Literature DB >> 32914238

Investigating the Theoretical Basis for In Vitro-In Vivo Extrapolation (IVIVE) in Predicting Drug Metabolic Clearance and Proposing Future Experimental Pathways.

Leslie Z Benet1, Jasleen K Sodhi2.   

Abstract

Extensive studies have been conducted to predict in vivo metabolic clearance from in vitro human liver metabolism parameters (i.e., in vitro-in vivo extrapolation (IVIVE)) with little success. Here, deriving IVIVE from first principles, we show that the product of fraction unbound in the blood and the predicted in vivo intrinsic clearance determined from hepatocyte or microsomal incubations is the lower boundary condition for in vivo hepatic clearance and the prerequisite for IVIVE predictions to be valid, regardless of extraction ratio. For 60-80% of drugs evaluated here, this product is markedly less than the in vivo measured clearance, a result that violates the lower boundary of the predictive relationship. This can only be explained by (a) suboptimal in vitro metabolic stability assay conditions, (b) significant error in the assumption that in vitro intrinsic clearance determinations will predict in vivo intrinsic clearance simply by scaling-up the amount of enzyme (in vitro incubation to in vivo liver), and/or (c) the methods of determining fraction unbound are incorrect. We further suggest that widely employed organ blood flow values underpredict the effective blood flow within the organ by approximately 2.5-fold, thus impacting IVIVE of high clearance compounds. We propose future pathways that should be investigated in terms of the relationship to experimentally measured clearance values, rather than model-dependent intrinsic clearance. IVIVE outcome can be improved by estimating the ratio of unbound drug concentration in the liver tissue to the liver plasma, examining the assumption of the free drug theory (i.e., there are no transporter effects at the blood cell membrane) and the finding that the upper limit of organ clearance may be greater than blood flow entering the organ.

Entities:  

Keywords:  IVIVE; fraction unbound; hepatic clearance; organ blood flow

Mesh:

Year:  2020        PMID: 32914238      PMCID: PMC7515557          DOI: 10.1208/s12248-020-00501-9

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  28 in total

Review 1.  Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications.

Authors:  A A Mangoni; S H D Jackson
Journal:  Br J Clin Pharmacol       Date:  2004-01       Impact factor: 4.335

2.  Evaluation of hepatic clearance prediction using in vitro data: emphasis on fraction unbound in plasma and drug ionisation using a database of 107 drugs.

Authors:  David Hallifax; J Brian Houston
Journal:  J Pharm Sci       Date:  2012-06-14       Impact factor: 3.534

Review 3.  Prediction of hepatic clearance in human from in vitro data for successful drug development.

Authors:  Masato Chiba; Yasuyuki Ishii; Yuichi Sugiyama
Journal:  AAPS J       Date:  2009-04-30       Impact factor: 4.009

4.  Clearance concepts in pharmacokinetics.

Authors:  M Rowland; L Z Benet; G G Graham
Journal:  J Pharmacokinet Biopharm       Date:  1973-04

Review 5.  Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance.

Authors:  J B Houston
Journal:  Biochem Pharmacol       Date:  1994-04-29       Impact factor: 5.858

6.  Use of Segregated Hepatocyte Scaling Factors and Cross-Species Relationships to Resolve Clearance Dependence in the Prediction of Human Hepatic Clearance.

Authors:  D Hallifax; J B Houston
Journal:  Drug Metab Dispos       Date:  2019-01-04       Impact factor: 3.922

7.  The effects of age and liver disease on the disposition and elimination of diazepam in adult man.

Authors:  U Klotz; G R Avant; A Hoyumpa; S Schenker; G R Wilkinson
Journal:  J Clin Invest       Date:  1975-02       Impact factor: 14.808

8.  The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data.

Authors:  R S Obach; J G Baxter; T E Liston; B M Silber; B C Jones; F MacIntyre; D J Rance; P Wastall
Journal:  J Pharmacol Exp Ther       Date:  1997-10       Impact factor: 4.030

9.  Clearance Prediction Methodology Needs Fundamental Improvement: Trends Common to Rat and Human Hepatocytes/Microsomes and Implications for Experimental Methodology.

Authors:  F L Wood; J B Houston; D Hallifax
Journal:  Drug Metab Dispos       Date:  2017-09-08       Impact factor: 3.922

10.  Translational learning from clinical studies predicts drug pharmacokinetics across patient populations.

Authors:  Markus Krauss; Ute Hofmann; Clemens Schafmayer; Svitlana Igel; Reinhold Kerb; Jochen Hampe; Lars Kuepfer; Matthias Schwab; Jan Schlender; Christian Mueller; Mario Brosch; Witigo von Schoenfels; Wiebke Erhart; Andreas Schuppert; Michael Block; Elke Schaeffeler; Gabriele Boehmer; Linus Goerlitz; Jan Hoecker; Joerg Lippert
Journal:  NPJ Syst Biol Appl       Date:  2017-03-28
View more
  7 in total

1.  In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species.

Authors:  David A Tess; Sangwoo Ryu; Li Di
Journal:  Pharm Res       Date:  2022-03-07       Impact factor: 4.200

Review 2.  Can In Vitro-In Vivo Extrapolation Be Successful? Recognizing the Incorrect Clearance Assumptions.

Authors:  Leslie Z Benet; Jasleen K Sodhi
Journal:  Clin Pharmacol Ther       Date:  2021-12-08       Impact factor: 6.903

Review 3.  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

4.  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

5.  Pharmacokinetic/pharmacodynamic modeling for dose selection for the first-in-human trial of the activated Factor XII inhibitor garadacimab (CSL312).

Authors:  Dipti Pawaskar; Xi Chen; Fiona Glassman; Frauke May; Anthony Roberts; Mark Biondo; Andrew McKenzie; Marc W Nolte; William J Jusko; Michael Tortorici
Journal:  Clin Transl Sci       Date:  2021-12-08       Impact factor: 4.689

Review 6.  Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom?

Authors:  Sebastian Frechen; Amin Rostami-Hodjegan
Journal:  Pharm Res       Date:  2022-04-20       Impact factor: 4.580

Review 7.  Drug-Drug Interactions in People Living With HIV at Risk of Hepatic and Renal Impairment: Current Status and Future Perspectives.

Authors:  Nicolas Cottura; Hannah Kinvig; Sandra Grañana-Castillo; Adam Wood; Marco Siccardi
Journal:  J Clin Pharmacol       Date:  2022-02-08       Impact factor: 2.860

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.