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Updated: Dec 9, 2025

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
Published on: December 3, 2020
Physiologically-Based Pharmacokinetic Model of Morphine and Morphine-3-Glucuronide in Nonalcoholic Steatohepatitis
Noora Sjöstedt1, Sibylle Neuhoff2, Kim L R Brouwer1
1Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Abstract:
Nonalcoholic steatohepatitis (NASH), the progressive form of nonalcoholic fatty liver disease, is increasing in prevalence. NASH-related alterations in hepatic protein expression (e.g., transporters) and in overall physiology may affect drug exposure by altering drug disposition and elimination. The aim of this study was to build a physiologically-based pharmacokinetic (PBPK) model to predict drug exposure in NASH by incorporating NASH-related changes in hepatic transporters. Morphine and morphine-3-glucuronide (M3G) were used as model compounds. A PBPK model of morphine with permeability-limited hepatic disposition was extended to include M3G disposition and enterohepatic recycling (EHR). The model captured the area under the plasma concentration-time curve (AUC) of morphine and M3G after intravenous morphine administration within 0.82-fold and 1.94-fold of observed values from 3 independent clinical studies for healthy adult subjects (6, 10, and 14 individuals). When NASH-related changes in multidrug resistance-associated protein 2 (MRP2) and MRP3 were incorporated into the model, the predicted M3G mean AUC in NASH was 1.34-fold higher compared to healthy subjects, which is slightly lower than the observed value (1.63-fold). Exploratory simulations on other physiological changes occurring in NASH (e.g., moderate decreases in glomerular filtration rate and portal vein blood flow) revealed that the effect of transporter changes was most prominent. Additionally, NASH-related transporter changes resulted in decreased morphine EHR, which could be important for drugs with extensive EHR. This study is an important first step to predict drug disposition in complex diseases such as NASH using PBPK modeling.
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