Cited by 54Pharmacogenetics and Drug MetabolismDrug Transport and Resistance MechanismsAnimal testing and alternatives

Toward a new paradigm for the efficient in vitro–in vivo extrapolation of metabolic clearance in humans from hepatocyte data

Poulin, Patrick, Haddad, Sami·2013·Journal of Pharmaceutical Sciences

Abstract

The objective of this study was to follow up a previous study on a comparative analysis of diverse in vitro-in vivo extrapolation (IVIVE) methods used for predicting hepatic metabolic clearance (CL) of drugs from intrinsic clearance (CLint ) data determined in microsomal incubations, but using hepatocyte data instead. Six IVIVE methods were compared: the "conventional and conventional bias-corrected methods," the "regression equation method," the "direct scaling method," the "Berezhkovskiy's method," and the "novel IVIVE method of Poulin et al." offering a new paradigm. A large and diverse dataset of 49 drugs were collected from the literature for hepatocyte data in human. Based on all statistical parameters, this study confirms that the novel IVIVE method of Poulin et al. shows the greatest prediction performance among the IVIVE methods tested by using hepatocyte data. The superior prediction performance of this novel IVIVE method is again most pronounced for (a) drugs highly bound in blood, (b) drugs bound to albumin, and (c) low CL drugs. Because the novel IVIVE method has been developed particularly to improve the prediction accuracy for drugs with such properties, this study confirms its utility. Furthermore, the results of the current comparative analysis performed using hepatocyte data confirm the findings of a previous analysis made with microsomal data. Overall, the proposed novel IVIVE method offers a new paradigm for the prediction of hepatic metabolic CL particularly for drugs, which have the aforementioned properties, and, hence, this would contribute to a more accurate CL prediction for small molecules in drug discovery and development, interspecies scaling, and can potentially be used for the optimization of driving factors of CL in an attempt to facilitate the simulation of drug disposition by using the physiologically based pharmacokinetics (PBPK) model.

Integrity signals

Registry

Crossref · 10.1002/jps.23502

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Poulin, P., & Haddad, S.
Affiliations
Université de Montréal (CA)
Corresponding author
Poulin, Patrick
Venue
Journal of Pharmaceutical Sciences
Publisher
Elsevier BV
Published
2013-09
Volume
102
Issue
9
Pages
3239-3251
ISSN
0022-3549 (ISSN-L)
OpenAlex
W1970977102
Cited by
54
References
6
Citations by year
2026: 1 · 2025: 3 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 7 · 2020: 5 · 2019: 9 · 2018: 6 · 2017: 4 · 2016: 3 · 2015: 2 · 2014: 2 · 2013: 5
FWCI
4.39
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Pharmaceutical Sciences (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
  • Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
  • Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
Keywords
in vitro-in vivo extrapolation, intrinsic clearance, drug metabolism, albumin binding, interspecies scaling, physiologically based pharmacokinetic modeling, drug disposition, small molecules
MeSH terms
Blood Proteins, Pharmaceutical Preparations, Humans, Metabolic Clearance Rate, Models, Biological, Protein Binding, Hepatocytes

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review