Literature DB >> 18381489

Prediction of pharmacokinetic drug-drug interactions using human hepatocyte suspension in plasma and cytochrome P450 phenotypic data. II. In vitro-in vivo correlation with ketoconazole.

Chuang Lu1, Panos Hatsis, Cicely Berg, Frank W Lee, Suresh K Balani.   

Abstract

Traditional cytochrome P450 (P450) based drug-drug interaction (DDI) predictions are based on the ratio of an inhibitor's physiological concentration [I] and its inhibition constant K(i). Determining [I] at the enzymatic site, although critical for predicting clinical DDIs, remains a technical challenge. In our previous study, a novel approach using cryopreserved human hepatocytes suspended in human plasma was investigated to mimic the in vivo concentration of ketoconazole at the enzymatic site (Lu et al., 2007), effectively eliminating the estimation of the elusive [I] value. P450 inhibition in this system appears to model that in vivo. Using the ketoconazole inhibition information in a human hepatocyte-plasma suspension together with quantitative P450 phenotypic information, we successfully predicted the pharmacokinetic DDIs for a small set of drugs, such as theophylline, tolbutamide, omeprazole, desipramine, midazolam, loratadine, cyclosporine, and alprazolam, as well as an investigational compound. For the applicability of this model on a wider scale the in vitro-in vivo correlation data set needed to be expanded. However, for most drugs in the literature there is not enough quantitative information on the involvement of individual P450s to predict DDIs retrospectively. To facilitate that, in this study we determined quantitative P450 phenotyping for seven marketed drugs: budesonide, buprenorphine, loratadine, sirolimus, tacrolimus, docetaxel, and methylprednisolone. Augmentation of the new data set with the one generated previously produced broader a database that provided further support for the wider applicability of this approach using ketoconazole as a potent CYP3A inhibitor. This application is predicted to be equally effective with other P450 inhibitors that are not substrates of efflux pumps.

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Year:  2008        PMID: 18381489     DOI: 10.1124/dmd.107.018796

Source DB:  PubMed          Journal:  Drug Metab Dispos        ISSN: 0090-9556            Impact factor:   3.922


  8 in total

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Authors:  Karthik Venkatakrishnan; Michael D Pickard; Lisa L von Moltke
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2.  Human hepatocyte assessment of imatinib drug-drug interactions - complexities in clinical translation.

Authors:  Jan H Beumer; Venkateswaran C Pillai; Robert A Parise; Susan M Christner; Brian F Kiesel; Michelle A Rudek; Raman Venkataramanan
Journal:  Br J Clin Pharmacol       Date:  2015-09-19       Impact factor: 4.335

3.  A Generic Model for Quantitative Prediction of Interactions Mediated by Efflux Transporters and Cytochromes: Application to P-Glycoprotein and Cytochrome 3A4.

Authors:  Michel Tod; S Goutelle; N Bleyzac; L Bourguignon
Journal:  Clin Pharmacokinet       Date:  2019-04       Impact factor: 6.447

4.  Text Mining Driven Drug-Drug Interaction Detection.

Authors:  Su Yan; Xiaoqian Jiang; Ying Chen
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2013

5.  Irreversible Enzyme Inhibition Kinetics and Drug-Drug Interactions.

Authors:  Michael Mohutsky; Stephen D Hall
Journal:  Methods Mol Biol       Date:  2021

6.  Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s.

Authors:  Bo Lindmark; Anna Lundahl; Kajsa P Kanebratt; Tommy B Andersson; Emre M Isin
Journal:  Br J Pharmacol       Date:  2018-04-19       Impact factor: 8.739

7.  Pharmacointeraction network models predict unknown drug-drug interactions.

Authors:  Aurel Cami; Shannon Manzi; Alana Arnold; Ben Y Reis
Journal:  PLoS One       Date:  2013-04-19       Impact factor: 3.240

8.  SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions.

Authors:  Saskia Preissner; Katharina Kroll; Mathias Dunkel; Christian Senger; Gady Goldsobel; Daniel Kuzman; Stefan Guenther; Rainer Winnenburg; Michael Schroeder; Robert Preissner
Journal:  Nucleic Acids Res       Date:  2009-11-24       Impact factor: 16.971

  8 in total

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