Toxicological impacts of sulfamethoxazole on freshwater organisms – toxicity and oxidative stress

The continuous expansion of the pharmaceutical sector has intensified aquatic contamination, as persistent active compounds enter water bodies through urban, industrial, and agricultural runoff. Among these contaminants, sulfamethoxazole (SMX)—a widely used sulphonamide antibiotic—raises ecological concerns due to its persistence and potential for bioaccumulation. To evaluate its ecotoxicological risks, this study conducted an integrated assessment combining biodegradability assays, multi-trophic acute toxicity screening, and tissue-specific sublethal biomarker analyses across the trophic chain: microalgae ( Selenastrum capricornutum ), crustaceans ( Daphnia magna and Heterocypris incongruens ), and fish ( Cyprinus carpio ). Although SMX exhibited moderate biodegradability, acute exposure assays revealed strong species-specific sensitivities. Microalgae proved particularly susceptible, exhibiting an EC 50 of 24.77 ± 6.43 mg/L, whereas no acute mortality was observed in crustaceans or fish at concentrations up to 100 mg/L. However, a 14-day sublethal exposure of Cyprinus carpio to 10 µg/L and 100 µg/L demonstrated that the absence of acute lethality does not equal biological safety. While hepatic and gonadal indices remained unaffected, SMX triggered marked metabolic disruption. The biochemical assays across the gills, intestine, and liver showed a significant, dose-dependent modulation of key antioxidant enzymes—superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase, and glutathione S-transferase—alongside altered transaminase activity. By linking degradation potential with multi-trophic responses and organ-specific enzymatic profiles, this work demonstrates that SMX exposure can induce biochemical stress without visible physiological damage. These findings emphasise that assessing pharmaceutical contamination in receiving surface waters requires sensitive tissue-level biomarkers alongside traditional lethality metrics to predict cumulative ecological risks accurately.