Pharmacogenetics of Antidepressant-Induced Disinhibition in Children
We are currently waiting for REB approval
Over 800,000 children and youth in Canada experience a significant mental health issue. A majority of these children experience symptoms associated with major depressive disorder, anxiety disorders, or obsessive-compulsive disorder. Antidepressants, such as selective-serotonin reuptake inhibitors (SSRIs) are the most frequently prescribed medications for these children. Although SSRIs are thought to be generally effective and well-tolerated, 10% - 20% of children treated with SSRIs experience behavioural disinhibition (i.e., hyperactivity, impulsivity, irritability) that can lead to devastating consequences (e.g., suicidal impulses, violence). Unfortunately, there are no clinically useful markers available to assist clinicians in predicting which children will experience this adverse event. To address this problem, we are proposing an innovative approach that will apply pharmacogenetics (i.e. the study of how drugs and genes interact) and machine learning (i.e. the construction of algorithms that can learn from and make predictions on data) to identify a panel of genetic variants that could be used to pre-emptively detect children at-risk for developing this adverse event. The discovery of these genetic markers would revolutionize how SSRIs are prescribed to children by giving clinicians and parents a simple, low cost, and personalized solution. This, in turn, would substantially reduce the distress inflicted on children and their families as well as alleviate the economic costs associated with SSRI-induced disinhibition.
Objectives and Aims: The objective of this study is to identify and validate a panel of genetic variants that could be used to pre-emptively detect children at-risk for developing side effects when taking SSRIs. Aim 1: To recruit and collect DNA from children with a history of SSRI therapy and perform comprehensive pharmacogenetic sequencing. Aim 2: To discover and validate a pharmacogenetic-based classifier for SSRI-induced disinhibition risk through the use of machine learning algorithms.