The term “pharmacogenetics (PGx)” was first coined in 1959 with the aim of identifying clinically meaningful genetic predictors of responses to drug treatments and their adverse effects. PGx addresses the limitations of the traditional non-biomarker based “trial-and-error” procedure in selecting psychotropic drugs for patients with psychiatric disorders, including schizophrenia. A combination of pharmacokinetics and pharmacodynamics underlie interindividual differences in drug responses and adverse effects; therefore, PGx focuses on genetic variants involved in pharmacokinetics and pharmacodynamics (Table).
Numerous genetic variants associated with antipsychotic responses and adverse effects in the treatment of schizophrenia have been identified. Based on such genetic information, specific recommendations for treatment with psychotropic drugs, including antipsychotics, have been proposed for clinical practice.
Clinical availability of PGx information
Most antipsychotics are metabolized by cytochrome P450 (CYP) enzymes, (primarily CYP1A2, CYP2D6, and CYP3A4), while other CYP enzymes such as CYP3A5 and CYP2C19 are involved in the metabolism of only a few antipsychotics. The genes encoding these CYP enzymes are polymorphic, resulting in interindividual differences in antipsychotic metabolic abilities.
Four classes of CYP metabolizer profiles have been established according to the multiallelic nature of the CYP enzyme genetic construct: poor, intermediate, normal (extensive), and ultra-rapid metabolizers. Most of the currently available clinical recommendations are based on gene variants that affect CYP enzyme activity.
Based on the CYP2D6 phenotype, the FDA provides information on PGx biomarkers in the drug labeling for nine antipsychotics (aripiprazole, aripiprazole lauroxil, brexpiprazole, clozapine, iloperidone, perphenazine, pimozide, risperidone, and thioridazine). For seven of these nine anti psychotics (aripiprazole, aripiprazole lauroxil, brexpiprazole, clozapine, iloperidone, pimozide, and thioridazine), dose adjustment recommendations are provided for individuals identified as CYP2D6 poor metabolizer. For example, it is recommended to administer half of the usual dose of aripiprazole to patients who are known to be CYP2D6 poor metabolizers because at standard doses the risk of adverse effects is increased with higher drug plasma levels.
The Pharmacogenomics Knowledgebase (PharmGKB), an NIH-funded resource, provides clinically relevant PGx information, including dosing guidelines and drug labels. In addition, drug labels or expert recommendations provided by several resources are summarized on the PharmGKB website (www.pharmgkb.org).
Dr Yoshida is a Postdoctoral Research Fellow, Centre for Addiction and Mental Health, Toronto, ON, Canada, and a Psychiatrist, Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; Dr Müller is Head, Pharmacogenetics Research Clinic, Centre for Addiction and Mental Health, and Professor, Department of Psychiatry, University of Toronto, ON. The authors report no conflicts of interest concerning the subject matter of this article.
1. Bousman C, Maruf A Al, Müller DJ. Towards the integration of pharmacogenetics in psychiatry: a minimum, evidence-based genetic testing panel. Curr Opin Psychiatry. 2018;32:7-15.
2. Walden LM, Brandl EJ, Changasi A, et al. Physicians’ opinions following pharmacogenetic testing for psychotropic medication. Psychiatry Res. 2015;229:913-918.
3. Rosenblat JD, Lee Y, McIntyre RS. The effect of pharmacogenomic testing on response and remission rates in the acute treatment of major depressive disorder: a meta-analysis. J Affect Disord. 2018;241:484-491.
4. Rahman T, Ash DM, Lauriello J, Rawlani R. Misleading guidance from pharmacogenomic testing. Am J Psychiatry. 2017;174:922-924.
5. Bousman CA, Dunlop BW. Genotype, phenotype, and medication recommendation agreement among commercial pharmacogenetic-based decision support tools. Pharmacogenomics J. 2018:1-10.
6. Zhang J-P, Malhotra AK. Recent progress in pharmacogenomics of antipsychotic drug response. Curr Psychiatry Rep. 2018;20:24.
7. Zai CC, Tiwari AK, Zai GC, Maes MS, et al. New findings in pharmacogenetics of schizophrenia. Curr Opin Psychiatry. 2018;31:200-212.
8. Hauser RA, Factor SA, Marder SR, et al. KINECT 3: a phase 3 randomized, double-blind, placebo-controlled trial of valbenazine for tardive dyskinesia. Am J Psychiatry. 2017;174:476-484.
9. Yoshida K, Müller DJ. Pharmacogenetics of antipsychotic drug treatment: update and clinical implications. Mol Neuropsychiatry. 2018;8:1-26.
10. Yoshida K, Tiwari AK, Maciukiewicz M, et al. A multi-genic prediction model for antipsychotic-induced weight gain. Presented at the 68th Canadian Psychiatric Association; Toronto; 2018.
11. Santoro ML, Ota V, de Jong S, et al. Polygenic risk score analyses of symptoms and treatment response in an antipsychotic-naive first episode of psychosis cohort. Transl Psychiatry. 2018;8:174.
12. Zhang J-P, Robinson D, Yu J, et al. Schizophrenia polygenic risk score as a predictor of antipsychotic efficacy in first-episode psychosis. Am J Psychiatry. November 2018; Epub ahead of print.
13. Dunnenberger HM, Crews KR, Hoffman JM, et al. Preemptive clinical pharmacogenetics implementation: current program in five United States medical centers. Ann Rev Pharmacol Toxicol. 2015;55:89-106.
14. PharmGKB. https://www.pharmgkb.org. Accessed January 27, 2019.
15. CPIC. Guidelines. https://cpicpgx.org/guidelines/. Accessed January 27, 2019.
16. Whirl-Carrillo M, McDonagh EM, Hebert JM, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92:414-417.
17. PharmGKB. Guideline Annotations. DPWG. https://www.pharmgkb.org/view/dosing-guidelines.do?source=DPWG#. Accessed January 27, 2019.