Bioaccumulation Assessment Using Predictive Approaches

Authors

  • John W Nichols,

    Corresponding author
    1. US Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Boulevard, Duluth, Minnesota 55804
    • US Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Boulevard, Duluth, Minnesota 55804
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  • Mark Bonnell,

    1. Environment Canada, Existing Substances Division, Place Vincent Massey, 20th Floor, 351 Saint Joseph Boulevard, Gatineau, Quebec K1A 0H3, Canada
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  • Sabcho D Dimitrov,

    1. University “Professor Assen Zlatarov”, Laboratory of Mathematical Chemistry, 1 Yakimov Street, 8010 Bourgas, Bulgaria
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  • Beate I Escher,

    1. Department of Environmental Toxicology (Utox), Swiss Federal Institute of Aquatic Science and Technology (Eawag), Überlandstrasse 133, PO Box 611, 8600 Dübendorf, Switzerland
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  • Xing Han,

    1. DuPont Haskell Global Centers for Health and Environmental Sciences, 1090 Elkton Road, Newark, Delaware 19714, USA
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  • Nynke I Kramer

    1. Utrecht University, Institute for Risk Assessment Sciences, PO Box 80176, Utrecht, The Netherlands
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  • Published on the Web 6/24/2009.

Abstract

Mandated efforts to assess chemicals for their potential to bioaccumulate within the environment are increasingly moving into the realm of data inadequacy. Consequently, there is an increasing reliance on predictive tools to complete regulatory requirements in a timely and cost-effective manner. The kinetic processes of absorption, distribution, metabolism, and elimination (ADME) determine the extent to which chemicals accumulate in fish and other biota. Current mathematical models of bioaccumulation implicitly or explicitly consider these ADME processes, but there is a lack of data needed to specify critical model input parameters. This is particularly true for compounds that are metabolized, exhibit restricted diffusion across biological membranes, or do not partition simply to tissue lipid. Here we discuss the potential of in vitro test systems to provide needed data for bioaccumulation modeling efforts. Recent studies demonstrate the utility of these systems and provide a “proof of concept” for the prediction models. Computational methods that predict ADME processes from an evaluation of chemical structure are also described. Most regulatory agencies perform bioaccumulation assessments using a weight-of-evidence approach. A strategy is presented for incorporating predictive methods into this approach. To implement this strategy it is important to understand the “domain of applicability” of both in vitro and structure-based approaches, and the context in which they are applied.

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