Plasma and hepatic concentrations of acetaminophen and its primary conjugates after oral administrations determined in experimental animals and humans and extrapolated by pharmacokinetic modeling

Abstract

1. Plasma concentrations of acetaminophen, its glucuronide and sulfate conjugates, and cysteinyl acetaminophen were experimentally determined after oral administrations of 10 mg/kg in humanised-liver mice, control mice, rats, common marmosets, cynomolgus monkeys, and minipigs; the results were compared with reported human pharmacokinetic data.

2. Among the animals tested, only rats predominantly converted acetaminophen to sulfate conjugates, rather than glucuronide conjugates. In contrast, the values of area under the plasma concentration curves of acetaminophen, its glucuronide and sulfate conjugates, and cysteinyl acetaminophen after oral administration of acetaminophen in marmosets and minipigs were consistent with those reported in humans under the present conditions.

3. Physiologically based pharmacokinetic (PBPK) models (consisting of the gut, liver, and central compartments) for acetaminophen and its primary metabolite could reproduce and estimate, respectively, the plasma and hepatic concentrations of acetaminophen in experimental animals and humans after single virtual oral doses.

4. The values of area under the curves of hepatic concentrations of acetaminophen estimated using PBPK models were correlated with the measured levels of cysteinyl acetaminophen (a deactivated metabolite) in plasma fractions in these species.

5. Consequently, using simple PBPK models and plasma data to predict hepatic chemical concentrations after oral doses could be helpful as an indicator of in vivo possible hepatotoxicity of chemicals such as acetaminophen.

Keywords:

• Physiologically based kinetic model
• humanised-liver mouse
• common marmoset
• cynomolgus monkey
• minipig
• paracetamol

Acknowledgments

We appreciate Drs. Erika Sasaki, Takashi Inoue, Norie Murayama, Yusuke Kamiya, and Ayane Nakano for their support of this study. The authors also greatly thank David Smallbones for copyediting a draft of this article.

Disclosure statement

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

Additional information

Funding

This work was financially supported partly by the METI Artificial Intelligence-based Substance Hazard Integrated Prediction System Project, Japan, and the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research [19K07205] and for Young Scientists [202021210].