title: A probabilistic approach for deriving acceptable human intake limits and human health risks from toxicological studies: general framework.
abstract: The use of uncertainty factors in the standard method for deriving acceptable intake or exposure limits for humans, such as the Reference Dose (RfD), may be viewed as a conservative method of taking various uncertainties into account. As an obvious alternative, the use of uncertainty distributions instead of uncertainty factors is gaining attention. This paper presents a comprehensive discussion of a general framework that quantifies both the uncertainties in the no-adverse-effect level in the animal (using a benchmark-like approach) and the uncertainties in the various extrapolation steps involved (using uncertainty distributions). This approach results in an uncertainty distribution for the no-adverse-effect level in the sensitive human subpopulation, reflecting the overall scientific uncertainty associated with that level. A lower percentile of this distribution may be regarded as an acceptable exposure limit (e.g., RfD) that takes account of the various uncertainties in a nonconservative fashion. The same methodology may also be used as a tool to derive a distribution for possible human health effects at a given exposure level. We argue that in a probabilistic approach the uncertainty in the estimated no-adverse-effect-level in the animal should be explicitly taken into account. Not only in this source of uncertainty too large to be ignored, it also has repercussions for the quantification of the other uncertainty distributions.
authors.affiliation: National Institute of Public Health and the Environment (RIVM), Laboratory for Health Effects Research, Bilthoven, The Netherlands.
authors.forename: W
authors.initials: W
authors.lastname: Slob
authors.forename: M N
authors.initials: MN
authors.lastname: Pieters
mesh_terms: D000818:Animals; D019985:Benchmarking; D006801:Humans; D015233:Models, Statistical; D019055:No-Observed-Adverse-Effect Level; D018570:Risk Assessment / Q000706:statistics & numerical data*; D014116:Toxicology / Q000706:statistics & numerical data*