Literature DB >> 17050792

Population-based assessments of clinical drug-drug interactions: qualitative indices or quantitative measures?

Honghui Zhou1.   

Abstract

Population-based assessments of drug-drug interactions have become more common since the introduction and acceptance of the population pharmacokinetic approach. Unlike traditional methods, population-based studies provide clinically relevant results that can be applied directly to a target patient population. Furthermore, population-based studies do not demand the traditional requirements of intensive pharmacokinetic sampling, rigorous inpatient stays, or stringent assessment schedules. As such, the population-based approach can effectively be used to confirm known drug-drug interactions and further characterize anticipated interactions. A prospectively designed analysis can also reveal drug-drug interactions that might otherwise have gone undetected with traditional methods. Ultimately, these results could help to alleviate clinicians' concerns about using widely marketed drugs in combination therapies and also reduce patients' risk of experiencing unacceptable side effects. This article intends to provide a balanced overview of the population-based approach and its merits, drawbacks, and potential utility in the assessment of drug-drug interactions during clinical drug development.

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Year:  2006        PMID: 17050792     DOI: 10.1177/0091270006294278

Source DB:  PubMed          Journal:  J Clin Pharmacol        ISSN: 0091-2700            Impact factor:   3.126


  7 in total

Review 1.  Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group.

Authors:  Peter L Bonate; Malidi Ahamadi; Nageshwar Budha; Amparo de la Peña; Justin C Earp; Ying Hong; Mats O Karlsson; Patanjali Ravva; Ana Ruiz-Garcia; Herbert Struemper; Janet R Wade
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-02-02       Impact factor: 2.745

2.  Non-Bayesian knowledge propagation using model-based analysis of data from multiple clinical studies.

Authors:  Jakob Ribbing; Andrew C Hooker; E Niclas Jonsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-11-08       Impact factor: 2.745

3.  Therapeutic protein drug-drug interactions: navigating the knowledge gaps-highlights from the 2012 AAPS NBC Roundtable and IQ Consortium/FDA workshop.

Authors:  Jane R Kenny; Maggie M Liu; Andrew T Chow; Justin C Earp; Raymond Evers; J Greg Slatter; Diane D Wang; Lei Zhang; Honghui Zhou
Journal:  AAPS J       Date:  2013-06-21       Impact factor: 4.009

4.  Therapeutic targeting of the IL-12/23 pathways: generation and characterization of ustekinumab.

Authors:  Jacqueline M Benson; Clifford W Sachs; George Treacy; Honghui Zhou; Charles E Pendley; Carrie M Brodmerkel; Gopi Shankar; Mary A Mascelli
Journal:  Nat Biotechnol       Date:  2011-07       Impact factor: 54.908

5.  Power estimation using a population pharmacokinetics model with optimal design by clinical trial simulations: application in pharmacokinetic drug-drug interaction studies.

Authors:  Shuying Yang; Misba Beerahee
Journal:  Eur J Clin Pharmacol       Date:  2010-12-02       Impact factor: 2.953

Review 6.  Update on Therapeutic Protein-Drug Interaction: Information in Labeling.

Authors:  Xing Jing; Ping Ji; Sarah J Schrieber; Elimika P Fletcher; Chandrahas Sahajwalla
Journal:  Clin Pharmacokinet       Date:  2020-01       Impact factor: 6.447

7.  Detecting signals of drug-drug interactions in a spontaneous reports database.

Authors:  Bharat T Thakrar; Sabine Borel Grundschober; Lucette Doessegger
Journal:  Br J Clin Pharmacol       Date:  2007-05-15       Impact factor: 4.335

  7 in total

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