M
Michael L. Dourson
Researcher at University of Cincinnati
Publications - 125
Citations - 5373
Michael L. Dourson is an academic researcher from University of Cincinnati. The author has contributed to research in topics: Risk assessment & Reference dose. The author has an hindex of 35, co-authored 120 publications receiving 4879 citations. Previous affiliations of Michael L. Dourson include United States Environmental Protection Agency & University of Cincinnati Academic Health Center.
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Journal ArticleDOI
Reference dose (RfD): Description and use in health risk assessments☆
D.G. Barnes,Michael L. Dourson +1 more
TL;DR: The concept of the "reference dose" is introduced in order to avoid use of prejudicial terms, to promote greater consistency in the assessment of noncarcinogenic chemicals, and to maintain the functional between risk assessment and risk management.
Journal ArticleDOI
Regulatory history and experimental support of uncertainty (safety) factors.
Michael L. Dourson,J.F. Stara +1 more
TL;DR: A synthesis of available literature on uncertainty (safety) factors which are used to estimate acceptable daily intakes (ADIs) for toxicants is presented, revealing reasonable qualitative biological premises, as well as specific biological data that support both the use and choice of these factors.
Journal ArticleDOI
Copper and Human Health: Biochemistry, Genetics, and Strategies for Modeling Dose-response Relationships
Bonnie Ransom Stern,Marc Solioz,Daniel Krewski,Peter Aggett,T.C. Aw,Scott Baker,Kenny S. Crump,Michael L. Dourson,Lynne T. Haber,Rick Hertzberg,Carl L. Keen,Bette Meek,Larisa Rudenko,Rita Schoeny,Wout Slob,Thomas B Starr +15 more
TL;DR: The dose-response modeling strategy envisioned here is designed to determine whether the existing toxicity data for copper excess or deficiency may be effectively utilized in defining the limits of the homeostatic range in humans and other species.
Journal ArticleDOI
Evolution of Science-Based Uncertainty Factors in Noncancer Risk Assessment
TL;DR: The results of this review support the use of data-derived uncertainty factors when appropriate scientific data are available and incorporation of all available scientific data into the risk assessment process fosters increased research and ultimately reduces uncertainty.