Responses of water fleas against a complex mixture of pollutants: Using Daphnia magna to understand cocktail effects

Natural freshwater systems rarely contain a single contaminant, yet regulatory ecotoxicology continues to rely heavily on single-chemical tests conducted in defined laboratory media. This discrepancy between how pollutants are tested and how organisms actually encounter them limits our ability to understand real, possibly synergistic, effects in the wild. In this study, the model organism, sentinel Daphnia magna, was exposed to a six-component cocktail spanning three contaminant classes: the pharmaceuticals, propranolol, diltiazem and metformin; the metals, nickel and lithium; and the herbicide, glyphosate. Exposures were conducted under standardised conditions, with acute and chronic toxicity characterised using dose-response curves and survival plots, respectively. To capture sub-lethal responses of the mixture alone, additional endpoints were measured: filtration activity, growth, a panel of key enzymatic markers, and untargeted metabolomic fingerprints. The toxicity of the mixture exceeded what individual-compound data would predict, pointing to non-additive interactions among components. Concentration-dependent changes were observed across every endpoint, with a clear decline in growth at 7, 14 and 21 days, and with feeding rate together with enzyme activities responding in a dose-dependent manner. These findings highlight the limitations of traditional risk assessment frameworks in predicting the toxicity of complex mixtures. Ongoing work aims to target two complementary goals: further parallel single-chemical assays on each of the six constituents, run under matched conditions to quantify the magnitude of synergism within the mixture; and exposures conducted in natural river water matrices, to test whether laboratory-derived biomarkers remain informative in field-relevant, realistic conditions. This broader integrative approach promotes the development of daphnid-based New Approach Methodologies (NAMs) as practical tools for improved early-warning systems in freshwater monitoring.