Adverse drug reactions are a leading cause of morbidity and mortality that costs billions of dollars for the healthcare system. In children, there is increased risk for adverse drug reactions with potentially lasting adverse effects into adulthood. The current pediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. We hypothesize that adverse drug reactions manifest from the interaction between drug exposure and dynamic biological processes during child growth and development.
While pediatric pharmacologists have studied and recognized this interaction, the evidence from these studies have focused on a few, well-known drug toxicities largely within animal models that have limited translation to children and their clinical care. Moreover, preclinical studies during drug development do not consider growth and maturation of children, which severely limits our knowledge of drug safety in this population. Post-marketing pediatric drug safety studies, on the other hand, leverage large amounts of observations to identify and characterize adverse drug events in the pediatric population after drugs enter the market. However, these observational studies have been limited to event surveillance and have not focused on evaluating why adverse drug events may manifest in children.
We hypothesize that by developing statistical methodologies with prior knowledge of dynamic, shared information during development, we can improve the detection of adverse drug events in children. We further hypothesize that detecting adverse drug events in this way also improves the evaluation of dynamic biological and physiological processes during child growth and development. In chapter 1, we described the pediatric drug safety landscape, dynamic processes from pediatric developmental biology, and motivation for a large-scale and data-driven approach to study the interaction between drug treatment and child development. In chapter 2, using drug event reports collected by the Food and Drug Administration (FDA), we evaluated statistical models for identifying temporal trends of adverse effects across childhood. We found the generalized additive model (GAM), as compared to a popular disproportionality method, show improved detection performance especially of rare pediatric adverse drug events. In chapter 3, we applied covariate-adjusted drug-event GAMs in a systematic way to develop a resource of nearly half a million adverse drug event (ADE) risk estimates across child development stages.
We showed that not only do significant ADEs through childhood recapitulate dynamic organ and system maturation, but we also provide granular, development-specific risk for known pediatric drug effects that were previously unknown. Importantly, this approach facilitated the evaluation of dynamic biological processes, such as drug-metabolizer gene expression levels across childhood, that we observed coincided with dynamic risk of adverse drug effects. In chapter 4, we performed several case studies showing population-level evidence for well-known pediatric adverse drug reactions using our generated resource. In addition, we developed an accessible web portal, the Pediatric Drug Safety portal (PDSportal), to retrieve from our resource the population-level evidence of user-specified adverse drug events in the pediatric population across child development stages.
In conclusion, we summarize three key research directions in data-driven pediatric drug safety research: quantifying child vs. adult drug safety profiles, predicting pre-clinical drug toxicity across childhood, and detecting genetic susceptibility of pediatric adverse drug events. Our results demonstrate that developing pediatric drug safety methods directly for children using data-driven approaches improves both identification and evaluation of adverse drug events during the period of child growth and development.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-5d9b-6738 |
| Date | January 2022 |
| Creators | Giangreco, Nicholas Paul |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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