Individualization of therapy in psychiatry: how personalized it may be?
- Authors: Skryabin V.Y.1
-
Affiliations:
- Russian Medical Academy of Continuous Professional Education
- Issue: Vol LVII, No 4 (2025)
- Pages: 306-313
- Section: Discussions
- URL: https://journals.rcsi.science/1027-4898/article/view/364030
- DOI: https://doi.org/10.17816/nb678686
- EDN: https://elibrary.ru/KOEDBP
- ID: 364030
Cite item
Abstract
This article provides a critical review of current approaches to personalized psychiatry, analyzing its potential, limitations, and ethical dilemmas. Despite progress in areas such as pharmacogenetics (e.g., the influence of CYP2D6 and CYP2C19 gene polymorphisms on psychotropic drug metabolism) and digital phenotyping (behavioral monitoring through wearable devices and machine learning algorithms), most methods remain within framework of research projects. Regulatory initiatives (FDA, CPIC) are gradually integrating genetic data into clinical recommendations; however, their implementation is limited by insufficient ethnic representation, low reproducibility, and small sample sizes. Digital tools, although capable of predicting relapses in affective or psychotic disorders, face challenges of data interpretation and risks of privacy breaches. Polygenic risk scores and biomarkers (e.g., neuroimaging patterns, cytokine levels) demonstrate limited predictive power at the individual level. Key risks of personalization include fragmented access to care, genetic-based stigmatization, and the replacement of clinical reasoning with algorithmic decision-making. Personalization is therefore justified only when supported by robust evidence (e.g., pharmacogenetics in treatment-resistant depression) and should complement rather than replace clinical practice. Successful integration requires validation of technologies, overcoming ethnic and socioeconomic barriers, and maintaining ethical standards and clinical expertise. Personalized psychiatry should be viewed not as a revolutionary paradigm, but as an evolutionary tool requiring cautious, stepwise implementation into clinical practice.
About the authors
Valentin Y. Skryabin
Russian Medical Academy of Continuous Professional Education
Author for correspondence.
Email: sardonios@yandex.ru
ORCID iD: 0000-0002-4942-8556
SPIN-code: 4895-5285
MD, Cand. Sci. (Medicine)
Russian Federation, MoscowReferences
- Abdullah-Koolmees H, van Keulen AM, Nijenhuis M, Deneer VHM. Pharmacogenetics Guidelines: Overview and Comparison of the DPWG, CPIC, CPNDS, and RNPGx Guidelines. Front Pharmacol. 2021;11:595219. doi: 10.3389/fphar.2020.595219
- Insel TR. Digital phenotyping: technology for a new science of behavior. JAMA. 2017;318(13):1215–1216. doi: 10.1001/jama.2017.11295
- Torous J, Jän Myrick K, Rauseo-Ricupero N, Firth J. Digital mental health and COVID-19: using technology today to accelerate the curve on access and quality tomorrow. JMIR Ment Health. 2020;7(3):e18848. doi: 10.2196/18848
- Bousman CA, Arandjelovic K, Mancuso SG, et al. Pharmacogenetic tests and depressive symptom remission: a meta-analysis of randomized controlled trials. Pharmacogenomics. 2019;20(1):37–47. doi: 10.2217/pgs-2018-0142
- Phillips EJ, Sukasem C, Whirl-Carrillo M, et al. Clinical pharmacogenetics implementation consortium guideline for HLA genotype and use of carbamazepine and oxcarbazepine: 2017 update. Clin Pharmacol Ther. 2018;103(4):574–581. doi: 10.1002/cpt.1004
- Wang Y, Tsuo K, Kanai M, et al. Challenges and opportunities for developing more generalizable polygenic risk scores. Annu Rev Biomed Data Sci. 2022;5:293–320. doi: 10.1146/annurev-biodatasci-111721-074830
- Bufano P, Laurino M, Said S, et al. Digital phenotyping for monitoring mental disorders: systematic review. J Med Internet Res. 2023;25:e46778. doi: 10.2196/46778
- Martinez-Martin N, Greely HT, Cho MK. Ethical development of digital phenotyping tools for mental health applications: Delphi study. JMIR Mhealth Uhealth. 2021;9(7):e27343. doi: 10.2196/27343
- Torous J, Bucci S, Bell IH, et al. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry. 2021;20(3):318–335. doi: 10.1002/wps.20883
- Huang FF, Wang PC, Yang XY, et al. Predicting responses to cognitive behavioral therapy in obsessive-compulsive disorder based on multilevel indices of rs-fMRI. J Affect Disord. 2023;323:345–353. doi: 10.1016/j.jad.2022.11.073
Supplementary files

