Characterizing the Effects of Race/Ethnicity on Acetaminophen Pharmacokinetics Using Physiologically Based Pharmacokinetic Modeling.

BACKGROUND AND OBJECTIVES

Acetaminophen (APAP, paracetamol) is currently the principal cause of acute liver failure in both the USA and the UK. However, relatively little is known about the influence of genes and race/ethnicity on the disposition of APAP and the extent to which genetic variation and ethnicity may predispose individuals to a higher risk of APAP-induced hepatotoxicity. The objective of this research was to develop subpopulation-specific physiologically based pharmacokinetic (PBPK) models for two genetically different groups (Western Europeans and East Asians) and then use the models to quantify the difference in absorption, distribution, metabolism, and excretion (ADME) of APAP between these groups.

METHODS

A comprehensive set of human pharmacokinetic data mined from the literature was divided into two groups based on ethnicity as an indicator of the expected abundance of phenol-metabolizing alleles. Using these datasets and a Bayesian hierarchical framework, subpopulation-specific physiologically based pharmacokinetic models for APAP were developed and tested for the two groups.

RESULTS

Model simulations were in good agreement with experimental data for both time-dependent parent and metabolite concentrations and summary pharmacokinetic parameters. In addition, simulations were conducted to characterize the difference between ADME in these groups with regard to urinary excretion and APAP area under the curve (AUC) in the liver. Although not dramatic at therapeutic dosing levels, these results demonstrated the divergence in the liver-specific APAP concentrations and AUC between the two groups and suggested that differences in glucuronidation capacity may play a role in this disparity.

CONCLUSIONS

Overall, the models developed in this study, and others created using this type of hierarchical methodology, are expected to be useful in quantifying ADME in a subpopulation-specific manner and reducing prediction uncertainty compared to that from generalized PBPK modeling approaches.