Literature DB >> 18853387

Reliability of human cryopreserved hepatocytes and liver microsomes as in vitro systems to predict metabolic clearance.

R Stringer1, P L Nicklin, J B Houston.   

Abstract

A total of 110 drugs, selected to cover a range of physicochemical and pharmacokinetic properties, were used to explore standard approaches to the prediction of in vivo metabolic clearance using drug-depletion profiles from human liver microsomes (HLMs) and cyropreserved hepatocytes. A total of 41 drugs (37% of the compounds tested) showed measurable depletion rates using HLMs (depletion by 20% or more over the time course). The most reliable correlations in terms of bias (average fold error (AFE) = 2.32) and precision (root mean square error (RMSE) = 3501) were observed by comparing in vivo intrinsic clearance (CL(int)), calculated using the parallel-tube model and incorporating the fraction unbound in blood, with in vitro CL(int) adjusted for microsomal binding. For these reference drugs, 29% of predictions were within two-fold of the observed values and 66% were within five-fold. Compared with HLMs, clearance predictions with cryopreserved hepatocytes (57 drugs) were of similar precision (RMSE = 3608) but showed more bias (AFE = 5.21) with 18% of predictions within two-fold of the observed values and 46% within five-fold. However, with a broad complement of drug-metabolizing enzymes, hepatocytes catalysed measurable CL(int) values for a greater proportion (52%) of the reference compounds and were particularly proficient at defining metabolic rates for drugs with predominantly phase 2 metabolic routes.

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Year:  2008        PMID: 18853387     DOI: 10.1080/00498250802446286

Source DB:  PubMed          Journal:  Xenobiotica        ISSN: 0049-8254            Impact factor:   1.908


  14 in total

Review 1.  Applications of human pharmacokinetic prediction in first-in-human dose estimation.

Authors:  Peng Zou; Yanke Yu; Nan Zheng; Yongsheng Yang; Hayley J Paholak; Lawrence X Yu; Duxin Sun
Journal:  AAPS J       Date:  2012-03-10       Impact factor: 4.009

2.  Prediction of human metabolic clearance from in vitro systems: retrospective analysis and prospective view.

Authors:  David Hallifax; Joanne A Foster; J Brian Houston
Journal:  Pharm Res       Date:  2010-07-27       Impact factor: 4.200

3.  Prediction of Metabolic Clearance for Low-Turnover Compounds Using Plated Hepatocytes with Enzyme Activity Correction.

Authors:  Bennett Ma; Roy Eisenhandler; Yuhsin Kuo; Paul Rearden; Ying Li; Peter J Manley; Sheri Smith; Karsten Menzel
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-04       Impact factor: 2.441

4.  Integrated pharmacokinetic-driven approach to screen candidate anticancer drugs for brain tumor chemotherapy.

Authors:  Hua Lv; Xiaoping Zhang; Jyoti Sharma; M V Ramana Reddy; E Premkumar Reddy; James M Gallo
Journal:  AAPS J       Date:  2012-11-22       Impact factor: 4.009

5.  Interlaboratory Variability in Human Hepatocyte Intrinsic Clearance Values and Trends with Physicochemical Properties.

Authors:  Christine M Bowman; Leslie Z Benet
Journal:  Pharm Res       Date:  2019-05-31       Impact factor: 4.200

6.  In Vitro-In Vivo Inaccuracy: The CYP3A4 Anomaly.

Authors:  Christine M Bowman; Leslie Z Benet
Journal:  Drug Metab Dispos       Date:  2019-09-24       Impact factor: 3.922

7.  In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction.

Authors:  Christine M Bowman; Leslie Z Benet
Journal:  J Pharm Sci       Date:  2019-02-25       Impact factor: 3.534

8.  Predicting when biliary excretion of parent drug is a major route of elimination in humans.

Authors:  Chelsea M Hosey; Fabio Broccatelli; Leslie Z Benet
Journal:  AAPS J       Date:  2014-07-09       Impact factor: 4.009

Review 9.  Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME.

Authors:  Patricio Godoy; Nicola J Hewitt; Ute Albrecht; Melvin E Andersen; Nariman Ansari; Sudin Bhattacharya; Johannes Georg Bode; Jennifer Bolleyn; Christoph Borner; Jan Böttger; Albert Braeuning; Robert A Budinsky; Britta Burkhardt; Neil R Cameron; Giovanni Camussi; Chong-Su Cho; Yun-Jaie Choi; J Craig Rowlands; Uta Dahmen; Georg Damm; Olaf Dirsch; María Teresa Donato; Jian Dong; Steven Dooley; Dirk Drasdo; Rowena Eakins; Karine Sá Ferreira; Valentina Fonsato; Joanna Fraczek; Rolf Gebhardt; Andrew Gibson; Matthias Glanemann; Chris E P Goldring; María José Gómez-Lechón; Geny M M Groothuis; Lena Gustavsson; Christelle Guyot; David Hallifax; Seddik Hammad; Adam Hayward; Dieter Häussinger; Claus Hellerbrand; Philip Hewitt; Stefan Hoehme; Hermann-Georg Holzhütter; J Brian Houston; Jens Hrach; Kiyomi Ito; Hartmut Jaeschke; Verena Keitel; Jens M Kelm; B Kevin Park; Claus Kordes; Gerd A Kullak-Ublick; Edward L LeCluyse; Peng Lu; Jennifer Luebke-Wheeler; Anna Lutz; Daniel J Maltman; Madlen Matz-Soja; Patrick McMullen; Irmgard Merfort; Simon Messner; Christoph Meyer; Jessica Mwinyi; Dean J Naisbitt; Andreas K Nussler; Peter Olinga; Francesco Pampaloni; Jingbo Pi; Linda Pluta; Stefan A Przyborski; Anup Ramachandran; Vera Rogiers; Cliff Rowe; Celine Schelcher; Kathrin Schmich; Michael Schwarz; Bijay Singh; Ernst H K Stelzer; Bruno Stieger; Regina Stöber; Yuichi Sugiyama; Ciro Tetta; Wolfgang E Thasler; Tamara Vanhaecke; Mathieu Vinken; Thomas S Weiss; Agata Widera; Courtney G Woods; Jinghai James Xu; Kathy M Yarborough; Jan G Hengstler
Journal:  Arch Toxicol       Date:  2013-08-23       Impact factor: 5.153

10.  Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.

Authors:  Urban Fagerholm; Sven Hellberg; Ola Spjuth
Journal:  Molecules       Date:  2021-04-28       Impact factor: 4.411

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