Pharmacogenetics of Antidepressant-Induced Disinhibition

Summary

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Background and Rationale: Antidepressants such as serotonin-selective reuptake inhibitors (SSRIs) are frequently prescribed to children to manage major depressive and anxiety disorders. Although SSRIs are thought to be generally effective and well-tolerated in children, 10%- 20% of children treated ...

Background and Rationale: Antidepressants such as serotonin-selective reuptake inhibitors (SSRIs) are frequently prescribed to children to manage major depressive and anxiety disorders. Although SSRIs are thought to be generally effective and well-tolerated in children, 10%- 20% of children treated with SSRIs experience behavioral disinhibition (i.e. activation, hyperactivity, impulsivity, insomnia) that can lead to devastating consequences (e.g. suicidal impulses, violence). There are currently no tools available to assist healthcare providers in predicting which children will experience behavioral disinhibition as a result of SSRI treatment. Research Question: Do children who experience SSRI-induced behavioral disinhibition (SIBD) have a distinct pharmacogenetic profile relative to children who do not have these adverse experiences? Methodology: Using a retrospective cohort study design, 120 SSRI-treated children diagnose with major depression, anxiety and OCD, aged 6 to 17 years of aged will be recruited from Child and Adolescent Addiction, Mental Health & Psychiatry (CAAMHP) Program in Calgary. Children with a current or past history of SSRI use will be identified via recruitment advertisements and by CAAMHP treatment teams operating within community clinics as well as inpatient units within the Alberta Children's Hospital and Foothills Medical Centre. Clinical data will be collected from the participant's medical record as well as information provided by the child's healthcare provider and caretaker using a customized data collection form. Saliva will be collected, processed and genotyped in accordance with standard procedures. Participants and their parents will complete self-report measures to gather information regarding demographics, SIBD, and other adverse side effects and drug reactions. Using machine learning (i.e. the construction of algorithms that can learn from and make predictions on data) we will identify and validate a panel of genetic variants that could be used to pre-emptively detect children at-risk for developing SSRI-induced behavioral disinhibition.

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