Toward a new paradigm for the efficient in vitro–in vivo extrapolation of metabolic clearance in humans from hepatocyte data
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
Cites
Statistics
Claims the paper makes
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?
Poulin et al.'s mechanistic IVIVE model overcomes the prediction of human CL for drugs with low CLint and high binding affinity for proteins commonly encountered when predicting human CL from in vitro data.
“This mechanistic IVIVE model overcomes the prediction of human CL for drugs with low CLint and high binding affinity for proteins commonly encountered when predicting human CL from in vitro data.”
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 [83]
Cites
Reduced elimination of IgG antibodies by engineering the variable region
Igawa, T., Tsunoda, H., Tachibana, T., Maeda, A., Mimoto, F., Moriyama, C., Nanami, M., Sekimori, Y., Nabuchi, Y., Aso, Y., et al. (2010) · Protein Engineering Design and Selection · doi:10.1093/protein/gzq009 · Cited by 265 · see citing works
Ring, B. J., Chien, J. Y., Adkison, K. K., Jones, H. M., Rowland, M., Jones, R. D., Yates, J. W. T., Ku, M. S., Gibson, C. R., He, H., et al. (2011) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.22552 · Cited by 206 · see citing works
Self-citationPoulin, P., Kenny, J. R., Hop, C. E. C. A., & Haddad, S. (2012) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.22792 · Cited by 109 · see citing works
Self-citationNovel In Vitro-In Vivo Extrapolation (IVIVE) Method to Predict Hepatic Organ Clearance in Rat
Umehara, K.-I., & Camenisch, G. (2012) · Pharmaceutical Research · doi:10.1007/s11095-011-0607-2 · Cited by 65 · see citing works
Poulin, P., Hop, C. E. C. A., Ho, Q., Halladay, J. S., Haddad, S., & Kenny, J. R. (2012) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.23288 · Cited by 64 · see citing works
Self-citationHallifax, D., & Houston, J. B. (2012) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.23202 · Cited by 60 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bender, A., & Cortes-Ciriano, I. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.11.037 · Cited by 232 · see citing works
Choi, G.-W., Lee, Y.-B., & Cho, H.-Y. (2019) · Pharmaceutics · doi:10.3390/pharmaceutics11040168 · Cited by 76 · see citing works
“[72,83,84] have developed a mechanistic IVIVE model based on two additional factors including pH differences between extracellular and intracellular water in liver, and protein-facilitated uptake induced by potential ionic interactions between protein-albumin bound drug complex and cell surface.”
Background
- Authors
- Poulin, P., & Haddad, S.
- Affiliations
- Université de Montréal (CA)
- Corresponding author
- Poulin, Patrick
- ORCID
- Haddad, Sami
- 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