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

Camenisch, Gian; Umehara, Ken‐ichi

doi:10.1002/bdd.1784

Cited by 76Drug Transport and Resistance MechanismsPharmacogenetics and Drug MetabolismDrug Solubulity and Delivery Systems

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

Camenisch, Gian, Umehara, Ken‐ichi·2012·Biopharmaceutics & Drug Disposition

DOI 10.1002/bdd.1784

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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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An extended clearance model has been developed in which permeability is an important factor affecting CLH.
“recently, an extended clearance model has been developed in which permeability is one of the important factors affecting the CLH [41,42].”
From Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling (2019) · cites this paper as [42]
The proposed physiology-based IVIVE method was used to predict rat hepatic clearance of 13 compounds with various physicochemical and PK characteristics.
“The proposed method was used to predict rat hepatic clearance of 13 compounds with various physicochemical and PK characteristics.”
From Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling (2019) · cites this paper as [42]
In the physiology-based IVIVE studies, the hepatic clearance of valsartan (class 2 compound based on BDDCS) was underestimated with the highest fold-error of 3.95.
“In these studies, the hepatic clearance of valsartan (class 2 compound based on BDDCS) was underestimated with the highest fold-error of 3.95.”
From Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling (2019) · cites this paper as [42]
A novel IVIVE method developed to predict hepatic organ clearance via physiology-based modeling reflects additional physiologically relevant information (hepatic uptake, metabolism, biliary excretion, and sinusoidal efflux) compared with the conventional method.
“This new method reflects additional physiologically relevant information (namely hepatic uptake, metabolism, biliary excretion, and sinusoidal efflux) compared with the conventional method.”
From Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling (2019) · cites this paper as [42]

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Authors: Camenisch, G., & Umehara, K.
Affiliations: Novartis Institutes for BioMedical Research (CH)
Drug–Drug Interaction Section, Drug Metabolism and Pharmacokinetics, Novartis Institutes of Biomedical Research CH‐4002 Basel Switzerland
Corresponding author: Camenisch, Gian
ORCID: Camenisch, Gian
Venue: Biopharmaceutics & Drug Disposition
Publisher: Wiley
Published: 2012-04-19
Volume: 33
Issue: 4
Pages: 179-194
ISSN: 0142-2782 (ISSN-L)
1099-081X
DOI: 10.1002/bdd.1784
OpenAlex: W2158305760
Cited by: 76
References: 47
Citations by year: 2025: 2 · 2024: 2 · 2023: 5 · 2022: 5 · 2021: 3 · 2020: 7 · 2019: 9 · 2018: 4 · 2017: 9 · 2016: 5 · 2015: 5 · 2014: 10 · 2013: 7 · 2012: 3
FWCI: 3.96
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Biopharmaceutics & Drug Disposition (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmaceutical Science › Drug Solubulity and Delivery Systems
Keywords: drug-drug interactions, in vitro-in vivo extrapolation, drug transporters, drug metabolism, intrinsic clearance, hepatocyte models, liver microsomes, bile secretion, decision tree
MeSH terms: Biological Transport, Decision Trees, Drug Interactions, Pharmaceutical Preparations, Humans, Microsomes, Liver, Models, Biological, Drug Design, Linear Models, Hepatocytes

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

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