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Conc-response vs biology-based approaches in ecotoxicity

Conc-response vs biology-based approaches in ecotoxicity. Jan Baas, Tjalling Jager & Bas Kooijman (VU-Theor Biol). Modeling effects of mixtures of chemical compounds. contract No. 003956. crossing must not be possible. survival prob. log conc. Assumptions of standard approach.

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Conc-response vs biology-based approaches in ecotoxicity

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  1. Conc-response vs biology-basedapproaches in ecotoxicity Jan Baas, Tjalling Jager & Bas Kooijman (VU-Theor Biol) Modeling effects of mixtures of chemical compounds contract No. 003956

  2. crossing must not be possible survival prob log conc Assumptions of standard approach Lethal effects: • Individuals have identical toxico-kinetics • They die for sure if internal conc exceeds threshold • Threshold varies among individuals (log-logistic distribution) Empirical counter-evidence: • Slope conc-response curve becomes steeper during exposure • LC50 of re-exposed cohort remains the same • Sublethal effects don’t support large differences among individuals Kooijman (1996) An alternative for NOEC exists, but the standard model has to be replaced first. Oikos75: 310--316

  3. too similar rarely significant Kooijman 1981 Water Res15:107-119 survival prob EC0 log conc Problems of standard approach • Incorporation of exposure time is problematic (translation from acute to chronic effects; links to pharmacology) • EC-small levels difficult to determine and model-sensitive (links to envir risk assessment) • Incompatible with NOEC/NEC NEC = EC0(∞) • Difficult to extrapolate from individual to population from one species to another, one chemical to another • Not applicable in case of varying exposure (peak exposure) • Problems in quantifying effects of mixtures

  4. Mixtures in standard approach Log-logistic survival model: c: external concentration; C: LC50, :slope Independent action: Concentration addition: Independent action differs from concentration addition Molecules of one compound have dependent action No mechanism behind concentration addition; implicit definition if (these problems don’t apply to biology based methods)

  5. Model comparison for Cd-Cu mixtureResults of spreadsheet by Jan Baas * Change from antagonism to synergism at about 2 * LC50 Conclusion: interaction depends on choice of CA vs IA model exposure time Jonker M.J., Svendsen C., Bedaux, J.J.M., Bongers, M. & Kammenga, J.E. (2005) Significance testing of synergistic/antagonistic, dose level-dependent, or dose ratio-dependent effects in mixture dose-response analysis. Environmental Toxicology and Chemistry, 24: 2701 - 2713.

  6. assimilation  maintenance costs defecation feeding food faeces growth costs assimilation reproduction costs reserve  hazard to embryo somatic maintenance  7 maturity maintenance  1-  maint tumour induction 6 maturation reproduction u endocr. disruption growth 7  lethal effects: hazard rate Mode of action affects translation to pop level 8 maturity offspring structure tumour 6 Modes of action of toxicants

  7. Primary DEB parameters assimilation {JEAm} max surface-specific assim rate  Lm feeding {b} surface- specific searching rate digestion yEX yield of reserve on food growth yVEyield of structure on reserve mobilization v energy conductance heating,osmosis {JET} surface-specific somatic maint. costs turnover,activity [JEM] volume-specific somatic maint. costs regulation,defence [JEJ] volume-specific maturity maint. costs allocation  partitioning fraction egg formation R reproduction efficiency life cycle [EJb] volume-specific maturity at birth life cycle [EJp] volume-specific maturity at puberty aging ha aging acceleration Kooijman 1986 J. Theor. Biol. 121: 269-282 maximum length Lm =  {JEAm} / [JEM]

  8. Change in target parameter Simplest basis: Change  internal conc that exceeds internal NEC • Rationale • effective molecules operate independently • approximation for small effects

  9. Conclusions Process-based model free of choice CA vs IA in effects on survival has one type of interaction for all exposure times needs 3 toxicity parameters per compound + n(n-1)/2 interaction parameters for mix of n compounds = 7 tox parameters per binary mixture Standard model needs 2 tox pars per compound per exposure time + 1 or 2exposure-time dependent interaction pars = 5-6 tox parameters per binary mixture per exposure time interaction complex for mixtures of more than 2 compounds is inconsistent for mixtures

  10. DEB tele course 2007 Cambridge Univ Press 2000 http://www.bio.vu.nl/thb/deb/ Free of financial costs; some 200 h effort investment Feb-April 2007; target audience: PhD students We encourage participation in groups that organize local meetings weekly French group of participants of the DEB tele course 2005: special issue of J. Sea Res. 2006 on DEB applications to bivalves Software package DEBtool for Octave/ Matlab freely downloadable Slides of this presentation are downloadable from http://www.bio.vu.nl/thb/users/bas/lectures/ Audience: thank you for your attention Organizers: thank you for the invitation

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