Literature DB >> 10516934

Assessment of the quality and quantity of drug-drug interaction studies in recent NDA submissions: study design and data analysis issues.

S M Huang1, L J Lesko, R L Williams.   

Abstract

This report investigates the quality and quantity of drug-drug interaction studies in recent new drug applications (NDAs). Eighty-nine studies contained in 14 NDAs submitted between December 1995 and November 1996 to the U.S. Food and Drug Administration (FDA) were reviewed. The results indicated that the median number of clinical drug-drug interaction studies per NDA was 6, almost double that of a 1994-1995 survey. In vitro metabolism data were present in 70% of the submissions. More than 50% of the submissions contained interaction studies using a battery of drugs (cimetidine, digoxin, or warfarin) without optimal use of the in vitro metabolism or in vivo mass balance data. Various study designs using a median number of 12 subjects were employed in the evaluation of drug-drug interactions. Some of the important study design factors such as dose size, dosing regimen, dosing duration, and timing of coadministration were considered, although not consistently, by the sponsors in their study design. Seventy-five percent of the studies used normal, healthy male subjects, and 25% used patients for whom the new molecular entities were intended. In 33% of the studies, female subjects were also recruited. Although the majority (80%) of the submissions still used p-values to determine the significance of drug interactions, 30% used a more relevant equivalence approach with 90% confidence intervals for key pharmacokinetic and/or pharmacodynamic parameter ratios to assess the extent of drug interactions. Overall, 82% of the studies concluded no interaction. Although population pharmacokinetic analysis can be a useful tool in studying drug-drug interactions, only 21% of the submissions used this approach. In summary, this assessment reveals that the quantity and quality of drug-drug interaction studies in NDAs have improved over the years. These improvements, as well as others that can be implemented, should result in more informative labeling and better patient care. FDA guidance for industry dealing with the design, analysis, and labeling language of in vivo metabolic drug-drug interactions has been developed to assist sponsors and FDA reviewers with these issues.

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Year:  1999        PMID: 10516934     DOI: 10.1177/00912709922011764

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


  10 in total

Review 1.  Manufacturer's drug interaction and postmarketing adverse event data: what are appropriate uses?

Authors:  W K Kraft; S A Waldman
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2.  Cost-efficient higher-order crossover designs in comparative bioavailability studies.

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4.  Quantitative drug interactions prediction system (Q-DIPS): a dynamic computer-based method to assist in the choice of clinically relevant in vivo studies.

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5.  Effect of St John's wort and ginseng on the pharmacokinetics and pharmacodynamics of warfarin in healthy subjects.

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6.  Semi-mechanistic population pharmacokinetic drug-drug interaction modelling of a long half-life substrate and itraconazole.

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7.  Nonnucleoside reverse transcriptase inhibitor pharmacokinetics in a large unselected cohort of HIV-infected women.

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Review 8.  Herb-drug interactions with St John's wort (Hypericum perforatum): an update on clinical observations.

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9.  Common clinical conditions - age, low BMI, ritonavir use, mild renal impairment - affect tenofovir pharmacokinetics in a large cohort of HIV-infected women.

Authors:  Sanjiv M Baxi; Ruth M Greenblatt; Peter Bacchetti; Rebecca Scherzer; Howard Minkoff; Yong Huang; Kathryn Anastos; Mardge Cohen; Stephen J Gange; Mary Young; Michael G Shlipak; Monica Gandhi
Journal:  AIDS       Date:  2014-01-02       Impact factor: 4.177

Review 10.  Clinical-pharmacological strategies to assess drug interaction potential during drug development.

Authors:  J Kuhlmann; W Mück
Journal:  Drug Saf       Date:  2001       Impact factor: 5.228

  10 in total

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