TY - JOUR

T1 - Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders

AU - Levchenko, Anastasia

AU - Nurgaliev, Timur

AU - Kanapin, Alexander

AU - Samsonova, Anastasia

AU - Gainetdinov, Raul R.

N1 - Publisher Copyright: © 2020 The Author(s)

PY - 2020/5

Y1 - 2020/5

N2 - A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mentalillness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category.In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a nextstep, a perspective on the path personalized psychiatry may take in the future is given, paying particular attentionto machine learning algorithms that can be used with the goal of handling multidimensional datasets.

AB - A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mentalillness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category.In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a nextstep, a perspective on the path personalized psychiatry may take in the future is given, paying particular attentionto machine learning algorithms that can be used with the goal of handling multidimensional datasets.

KW - Neuroscience

KW - Bioinformatics

KW - genetics

KW - Pharmaceutical science

KW - Molecular biology

KW - Pathophysiology

KW - Mathematical biosciences

KW - psychiatry

KW - Evidence-based medicine

KW - Biomarker

KW - HUMAN BRAIN

KW - machine learning

KW - Pharmacotherapy

KW - RDoC

KW - schizophrenia

KW - Schizophrenia

KW - Machine learning

KW - Human brain

KW - Genetics

KW - Psychiatry

KW - RESEARCH DOMAIN CRITERIA

KW - SYNTHASE KINASE 3-BETA

KW - IMPAIRED GLUCOSE-TOLERANCE

KW - DRUG-NAIVE PATIENTS

KW - GENOME-WIDE ASSOCIATION

KW - WFSBP TASK-FORCE

KW - NMDA-RECEPTOR ENCEPHALITIS

KW - BIOLOGICAL MARKERS CRITERIA

KW - MAJOR DEPRESSIVE DISORDER

KW - MISMATCH NEGATIVITY DEFICITS

UR - https://www.cell.com/heliyon/fulltext/S2405-8440(20)30835-5

UR - http://www.scopus.com/inward/record.url?scp=85084793807&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/17f9b840-7e05-301a-b805-4a1d6e574292/

U2 - 10.1016/j.heliyon.2020.e03990

DO - 10.1016/j.heliyon.2020.e03990

M3 - Review article

C2 - 32462093

VL - 6

JO - Heliyon

JF - Heliyon

SN - 2405-8440

IS - 5

M1 - e03990

ER -