Physiologically Based Pharmacokinetic Models for Prediction of Complex CYP2C8 and OATP1B1 (SLCO1B1) Drug-Drug-Gene Interactions: A Modeling Network of Gemfibrozil, Repaglinide, Pioglitazone, Rifampicin, Clarithromycin and Itraconazole.


Journal

Clinical pharmacokinetics
ISSN: 1179-1926
Titre abrégé: Clin Pharmacokinet
Pays: Switzerland
ID NLM: 7606849

Informations de publication

Date de publication:
12 2019
Historique:
pubmed: 28 5 2019
medline: 12 9 2020
entrez: 27 5 2019
Statut: ppublish

Résumé

Drug-drug interactions (DDIs) and drug-gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the effect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogenous regarding their genetic background. The first objective of this study was to provide whole-body physiologically based pharmacokinetic (PBPK) models of important cytochrome P450 (CYP) 2C8 perpetrator and victim drugs, built and evaluated for DDI and DGI studies. The second objective was to apply these models to describe complex interactions with more than two interacting partners. PBPK models of the CYP2C8 and organic-anion-transporting polypeptide (OATP) 1B1 perpetrator drug gemfibrozil (parent-metabolite model) and the CYP2C8 victim drugs repaglinide (also an OATP1B1 substrate) and pioglitazone were developed using a total of 103 clinical studies. For evaluation, these models were applied to predict 34 different DDI studies, establishing a CYP2C8 and OATP1B1 PBPK DDI modeling network. The newly developed models show a good performance, accurately describing plasma concentration-time profiles, area under the plasma concentration-time curve (AUC) and maximum plasma concentration (C Whole-body PBPK models of gemfibrozil, repaglinide, and pioglitazone have been built and qualified for DDI and DGI prediction. PBPK modeling is applicable to investigate complex interactions between multiple drugs and genetic polymorphisms.

Sections du résumé

BACKGROUND
Drug-drug interactions (DDIs) and drug-gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the effect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogenous regarding their genetic background.
OBJECTIVES
The first objective of this study was to provide whole-body physiologically based pharmacokinetic (PBPK) models of important cytochrome P450 (CYP) 2C8 perpetrator and victim drugs, built and evaluated for DDI and DGI studies. The second objective was to apply these models to describe complex interactions with more than two interacting partners.
METHODS
PBPK models of the CYP2C8 and organic-anion-transporting polypeptide (OATP) 1B1 perpetrator drug gemfibrozil (parent-metabolite model) and the CYP2C8 victim drugs repaglinide (also an OATP1B1 substrate) and pioglitazone were developed using a total of 103 clinical studies. For evaluation, these models were applied to predict 34 different DDI studies, establishing a CYP2C8 and OATP1B1 PBPK DDI modeling network.
RESULTS
The newly developed models show a good performance, accurately describing plasma concentration-time profiles, area under the plasma concentration-time curve (AUC) and maximum plasma concentration (C
CONCLUSIONS
Whole-body PBPK models of gemfibrozil, repaglinide, and pioglitazone have been built and qualified for DDI and DGI prediction. PBPK modeling is applicable to investigate complex interactions between multiple drugs and genetic polymorphisms.

Identifiants

pubmed: 31129789
doi: 10.1007/s40262-019-00777-x
pii: 10.1007/s40262-019-00777-x
pmc: PMC6885506
doi:

Substances chimiques

Carbamates 0
Liver-Specific Organic Anion Transporter 1 0
Piperidines 0
SLCO1B1 protein, human 0
Itraconazole 304NUG5GF4
repaglinide 668Z8C33LU
CYP2C8 protein, human EC 1.14.14.1
Cytochrome P-450 CYP2C8 EC 1.14.14.1
Clarithromycin H1250JIK0A
Gemfibrozil Q8X02027X3
Rifampin VJT6J7R4TR
Pioglitazone X4OV71U42S

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1595-1607

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Auteurs

Denise Türk (D)

Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany.

Nina Hanke (N)

Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany.

Sarah Wolf (S)

Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany.

Sebastian Frechen (S)

Clinical Pharmacometrics, Bayer AG, Leverkusen, Germany.

Thomas Eissing (T)

Clinical Pharmacometrics, Bayer AG, Leverkusen, Germany.

Thomas Wendl (T)

Clinical Pharmacometrics, Bayer AG, Leverkusen, Germany.

Matthias Schwab (M)

Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.
Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany.
Department of Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany.

Thorsten Lehr (T)

Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany. thorsten.lehr@mx.uni-saarland.de.

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Classifications MeSH