Literature DB >> 9597163

Quantitative prediction of in vivo drug clearance and drug interactions from in vitro data on metabolism, together with binding and transport.

K Ito1, T Iwatsubo, S Kanamitsu, Y Nakajima, Y Sugiyama.   

Abstract

It is of great importance to predict in vivo pharmacokinetics in humans based on in vitro data. We summarize recent findings of the quantitative prediction of the hepatic metabolic clearance from in vitro studies using human liver microsomes, hepatocytes, or P450 isozyme recombinant systems. Furthermore, we propose a method to predict pharmacokinetic alterations caused by drug-drug interactions that is based on in vitro metabolic inhibition studies using human liver microsomes or human enzyme expression systems. Although we attempt to avoid the false negative prediction, the inhibitory effect was underestimated in some cases, indicating the possible contribution of the active transport into hepatocytes and/or interactions at the processes other than the hepatic metabolism, such as the metabolism and transport processes during gastrointestinal absorption.

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Year:  1998        PMID: 9597163     DOI: 10.1146/annurev.pharmtox.38.1.461

Source DB:  PubMed          Journal:  Annu Rev Pharmacol Toxicol        ISSN: 0362-1642            Impact factor:   13.820


  43 in total

1.  Quantitative prediction of in vivo drug-drug interactions from in vitro data based on physiological pharmacokinetics: use of maximum unbound concentration of inhibitor at the inlet to the liver.

Authors:  S Kanamitsu; K Ito; Y Sugiyama
Journal:  Pharm Res       Date:  2000-03       Impact factor: 4.200

2.  Prediction of in vivo interaction between triazolam and erythromycin based on in vitro studies using human liver microsomes and recombinant human CYP3A4.

Authors:  S Kanamitsu; K Ito; C E Green; C A Tyson; N Shimada; Y Sugiyama
Journal:  Pharm Res       Date:  2000-04       Impact factor: 4.200

Review 3.  Prediction of hepatic metabolic clearance: comparison and assessment of prediction models.

Authors:  J Zuegge; G Schneider; P Coassolo; T Lavé
Journal:  Clin Pharmacokinet       Date:  2001       Impact factor: 6.447

4.  Effects of intestinal CYP3A4 and P-glycoprotein on oral drug absorption--theoretical approach.

Authors:  K Ito; H Kusuhara; Y Sugiyama
Journal:  Pharm Res       Date:  1999-02       Impact factor: 4.200

5.  The use of in vitro metabolic stability for rapid selection of compounds in early discovery based on their expected hepatic extraction ratios.

Authors:  Yan Yi Lau; Gopal Krishna; Nathan P Yumibe; Diane E Grotz; Elpida Sapidou; Laura Norton; Inhou Chu; Cliff Chen; A D Soares; Chin-Chung Lin
Journal:  Pharm Res       Date:  2002-11       Impact factor: 4.200

6.  High-throughput screening assays for CYP2B6 metabolism and inhibition using fluorogenic vivid substrates.

Authors:  Bryan D Marks; Tony A Goossens; Heidi A Braun; Mary S Ozers; Ronald W Smith; Connie Lebakken; Olga V Trubetskoy
Journal:  AAPS PharmSci       Date:  2003

7.  Inter-individual variability in levels of human microsomal protein and hepatocellularity per gram of liver.

Authors:  Z E Wilson; A Rostami-Hodjegan; J L Burn; A Tooley; J Boyle; S W Ellis; G T Tucker
Journal:  Br J Clin Pharmacol       Date:  2003-10       Impact factor: 4.335

8.  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

9.  Improved proteostasis in the secretory pathway rescues Alzheimer's disease in the mouse.

Authors:  Yajing Peng; Mi Jin Kim; Rikki Hullinger; Kenneth J O'Riordan; Corinna Burger; Mariana Pehar; Luigi Puglielli
Journal:  Brain       Date:  2016-01-19       Impact factor: 13.501

10.  Quantitative drug interactions prediction system (Q-DIPS): a dynamic computer-based method to assist in the choice of clinically relevant in vivo studies.

Authors:  P Bonnabry; J Sievering; T Leemann; P Dayer
Journal:  Clin Pharmacokinet       Date:  2001       Impact factor: 6.447

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