Physiologically based modeling of the pharmacokinetics of acetaminophen and its major metabolites in humans using a Bayesian population approach.

The principal aim of this study was to develop, validate, and demonstrate a physiologically based pharmacokinetic (PBPK) model to predict and characterize the absorption, distribution, metabolism, and excretion of acetaminophen (APAP) in humans. A PBPK model was created that included pharmacologically and toxicologically relevant tissue compartments and incorporated mechanistic descriptions of the absorption and metabolism of APAP, such as gastric emptying time, cofactor kinetics, and transporter-mediated movement of conjugated metabolites in the liver. Through the use of a hierarchical Bayesian framework, unknown model parameters were estimated using a large training set of data from human pharmacokinetic studies, resulting in parameter distributions that account for data uncertainty and inter-study variability. Predictions from the model showed good agreement to a diverse test set of data across several measures, including plasma concentrations over time, renal clearance, APAP absorption, and pharmacokinetic and exposure metrics. The utility of the model was then demonstrated through predictions of cofactor depletion, dose response of several pharmacokinetic endpoints, and the relationship between APAP biomarker levels in the plasma and those in the liver. The model addressed several limitations in previous PBPK models for APAP, and it is anticipated that it will be useful in predicting the pharmacokinetics of APAP in a number of contexts, such as extrapolating across doses, estimating internal concentrations, quantifying population variability, assessing possible impacts of drug coadministration, and, when coupled with a suitable pharmacodynamic model, predicting toxicity.