Prediction of response to drug therapy in psychiatric disorders

Shani Stern, Sara Linker, Krishna C. Vadodaria, Maria C. Marchetto, Fred H. Gage

Research output: Contribution to journalReview articlepeer-review

Abstract

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

Original languageEnglish
Article number180031
JournalOpen Biology
Volume8
Issue number5
DOIs
StatePublished - 2018
Externally publishedYes

Bibliographical note

Funding Information:
Data accessibility. This article has no additional data. Competing interests. We declare we have no competing interests. Funding. This work was supported by the Paul G. Allen Family Foundation, Bob and Mary Jane Engman, the Leona M. and Harry B.

Funding Information:
Helmsley Charitable Trust grant no. 2012-PG-MED002, Annette C. Merle-Smith, R01 MH095741 (to F.H.G.), U19MH106434 (to F.H.G.) and by the G. Harold and Leila Y. Mathers Foundation.

Publisher Copyright:
© 2018 The Authors.

Keywords

  • Autism spectrum disorder
  • Bipolar disorder
  • Classification
  • Major depression
  • Prediction
  • Schizophrenia

ASJC Scopus subject areas

  • Neuroscience (all)
  • Immunology
  • Biochemistry, Genetics and Molecular Biology (all)

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