Leveraging artificial intelligence methods and electronic health records for pediatric pharmacovigilance

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.

Grant Number: 1R21HD113234-01
NIH Institute/Center: NIH
Principal Investigator: Cosmin Bejan