firm providing advice to public and private organizations on environmental, toxicological, and human health risk analysis issues. Toxicology Excellence for Risk Assessment, the employer of Lynne T. Haber and Michael Dourson, is a not-for-profit organization providing advice to public and private organizations on toxicological and risk assessment issues. The Hamner Institutes for Health Sciences, the employer of Melvin E. Andersen, is a not-for-profit organization conducting research for private and public sponsors on human health risk issues. Indiana University, the employer of James E. Klaunig, The University of Ottawa, and The University of Nebraska Medical Center are all traditional academic institutions. Roger O. McClellan is an independent advisor to private and public organizations on toxicological and human health risk analysis issues. The Dow Chemical Company, the employer of Paul S. Price, is a private firm developing, producing, and marketing a broad range of chemical products. The authors have the sole responsibility for the writing and contents of the paper. The views and opinions expressed are not necessarily those of the American Chemistry Council. One of the authors, Roger O. McClellan, serves as the Editor of Critical Reviews in Toxicology and recuses himself from the Journal{\textquoteright}s review of the manuscript; the review was coordinated by David Warheit, a member of the Journal{\textquoteright}s Editorial Advisory Board.",
year = "2011",
month = jan,
doi = "10.3109/10408444.2010.536524",
language = "English (US)",
volume = "41",
pages = "1--19",
journal = "Critical reviews in toxicology",
issn = "1040-8444",
publisher = "Informa Healthcare",
number = "1",
}