Literature DB >> 22890957

Comparative assessment of In Vitro-In Vivo extrapolation methods used for predicting hepatic metabolic clearance of drugs.

Patrick Poulin1, Cornelis E C A Hop, Quynh Ho, Jason S Halladay, Sami Haddad, Jane R Kenny.   

Abstract

The purpose of this study was to perform a comparative analysis of various in vitro--in vivo extrapolation (IVIVE) methods used for predicting hepatic metabolic clearance (CL) of drugs on the basis of intrinsic CL data determined in microsomes. Five IVIVE methods were evaluated: the "conventional and conventional bias-corrected methods" using the unbound fraction in plasma (fu(p) ), the "Berezhkovskiy method" in which the fu(p) is adjusted for drug ionization, the "Poulin et al. method" using the unbound fraction in liver (fu(liver) ), and the "direct scaling method," which does not consider any binding corrections. We investigated the effects of the following scenarios on the prediction of CL: the use of preclinical or human datasets, the extent of plasma protein binding, the magnitude of CL in vivo, and the extent of drug disposition based on biopharmaceutics drug disposition classification system (BDDCS) categorization. A large and diverse dataset of 139 compounds was collected, including those from the literature and in house from Genentech. The results of this study confirm that the Poulin et al. method is robust and showed the greatest accuracy as compared with the other IVIVE methods in the majority of prediction scenarios studied here. The difference across the prediction methods is most pronounced for (a) albumin-bound drugs, (b) highly bound drugs, and (c) low CL drugs. Predictions of CL showed relevant interspecies differences for BDDCS class 2 compounds; the direct scaling method showed the greatest predictivity for these compounds, particularly for a reduced dataset in rat that have unexpectedly high CL in vivo. This result is a reflection of the direct scaling method's natural tendency to overpredict the true metabolic CL. Overall, this study should facilitate the use of IVIVE correlation methods in physiologically based pharmacokinetics (PBPK) model.
Copyright © 2012 Wiley Periodicals, Inc.

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Year:  2012        PMID: 22890957     DOI: 10.1002/jps.23288

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  8 in total

1.  The Presence of a Transporter-Induced Protein Binding Shift: A New Explanation for Protein-Facilitated Uptake and Improvement for In Vitro-In Vivo Extrapolation.

Authors:  Christine M Bowman; Hideaki Okochi; Leslie Z Benet
Journal:  Drug Metab Dispos       Date:  2019-01-23       Impact factor: 3.922

Review 2.  Dose selection based on physiologically based pharmacokinetic (PBPK) approaches.

Authors:  Hannah M Jones; Kapil Mayawala; Patrick Poulin
Journal:  AAPS J       Date:  2012-12-27       Impact factor: 4.009

3.  Physiology-based IVIVE predictions of tramadol from in vitro metabolism data.

Authors:  Huybrecht T'jollyn; Jan Snoeys; Pieter Colin; Jan Van Bocxlaer; Pieter Annaert; Filip Cuyckens; An Vermeulen; Achiel Van Peer; Karel Allegaert; Geert Mannens; Koen Boussery
Journal:  Pharm Res       Date:  2014-07-22       Impact factor: 4.200

4.  Metabolic Profiling of Human Long-Term Liver Models and Hepatic Clearance Predictions from In Vitro Data Using Nonlinear Mixed-Effects Modeling.

Authors:  Nicole A Kratochwil; Christophe Meille; Stephen Fowler; Florian Klammers; Aynur Ekiciler; Birgit Molitor; Sandrine Simon; Isabelle Walter; Claudia McGinnis; Johanna Walther; Brian Leonard; Miriam Triyatni; Hassan Javanbakht; Christoph Funk; Franz Schuler; Thierry Lavé; Neil J Parrott
Journal:  AAPS J       Date:  2017-01-03       Impact factor: 4.009

Review 5.  Prediction of drug disposition on the basis of its chemical structure.

Authors:  David Stepensky
Journal:  Clin Pharmacokinet       Date:  2013-06       Impact factor: 6.447

Review 6.  Successful and Unsuccessful Prediction of Human Hepatic Clearance for Lead Optimization.

Authors:  Jasleen K Sodhi; Leslie Z Benet
Journal:  J Med Chem       Date:  2021-03-25       Impact factor: 7.446

7.  Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data.

Authors:  Jaydeep Yadav; Mehdi El Hassani; Jasleen Sodhi; Volker M Lauschke; Jessica H Hartman; Laura E Russell
Journal:  Drug Metab Rev       Date:  2021-05-25       Impact factor: 6.984

8.  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

  8 in total

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