Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children

Nicholas P Giangreco,Nicholas P Tatonetti

doi:10.1186/s13040-021-00264-9

Abstract

BackgroundIdentifying adverse drugs effects (ADEs) in children, overall and within pediatric age groups, is essential for preventing disability and death from marketed drugs. At the same time, however, detection is challenging due to dynamic biological processes during growth and maturation, called ontogeny, that alter pharmacokinetics and pharmacodynamics. As a result, methodologies in pediatric drug safety have been limited to event surveillance and have not focused on investigating adverse event mechanisms. There is an opportunity to identify drug event patterns within observational databases for evaluating ontogenic-mediated adverse event mechanisms. The first step of which is to establish statistical models that can identify temporal trends of adverse effects across childhood.ResultsUsing simulation, we evaluated a population stratification method (the proportional reporting ratio or PRR) and a population modeling method (the generalized additive model or GAM) to identify and quantify ADE risk at varying reporting rates and dynamics. We found that GAMs showed improved performance over the PRR in detecting dynamic drug event reporting across child development stages. Moreover, GAMs exhibited normally distributed and robust ADE risk estimation at all development stages by sharing information across child development stages.ConclusionsOur study underscores the opportunity for using population modeling techniques, which leverage drug event reporting across development stages, as biologically-inspired detection methods for evaluating ontogenic mechanisms.

Highlights

Identifying adverse drugs effects (ADEs) in children, overall and within pediatric age groups, is essential for preventing disability and death from marketed drugs
We found that the generalized additive model (GAM) had higher area under the receiver operating characteristic (AUROC) and power across stages (Fig. 4 and Figure S2) and similar performance within each child development stage (Figure S3, 4) when detecting drug event reporting dynamics compared to the proportional reporting ratio (PRR)
We found that the GAM had more robust and higher overall performance and sensitivity (Fig. 6 and Figure S8) to detect the various drug event reporting dynamics as adverse drug events became rare at child development stages

Summary

Introduction

Identifying adverse drugs effects (ADEs) in children, overall and within pediatric age groups, is essential for preventing disability and death from marketed drugs. Methodologies in pediatric drug safety have been limited to event surveillance and have not focused on investigating adverse event mechanisms. Adverse drug events (ADEs) in children are common and can result in injury and death [1, 2]. The mechanisms may include varying protein activity [9, 10] as well as include functional and structural changes that occur during maturation [11, 12] These ontogenic changes can alter pharmacodynamics and pharmacokinetics resulting in adverse effects, as is the case for doxorubicin-induced cardiotoxicity [13] and valproate-induced hepatotoxicity [14]. There is an opportunity to combine known ontogenic biology with real-world pediatric drug effect data to identify ontogenicmediated adverse events

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Conclusion