Prioritizing PBT Substances

  • Lars Carlsen
  • John D. Walker

Abstract

The interplay between partial order ranking and Quantitative Structure Activity Relationships (QSARs) constitute a strong decision support tool. By means of partial order ranking it is possible to prioritize and select chemicals for decision-making among a group of substances based on simultaneous evaluation of data related to different endpoints. In the absence of experimental data, QSARs are used to provide estimates. In the present chapter, the identification of chemicals with Persistence and Bioconcentration (PB) potential is used to illustrate the interplay between partial order ranking and QSARs. The endpoints biodegradation and bioconcentration were obtained using the BioWin and BCFWin modules from http://www.epa.gov/oppt/exposure/docs/episuitedl.htm. Partial order theory was used to rank chemicals for PB potential based on QSAR estimates. The proposed approach is suggested as a decision support tool to facilitate pollution prevention activities by regulated and regulatory communities.

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References

  1. Boethling RS, Howard PH, Meylan WM, Stiteler W, Beauman J, Tirado N (1994) Group contribution method for predicting probability and rate of aerobic biodegradation. Environ Sci Technol 28:459–65CrossRefGoogle Scholar
  2. Brüggemann R, Halfon E, Bücherl C (1995) Theoretical base of the program “Hasse”, GSF-Bericht 20/95, Neuherberg; The software may be obtained by contacting Dr. R. BrüggemannGoogle Scholar
  3. Brüggemann R, Pudenz S, Carlsen L, Sørensen PB, Thomsen M, Mishra RK (2001a) The use of Hasse diagrams as a potential approach for inverse QSAR. SAR QSAR Environ Res 11:473–487Google Scholar
  4. Brüggemann R, Halfon E, Welzl G, Voigt K, Steinberg CEW (2001b) Applying the concept of partially ordered sets on the ranking of near-shore sediments by a battery of tests. J Chem Inf Comput Sci 41:918–925CrossRefGoogle Scholar
  5. Carlsen L, Sørensen PB, Thomsen M (2001) Partial order ranking based QSAR’s: Estimation of solubilities and octanol-water partitioning. Chemosphere 43:295–302CrossRefGoogle Scholar
  6. Carlsen L, Sørensen PB, Thomsen M, Brüggemann R (2002) QSAR’s Based on Partial Order Ranking. SAR QSAR Environ Res 13:153–165CrossRefGoogle Scholar
  7. Carlsen L, Walker JD (2003) QSARs for prioritizing PBT substances to promote pollution prevention. QSAR Comb Sci 22:49–57CrossRefGoogle Scholar
  8. Halfon E, Reggiani MG (1986) On the ranking of chemicals for environmental hazard, Environ Sci Technol 20:1173–1179CrossRefGoogle Scholar
  9. Hasse H (1952) Über die Klassenzahl abelscher Zahlkörper. Akademie Verlag, BerlinGoogle Scholar
  10. Lerche DB, Brüggemann R, Sørensen PB, Carlsen L, Nielsen OJ (2002) Comparison of Hasse Diagram Technique with three multicriteria analysis for ranking chemical substances. J Chem Inf Comput Sci 42:1086–1098CrossRefGoogle Scholar
  11. Meylan WM, Howard PH, Boethling RS, Aronson D, Printup H, Gouchie S (1999) Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient. Environmental Toxicology and Chemistry 18:664–672CrossRefGoogle Scholar
  12. Patil GP, Taillie C (2005) Multiple indicators, partially ordered sets, and linear extensions: Multi-criterion ranking and prioritization. Environmental and Ecological Statistics 11:199–228CrossRefGoogle Scholar
  13. Walker JD, Carlsen L (2002) QSARs for identifying and prioritizing substances with persistence and bioconcentration potential. SAR QSAR Environ Res 13:713–725CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lars Carlsen
    • 1
  • John D. Walker
    • 2
  1. 1.Awareness CenterRoskildeDenmark
  2. 2.TSCA Interagency Testing Committee (ITC), Office of Pollution Prevention and Toxics (7401)U.S. Environmental Protection AgencyWashington, D.C.USA

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