Intra- and Interlaboratory Evaluation of an Assay of Soil Arsenic Relative Bioavailability in Mice.
Karen D BradhamCarina HerdePaul HerdeAlbert L JuhaszKaren Herbin-DavisBrittany ElekAmy FarthingGary L DiamondDavid J ThomasPublished in: Journal of agricultural and food chemistry (2020)
Hand-to-mouth activity in children can be an important route for ingestion of soil and dust contaminated with inorganic arsenic. Estimating the relative bioavailability of arsenic present in these media is a critical element in assessing the risks associated with aggregate exposure to this toxic metalloid during their early life. Here, we evaluated the performance of a mouse assay for arsenic bioavailability in two laboratories using a suite of 10 soils. This approach allowed us to examine both intralaboratory and interlaboratory variations in assay performance. Use of a single vendor for preparation of all amended test diets and of a single laboratory for arsenic analysis of samples generated in the participating laboratories minimized contributions of these potential sources of variability in assay performance. Intralaboratory assay data showed that food and water intake and cumulative urine and feces production remained stable over several years. The stability of these measurements accounted for the reproducibility of estimates of arsenic bioavailability obtained from repeated intralaboratory assays using sodium arsenate or soils as the test material. Interlaboratory comparisons found that estimates of variables used to evaluate assay performance (recovery and urinary excretion factor) were similar in the two laboratories. For all soils, estimates of arsenic relative bioavailability obtained in the two laboratories were highly correlated (r2 = 0.94 and slope = 0.9) in a linear regression model. Overall, these findings show that this mouse assay for arsenic bioavailability provides reproducible estimates using a variety of test soils. This robust model may be adaptable for use in other laboratory settings.
Keyphrases
- heavy metals
- drinking water
- high throughput
- health risk assessment
- human health
- risk assessment
- health risk
- sewage sludge
- early life
- climate change
- adipose tissue
- skeletal muscle
- metabolic syndrome
- body mass index
- insulin resistance
- machine learning
- artificial intelligence
- plant growth
- simultaneous determination
- wild type