Can in vitro assays account for interactions between inorganic co-contaminants observed during in vivo relative bioavailability assessment?
2018
Ollson, Cameron J. | Smith, Euan | Juhasz, Albert L.
In vitro assays act as surrogate measurements of relative bioavailability (RBA) for inorganic contaminants. The values derived from these assays are routinely used to refine human health risk assessments (HHRA). Extensive in vitro research has been performed on three major inorganic contaminants; As, Cd and Pb. However, the majority of these studies have evaluated the contaminants individually, even in cases when they are found as co-contaminants. Recently, in vivo studies (animal model) have determined that when the three aforementioned contaminants are present in the same soil matrix, they have the ability to influence each other's individual bioavailability. Since in vitro assays are used to inform HHRA, this study investigated whether bioaccessibility methods including the Solubility/Bioavailability Research Consortium (SBRC) assay, and physiologically based extraction test (PBET), have the ability to detect interactions between As, Cd and Pb. Using a similar dosing methodology to recently published in vivo studies, spiked aged (12 years) soil was assessed by evaluating contaminant bioaccessibility individually, in addition to tertiary combinations. In two spiked aged soils (grey and brown chromosols), there was no influence on contaminant bioaccessibility when As, Cd and Pb we present as co-contaminants. However, in a red ferrosol, the presence of As and Pb significantly decreased (p < 0.05) the bioaccessibility of Cd when assessed using gastric and intestinal phases of the SBRC assay and the PBET. Conceivable, differences in key physico-chemical properties (TOC, Fe, Al, P) between the study soils influenced contaminant interactions and bioaccessibility outcomes. Although bioaccessibility methods may not account for interactions between elements as demonstrated in in vivo models, in vitro assessment provides a conservative prediction of contaminant RBA under co-contaminant scenarios.
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