Advancing component-based mixture risk assessment using new approach methodologies: An environmental case study

The next-generation risk assessment vision promotes mechanism-based hazard assessment using new approach methodologies (NAMs) and the evaluation of chemical mixture exposure. Here, we present a refined component-based mixture risk assessment (CBMRA) approach that integrates a quantitative adverse outcome pathway (qAOP) framework with high-throughput screening (HTS) data. HTS data are used to identify qAOP-active chemicals in mixtures and to scale their toxic contributions based on relative potency within the qAOP applicability domain. The approach was demonstrated with an environmentally relevant case study using a previously published probabilistic Bayesian Network qAOP model from AOP-Wiki AOP#245 ("Reduction in photophosphorylation leading to growth inhibition in aquatic plants"), applied to pesticide mixtures reported in European rivers from the European Environment Agency's Pesticide Indicator Dataset (WISE Statistics - Pesticides, 1990-2021). ToxCast and Tox21 data, in silico gap-filling methods, NAM tools and expert knowledge were used to estimate pesticide relative potency and support its application within the framework. Results indicate that 43% of mixtures contained qAOP-active compounds, but cumulative concentrations remained below the threshold required to impair growth in the model species Lemna minor. Overall, the study presents a methodological framework integrating NAM concepts, existing data and tools for mechanistic mixture risk assessment. Performance depends on HTS data quality, uncertainty in the relative potency application, and qAOP relevance. Continued NAM development is expected to strengthen the scientific basis and practical relevance of NAM-based CBMRA for mechanistically informed mixture risk screening. This framework can help prioritize mixtures for further testing or refine safety factors in risk assessment.