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A Probabilistic Approach for Deriving Acceptable Human Intake Limits and Human Health Risks from Toxicological Studies: General Framework

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  • W. Slob
  • M. N. Pieters

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 is this source of uncertainty too large to be ignored, it also has repercussions for the quantification of the other uncertainty distributions.

Suggested Citation

  • W. Slob & M. N. Pieters, 1998. "A Probabilistic Approach for Deriving Acceptable Human Intake Limits and Human Health Risks from Toxicological Studies: General Framework," Risk Analysis, John Wiley & Sons, vol. 18(6), pages 787-798, December.
  • Handle: RePEc:wly:riskan:v:18:y:1998:i:6:p:787-798
    DOI: 10.1111/j.1539-6924.1998.tb01121.x
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    References listed on IDEAS

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    1. Wout Slob, 1994. "Uncertainty Analysis in Multiplicative Models," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 571-576, August.
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    Cited by:

    1. Hilko Van Der Voet & Wout Slob, 2007. "Integration of Probabilistic Exposure Assessment and Probabilistic Hazard Characterization," Risk Analysis, John Wiley & Sons, vol. 27(2), pages 351-371, April.
    2. Susan Dekkers & Jan Telman & Monique A. J. Rennen & Marco J. Appel & Cees De Heer, 2006. "Within‐Animal Variation as an Indication of the Minimal Magnitude of the Critical Effect Size for Continuous Toxicological Parameters Applicable in the Benchmark Dose Approach," Risk Analysis, John Wiley & Sons, vol. 26(4), pages 867-880, August.
    3. Kristi Kuljus & Dietrich Von Rosen & Salomon Sand & Katarina Victorin, 2006. "Comparing Experimental Designs for Benchmark Dose Calculations for Continuous Endpoints," Risk Analysis, John Wiley & Sons, vol. 26(4), pages 1031-1043, August.
    4. Kenneth T. Bogen, 2005. "Risk Analysis for Environmental Health Triage," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1085-1095, October.
    5. Kan Shao & Jeffrey S. Gift, 2014. "Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 101-120, January.
    6. Martí Nadal & Vikas Kumar & Marta Schuhmacher & José L. Domingo, 2008. "Applicability of a Neuroprobabilistic Integral Risk Index for the Environmental Management of Polluted Areas: A Case Study," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 271-286, April.
    7. Salomon J. Sand & Dietrich Von Rosen & Agneta Falk Filipsson, 2003. "Benchmark Calculations in Risk Assessment Using Continuous Dose‐Response Information: The Influence of Variance and the Determination of a Cut‐Off Value," Risk Analysis, John Wiley & Sons, vol. 23(5), pages 1059-1068, October.
    8. Dette, Holger & Pepelyshev, Andrey & Shpilev, Piter & Wong, Weng Kee, 2009. "Optimal designs for estimating critical effective dose under model uncertainty in a dose response study," Technical Reports 2009,07, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    9. Paul S. Price & Heli M. Hollnagel & Jack M. Zabik, 2009. "Characterizing the Noncancer Toxicity of Mixtures Using Concepts from the TTC and Quantitative Models of Uncertainty in Mixture Toxicity," Risk Analysis, John Wiley & Sons, vol. 29(11), pages 1534-1548, November.
    10. Dette, Holger & Pepelyshev, Andrey & Shpilev, Piter & Wong, Weng Kee, 2009. "Optimal designs for estimating critical effective dose under model uncertainty in a dose response study," Technical Reports 2009,09, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    11. Signe M. Jensen & Felix M. Kluxen & Christian Ritz, 2019. "A Review of Recent Advances in Benchmark Dose Methodology," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2295-2315, October.
    12. Roger Cooke, 2010. "Conundrums with Uncertainty Factors," Risk Analysis, John Wiley & Sons, vol. 30(3), pages 330-339, March.
    13. Wouter Fransman & Harrie Buist & Eelco Kuijpers & Tobias Walser & David Meyer & Esther Zondervan‐van den Beuken & Joost Westerhout & Rinke H. Klein Entink & Derk H. Brouwer, 2017. "Comparative Human Health Impact Assessment of Engineered Nanomaterials in the Framework of Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 37(7), pages 1358-1374, July.
    14. Mirjam Moerbeek & Aldert H. Piersma & Wout Slob, 2004. "A Comparison of Three Methods for Calculating Confidence Intervals for the Benchmark Dose," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 31-40, February.

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