The Pharmacogenetics of Antidepressant-Induced Disinhibition (PGx-AID) study will address key gaps in knowledge and strengthen Canada’s capacity to identify innovative approaches to improving drug safety in children. The study will specifically address knowledge gaps and build capacity in antidepressant safety through the use of 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). Focus will be given to a severe adverse drug reaction associated with selective-serotonin reuptake inhibitors (SSRIs) therapy known as behavioural disinhibition. SSRI-induced behavioural disinhibition (SIBD) is characterized by a rapid onset of aggression, agitation, impulsivity, and hyperactivity following commencement of SSRI therapy that results in socially inappropriate behaviour and 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 behavioural disinhibition as a result of SSRI treatment. SIBD occurs in 10-20% of children/youth who take an SSRI. Given that SSRI use among children in Canada is predicted to steadily increase, solutions to curb the incidence of SIBD are desperately needed. The proposed project provides one such solution through the identification of genetic markers associated with SIBD. Discovery of such markers would revolutionize how SSRIs are prescribed to children by giving healthcare providers and parents a simple, low cost, personalized tool for assessing the risk for SIBD. This in turn, would substantially reduce the distress inflicted on children and their families as well as alleviate the economic costs associated with SIBD.
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 SIBD.
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 SIBD risk through the use of machine learning algorithms.