Literature DB >> 19418230

Predicting drug-drug interactions: an FDA perspective.

Lei Zhang1, Yuanchao Derek Zhang, Ping Zhao, Shiew-Mei Huang.   

Abstract

Pharmacokinetic drug interactions can lead to serious adverse events, and the evaluation of a new molecular entity's drug-drug interaction potential is an integral part of drug development and regulatory review prior to its market approval. Alteration of enzyme and/or transporter activities involved in the absorption, distribution, metabolism, or excretion of a new molecular entity by other concomitant drugs may lead to a change in exposure leading to altered response (safety or efficacy). Over the years, various in vitro methodologies have been developed to predict drug interaction potential in vivo. In vitro study has become a critical first step in the assessment of drug interactions. Well-executed in vitro studies can be used as a screening tool for the need for further in vivo assessment and can provide the basis for the design of subsequent in vivo drug interaction studies. Besides in vitro experiments, in silico modeling and simulation may also assist in the prediction of drug interactions. The recent FDA draft drug interaction guidance highlighted the in vitro models and criteria that may be used to guide further in vivo drug interaction studies and to construct informative labeling. This report summarizes critical elements in the in vitro evaluation of drug interaction potential during drug development and uses a case study to highlight the impact of in vitro information on drug labeling.

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Year:  2009        PMID: 19418230      PMCID: PMC2691466          DOI: 10.1208/s12248-009-9106-3

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  39 in total

1.  CYP3A4 drug interactions: correlation of 10 in vitro probe substrates.

Authors:  K E Kenworthy; J C Bloomer; S E Clarke; J B Houston
Journal:  Br J Clin Pharmacol       Date:  1999-11       Impact factor: 4.335

2.  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

Review 3.  Drug metabolism and drug interactions: application and clinical value of in vitro models.

Authors:  Karthik Venkatakrishnan; Lisa L von Moltke; R Scott Obach; David J Greenblatt
Journal:  Curr Drug Metab       Date:  2003-10       Impact factor: 3.731

4.  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

5.  The effects of dose staggering on metabolic drug-drug interactions.

Authors:  Jiansong Yang; Maria Kjellsson; Amin Rostami-Hodjegan; Geoffrey T Tucker
Journal:  Eur J Pharm Sci       Date:  2003-10       Impact factor: 4.384

Review 6.  Database analyses for the prediction of in vivo drug-drug interactions from in vitro data.

Authors:  Kiyomi Ito; Hayley S Brown; J Brian Houston
Journal:  Br J Clin Pharmacol       Date:  2004-04       Impact factor: 4.335

7.  Elucidation of distinct ligand binding sites for cytochrome P450 3A4.

Authors:  N A Hosea; G P Miller; F P Guengerich
Journal:  Biochemistry       Date:  2000-05-23       Impact factor: 3.162

8.  Fluvoxamine-theophylline interaction: gap between in vitro and in vivo inhibition constants toward cytochrome P4501A2.

Authors:  C Yao; K L Kunze; E D Kharasch; Y Wang; W F Trager; I Ragueneau; R H Levy
Journal:  Clin Pharmacol Ther       Date:  2001-11       Impact factor: 6.875

9.  Mibefradil is a P-glycoprotein substrate and a potent inhibitor of both P-glycoprotein and CYP3A in vitro.

Authors:  C Wandel; R B Kim; F P Guengerich; A J Wood
Journal:  Drug Metab Dispos       Date:  2000-08       Impact factor: 3.922

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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  60 in total

1.  Drug-drug interaction through molecular structure similarity analysis.

Authors:  Santiago Vilar; Rave Harpaz; Eugenio Uriarte; Lourdes Santana; Raul Rabadan; Carol Friedman
Journal:  J Am Med Inform Assoc       Date:  2012-05-30       Impact factor: 4.497

Review 2.  Quantitative clinical pharmacology is transforming drug regulation.

Authors:  Carl C Peck
Journal:  J Pharmacokinet Pharmacodyn       Date:  2010-10-27       Impact factor: 2.745

3.  Downregulation of Organic Anion Transporting Polypeptide (OATP) 1B1 Transport Function by Lysosomotropic Drug Chloroquine: Implication in OATP-Mediated Drug-Drug Interactions.

Authors:  Khondoker Alam; Sonia Pahwa; Xueying Wang; Pengyue Zhang; Kai Ding; Alaa H Abuznait; Lang Li; Wei Yue
Journal:  Mol Pharm       Date:  2016-02-01       Impact factor: 4.939

4.  Use of different parameters and equations for calculation of IC₅₀ values in efflux assays: potential sources of variability in IC₅₀ determination.

Authors:  Donna A Volpe; Salaheldin S Hamed; Lei K Zhang
Journal:  AAPS J       Date:  2013-12-13       Impact factor: 4.009

5.  Impact of genetic polymorphism on drug-drug interactions mediated by cytochromes: a general approach.

Authors:  Michel Tod; Christina Nkoud-Mongo; François Gueyffier
Journal:  AAPS J       Date:  2013-09-12       Impact factor: 4.009

6.  Inhibition of CYP2D6-mediated tramadol O-demethylation in methadone but not buprenorphine maintenance patients.

Authors:  Janet K Coller; Jennifer R Michalakas; Heather M James; Aaron L Farquharson; Joel Colvill; Jason M White; Andrew A Somogyi
Journal:  Br J Clin Pharmacol       Date:  2012-11       Impact factor: 4.335

7.  Ser100-Phosphorylated RORα Orchestrates CAR and HNF4α to Form Active Chromatin Complex in Response to Phenobarbital to Regulate Induction of CYP2B6.

Authors:  Muluneh Fashe; Takuyu Hashiguchi; Masahiko Negishi; Tatsuya Sueyoshi
Journal:  Mol Pharmacol       Date:  2020-01-10       Impact factor: 4.436

8.  Breast cancer resistance protein (ABCG2) determines distribution of genistein phase II metabolites: reevaluation of the roles of ABCG2 in the disposition of genistein.

Authors:  Zhen Yang; Wei Zhu; Song Gao; Taijun Yin; Wen Jiang; Ming Hu
Journal:  Drug Metab Dispos       Date:  2012-06-26       Impact factor: 3.922

Review 9.  Combining targeted therapies: practical issues to consider at the bench and bedside.

Authors:  Jordi Rodon; Jose Perez; Razelle Kurzrock
Journal:  Oncologist       Date:  2010-01-15

10.  In Vivo Imaging of Human MDR1 Transcription in the Brain and Spine of MDR1-Luciferase Reporter Mice.

Authors:  Kazuto Yasuda; Cynthia Cline; Yvonne S Lin; Rachel Scheib; Samit Ganguly; Ranjit K Thirumaran; Amarjit Chaudhry; Richard B Kim; Erin G Schuetz
Journal:  Drug Metab Dispos       Date:  2015-08-17       Impact factor: 3.922

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