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Front. Pharmacol., 29 May 2019 | https://doi.org/10.3389/fphar.2019.00617

Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches

  • 1Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Italy
  • 2Department of Neuroscience, Unit of Functional Pharmacology, Uppsala University, Sweden
  • 3Department of Psychiatry, Dalhousie University, Canada

Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the 8th leading cause of disability according to the World Health Organization and is associated with a significant reduction in life expectancy. Antipsychotics represent the first-choice treatment in SCZ, but approximately 30% of patients fail to respond to acute treatment. These patients are generally defined as treatment resistant and are eligible for clozapine treatment. Treatment resistant patients show a more severe course of the diseases, but it has been suggested that treatment resistant schizophrenia (TRS) may constitute a distinct phenotype more than just a more severe form of SCZ. TRS is heritable, and genetic has been shown to play an important role in modulating response to antipsychotics. Important efforts have been put into place in order to better understand the genetic architecture of TRS, with the main goal of identifying reliable predictive markers that might improve the management and quality of life of TRS patients. However, the number of candidate gene and genome-wide association studies specifically focused on TRS is limited, and to date findings do not allow to disentangle its polygenic nature. More recent studies implemented polygenic risk score, gene based and machine learning methods to explore the genetics of TRS, reporting promising findings. In this review, we present an overview on the genetics of TRS, particularly focusing our discussion on studies implementing polygenic approaches.

Keywords: Schizophrenia, antipsychotic, response, Clozapine, Pharmacogenetics, Polygenic risk score (PRS), treatment resistant schizophrenia - TRS

Citation: Pisanu C and Squassina A (2019). Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches. Front. Pharmacol. 10:617. doi: 10.3389/fphar.2019.00617

Received: 15 Feb 2019; Accepted: 15 May 2019;
Published online: 29 May 2019.

Edited by:

Francisco Ciruela, University of Barcelona, Spain

Reviewed by:

Stefano Comai, Vita-Salute San Raffaele University, Italy
Clement Zai, Centre for Addiction and Mental Health (CAMH), Canada  

Copyright: © 2019 Pisanu and Squassina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Alessio Squassina, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Italy, squassina@unica.it