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Transl Psychiatry. 2018 Dec 13;8(1):276. doi: 10.1038/s41398-018-0330-4.

Improving pharmacogenetic prediction of extrapyramidal symptoms induced by antipsychotics.

Author information

1
Department of Medicine, University of Barcelona, Barcelona, Spain.
2
Department of Clinical Foundations, Pharmacology Unit, University of Barcelona, Barcelona, Spain.
3
Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Barcelona, Spain.
4
Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, Madrid, Spain.
5
Hospital Ramon y Cajal, Universidad de Alcala, IRYCIS, Madrid, Spain.
6
Department of Psychiatry, Complejo Hospitalario de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain.
7
The August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain.
8
Department of Medicine, University of Barcelona, Barcelona, Spain. bernardo@clinic.ub.es.
9
Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Barcelona, Spain. bernardo@clinic.ub.es.
10
The August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain. bernardo@clinic.ub.es.
11
Barcelona Clínic Schizophrenia Unit, Hospital Clínic de Barcelona, Barcelona, Spain. bernardo@clinic.ub.es.
12
Department of Clinical Foundations, Pharmacology Unit, University of Barcelona, Barcelona, Spain. sergimash@ub.edu.
13
Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Barcelona, Spain. sergimash@ub.edu.
14
The August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain. sergimash@ub.edu.

Abstract

In previous work we developed a pharmacogenetic predictor of antipsychotic (AP) induced extrapyramidal symptoms (EPS) based on four genes involved in mTOR regulation. The main objective is to improve this predictor by increasing its biological plausibility and replication. We re-sequence the four genes using next-generation sequencing. We predict functionality "in silico" of all identified SNPs and test it using gene reporter assays. Using functional SNPs, we develop a new predictor utilizing machine learning algorithms (Discovery Cohort, N = 131) and replicate it in two independent cohorts (Replication Cohort 1, N = 113; Replication Cohort 2, N = 113). After prioritization, four SNPs were used to develop the pharmacogenetic predictor of AP-induced EPS. The model constructed using the Naive Bayes algorithm achieved a 66% of accuracy in the Discovery Cohort, and similar performances in the replication cohorts. The result is an improved pharmacogenetic predictor of AP-induced EPS, which is more robust and generalizable than the original.

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