Pharmacogenomics with antidepressants in the STAR*D study

Eugene Lin, Po-See Chen

Research output: Contribution to journalReview articlepeer-review

40 Citations (Scopus)

Abstract

Major depressive disorder is one of the most common psychiatric disorders worldwide. No single antidepressant has been shown to be more effective than any other in lifting depression, and the effectiveness of any particular antidepressant in an individual is difficult to predict; therefore, doctors must prescribe antidepressants based on educated guesses. SNPs can be used in clinical association studies to determine the contribution of genes to drug efficacy. Evidence is accumulating to suggest that the efficacy of antidepressants results from the combined effects of a number of genetic variants, such as SNPs. Although there are not enough data currently available to prove this hypothesis, an increasing number of genetic variants associated with antidepressant response are being discovered. In this article, we review the pharmacogenomics of the drug efficacy of antidepressants in major depressive disorder. First, we survey the SNPs and genes identified as genetic markers that are correlated and associated with the drug efficacy of antidepressants in the Sequenced Treatment Alternatives for Depression (STAR*D) study. Second, we investigate candidate genes that have been suggested as contributing to treatment-emergent suicidal ideation during the course of antidepressant treatment in the STAR*D study. Third, we briefly describe the pharmacokinetic genes examined in the STAR*D study, and finally, we summarize the limitations with respect to the pharmacogenomics studies in the STAR*D study. Future research with independent replication in large sample sizes is needed to confirm the role of the candidate genes identified in the STAR*D study in antidepressant treatment response.

Original languageEnglish
Pages (from-to)935-946
Number of pages12
JournalPharmacogenomics
Volume9
Issue number7
DOIs
Publication statusPublished - 2008 Sep 1

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Genetics
  • Pharmacology

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