Review Article Open Access

Pharmacogenomics of Antidepressant Medications

Abstract

Tricyclic antidepressants and SSRIs, prescribed for major depressive disorders, anxiety and/or neuropathic pain, are associated with therapeutic failures in approximately 40% of patients after initial dosing. Pharmacogenetic variances play a significant role in these failures and therefore, using genetic data in decision-making to personalize dosing may both enhance efficacy and reduce adverse effects. Polymorphisms affect several pharmacodynamic and pharmacokinetic variables that modulate efficacy and adverse effects; however, clinical data at this time most strongly associate variant diplotypes of cytochrome P450 (CYP) enzymes with potentially altered metabolism. Four potential patient phenotypes can result from these variants: normal-, poor-, and intermediate- and ultrarapid-drug metabolizers. Plasma concentrations of amitriptyline, nortriptyline and fluvoxamine, paroxetine, citalopram, escitalopram, and sertraline are strongly influenced by the actions of two CYP450s: CYP2D6 and CYP2C19. These CYPs are two of the most polymorphic cytochromes. Pharmacogenetic genotyping of these CYPs has led to phenotype-guided TCA and SSRI recommendations, although current guidelines are limited to drugs with sufficient accumulated clinical evidence. Overall, these guidelines call for patients phenotyped as CYP2D6 or CYP2C19 poor or ultrarapid metabolizers to consider modifications such as dose adjustments or alternative antidepressants. Most newer antidepressants are not supported by the volume or strength of clinical data as are TCAs and SSRIs and thus fewer genomic-based guidelines exist for these newer drugs, although some recommendations are being made. Pharmacogenomic testing is likely to be most useful in early treatment and is limited by identification of known variants, sufficient clinical data sets, epigenetic factors such as pheno-conversion, other drugdrug interactions and comorbidities such as liver disease.

Amanpreet Kooner,  Inder Sehgal

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