Literature DB >> 22407504

Predicting human hepatic clearance from in vitro drug metabolism and transport data: a scientific and pharmaceutical perspective for assessing drug-drug interactions.

Gian Camenisch1, Ken-ichi Umehara.   

Abstract

OBJECTIVES: Membrane transporters and metabolism are major determinants of the hepatobiliary elimination of drugs. This work investigates several key questions for drug development. Such questions include which drugs demonstrate transporter-based clearance in the clinic, and which in vitro methods are most suitable for drug classification, i.e. transporter- vs metabolism-dependent compound class categories. Additional questions posed are: what is the expected quantitative change in exposure in the presence of a transporter- and/or metabolism-inhibiting drug, and which criteria should trigger follow-up clinical drug-drug interaction studies.
METHODS: A well-established method for (human) liver clearance prediction that considers all four physiological processes driving hepatic drug elimination (namely sinusoidal uptake and efflux, metabolism and biliary secretion) was applied. Suspended hepatocytes, liver microsomes and sandwich-cultured hepatocytes were used as in vitro models to determine the individual intrinsic clearance for 13 selected compounds with various physicochemical and pharmacokinetic properties.
RESULTS: Using this in vitro-in vivo extrapolation method a good linear correlation was observed between predicted and reported human hepatic clearances. Linear regression analysis revealed much improved correlations compared with other prediction methods.
CONCLUSIONS: The presented approach serves as a basis for accurate compound categorization within the Biopharmaceutics Drug Disposition Classification System (BDDCS) and was applied to anticipate metabolism- and transporter-based drug-drug interactions using different static prediction methods. A decision tree proposal is provided and helps to guide clinical studies on active processes influencing hepatic elimination. All recommendations in this paper are generally intended to support early pre-clinical and clinical drug development and the filing of a new drug application.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2012        PMID: 22407504     DOI: 10.1002/bdd.1784

Source DB:  PubMed          Journal:  Biopharm Drug Dispos        ISSN: 0142-2782            Impact factor:   1.627


  20 in total

1.  Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS).

Authors:  Manthena V Varma; Stefanus J Steyn; Charlotte Allerton; Ayman F El-Kattan
Journal:  Pharm Res       Date:  2015-07-09       Impact factor: 4.200

2.  Reliable Rate Measurements for Active and Passive Hepatic Uptake Using Plated Human Hepatocytes.

Authors:  Yi-An Bi; Renato J Scialis; Sarah Lazzaro; Sumathy Mathialagan; Emi Kimoto; Julie Keefer; Hui Zhang; Anna M Vildhede; Chester Costales; A David Rodrigues; Larry M Tremaine; Manthena V S Varma
Journal:  AAPS J       Date:  2017-02-10       Impact factor: 4.009

3.  When Does the Rate-Determining Step in the Hepatic Clearance of a Drug Switch from Sinusoidal Uptake to All Hepatobiliary Clearances? Implications for Predicting Drug-Drug Interactions.

Authors:  Gabriela I Patilea-Vrana; Jashvant D Unadkat
Journal:  Drug Metab Dispos       Date:  2018-08-16       Impact factor: 3.922

Review 4.  Drug Disposition Classification Systems in Discovery and Development: A Comparative Review of the BDDCS, ECCS and ECCCS Concepts.

Authors:  Gian P Camenisch
Journal:  Pharm Res       Date:  2016-07-20       Impact factor: 4.200

5.  Prediction of Drug Clearance from Enzyme and Transporter Kinetics.

Authors:  Priyanka R Kulkarni; Amir S Youssef; Aneesh A Argikar
Journal:  Methods Mol Biol       Date:  2021

6.  Different interaction profiles of direct-acting anti-hepatitis C virus agents with human organic anion transporting polypeptides.

Authors:  Tomomi Furihata; Shogo Matsumoto; Zhongguo Fu; Akihito Tsubota; Yuchen Sun; Sayaka Matsumoto; Kaoru Kobayashi; Kan Chiba
Journal:  Antimicrob Agents Chemother       Date:  2014-05-27       Impact factor: 5.191

Review 7.  How Transporters Have Changed Basic Pharmacokinetic Understanding.

Authors:  Leslie Z Benet; Christine M Bowman; Jasleen K Sodhi
Journal:  AAPS J       Date:  2019-09-03       Impact factor: 4.009

8.  High prevalence of potential drug-drug interactions in patients with castration-resistant prostate cancer treated with abiraterone acetate.

Authors:  Rehana Jamani; Esther K Lee; Scott R Berry; Ronak Saluja; Carlo DeAngelis; Angie Giotis; Urban Emmenegger
Journal:  Eur J Clin Pharmacol       Date:  2016-08-25       Impact factor: 2.953

9.  Prediction of Cyclosporin-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Model Characterizing Interplay of Drug Transporters and Enzymes.

Authors:  Yiting Yang; Ping Li; Zexin Zhang; Zhongjian Wang; Li Liu; Xiaodong Liu
Journal:  Int J Mol Sci       Date:  2020-09-24       Impact factor: 5.923

Review 10.  BDDCS, the Rule of 5 and drugability.

Authors:  Leslie Z Benet; Chelsea M Hosey; Oleg Ursu; Tudor I Oprea
Journal:  Adv Drug Deliv Rev       Date:  2016-05-13       Impact factor: 15.470

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