Literature DB >> 25760528

Prediction of hepatic and intestinal glucuronidation using in vitro-in vivo extrapolation.

Yoichi Naritomi1, Fumihiro Nakamori2, Takako Furukawa2, Kenji Tabata2.   

Abstract

The accurate prediction of hepatic (Fh) and intestinal availability (Fg) is vital for determining human pharmacokinetics. To predict these PK parameters for cytochrome P450 (P450) metabolism, in vitro-in vivo extrapolation (IVIVE) using hepatic microsomes, hepatocytes, and intestinal microsomes has been actively investigated. However, IVIVE has not been sufficiently evaluated for non-P450 enzymes. UDP-glucuronosyltransferase (UGT) is a non-P450 enzyme that catalyzes glucuronidation, a major pathway for drugs possessing carboxylic acid, hydroxyl, and amine moieties. In drug metabolism, UGT is the most important enzyme after P450, and prediction of Fh for UGT substrates has mainly been attempted using hepatic models based on the clearance concepts. While various approaches for achieving improved prediction of clearance have been investigated--such as the addition of bovine serum albumin to microsomal incubation mixtures--optimized in vitro methods that utilize both hepatic microsomes and hepatocytes for more accurate prediction are still required. Although application of the simplified intestinal availability (SIA) model is effective in predicting the Fg of UGT substrates, this model is limited to compounds with high oral absorption. In this review, we discuss the current state, issues, and future directions of predicting Fh and Fg for glucuronidation.
Copyright © 2014 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Availability; Clearance; Glucuronidation; Intestine; In vitro–in vivo extrapolation; Liver; Prediction; UDP-glucuronosyltransferase

Mesh:

Substances:

Year:  2014        PMID: 25760528     DOI: 10.1016/j.dmpk.2014.10.001

Source DB:  PubMed          Journal:  Drug Metab Pharmacokinet        ISSN: 1347-4367            Impact factor:   3.614


  7 in total

Review 1.  Challenges and Opportunities with Non-CYP Enzymes Aldehyde Oxidase, Carboxylesterase, and UDP-Glucuronosyltransferase: Focus on Reaction Phenotyping and Prediction of Human Clearance.

Authors:  Upendra A Argikar; Philip M Potter; J Matthew Hutzler; Punit H Marathe
Journal:  AAPS J       Date:  2016-08-05       Impact factor: 4.009

2.  The use of PBPK modeling across the pediatric age range using propofol as a case.

Authors:  Robin Michelet; Jan Van Bocxlaer; Karel Allegaert; An Vermeulen
Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-10-08       Impact factor: 2.745

Review 3.  The Ontogeny of UDP-glucuronosyltransferase Enzymes, Recommendations for Future Profiling Studies and Application Through Physiologically Based Pharmacokinetic Modelling.

Authors:  Justine Badée; Stephen Fowler; Saskia N de Wildt; Abby C Collier; Stephan Schmidt; Neil Parrott
Journal:  Clin Pharmacokinet       Date:  2019-02       Impact factor: 6.447

4.  Challenges and Opportunities with Predicting in Vivo Phase II Metabolism via Glucuronidation from in Vitro Data.

Authors:  Shufan Ge; Yifan Tu; Ming Hu
Journal:  Curr Pharmacol Rep       Date:  2016-11-08

Review 5.  Predicting Drug Extraction in the Human Gut Wall: Assessing Contributions from Drug Metabolizing Enzymes and Transporter Proteins using Preclinical Models.

Authors:  Sheila Annie Peters; Christopher R Jones; Anna-Lena Ungell; Oliver J D Hatley
Journal:  Clin Pharmacokinet       Date:  2016-06       Impact factor: 6.447

6.  New Perspectives on Acyl Glucuronide Risk Assessment in Drug Discovery: Investigation of In vitro Stability, In situ Reactivity, and Bioactivation.

Authors:  Mithat Gunduz; Upendra A Argikar; Amanda L Cirello; Jennifer L Dumouchel
Journal:  Drug Metab Lett       Date:  2018

7.  Prediction of cytochrome P450-mediated drug clearance in humans based on the measured activities of selected CYPs.

Authors:  Jie Gao; Jie Wang; Na Gao; Xin Tian; Jun Zhou; Yan Fang; Hai-Feng Zhang; Qiang Wen; Lin-Jing Jia; Dan Zou; Hai-Ling Qiao
Journal:  Biosci Rep       Date:  2017-11-21       Impact factor: 3.840

  7 in total

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