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An Air-liquid Interface Bronchial Epithelial Model for Realistic, Repeated Inhalation Exposure to Airborne Particles for Toxicity Testing
Published on: May 13, 2020
Effect-based aggregate exposure pathways for tracking mixture potency across the source-to-target site continuum
1Toxicology Centre, University of Saskatchewan, Saskatoon, SK, S7N 5B3, Canada; School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK S7N 5CN, Canada.
Abstract:
Environmental exposures often arise from multiple sources, diverse chemical stressors, and interconnected exposure pathways, making it difficult to mechanistically link external exposures to biological effects. The aggregate exposure pathway (AEP) framework was proposed a decade ago to address this challenge by organizing exposure data from source to target site through measurable states of the stressor (e.g., amount, size, properties, composition). However, AEPs have struggled to gain traction probably since exposure data remain poorly interoperable. An effect-based AEP framework is proposed that adopt bioanalytical equivalence quotients as bioassay endpoint-specific potency metrics for tracking mixture activity across exposure states. Using a hypothetical contaminated river with fish as the biological receptor, two interconnected AEPs comprising eleven key exposure states were constructed. Weight-of-evidence assessment showed that potential linkages (essentiality) between key exposure states were strongest at source and in environmental media but weaker for internal exposure states, while key translational relationships were generally strong in theoretical plausibility but showed stronger empirical support and quantitative understanding in environmental media than in complex environmental and biological matrices. These findings revealed data gaps probably caused by current limitations in extracting complex chemical mixtures and quantifying bioanalytical equivalence quotients in complex matrices. This effect-based AEP framework provides a quantitative and scalable basis for mechanistic mixture risk assessment in the exposome era.
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