Evaluating the mouse model for estimation of arsenic bioavailability: Comparison of estimates of absolute bioavailability of inorganic arsenic in mouse, humans, and other species

ABSTRACT

Accurate assessment of adverse health effects attributable to ingestion of inorganic arsenic (As) present in contaminated soils requires determination of the internal dose of metal provided by ingested soil. This calculation requires estimation of the oral bioavailability of soil-borne (As). Animal models to assess the bioavailability of soil (As) are frequently used as surrogates for determination of this variable in humans. A mouse assay has been widely applied to estimate the bioavailability of As in soils at sites impacted by mining, smelting, and pesticides. In the mouse assay, the relative bioavailability (RBA) of soil (As) is determined as the ratio of the fraction of the ingested arsenic dose excreted in urine after consumption of diets containing a test soil or the soluble reference compound, sodium arsenate. The aim of the current study was to compare (As) bioavailability measured in the mouse assay with reported estimates in humans. Here, a pharmacokinetic model based on excretion of arsenic in urine and feces was used to estimate the absolute bioavailability (ABA) of As in mice that received an oral dose of sodium arsenate. Based upon this analysis, in mice that consumed diet amended with sodium arsenate, the ABA was 85%. This estimate of arsenic ABA for the mouse is comparable to estimates in humans who consumed (As) in drinking water and diet, and to estimates of ABA in monkeys and swine exposed to sodium arsenate. The concordance of estimates for ABA in mice and humans provides further support for use of the mouse model in human health risk assessment. Sodium arsenate ABA also provides a basis for estimating soil arsenic ABA from RBA estimates obtained in the mouse model.

KEYWORDS:

• Arsenic
• bioavailability
• pharmacokinetics
• human
• mouse

Acknowledgments

Portions of this work were funded by the U.S. Environmental Protection Agency, Office of Superfund Remediation and Technology Innovation (OSRTI), under Contract 68HERH19D0022. We are grateful for valuable input on Magnolia code from Conrad Housand of Magnolia Sciences.

Data Availability Statement

Data presented in this publication are available from the corresponding author on reasonable request.

Disclaimer

The manuscript has been reviewed in accordance with EPA policy and approved for publication. Approval does not signify that content necessarily reflects views and policies of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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Additional information

Funding

This work was supported by the [US Environmental Protection Agency Contract 68HERH19D0022].