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Estimating Hepatotoxic Doses Using High-Content Imaging in Primary Hepatocytes.

Imran Shah1, Todor Antonijevic1,2, Bryant Chambers1

  • 1Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA.

Toxicological Sciences : an Official Journal of the Society of Toxicology
|July 21, 2021
PubMed
Summary

New approach methodologies (NAMs) using rat liver cells and high-content imaging (HCI) provide conservative estimates for chemical risk assessment. These NAMs estimate point of departure (POD) values closer to traditional in vivo studies when using relevant cell types.

Keywords:
computational toxicologyhepatotoxicityhigh-content imagingpoint of departuretoxicokinetics

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Area of Science:

  • Toxicology
  • In vitro toxicology
  • Chemical risk assessment

Background:

  • Estimating point of departure (POD) values from in vitro data is crucial for new approach methodologies (NAMs) in chemical risk assessment.
  • This study evaluates a NAM for hepatotoxicity using rat primary hepatocytes and high-content imaging (HCI).

Purpose of the Study:

  • To assess the utility of a NAM integrating rat primary hepatocytes, HCI, and toxicokinetic modeling for estimating hepatotoxicity POD values.
  • To compare POD estimates from this NAM with traditional in vivo lowest observed adverse effect levels (LOAELs) and other NAMs (ToxCast, Tox21).

Main Methods:

  • Rat primary hepatocytes were exposed to 51 hepatotoxic chemicals across 10 concentrations.
  • High-content imaging (HCI) measured cellular responses (e.g., endoplasmic reticulum stress, mitochondrial function, steatosis, apoptosis) at 24, 48, and 72 hours.
  • Toxicokinetic modeling was used to estimate administered equivalent doses (AEDs) from in vitro data.

Main Results:

  • AEDs from the rat hepatocyte NAM were 4.1-fold and 8.1-fold lower than subchronic and chronic LOAELs, respectively.
  • AEDs from ToxCast and Tox21 assays were substantially lower (89.8-fold and 168-fold) than LOAELs.
  • NAMs using diverse cell types yielded conservative POD estimates, while those using the same species/cell type approached in vivo PODs.

Conclusions:

  • NAMs integrating in vitro data, HCI, and toxicokinetic modeling can provide valuable POD estimates for chemical risk assessment.
  • The choice of cell type and modeling approach influences the conservatism of NAM-derived POD estimates.
  • NAMs based on the same species and cell type as the in vivo adverse outcome may offer more refined risk assessment predictions.