Literature DB >> 23327581

Consistency of drug-drug and gene-drug interaction information in US FDA-approved drug labels.

Daniela J Conrado1, Hobart L Rogers, Issam Zineh, Michael A Pacanowski.   

Abstract

AIM: To characterize concordance between clinically relevant drug-drug interactions (DDIs) related to CYP2C19, CYP2D6 and CYP2C9 and their analogous gene-drug interactions (GDIs) in US FDA-approved drug labeling.
METHODS: We selected prototypical CYP2C19, CYP2D6 and CYP2C9 inhibitors and abstracted all respective interacting drugs via a tertiary resource used in the clinical setting. We then selected only CYP2C19, CYP2D6 and CYP2C9 metabolism-related DDIs requiring enhanced clinical monitoring, dose adjustment or use of alternative drugs. Labeling and management strategies on DDIs and GDIs were compared.
RESULTS: Among the drug labels with DDI information, 73% of them describe the analogous GDI. Of the 65 drug labels, 43 and 17% had specific management recommendations for DDIs and GDIs, respectively. In general, GDI management recommendations were concordant with DDI management recommendations in terms of specific dose adjustments or use of alternative drugs.
CONCLUSION: The FDA has recognized genetic differences in drug metabolism where clinically relevant DDIs trigger dose adjustment or use of alternative drugs.

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Year:  2013        PMID: 23327581     DOI: 10.2217/pgs.12.203

Source DB:  PubMed          Journal:  Pharmacogenomics        ISSN: 1462-2416            Impact factor:   2.533


  12 in total

Review 1.  The role of ADME pharmacogenomics in early clinical trials: perspective of the Industry Pharmacogenomics Working Group (I-PWG).

Authors:  Larry Tremaine; William Brian; Terrye DelMonte; Stephan Francke; Peter Groenen; Keith Johnson; Lei Li; Kimberly Pearson; Jean-Claude Marshall
Journal:  Pharmacogenomics       Date:  2015-11-30       Impact factor: 2.533

2.  Comparing cytochrome P450 pharmacogenetic information available on United States drug labels and European Union Summaries of Product Characteristics.

Authors:  J Reis-Pardal; A Rodrigues; E Rodrigues; F Fernandez-Llimos
Journal:  Pharmacogenomics J       Date:  2016-05-31       Impact factor: 3.550

3.  Clinical validity: Combinatorial pharmacogenomics predicts antidepressant responses and healthcare utilizations better than single gene phenotypes.

Authors:  C A Altar; J M Carhart; J D Allen; D K Hall-Flavin; B M Dechairo; J G Winner
Journal:  Pharmacogenomics J       Date:  2015-02-17       Impact factor: 3.550

Review 4.  A Scoping Review of the Evidence Behind Cytochrome P450 2D6 Isoenzyme Inhibitor Classifications.

Authors:  Emily J Cicali; D Max Smith; Benjamin Q Duong; Lukas G Kovar; Larisa H Cavallari; Julie A Johnson
Journal:  Clin Pharmacol Ther       Date:  2020-02-13       Impact factor: 6.875

5.  Integrating pharmacogenetic testing into primary care.

Authors:  Susanne B Haga
Journal:  Expert Rev Precis Med Drug Dev       Date:  2017-11-03

Review 6.  Ethical and public policy challenges for pharmacogenomics.

Authors:  Elliot S Gershon; Ney Alliey-Rodriguez; Kay Grennan
Journal:  Dialogues Clin Neurosci       Date:  2014-12       Impact factor: 5.986

7.  Pharmacogenetic information for patients on drug labels.

Authors:  Susanne B Haga; Rachel Mills; Jivan Moaddeb
Journal:  Pharmgenomics Pers Med       Date:  2014-10-03

8.  CYP2C9 genotype vs. metabolic phenotype for individual drug dosing--a correlation analysis using flurbiprofen as probe drug.

Authors:  Silvia Vogl; Roman W Lutz; Gilbert Schönfelder; Werner K Lutz
Journal:  PLoS One       Date:  2015-03-16       Impact factor: 3.240

9.  Pharmacogenomic biomarker information differences between drug labels in the United States and Hungary: implementation from medical practitioner view.

Authors:  Reka Varnai; Istvan Szabo; Greta Tarlos; Laszlo Jozsef Szentpeteri; Attila Sik; Sandor Balogh; Csilla Sipeky
Journal:  Pharmacogenomics J       Date:  2019-12-02       Impact factor: 3.550

10.  Comparing sensitivity to change using the 6-item versus the 17-item Hamilton depression rating scale in the GUIDED randomized controlled trial.

Authors:  Boadie W Dunlop; Sagar V Parikh; Anthony J Rothschild; Michael E Thase; Charles DeBattista; Charles R Conway; Brent P Forester; Francis M Mondimore; Richard C Shelton; Matthew Macaluso; Jennifer Logan; Paul Traxler; James Li; Holly Johnson; John F Greden
Journal:  BMC Psychiatry       Date:  2019-12-27       Impact factor: 3.630

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