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Licensed Unlicensed Requires Authentication Published by De Gruyter September 1, 2020

Using a pharmacogenetic clinical decision support system to improve psychopharmacotherapy dosing in patients with affective disorders

  • Michael Zastrozhin EMAIL logo , Valentin Skryabin ORCID logo , Alexander Sorokin , Oleg Buzik , Inessa Bedina , Elena Grishina , Kristina Ryzhikova , Valery Shipitsyn , Evgeny Bryun and Dmitry Sychev



Although pharmacogenetic tests provide the information on a genotype and the predicted phenotype, these tests do not themselves provide the interpretation of data for a physician. Currently, there are approximately two dozen pharmacogenomic clinical decision support systems (CDSSs) used in psychiatry. Implementation of the CDSSs forming the recommendations on drug and dose selection according to the results of pharmacogenetic testing is an urgent task. Fulfillment of this task will allow increasing the efficacy of therapy and decreasing the risk of undesirable side effects.


The study included 118 male patients (48 in the main group and 70 in the control group) with affective disorders and comorbid alcohol use disorder. To evaluate the efficacy and safety of therapy, several international psychometric scales and rating scales to measure side effects were used. Genotyping was performed using the real-time polymerase chain reaction with allele-specific hybridization. Pharmacogenetic testing results were interpreted using free software PGX2 (LLE Medicine, Russian Federation, Biomedical Cluster of Skolkovo, Moscow Innovative Cluster;


The statistically significant differences across the scores on psychometric scales were revealed. For instance, the total score on the Hamilton Rating Scale for Depression by day 9 was 9.0 [8.0; 10.0] for the main group and 11.0 [10.0; 12.0] (p<0.001) for the control group and by day 16 it was 4.0 [2.0; 6.0] for the main group and 14.0 [13.0; 14.0] (p<0.001) for the control group. The UKU Side-Effect Rating Scale (UKU) also revealed a statistically significant difference. The total score on the UKU scale by day 9 was 4.0 [4.0; 5.0] for the main group and 5.0 [5.0; 6.0] (p<0.001) for the control group and by day 16 this difference grew significantly: 3.0 [0.0; 4.2] for the main group and 9.0 [7.0; 11.0] (p<0.001) for the control group.


Pharmacogenetic-guided personalization of the drug dose in patients with affective disorders and comorbid alcohol use disorder can reduce the risk of undesirable side effects and pharmacoresistance. It allows recommending the use of pharmacogenetic CDSSs for optimizing drug dosage.

Corresponding author: Michael Zastrozhin, PhD, Department of Healthcare, Moscow Research and Practical Center on Addictions of the Moscow, 37/1 Lyublinskaya Street, Moscow, 109390, Russia; and Russian Medical Academy of Continuous Professional Education of the Ministry of Health of the Russian Federation, 2/1 Barrikadnaya Street, Moscow, 123995, Russia, E-mail:

Funding source: Russian Federation

Award Identifier / Grant number: МК-2460.2018.7

Funding source: The Russian Science Foundation

Award Identifier / Grant number: 18-75-10073


The authors thank Dr. Marco Torrado, PhD, for his comments, suggestions, and proofreading.

  1. Research funding: This research was conducted with the financial support of a grant from the President of the Russian Federation to assist young Russian scientists holding Ph.D. degrees (project No. МК-2460.2018.7) and support of The Russian Science Foundation, project 18-75-10073 “Conducting research by scientific groups under the leadership of young scientists of Presidential program of research projects, implemented by leading scientists, including young scientists”.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: The study was approved by the local Ethics Committee of the Russian Medical Academy of Continuous Professional Education of the Ministry of Health of the Russian Federation (Protocol No. 6 from May 16, 2017).


1. Regier, DA, Kuhl, EA, Kupfer, DJ. The DSM-5: classification and criteria changes. World Psychiatr 2013;12:92–8. in Google Scholar

2. Akiskal, HS, Walker, P, Puzantian, VR, King, D, Rosenthal, TL, Dranon, M. Bipolar outcome in the course of depressive illness. Phenomenologic, familial, and pharmacologic predictors. J Affect Disord 1983;5:115. in Google Scholar

3. Bell, RA, Franks, P, Duberstein, PR, Epstein, RM, Feldman, MD, Fernandez y Garcia, E, et al. Suffering in silence: reasons for not disclosing depression in primary care. Ann Fam Med 2011;9:439–46. in Google Scholar

4. Amsterdam, JD, Hornig-Rohan, M. Treatment algorithms in treatment-resistant depression. Psychiatr Clin North Am 1996;19:371–86. in Google Scholar

5. Greden, JF. The burden of disease for treatment-resistant depression. J Clin Psychiatr 2001;62:26–31.Search in Google Scholar

6. Hase, ME, Haight, BR, Richard, N, Rockett, CB, Mitton, M, Modell, JG. Remission rates following antidepressant therapy with bupropion or selective serotonin reuptake inhibitors: a meta-analysis of original data from 7 randomized controlled trials. J Clin Psychiatr 2005;66:974–81. in Google Scholar

7. Moncrieff, J, Kirsch, I. Efficacy of antidepressants in adults. BMJ 2005;331:155–7. in Google Scholar

8. Rush, AJ, Trivedi, MH, Wisniewski, SR, Nierenberg, AA, Stewart, JW, Warden, D. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatr 2006;163:1905–17. in Google Scholar

9. Trivedi, MH, Rush, AJ, Wisniewski, SR, Nierenberg, AA, Warden, D, Ritz, L. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatr 2006;163:28–40. in Google Scholar

10. Nierenberg, AA, DeCecco, LM. Definitions of antidepressant treatment response, remission, nonresponse, partial response, and other relevant outcomes: a focus on treatment-resistant depression. J Clin Psychiatr 2001;62:5–9.Search in Google Scholar

11. Nierenberg, AA, Keefe, BR, Leslie, VC, Alpert, JE, Pava, JA, Worthington, JJ3rd, et al. Residual symptoms in depressed patients who respond acutely to fluoxetine. J Clin Psychiatr 1999;60:221–5. in Google Scholar

12. Nierenberg, AA, Wright, EC. Evolution of remission as the new standard in the treatment of depression. J Clin Psychiatr 1999;60:7–11.Search in Google Scholar

13. Frank, E, Prien, RF, Jarrett, RB, Keller, MB, Kupfer, DJ, Lavori, PW. Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence. Arch Gen Psychiatr 1991;48:851–5. in Google Scholar

14. Thase, ME, Nierenberg, AA, Vrijland, P, van Oers, HJ, Schutte, AJ, Simmons, JH. Remission with mirtazapine and selective serotonin reuptake inhibitors: a meta-analysis of individual patient data from 15 controlled trials of acute phase treatment of major depression. Int Clin Psychopharm 2010;25:189–98. in Google Scholar

15. Singh, A. Pharmacogenomics–the potential of genetically guided prescribing. Aust Fam Physician 2007;36:820–4.Search in Google Scholar

16. Singh, A, Berk, M. Genetically guided prescribing: hope or hype? Acta Neuropsychiatr 2008;20:50–1. in Google Scholar

17. Goodwin, GM, Haddad, PM, Ferrier, IN, Aronson, JK, Barnes, T, Cipriani, A, et al. Evidence-based guidelines for treating bipolar disorder: revised third edition recommendations from the British Association for Psychopharmacology. J Psychopharmacol 2016;30:495–553. in Google Scholar

18. Baumann, P, Bergemann, N. AGNP Consensus guidelines for therapeutic drug monitoring in psychiatry: update 2011. Pharmacopsychiatry 2011;44:195–235. in Google Scholar

19. Bertilsson, L, Dahl, ML, Dalén, P, Al-Shurbaji, A. Molecular genetics of CYP2D6: clinical relevance with focus on psychotropic drugs. Br J Clin Pharmacol 2002;53:111–22. in Google Scholar

20. Lin, JH, Lu, AY. Inhibition and induction of cytochrome P450 and the clinical implications. Clin Pharmacokinet 1998;35:361–90. in Google Scholar

21. Shen, H, He, MM, Liu, H, Wrighton, SA, Wang, L, Guo, B. Comparative metabolic capabilities and inhibitory profiles of CYP2D6.1, CYP2D6.10, and CYP2D6.17. Drug Metab Dispos 2007;35:1292–300. in Google Scholar

22. Shah, P, Kerns, E, Nguyen, DT, Obach, RS, Wang, AQ, Zakharov, A, et al. An automated high-throughput metabolic stability assay using an integrated high-resolution accurate mass method and automated data analysis software. Drug Metab Dispos 2016;44:1653–61. in Google Scholar

23. Mizutani, T. PM frequencies of major CYPs in Asians and Caucasians. Drug Metab Rev 2003;35:99–106. in Google Scholar

24. Werk, AN, Cascorbi, I. Functional gene variants of CYP3A4. Clin Pharmacol Ther 2014;96:340–8. in Google Scholar

25. Williams, JA, Ring, BJ, Cantrell, VE, Jones, DR, Eckstein, J, Ruterbories, K, et al. Comparative metabolic capabilities of CYP3A4, CYP3A5, and CYP3A7. Drug Metab Dispos 2002;30:883–91. in Google Scholar

26. Tavira, B, Coto, E, Diaz-Corte, C, Alvarez, V, López-Larrea, C, Ortega, F. A search for new CYP3A4 variants as determinants of tacrolimus dose requirements in renal-transplanted patients. Pharmacogenet Genom 2013;23:445–8. in Google Scholar

27. Lamba, V, Ghodke, Y, Guan, W, Tracy, TS. MicroRNA-34a is associated with expression of key hepatic transcription factors and cytochromes P450. Biochem Biophys Res Commun Mar 7 2014;445:404–11. in Google Scholar

28. Lee, SJ, Usmani, KA, Chanas, B, Ghanayem, B, Xi, T, Hodgson, E, et al. Genetic findings and functional studies of human CYP3A5 single nucleotide polymorphisms in different ethnic groups. Pharmacogenetics 2003;13:461–72. in Google Scholar

29. Wolking, S, Schaeffeler, E, Lerche, H, Schwab, M, Nies, AT. Impact of genetic polymorphisms of ABCB1 (MDR1, P-glycoprotein) on drug disposition and potential clinical implications: update of the literature. Clin Pharmacokinet 2015;54:709–35. in Google Scholar

30. Bousman, CA, Hopwood, M. Commercial pharmacogenetic-based decision-support tools in psychiatry. Lancet Psychiat 2016;3:585–90. in Google Scholar

31. Singh, AB. Improved antidepressant remission in major depression via a pharmacokinetic pathway polygene pharmacogenetic report. Clin Psychopharm Neu 2015;13:150–6. in Google Scholar

32. Winner, JG, Carhart, JM, Altar, CA, Allen, JD, Dechairo, BM. A prospective, randomized, double-blind study assessing the clinical impact of integrated pharmacogenomic testing for major depressive disorder. Discov Med 2013;16:219–27.Search in Google Scholar

33. Flannery, BA, Volpicelli, JR, Pettinati, HM. Psychometric properties of the Penn alcohol craving scale. Alcohol Clin Exp Res 1999;23:1289–95. in Google Scholar

34. Busner, J, Targum, SD. The clinical global impressions scale: applying a research tool in clinical practice. Psychiatry (Edgmont) 2007;4:28–37.Search in Google Scholar

35. Snaith, RP. The hospital anxiety and depression scale. Health Qual Life Out 2003;1:29. in Google Scholar

36. Hamilton, M. The assessment of anxiety states by rating. Br J Med Psychol 1959;32:50–5. in Google Scholar

37. Lingjaerde, O, Ahlfors, UG, Bech, P, Dencker, SJ, Elgen, K. The UKU side effect rating scale. A new comprehensive rating scale for psychotropic drugs and a cross-sectional study of side effects in neuroleptic-treated patients. Acta Psychiatr Scand Suppl 1987;334:1–100. in Google Scholar

38. Spina, E, de Leon, J. Clinical applications of CYP genotyping in psychiatry. J Neural Transm 2015;122:5–28. in Google Scholar

39. Hicks, JK, Bishop, JR, Sangkuhl, K, Muller, DJ, Ji, Y, Leckband, SG, et al. Clinical pharmacogenetics implementation consortium (CPIC) guideline for CYP2D6 and CYP2C19 genotypes and dosing of selective serotonin reuptake inhibitors. Clin Pharmacol Ther 2015;98:127–34. in Google Scholar

40. Zastrozhin, MS, Sorokin, AS, Agibalova, TV, Grishina, EA, Antonenko, AР, Rozochkin, IN, et al. Using a personalized clinical decision support system for bromdihydrochlorphenylbenzodiazepine dosing in patients with anxiety disorders based on the pharmacogenomic markers. Hum Psychopharm 2018;25:e2677. in Google Scholar

41. Xie, HG, Wood, AJ, Kim, RB, Stein, CM, Wilkinson, GR. Genetic variability in CYP3A5 and its possible consequences. Pharmacogenomics 2004;5:243–72. in Google Scholar

42. Zhu, X, Yun, W, Sun, X, Qiu, F, Zhao, L, Guo, Y. Effects of major transporter and metabolizing enzyme gene polymorphisms on carbamazepine metabolism in Chinese patients with epilepsy. Pharmacogenomics 2014;15:1867–89. in Google Scholar

43. Seo, T, Ishitsu, T, Ueda, N, Nakada, N, Yurube, K, Ueda, K, et al. ABCB1 polymorphisms influence the response to antiepileptic drugs in Japanese epilepsy patients. Pharmacogenomics 2006;7:551–61. in Google Scholar

44. Puranik, YG, Birnbaum, AK, Marino, SE, Ahmed, G, Cloyd, JC, Remmel, RP, et al. Association of carbamazepine major metabolism and transport pathway gene polymorphisms and pharmacokinetics in patients with epilepsy. Pharmacogenomics 2013;14:35–45. in Google Scholar

45. Panomvana, D, Traiyawong, T, Towanabut, S. Effect of CYP3A5 genotypes on the pharmacokinetics of carbamazepine when used as monotherapy or co-administered with phenytoin, phenobarbital or valproic acid in Thai patients. J Pharm Pharmaceut Sci 2013;16:502–10. in Google Scholar

46. Hall-Flavin, DK, Winner, JG, Allen, JD, Jordan, JJ, Nesheim, RS, Snyder, KA, et al. Using a pharmacogenomic algorithm to guide the treatment of depression. Transl Psychiatry Oct 16 2012;2:e172. in Google Scholar

47. Zarkin, GA, Bray, JW, Aldridge, A, Mills, M, Cisler, RA, Couper, D, et al. The effect of alcohol treatment on social costs of alcohol dependence: results from the COMBINE study. Med Care 2010;48:396–401. in Google Scholar

48. Posternak, MA, Zimmerman, M. Is there a delay in the antidepressant effect? A meta-analysis. J Clin Psychiatr 2005;66:148–58. in Google Scholar

49. Taylor, MJ, Freemantle, N, Geddes, JR, Bhagwagar, Z. Early onset of selective serotonin reuptake inhibitor antidepressant action: systematic review and meta-analysis. Arch Gen Psychiatr 2006;63:1217–23. in Google Scholar

Received: 2020-01-09
Accepted: 2020-07-05
Published Online: 2020-09-01

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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