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Challenges and Future Prospects of Precision Medicine in Psychiatry

Authors Manchia M, Pisanu C, Squassina A, Carpiniello B

Received 8 February 2020

Accepted for publication 14 April 2020

Published 23 April 2020 Volume 2020:13 Pages 127—140


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Martin Bluth

Mirko Manchia,1– 3,* Claudia Pisanu,4,* Alessio Squassina,4,5 Bernardo Carpiniello1,2

1Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy; 2Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy; 3Department of Pharmacology, Dalhousie University, Halifax, NS, Canada; 4Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy; 5Department of Psychiatry, Dalhousie University, Halifax, NS, Canada

*These authors contributed equally to this work

Correspondence: Alessio Squassina

Abstract: Precision medicine is increasingly recognized as a promising approach to improve disease treatment, taking into consideration the individual clinical and biological characteristics shared by specific subgroups of patients. In specific fields such as oncology and hematology, precision medicine has already started to be implemented in the clinical setting and molecular testing is routinely used to select treatments with higher efficacy and reduced adverse effects. The application of precision medicine in psychiatry is still in its early phases. However, there are already examples of predictive models based on clinical data or combinations of clinical, neuroimaging and biological data. While the power of single clinical predictors would remain inadequate if analyzed only with traditional statistical approaches, these predictors are now increasingly used to impute machine learning models that can have adequate accuracy even in the presence of relatively small sample size. These models have started to be applied to disentangle relevant clinical questions that could lead to a more effective management of psychiatric disorders, such as prediction of response to the mood stabilizer lithium, resistance to antidepressants in major depressive disorder or stratification of the risk and outcome prediction in schizophrenia. In this narrative review, we summarized the most important findings in precision medicine in psychiatry based on studies that constructed machine learning models using clinical, neuroimaging and/or biological data. Limitations and barriers to the implementation of precision psychiatry in the clinical setting, as well as possible solutions and future perspectives, will be presented.

Keywords: machine learning, pharmacogenomics, predictive models, risk stratification, personalized therapy

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