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

Abstract

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness 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 next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

Keywords: Bioinformatics; Biomarker; Evidence-based medicine; Genetics; Human brain; Machine learning; Mathematical biosciences; Molecular biology; Neuroscience; Pathophysiology; Pharmaceutical science; Pharmacotherapy; Psychiatry; RDoC; Schizophrenia.

Figure 1

A possible case scenario of personalized approach in psychiatry. A number of biomarkers, including transcriptomic and proteomic profiling and response to medication, along with relevant genetic and epigenetic factors are considered for the following data analysis.

Figure 2

Machine learning algorithm applications. Multidimensional data may be analyzed by a machine learning algorithm that integrates the genotype, epigenotype, and phenotype of an individual to extract information about molecular pathways implicated in the pathogenesis. Genetic, epigenetic, and therapeutic drug monitoring data from the individual is also used to choose the safest and most efficient pharmacological agents, and, at the same time, information about the pathways is used to select the most appropriate pharmacological agents. Alternative therapeutic approaches, such as brain stimulation techniques, can also be chosen.