Nicholas P Giangreco1, Nicholas P Tatonetti2. 1. Departments of Systems Biology and Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, 10032, USA. 2. Departments of Systems Biology and Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, 10032, USA. npt2105@cumc.columbia.edu.
Abstract
BACKGROUND: Identifying 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. RESULTS: Using 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. CONCLUSIONS: Our 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.
BACKGROUND: Identifying 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. RESULTS: Using 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. CONCLUSIONS: Our 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.
Authors: Janneke M Brussee; Elisa A M Calvier; Elke H J Krekels; Pyry A J Välitalo; Dick Tibboel; Karel Allegaert; Catherijne A J Knibbe Journal: Expert Rev Clin Pharmacol Date: 2016-06-27 Impact factor: 5.045
Authors: Sandra de Bie; Carmen Ferrajolo; Sabine M J M Straus; Katia M C Verhamme; Jan Bonhoeffer; Ian C K Wong; Miriam C J M Sturkenboom Journal: PLoS One Date: 2015-06-19 Impact factor: 3.240