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Psychiatric Disorders Journals | Open Access Journals
Journal of Brain Research

Journal of Brain Research

ISSN: 2684-4583

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Psychiatric Disorders Journals

Pharmacogenomic tests used to guide the clinical treatment of major depressive disorder (MDD) must be carefully validated. An important assessment of validity is the ability to predict blood levels of drugs, which reflect impaired metabolism. Historically, the metabolic impact of individual genes has been assessed; however, we now know that several genes are often involved in drug metabolism. Here, we assessed the ability of individual pharmacokinetic genes (CYP2C19, CYP2D6, CYP3A4) and a combinatorial pharmacogenomic test (GeneSight Psychotropic®; weighted evaluation of the three genes) to predict blood levels of citalopram / escitalopram in patients with MDD. Patients from the genomic trial to improve depression decisions (GUIDED) who were taking citalopram / escitalopram during screening and who had data on blood levels were included (N = 191). In the multivariate analysis of the individual genes and of the combinatorial pharmacogenomic test separately (adjusted for age, smoking status), the F statistic for the combinatorial pharmacogenomic test was 1.7 to 2.9 times higher than the individual genes, showing that it explained a greater variance in citalopram / escitalopram blood levels. In the multivariate analysis of individual genes and the combinatorial pharmacogenomics test together, only the combinatorial pharmacogenomics test remained significant. Overall, this demonstrates that the combinatorial pharmacogenomic test was a better predictor of citalopram / escitalopram blood levels compared to individual genes.

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