UMMS Affiliation
Department of Psychiatry
Publication Date
2020-05-17
Document Type
Article
Disciplines
Genetic Phenomena | Genetics and Genomics | Mental and Social Health | Pharmacology, Toxicology and Environmental Health | Psychiatry | Psychiatry and Psychology
Abstract
Pharmacogenomic tests used to guide clinical treatment for major depressive disorder (MDD) must be thoroughly validated. One important assessment of validity is the ability to predict medication blood levels, which reflect altered metabolism. Historically, the metabolic impact of individual genes has been evaluated; however, we now know that multiple genes are often involved in medication metabolism. Here, we evaluated the ability of individual pharmacokinetic genes (CYP2C19, CYP2D6, CYP3A4) and a combinatorial pharmacogenomic test (GeneSight Psychotropic(R); weighted assessment of all three genes) to predict citalopram/escitalopram blood levels in patients with MDD. Patients from the Genomics Used to Improve DEpression Decisions (GUIDED) trial who were taking citalopram/escitalopram at screening and had available blood level data were included (N=191). In multivariate analysis of the individual genes and 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 more variance in citalopram/escitalopram blood levels. In multivariate analysis of the individual genes and combinatorial pharmacogenomic test together, only the combinatorial pharmacogenomic test remained significant. Overall, this demonstrates that the combinatorial pharmacogenomic test was a superior predictor of citalopram/escitalopram blood levels compared to individual genes.
Keywords
Citalopram, Depression, Escitalopram, GeneSight, Medication Blood Levels, Pharmacokinetics
Rights and Permissions
© 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).T
DOI of Published Version
10.1016/j.psychres.2020.113017
Source
Shelton RC, Parikh SV, Law RA, Rothschild AJ, Thase ME, Dunlop BW, DeBattista C, Conway CR, Forester BP, Macaluso M, Hain DT, Aguilar AL, Brown K, Lewis DJ, Jablonski MR, Greden JF. Combinatorial Pharmacogenomic Algorithm is Predictive of Citalopram and Escitalopram Metabolism in Patients with Major Depressive Disorder. Psychiatry Res. 2020 May 17;290:113017. doi: 10.1016/j.psychres.2020.113017. Epub ahead of print. PMID: 32485484. Link to article on publisher's site
Journal/Book/Conference Title
Psychiatry research
Related Resources
PubMed ID
32485484
Repository Citation
Shelton RC, Rothschild AJ. (2020). Combinatorial Pharmacogenomic Algorithm is Predictive of Citalopram and Escitalopram Metabolism in Patients with Major Depressive Disorder. Psychiatry Publications. https://doi.org/10.1016/j.psychres.2020.113017. Retrieved from https://escholarship.umassmed.edu/psych_pp/953
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Genetic Phenomena Commons, Genetics and Genomics Commons, Mental and Social Health Commons, Pharmacology, Toxicology and Environmental Health Commons, Psychiatry Commons, Psychiatry and Psychology Commons
Comments
Full author list omitted for brevity. For the full list of authors, see article.