Leveraging artificial intelligence methods and electronic health records for pediatric pharmacovigilance

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PROJECT SUMMARY One overarching goal of the US Food and Drug Administration is to effectively implement post-market pharmacovigilance capabilities of already approved medications. Achieving this goal for pediatric population is particularly challenging. For example, little is currently known about the safety, risks, drug-interactions, and teratogenic effects of many drugs used during pregnancy due to the strict regulations imposed for the participation of pregnant women in drug development trials. Further, safety and efficacy of many drugs for pediatric use is scarce due to the lack of clinical trials on children. For this reason, pediatric practice often involves “off-label” use of drugs with unknown side effects. This may cause unpredictable and tragic effects in pediatric patients including severe adverse drug reactions and toxicity that can affect their development and future reproductive capacity. The availability of large volumes of real-world healthcare data such as electronic health records (EHRs) provides an opportunity to meet the critical need of effectively investigating the effect of drug exposures on pediatric populations at large scale. Our goal is to conduct drug- and phenome-wide association studies on a large EHR database of mother-child dyads that will allow us to study adverse pediatric outcomes associated with 1) drug and substance use exposures of mothers during and before pregnancy; and 2) drug exposures of children during all their developmental milestones. Secondary analyses will include associations between substance use exposure of mothers and pediatric outcomes, drug-drug interaction wide association studies, and drug-substance use interaction wide association studies. Further, we will leverage artificial intelligence methods such as natural language processing (NLP) and machine learning to address exposure misclassification and improve pediatric outcome identification for the proposed studies. Our project aims are to: 1) conduct high-throughput pharmacoepidemiologic studies to identify adverse pediatric outcomes, and 2) evaluate the clinical utility of a real-time pediatric pharmacovigilance system using stakeholder engagement strategies. The expected outcome of this proposal is a stakeholder-informed tool to monitor adverse drug reactions of children in real-time. This will pave the way towards the deployment of a clinical decision support system for early detection of adverse drug reactions in pediatric populations and for real-time identification of patients who are at risk of such negative outcomes.