Literature DB >> 18483837

The quantitative prediction of CYP-mediated drug interaction by physiologically based pharmacokinetic modeling.

Motohiro Kato1, Yoshihisa Shitara, Hitoshi Sato, Kunihiro Yoshisue, Masaru Hirano, Toshihiko Ikeda, Yuichi Sugiyama.   

Abstract

PURPOSE: The objective is to confirm if the prediction of the drug-drug interaction using a physiologically based pharmacokinetic (PBPK) model is more accurate. In vivo Ki values were estimated using PBPK model to confirm whether in vitro Ki values are suitable.
METHOD: The plasma concentration-time profiles for the substrate with coadministration of an inhibitor were collected from the literature and were fitted to the PBPK model to estimate the in vivo Ki values. The AUC ratios predicted by the PBPK model using in vivo Ki values were compared with those by the conventional method assuming constant inhibitor concentration.
RESULTS: The in vivo Ki values of 11 inhibitors were estimated. When the in vivo Ki values became relatively lower, the in vitro Ki values were overestimated. This discrepancy between in vitro and in vivo Ki values became larger with an increase in lipophilicity. The prediction from the PBPK model involving the time profile of the inhibitor concentration was more accurate than the prediction by the conventional methods.
CONCLUSION: A discrepancy between the in vivo and in vitro Ki values was observed. The prediction using in vivo Ki values and the PBPK model was more accurate than the conventional methods.

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Year:  2008        PMID: 18483837     DOI: 10.1007/s11095-008-9607-2

Source DB:  PubMed          Journal:  Pharm Res        ISSN: 0724-8741            Impact factor:   4.200


  23 in total

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Journal:  J Pharm Sci       Date:  2002-05       Impact factor: 3.534

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Authors:  Rupert P Austin; Patrick Barton; Scott L Cockroft; Mark C Wenlock; Robert J Riley
Journal:  Drug Metab Dispos       Date:  2002-12       Impact factor: 3.922

3.  Substrate-dependent modulation of CYP3A4 catalytic activity: analysis of 27 test compounds with four fluorometric substrates.

Authors:  D M Stresser; A P Blanchard; S D Turner; J C Erve; A A Dandeneau; V P Miller; C L Crespi
Journal:  Drug Metab Dispos       Date:  2000-12       Impact factor: 3.922

4.  The conduct of in vitro and in vivo drug-drug interaction studies: a Pharmaceutical Research and Manufacturers of America (PhRMA) perspective.

Authors:  Thorir D Bjornsson; John T Callaghan; Heidi J Einolf; Volker Fischer; Lawrence Gan; Scott Grimm; John Kao; S Peter King; Gerald Miwa; Lan Ni; Gondi Kumar; James McLeod; R Scott Obach; Stanley Roberts; Amy Roe; Anita Shah; Fred Snikeris; John T Sullivan; Donald Tweedie; Jose M Vega; John Walsh; Steven A Wrighton
Journal:  Drug Metab Dispos       Date:  2003-07       Impact factor: 3.922

5.  Which concentration of the inhibitor should be used to predict in vivo drug interactions from in vitro data?

Authors:  Kiyomi Ito; Koji Chiba; Masato Horikawa; Michi Ishigami; Naomi Mizuno; Jun Aoki; Yasumasa Gotoh; Takafumi Iwatsubo; Shin-ichi Kanamitsu; Motohiro Kato; Iichiro Kawahara; Kayoko Niinuma; Akiko Nishino; Norihito Sato; Yuko Tsukamoto; Kaoru Ueda; Tomoo Itoh; Yuichi Sugiyama
Journal:  AAPS PharmSci       Date:  2002

6.  The intestinal first-pass metabolism of substrates of CYP3A4 and P-glycoprotein-quantitative analysis based on information from the literature.

Authors:  Motohiro Kato; Koji Chiba; Akihiro Hisaka; Michi Ishigami; Makoto Kayama; Naomi Mizuno; Yoshinori Nagata; Susumu Takakuwa; Yuko Tsukamoto; Kaoru Ueda; Hiroyuki Kusuhara; Kiyomi Ito; Yuichi Sugiyama
Journal:  Drug Metab Pharmacokinet       Date:  2003       Impact factor: 3.614

Review 7.  Prediction of pharmacokinetic alterations caused by drug-drug interactions: metabolic interaction in the liver.

Authors:  K Ito; T Iwatsubo; S Kanamitsu; K Ueda; H Suzuki; Y Sugiyama
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Authors:  M Ishigam; M Uchiyama; T Kondo; H Iwabuchi; S Inoue; W Takasaki; T Ikeda; T Komai; K Ito; Y Sugiyama
Journal:  Pharm Res       Date:  2001-05       Impact factor: 4.200

9.  Role of itraconazole metabolites in CYP3A4 inhibition.

Authors:  Nina Isoherranen; Kent L Kunze; Kyle E Allen; Wendel L Nelson; Kenneth E Thummel
Journal:  Drug Metab Dispos       Date:  2004-07-08       Impact factor: 3.922

10.  Characterization of the selectivity and mechanism of human cytochrome P450 inhibition by the human immunodeficiency virus-protease inhibitor nelfinavir mesylate.

Authors:  J H Lillibridge; B H Liang; B M Kerr; S Webber; B Quart; B V Shetty; C A Lee
Journal:  Drug Metab Dispos       Date:  1998-07       Impact factor: 3.922

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  18 in total

1.  Physiologically based modeling of pravastatin transporter-mediated hepatobiliary disposition and drug-drug interactions.

Authors:  Manthena V S Varma; Yurong Lai; Bo Feng; John Litchfield; Theunis C Goosen; Arthur Bergman
Journal:  Pharm Res       Date:  2012-05-26       Impact factor: 4.200

Review 2.  Predicting drug-drug interactions: an FDA perspective.

Authors:  Lei Zhang; Yuanchao Derek Zhang; Ping Zhao; Shiew-Mei Huang
Journal:  AAPS J       Date:  2009-05-06       Impact factor: 4.009

3.  Response to the comment on the article "physiologically based modeling of pravastatin transporter-mediated hepatobiliary disposition and drug-drug interactions".

Authors:  Manthena V S Varma; Yurong Lai; Bo Feng; John Litchfield; Theunis C Goosen; Arthur Bergman
Journal:  Pharm Res       Date:  2013-03-06       Impact factor: 4.200

Review 4.  Drug-drug interaction studies: regulatory guidance and an industry perspective.

Authors:  Thomayant Prueksaritanont; Xiaoyan Chu; Christopher Gibson; Donghui Cui; Ka Lai Yee; Jeanine Ballard; Tamara Cabalu; Jerome Hochman
Journal:  AAPS J       Date:  2013-03-30       Impact factor: 4.009

5.  Physiologically Based Pharmacokinetic (PBPK) Modeling of Pitavastatin and Atorvastatin to Predict Drug-Drug Interactions (DDIs).

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Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-08       Impact factor: 2.441

Review 6.  Computational approaches to analyse and predict small molecule transport and distribution at cellular and subcellular levels.

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7.  Human blood concentrations of cotinine, a biomonitoring marker for tobacco smoke, extrapolated from nicotine metabolism in rats and humans and physiologically based pharmacokinetic modeling.

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Journal:  Int J Environ Res Public Health       Date:  2010-09-01       Impact factor: 3.390

8.  Prediction of Cyclosporin-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Model Characterizing Interplay of Drug Transporters and Enzymes.

Authors:  Yiting Yang; Ping Li; Zexin Zhang; Zhongjian Wang; Li Liu; Xiaodong Liu
Journal:  Int J Mol Sci       Date:  2020-09-24       Impact factor: 5.923

Review 9.  Prediction of pharmacokinetics and drug-drug interactions when hepatic transporters are involved.

Authors:  Rui Li; Hugh A Barton; Manthena V Varma
Journal:  Clin Pharmacokinet       Date:  2014-08       Impact factor: 6.447

Review 10.  Combining Chimeric Mice with Humanized Liver, Mass Spectrometry, and Physiologically-Based Pharmacokinetic Modeling in Toxicology.

Authors:  Hiroshi Yamazaki; Hiroshi Suemizu; Marina Mitsui; Makiko Shimizu; F Peter Guengerich
Journal:  Chem Res Toxicol       Date:  2016-07-05       Impact factor: 3.739

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