Departments of Systems Biology and Biomedical Informatics, Columbia University, 622 W. 168(th) Street, New York, NY 10032, USA.
Departments of Systems Biology and Biomedical Informatics, Columbia University, 622 W. 168(th) Street, New York, NY 10032, USA.
Med. 2022 Aug 12;3(8):579-595.e7. doi: 10.1016/j.medj.2022.06.001. Epub 2022 Jun 24.
Adverse drug effects (ADEs) in children are common and may result in disability and death, necessitating post-marketing monitoring of their use. Evaluating drug safety is especially challenging in children due to the processes of growth and maturation, which can alter how children respond to treatment. Current drug safety-signal-detection methods do not account for these dynamics.
We recently developed a method called disproportionality generalized additive models (dGAMs) to better identify safety signals for drugs across child-development stages.
We used dGAMs on a database of 264,453 pediatric adverse-event reports and found 19,438 ADEs signals associated with development and validated these signals against a small reference set of pediatric ADEs. Using our approach, we can hypothesize on the ontogenic dynamics of ADE signals, such as that montelukast-induced psychiatric disorders appear most significant in the second year of life. Additionally, we integrated pediatric enzyme expression data and found that pharmacogenes with dynamic childhood expression, such as CYP2C18 and CYP27B1, are associated with pediatric ADEs.
We curated KidSIDES, a database of pediatric drug safety signals, for the research community and developed the Pediatric Drug Safety portal (PDSportal) to facilitate evaluation of drug safety signals across childhood growth and development.
This study was supported by grants from the National Institutes of Health (NIH).
儿童药物不良反应(ADE)很常见,可能导致残疾和死亡,因此需要对其使用进行上市后监测。由于生长和成熟过程会改变儿童对治疗的反应,因此评估儿童的药物安全性尤其具有挑战性。当前的药物安全性信号检测方法没有考虑到这些动态变化。
我们最近开发了一种称为比例广义加性模型(dGAMs)的方法,以更好地识别跨越儿童发育阶段的药物安全性信号。
我们在一个包含 264453 例儿科不良事件报告的数据库中使用 dGAMs 发现了 19438 个与发育相关的 ADE 信号,并针对一个小型儿科 ADE 参考集对这些信号进行了验证。通过我们的方法,我们可以假设 ADE 信号的个体发育动态,例如孟鲁司特引起的精神障碍在生命的第二年最为显著。此外,我们整合了儿科酶表达数据,发现具有儿童期动态表达的药物基因,如 CYP2C18 和 CYP27B1,与儿科 ADE 相关。
我们为研究界整理了儿科药物安全性信号数据库 KidSIDES,并开发了儿科药物安全门户(PDSportal),以促进在儿童生长发育过程中评估药物安全性信号。
这项研究得到了美国国立卫生研究院(NIH)的资助。