Literature DB >> 26692192

Development of a Physiologically Based Pharmacokinetic Model for Itraconazole Pharmacokinetics and Drug-Drug Interaction Prediction.

Yuan Chen1, Fang Ma2,3, Tong Lu4, Nageshwar Budha4, Jin Yan Jin4, Jane R Kenny2, Harvey Wong2,5, Cornelis E C A Hop2, Jialin Mao2.   

Abstract

BACKGROUND AND OBJECTIVES: Physiologically based pharmacokinetic (PBPK) modeling for itraconazole has been challenging due to highly variable in vitro d ata used for 'bottom-up' model building. Under-prediction of pharmacokinetics and drug-drug interactions (DDIs) following multiple doses of itraconazole has limited the use of PBPK model simulation to aid an itraconazole clinical DDI study design. The aim of this work is to develop an itraconazole PBPK model predominantly using a 'top-down' approach to enable a more accurate pharmacokinetic and DDI prediction.
METHODS: An itraconazole PBPK model describing itraconazole and hydroxyl-itraconazole (OH-ITZ) was constructed in Simcyp(®). The key parameters that govern the pharmacokinetic profile, including non-linear clearance (i.e., maximum rate of reaction [V max] and the Michaelis-Menten constant [K m]) and volume of distribution for both itraconazole and OH-ITZ, were redefined by leveraging existing in vivo data. Model verification was performed by comparing the simulated itraconazole and OH-ITZ pharmacokinetic profiles with the observed clinical data. Finally, the model was used to simulate clinical DDIs between itraconazole and midazolam.
RESULTS: The developed PBPK model well-described the pharmacokinetics of itraconazole and OH-ITZ, and particularly captured their accumulation after repeated doses of itraconazole. This was verified with the observed data from 29 clinical studies where itraconazole solution or capsule was given as a single or multiple dose. The predicted DDI between itraconazole and midazolam was within 1.25-fold of the observed data for seven of ten studies and within 1.5-fold for nine of ten studies.
CONCLUSION: The improvement of the itraconazole PBPK model increased our confidence in using PBPK model simulations to optimize clinical itraconazole DDI study design.

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Year:  2016        PMID: 26692192     DOI: 10.1007/s40262-015-0352-5

Source DB:  PubMed          Journal:  Clin Pharmacokinet        ISSN: 0312-5963            Impact factor:   6.447


  38 in total

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Journal:  Antimicrob Agents Chemother       Date:  1993-04       Impact factor: 5.191

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6.  Prediction of human volume of distribution values for neutral and basic drugs. 2. Extended data set and leave-class-out statistics.

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Journal:  J Med Chem       Date:  2004-02-26       Impact factor: 7.446

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Journal:  Clin Pharmacol Ther       Date:  2007-05-09       Impact factor: 6.875

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Authors:  D Vijaya Bharathi; Kishore Kumar Hotha; P V Vidya Sagar; Sanagapati Sirish Kumar; Pandu Ranga Reddy; A Naidu; Ramesh Mullangi
Journal:  J Chromatogr B Analyt Technol Biomed Life Sci       Date:  2008-04-25       Impact factor: 3.205

9.  Pharmacokinetic modeling of the dosing interval dependency for the interaction between itraconazole and triazolam.

Authors:  A Toi; H Ohtani; M Tsujimoto; Y Sawada
Journal:  Int J Clin Pharmacol Ther       Date:  2010-06       Impact factor: 1.366

10.  Quantitative evaluation of pharmacokinetic inhibition of CYP3A substrates by ketoconazole: a simulation study.

Authors:  Ping Zhao; Isabelle Ragueneau-Majlessi; Lei Zhang; John M Strong; Kellie S Reynolds; Rene H Levy; Kenneth E Thummel; Shiew-Mei Huang
Journal:  J Clin Pharmacol       Date:  2009-03       Impact factor: 3.126

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2.  Recommendations for the Design of Clinical Drug-Drug Interaction Studies With Itraconazole Using a Mechanistic Physiologically-Based Pharmacokinetic Model.

Authors:  Yuan Chen; Tamara D Cabalu; Ernesto Callegari; Heidi Einolf; Lichuan Liu; Neil Parrott; Sheila Annie Peters; Edgar Schuck; Pradeep Sharma; Helen Tracey; Vijay V Upreti; Ming Zheng; Andy Z X Zhu; Stephen D Hall
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Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-11-06

5.  Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator.

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Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-05-02

6.  Prediction of the drug-drug interaction potential of the α1-acid glycoprotein bound, CYP3A4/CYP2C9 metabolized oncology drug, erdafitinib.

Authors:  Loeckie De Zwart; Jan Snoeys; Frank Jacobs; Lilian Y Li; Italo Poggesi; Peter Verboven; Ivo Goris; Ellen Scheers; Inneke Wynant; Mario Monshouwer; Rao N V S Mamidi
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