The application of predictive models in the environmental risk assessment of ECONOR

Environmental risk assessment of products requires information on the physico-chemical properties, persistence and ecotoxicity of the product, its constituents and possible metabolic and degradation products. Experimental investigations are usually required to generate this information and consequently risk assessment can be costly and time consuming. One possible approach to minimising the amount of experimental testing is to supplement experimental data with data predicted using models such as quantitative structure-activity relationships (QSARs). Using these models, information can be generated based primarily on the knowledge of the chemical structure of the substance(s) under investigation. In this study predictive models were used to assess the environmental risk of the veterinary medicine, ECONOR which contains the active ingredient valnemulin. Available experimental data on the properties, degradability and ecotoxicity of valnemulin was supplemented with predicted data. Where possible, experimental data was used to validate the predicted approaches and this indicated that the predictions were accurate. Information on usage, properties and degradability was input to fate models to predict environmental concentrations (PECs) of valnemulin in soil, pore water and groundwater. Comparison of PECs with experimental and predicted ecotoxicity data for valnemulin indicated that that even under 'worst case' scenarios the environmental risk posed by valnemulin was low.