Literature DB >> 11465042

Sense and nonsense in the prediction of drug-drug interactions.

J H Lin1.   

Abstract

Drug interactions are always a major concern in medicine and within the pharmaceutical industry. Fatal drug interactions have been reported, and several prominent drugs have been withdrawn from the market because of serious adverse reactions related to drug interactions. Therefore, drug interactions represent not only a medical problem for clinicians, but also an economic loss for pharmaceutical companies. Today, many pharmaceutical companies are predicting potential interactions of new drug candidates in an attempt to minimize such losses and to more effectively safeguard the welfare of patients. Can in vivo drug interactions be predicted accurately from in vitro metabolic studies? Should the prediction be qualitative or quantitative? These are the fundamental questions that industrial drug metabolism scientists must confront daily. Prediction of in vivo drug interactions from in vitro metabolic data is highly controversial, because of the complexities of factors that are involved in drug interactions. Some scientists believe that quantitative prediction of drug interaction is possible, whereas others are less optimistic, and believe that quantitative prediction is extremely difficult, if not impossible. The purpose of this review is to present and discuss the technical problems inherent in estimating in vitro Ki values and in measuring inhibitor concentration at the active-site of enzymes. Theoretic considerations are briefly reviewed, and representative examples are drawn from literature to illustrate the sense and nonsense in predicting in vivo drug interactions.

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Year:  2000        PMID: 11465042     DOI: 10.2174/1389200003338947

Source DB:  PubMed          Journal:  Curr Drug Metab        ISSN: 1389-2002            Impact factor:   3.731


  13 in total

Review 1.  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

2.  Effects of commonly used excipients on the expression of CYP3A4 in colon and liver cells.

Authors:  Leslie Tompkins; Caitlin Lynch; Sam Haidar; James Polli; Hongbing Wang
Journal:  Pharm Res       Date:  2010-05-26       Impact factor: 4.200

3.  Qualitative pharmacokinetic modeling of drugs.

Authors:  Richard Boyce; Carol Collins; John Horn; Ira J Kalet
Journal:  AMIA Annu Symp Proc       Date:  2005

4.  Prediction of in vivo drug-drug interactions from in vitro data: impact of incorporating parallel pathways of drug elimination and inhibitor absorption rate constant.

Authors:  Hayley S Brown; Kiyomi Ito; Aleksandra Galetin; J Brian Houston
Journal:  Br J Clin Pharmacol       Date:  2005-11       Impact factor: 4.335

5.  In vitro characterization of the human biotransformation and CYP reaction phenotype of ET-743 (Yondelis, Trabectidin), a novel marine anti-cancer drug.

Authors:  Esther F A Brandon; Rolf W Sparidans; Kees-Jan Guijt; Sjoerd Löwenthal; Irma Meijerman; Jos H Beijnen; Jan H M Schellens
Journal:  Invest New Drugs       Date:  2006-01       Impact factor: 3.850

Review 6.  CYP induction-mediated drug interactions: in vitro assessment and clinical implications.

Authors:  Jiunn H Lin
Journal:  Pharm Res       Date:  2006-05-26       Impact factor: 4.200

7.  Quantitative prediction of in vivo inhibitory interactions involving glucuronidated drugs from in vitro data: the effect of fluconazole on zidovudine glucuronidation.

Authors:  Verawan Uchaipichat; Leanne K Winner; Peter I Mackenzie; David J Elliot; J Andrew Williams; John O Miners
Journal:  Br J Clin Pharmacol       Date:  2006-04       Impact factor: 4.335

Review 8.  Predicting the clinical relevance of drug interactions from pre-approval studies.

Authors:  Silvio Caccia; Silvio Garattini; Luca Pasina; Alessandro Nobili
Journal:  Drug Saf       Date:  2009       Impact factor: 5.606

Review 9.  Factors affecting the clinical development of cytochrome p450 3A substrates.

Authors:  Megan A Gibbs; Natilie A Hosea
Journal:  Clin Pharmacokinet       Date:  2003       Impact factor: 6.447

10.  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

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