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

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. Oikos 75: crossing must not be possible survival prob 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 log conc survival prob EC0 too similar rarely significant Kooijman 1981 Water Res 15:

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) Mixtures in standard approach

Model comparison for Cd-Cu mixture Results of spreadsheet Conclusion: interaction depends on choice of CA vs IA model exposure time Time (days)Interactions with CA as base model Interactions with IA as base model 2 - 3No interaction, CASynergism 4 – 5Synergism 6 Dose level dependent Synergism 7 - 9Dose level dependent Synergism No interaction, CADose level dependent Synergism 16Dose Ratio interactionDose level dependent Synergism 17No interaction, CADose Ratio low doses S, high doses A*Dose level dependent Synergism * Change from antagonism to synergism at about 2 * LC50 by Jan Baas 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:

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

Primary DEB parameters assimilation {J EAm } max surface-specific assim rate  L m feeding {b} surface- specific searching rate digestion y EX yield of reserve on food growth y VE yield of structure on reserve mobilization venergy conductance heating,osmosis {J ET } surface-specific somatic maint. costs turnover,activity [J EM ] volume-specific somatic maint. costs regulation,defence[J EJ ] volume-specific maturity maint. costs allocation  partitioning fraction egg formation  R reproduction efficiency life cycle[E J b ] volume-specific maturity at birth life cycle [E J p ] volume-specific maturity at puberty aging h a aging acceleration maximum length L m =  {J EAm } / [J EM ] Kooijman 1986 J. Theor. Biol. 121:

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

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 2 exposure-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

DEB tele course 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 on DEB applications to bivalves Software package DEBtool for Octave/ Matlab freely downloadable Slides of this presentation are downloadable from Cambridge Univ Press 2000 Audience : thank you for your attention Organizers : thank you for the invitation