Literature DB >> 3665341

An evaluation of population pharmacokinetics in therapeutic trials. Part II. Detection of a drug-drug interaction.

T H Grasela1, E J Antal, L Ereshefsky, B G Wells, R L Evans, R B Smith.   

Abstract

The use of observational data, collected during the routine clinical care of patients, has been advocated as a means to obtain clinically relevant information regarding the pharmacokinetic parameters of drugs. However, the validity of this approach and its proper role in new drug development is unclear. This study was performed to evaluate the ability of three approaches to estimate population pharmacokinetic parameters: the traditional approach, mixed-effect modeling, and a simple pharmacokinetic screen. The evaluation was performed with data collected during a multicenter, open-label study evaluating the efficacy, safety, and pharmacokinetics of imipramine and alprazolam in combination. The traditional pharmacokinetic study demonstrated a 20% decrease in the clearance of imipramine in the presence of 4 mg/day alprazolam. Mixed-effect modeling extends these findings by suggesting that the interaction is dependent on the simultaneous concentration of alprazolam, a finding that was not possible under the study design typically used for traditional pharmacokinetic studies. Although the simple screen suggests the presence of the drug-drug interaction, limited information regarding pharmacokinetic parameters is available and those parameters that can be estimated are biased.

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Year:  1987        PMID: 3665341     DOI: 10.1038/clpt.1987.174

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  16 in total

1.  Drug-drug pharmacodynamic interaction detection by a nonparametric population approach. Influence of design and of interindividual variability.

Authors:  Y Merlé; A Mallet; E Schmautz
Journal:  J Pharmacokinet Biopharm       Date:  1999-10

2.  Evaluation of hypothesis testing for comparing two populations using NONMEM analysis.

Authors:  D B White; C A Walawander; D Y Liu; T H Grasela
Journal:  J Pharmacokinet Biopharm       Date:  1992-06

3.  Covariance analysis of laboratory variance in steady-state serum phenytoin concentrations.

Authors:  H Costeff; Z Groswasser; N Soroker; G van Belle
Journal:  Clin Pharmacokinet       Date:  1991-04       Impact factor: 6.447

4.  An evaluation of point and interval estimates in population pharmacokinetics using NONMEM analysis.

Authors:  D B White; C A Walawander; Y Tung; T H Grasela
Journal:  J Pharmacokinet Biopharm       Date:  1991-02

Review 5.  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

Review 6.  Recommended reading in population pharmacokinetic pharmacodynamics.

Authors:  Peter L Bonate
Journal:  AAPS J       Date:  2005-10-05       Impact factor: 4.009

Review 7.  Integrated pharmacokinetics and pharmacodynamics in drug development.

Authors:  Jasper Dingemanse; Silke Appel-Dingemanse
Journal:  Clin Pharmacokinet       Date:  2007       Impact factor: 6.447

Review 8.  Expanding clinical applications of population pharmacodynamic modelling.

Authors:  C Minto; T Schnider
Journal:  Br J Clin Pharmacol       Date:  1998-10       Impact factor: 4.335

Review 9.  Drug interactions at the renal level. Implications for drug development.

Authors:  P L Bonate; K Reith; S Weir
Journal:  Clin Pharmacokinet       Date:  1998-05       Impact factor: 6.447

10.  The influence of assay variability on pharmacokinetic parameter estimation.

Authors:  D A Graves; C S Locke; K T Muir; R P Miller
Journal:  J Pharmacokinet Biopharm       Date:  1989-10
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