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MINI REVIEW article

Front. Pharmacol.
Sec. Neuropharmacology
Volume 10 - 2019 | doi: 10.3389/fphar.2019.00617
This article is part of the Research Topic Decreasing the Impact of Treatment Resistance in Schizophrenia: Identifying Novel Molecular Targets/ Pathways to Increase Treatment Efficacy View all 16 articles

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

Provisionally accepted
Claudia Pisanu Claudia Pisanu 1,2Alessio Squassina Alessio Squassina 1,3*
  • 1 Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Italy
  • 2 Department of Neuroscience, Unit of Functional Pharmacology, Uppsala University, Sweden
  • 3 Department of Psychiatry, Dalhousie University, Canada

The final, formatted version of the article will be published soon.

    Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the eighth 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 disease, but it has been suggested that treatment-resistant schizophrenia (TRS) may constitute a distinct phenotype that is more than just a more severe form of SCZ. TRS is heritable, and genetics 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 the disentanglement of 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

    Received: 15 Feb 2019; Accepted: 15 May 2019.

    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) or licensor 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: Alessio Squassina, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.