title: Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures.
abstract: In freshwater systems located in agricultural areas, organisms are exposed to a multitude of toxicologically and structurally different pesticides. For regulatory purposes it is of major importance whether the combined hazard of these substances can be predictively assessed from the single substance toxicity. For artificially designed multi-component mixtures, it has been shown that the mixture toxicity can be predicted by concentration addition (CA) in case of similarly acting substances and by independent action (IA), if mixtures are composed of dissimilarly acting substances. This study aimed to analyse whether these concepts may also be used to predictively assess the toxicity of environmentally realistic mixtures. For this purpose a mixture of 25 pesticides, which reflects a realistic exposure scenario in field run-off water, was studied for its effects on the reproduction of the freshwater alga Scenedesmus vacuolatus. The toxicity of the tested mixtures showed a good predictability by CA. This is consistent with the finding that the toxicity was dominated by a group of similarly acting photosystem II inhibitors, although the mixture included substances with diverse and partly unknown mechanisms of action. IA slightly underestimated the actual mixture toxicity. However, the EC(50) values that can be derived from each prediction, according to CA respectively IA, only differed by a factor of 1.3. The finding of such a small difference is partly explainable by the fact that only few components dominate the mixture scenario in terms of so-called toxic units (TUs). This connection is established by developing an equation that allows to calculate the maximum possible ratio between corresponding predictions of effect concentrations by IA and CA for any given ratio of the TUs of mixture components, irrespective of their individual concentration-response functions and independent from their mechanisms of action. To evaluate whether small quantitative differences between EC(50) values predicted by CA and IA are an exception or rather the rule for agricultural exposure scenarios, this calculation was applied on an additional set of 18 pesticide exposure scenarios that were taken from the literature. For these scenarios, EC(50) values predicted by IA can never exceed those predicted by CA by more than a factor of 2.5. The findings of this study support the view that CA provides a precautious but not overprotective approach to the predictive hazard assessment of pesticide mixtures under realistic exposure scenarios, irrespective of the similarity or dissimilarity of their mechanisms of action.
authors.affiliation: Department of Biology and Chemistry, University of Bremen, Germany. marion.junghans@cawag.ch
authors.forename: Marion
authors.initials: M
authors.lastname: Junghans
authors.forename: Thomas
authors.initials: T
authors.lastname: Backhaus
authors.forename: Michael
authors.initials: M
authors.lastname: Faust
authors.forename: Martin
authors.initials: M
authors.lastname: Scholze
authors.forename: L H
authors.initials: LH
authors.lastname: Grimme
mesh_terms: D000818:Animals; D001681:Biological Assay; D004305:Dose-Response Relationship, Drug; D004338:Drug Combinations; D008954:Models, Biological; D010575:Pesticides / Q000737:chemistry / Q000145:classification / Q000633:toxicity*; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D012098:Reproduction / Q000187:drug effects; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data; D044444:Scenedesmus / Q000187:drug effects* / Q000502:physiology
chemical_list: D004338:Drug Combinations; D010575:Pesticides