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Updated: Jul 29, 2025

Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults
Published on: April 22, 2022
In vitro-based prediction of human plasma concentrations of food-related compounds
Takashi Kitaguchi1, Mina Ito1, Katsutoshi Ohno1
1Global Food Safety Institute, Nissin Foods Holdings Co., Ltd., Hachioji, Tokyo, Japan.
Abstract:
Efforts have been made to replace animal experiments in safety evaluations, including in vitro-based predictions of human internal exposures, such as predicting peak plasma concentration (Cmax) values for xenobiotics and comparing these values with in vitro-based toxicity endpoints. Herein, the authors predicted the Cmax values of food-related compounds in humans based on existing and novel in vitro techniques. In this study, 20 food-related compounds, which have been previously reported in human pharmacokinetic or toxicokinetic studies, were evaluated. Human induced pluripotent stem cell-derived small intestinal epithelial cells (hiPSC-SIEC) and Caco-2 cells, HepaRG cells, equilibrium dialysis of human plasma, and LLC-PK1 cell monolayer were used to assess intestinal absorption and availability, hepatic metabolism, unbound plasma fraction, and secretion and reabsorption in renal tubular cells, respectively. After conversion of these parameters into human kinetic parameters, the plasma concentration profiles of these compounds were predicted using in silico methods, and the obtained Cmax values were found to be between 0.017 and 183 times the reported Cmax values. When the in silico-predicted parameters were modified with in vitro data, the predicted Cmax values came within 0.1-10 times the reported values because the metabolic activities of hiPSC-SIECs, such as uridine 5’-diphospho-glucuronosyl transferase, are more similar to those of human primary enterocytes. Thus, combining in vitro test results with the plasma concentration simulations resulted in more accurate and transparent predictions of Cmax values of food-related compounds than those obtained using in silico-derived predictions alone. This method facilitates accurate safety evaluation without the need for animal experiments.
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