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Updated: Aug 6, 2026

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
Published on: December 3, 2020
Prediction of in vivo drug clearance from in vitro data. II: potential inter-ethnic differences
S Inoue1, E M Howgate, K Rowland-Yeo
1Academic Unit of Clinical Pharmacology, Division of Clinical Sciences (South), University of Sheffield Royal Hallamshire Hospital, Sheffield, UK.
Abstract:
Potential differences in drug clearance between Japanese and Caucasians were investigated by integrating data on demography, liver size, the abundance of the major cytochromes P450 and in vitro metabolic parameters. Eleven drugs (alprazolam, caffeine, chlorzoxazone, cyclosporine, midazolam, omeprazole, sildenafil, tolbutamide, triazolam, S-warfarin and zolpidem) fulfilled the entry criteria of the study (i.e. the necessary in vitro metabolism data were available and clearance values had been reported both in Caucasians and Japanese). Values of relevant biological variables were obtained from the literature, and clearance predictions were made using the Simcyp Population-Based ADME Simulator. The ratios of observed oral clearance (CLp.o.) values in Caucasians compared with Japanese ranged from 0.6 to 2.8 (integrating data from 82 sources). The CLp.o. values for alprazolam, caffeine and zolpidem were not statistically different between Caucasian and Japanese (p>0.05), whereas those for chorzoxazone, cyclosporine, omeprazole, tolbutamide and triazolam were higher in Caucasians (p<0.05), and those for midazolam, sildenafil and S-warfarin were higher in Japanese (p<0.05). CLp.o. values, predicted from in vitro data, were within 3-fold of observed in vivo values for seven of the 11 drugs in Japanese. Values for the predicted ratios ranged from 1.6 to 4.9. The predicted ratios were not significantly different from observed ratios for cyclosporine, omeprazole, tolbutamide and triazolam. Only partial success in predicting ethnic differences in clearance indicates the need for larger and more reliable databases on relevant variables. With such information, in silico predictions might be used with more confidence to decrease the need for repeating pharmacokinetic studies in different ethnic groups.
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