Literature DB >> 28068459

Does Pharmacogenomic Testing Improve Clinical Outcomes for Major Depressive Disorder? A Systematic Review of Clinical Trials and Cost-Effectiveness Studies.

Joshua D Rosenblat1, Yena Lee2, Roger S McIntyre3,4.   

Abstract

OBJECTIVE: Pharmacogenomic testing has become scalable and available to the general public. Pharmacogenomics has shown promise for predicting antidepressant response and tolerability in the treatment of major depressive disorder (MDD). In theory, pharmacogenomics can improve clinical outcomes by guiding antidepressant selection and dosing. The current systematic review examines the extant literature to determine the impact of pharmacogenomic testing on clinical outcomes in MDD and assesses its cost-effectiveness. DATA SOURCES: The MEDLINE/PubMed and Google Scholar databases were systematically searched for relevant articles published prior to October 2015. Search terms included various combinations of the following: major depressive disorder (MDD), depression, mental illness, mood disorder, antidepressant, response, remission, outcome, pharmacogenetic, pharmacogenomics, pharmacodynamics, pharmacokinetic, genetic testing, genome wide association study (GWAS), CYP450, personalized medicine, cost-effectiveness, and pharmacoeconomics. STUDY SELECTION: Of the 66 records identified from the initial search, relevant clinical studies, written in English, assessing the cost-effectiveness and/or efficacy of pharmacogenomic testing for MDD were included. DATA EXTRACTION: Each publication was critically examined for relevant data.
RESULTS: Two nonrandomized, open-label, 8-week, prospective studies reported overall greater improvement in depressive symptom severity in the group of MDD subjects receiving psychiatric care guided by results of combinatorial pharmacogenomic testing (GeneSight) when compared to the unguided group. One industry-sponsored, randomized, double-blind, 10-week prospective study reported a trend for improved outcomes for the GeneSight-guided group; however, the trend did not reach statistical significance. Another industry-sponsored, randomized, double-blind, 12-week prospective study reported a 2.5-fold increase in remission rates in the CNSDose-guided group (P < .0001). One naturalistic, unblinded, industry-sponsored study showed clinical improvement when pharmacogenomics testing guided prescribing; however, this study lacked a control group. A single cost-effectiveness study concluded that single gene testing was not cost-effective. Conversely, a separate study reported that combinatorial pharmacogenomic testing is cost-effective.
CONCLUSIONS: A limited number of studies have shown promise for the clinical utility of pharmacogenomic testing; however, cost-effectiveness of pharmacogenomics, as well as demonstration of improved health outcomes, is not yet supported with replicated evidence. © Copyright 2017 Physicians Postgraduate Press, Inc.

Entities:  

Mesh:

Year:  2017        PMID: 28068459     DOI: 10.4088/JCP.15r10583

Source DB:  PubMed          Journal:  J Clin Psychiatry        ISSN: 0160-6689            Impact factor:   4.384


  34 in total

Review 1.  The role of depression pharmacogenetic decision support tools in shared decision making.

Authors:  Katarina Arandjelovic; Harris A Eyre; Eric Lenze; Ajeet B Singh; Michael Berk; Chad Bousman
Journal:  J Neural Transm (Vienna)       Date:  2017-10-29       Impact factor: 3.575

2.  Depression in the Primary Care Setting. Reply.

Authors:  Lawrence T Park; Carlos A Zarate
Journal:  N Engl J Med       Date:  2019-06-06       Impact factor: 91.245

3.  Next-Step Treatment Considerations for Patients With Treatment-Resistant Depression That Responds to Low-Dose Intravenous Ketamine.

Authors:  William V Bobo; Patricio Riva-Posse; Fernando S Goes; Sagar V Parikh
Journal:  Focus (Am Psychiatr Publ)       Date:  2020-04-23

Review 4.  Pharmacogenetic Decision Support Tools: A New Paradigm for Late-Life Depression?

Authors:  Ryan Abbott; Donald D Chang; Harris A Eyre; Chad A Bousman; David A Merrill; Helen Lavretsky
Journal:  Am J Geriatr Psychiatry       Date:  2017-05-25       Impact factor: 4.105

Review 5.  Deep learning in pharmacogenomics: from gene regulation to patient stratification.

Authors:  Alexandr A Kalinin; Gerald A Higgins; Narathip Reamaroon; Sayedmohammadreza Soroushmehr; Ari Allyn-Feuer; Ivo D Dinov; Kayvan Najarian; Brian D Athey
Journal:  Pharmacogenomics       Date:  2018-04-26       Impact factor: 2.533

6.  Polypharmacy: a healthcare conundrum with a pharmacogenetic solution.

Authors:  Cierra N Sharp; Mark W Linder; Roland Valdes
Journal:  Crit Rev Clin Lab Sci       Date:  2019-11-02       Impact factor: 6.250

Review 7.  Rapid evidence review of the comparative effectiveness, harms, and cost-effectiveness of pharmacogenomics-guided antidepressant treatment versus usual care for major depressive disorder.

Authors:  Kimberly Peterson; Eric Dieperink; Johanna Anderson; Erin Boundy; Lauren Ferguson; Mark Helfand
Journal:  Psychopharmacology (Berl)       Date:  2017-04-29       Impact factor: 4.530

Review 8.  Depression in the Primary Care Setting.

Authors:  Lawrence T Park; Carlos A Zarate
Journal:  N Engl J Med       Date:  2019-02-07       Impact factor: 91.245

9.  Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis.

Authors:  Alik S Widge; M Taha Bilge; Rebecca Montana; Weilynn Chang; Carolyn I Rodriguez; Thilo Deckersbach; Linda L Carpenter; Ned H Kalin; Charles B Nemeroff
Journal:  Am J Psychiatry       Date:  2018-10-03       Impact factor: 18.112

10.  Antidepressant Outcomes Predicted by Genetic Variation in Corticotropin-Releasing Hormone Binding Protein.

Authors:  Chloe P O'Connell; Andrea N Goldstein-Piekarski; Charles B Nemeroff; Alan F Schatzberg; Charles Debattista; Tania Carrillo-Roa; Elisabeth B Binder; Boadie W Dunlop; W Edward Craighead; Helen S Mayberg; Leanne M Williams
Journal:  Am J Psychiatry       Date:  2017-12-15       Impact factor: 18.112

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