Personalized psychopharmacotherapy: State of problem

In this review the significance of personalized medicine (PM) concepts in diagnostics and treatment of mental disorders was widely discussed in various contexts in publications of the researchers.The aim of this review was to present the primary modern instruments for psychopharmacotherapy individualization and to prove the importance of PM concepts application.Material and methods. System analysis of scientific publications, devoted to the present-day methods of psychopharmacotherapy personification, was carried out. The basic effective approaches, such as genotyping, biomarkerslevels determination, and therapeutic drug monitoring (TDM), were considered. Examples of genetic tests were given, which allowed optimizing treatment of mental disorders. The conception of biomarkers as diagnostic and therapeutic indicators for personalized medicine was considered. The review emphasized the necessity of development of data base (DB), including all patients’ parameters aggregated during TDM with following nonlinear mixed effects modeling (NLME) for prediction of individual therapeutic response. Other prospective directions of psychopharmacotherapy individualization were described briefly: proteomics, metabolomics, RNAomics, transcriptomics. Problems of deployment of personalized medicine`s and translational medicine (TM) principles to routine clinical practice was given consideration.Conclusion. The presented data on the efficacy of conceptual approaches to individualization of diagnostics and therapy of mental disorders substantiate the necessity of their introduction to psychiatric practice.

психические расстройства, психофармакотерапия, персонализированная медицина, фармакогенетика, биомаркеры, терапевтический лекарственный мониторинг, нелинейное моделирование смешанных эффектов, протеомика, метаболомика, трансляционная медицина, mental disorders, psychopharmacotherapy, personalized medicine, pharmacogenetics, biomarkers, therapeutic drug monitoring, nonlinear mixed effects modelling, proteomics, metabolomics, translational medicine

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