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Development and evaluation of a high throughput inhalation model for organic chemicals

Journal of Exposure Science & Environmental Epidemiology volume 30, pages 866–877 (2020)Cite this article

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Abstract

Currently it is difficult to prospectively estimate human toxicokinetics (particularly for novel chemicals) in a high-throughput manner. The R software package httk has been developed, in part, to address this deficiency, and the aim of this investigation was to develop a generalized inhalation model for httk. The structure of the inhalation model was developed from two previously published physiologically based models from Jongeneelen and Berge (Ann Occup Hyg 55:841–864, 2011) and Clewell et al. (Toxicol Sci 63:160–172, 2001), while calculated physicochemical data was obtained from EPA’s CompTox Chemicals Dashboard. In total, 142 exposure scenarios across 41 volatile organic chemicals were modeled and compared to published data. The slope of the regression line of best fit between log-transformed simulated and observed blood and exhaled breath concentrations was 0.46 with an r2 = 0.45 and a root mean square error (RMSE) of direct comparison between the log-transformed simulated and observed values of 1.11. Approximately 5.1% (n = 108) of the data points analyzed were >2 orders of magnitude different than expected. The volatile organic chemicals examined in this investigation represent small, generally lipophilic molecules. Ultimately this paper details a generalized inhalation component that integrates with the httk physiologically based toxicokinetic model to provide high-throughput estimates of inhalation chemical exposures.

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Fig. 1
Fig. 2: Log-transformed observed (y-axis) vs. simulated (x-axis) blood (μM) and exhaled air (ppm) concentrations.
Fig. 3: Log-transformed simulated minus observed concentrations for each exposure scenario.
Fig. 4: Observed vs. simulated aggregated measures.

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Acknowledgements

The authors would like to thank Dr. Lisa Sweeney for her review of the material and Dr. Greg Honda for his help in getting oriented to the httk package. The United States Environmental Protection Agency (EPA) through its Office of Research and Development (ORD) funded the efforts of RRS, RGP, MAS, and JFW. The views expressed in this publication are those of the authors and do not necessarily represent the views or policies of the U.S. EPA. Reference to commercial products or services does not constitute endorsement. This project was supported by appointments to the Internship/Research Participation Program at ORD and administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and U.S. EPA. Finally, we thank Drs. Elaina Kenyon, Jason Lambert, and Rogelio Tornero-Velez at the U.S. EPA and Dr. Jui-Hua Hsieh at the National Toxicology Program for their helpful internal reviews of the manuscript.

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Authors and Affiliations

  1. United States Air Force, 711th Human Performance Wing, Airman Readiness Optimization, Wright-Patterson AFB, Wright-Patterson AFB, OH, 45433, USA

    Matthew W. Linakis & Jeffery M. Gearhart

  2. UES, Inc., Dayton, OH, 45432, USA

    Matthew W. Linakis

  3. Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA

    Risa R. Sayre, Robert G. Pearce & Mark A. Sfeir

  4. National Center for Computational Toxicology, United States Environmental Protection Agency, Research Triangle Park, NC, 27711, USA

    Risa R. Sayre, Robert G. Pearce, Mark A. Sfeir & John F. Wambaugh

  5. National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27711, USA

    Nisha S. Sipes

  6. United States Air Force, 711th Human Performance Wing, Molecular Bioeffects, Wright-Patterson AFB, Wright-Patterson AFB, OH, 45433, USA

    Heather A. Pangburn

  7. Henry M. Jackson Foundation for the Advancement of Military Medicine, Wright-Patterson AFB, Wright-Patterson AFB, OH, 45433, USA

    Jeffery M. Gearhart

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Linakis, M.W., Sayre, R.R., Pearce, R.G. et al. Development and evaluation of a high throughput inhalation model for organic chemicals. J Expo Sci Environ Epidemiol 30, 866–877 (2020). https://doi.org/10.1038/s41370-020-0238-y

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