| Literature DB >> 31502691 |
Youwei Bi1, Jiang Liu1, Lingjue Li1, Jingyu Yu1, Atul Bhattaram1, Michael Bewernitz1, Ruo-Jing Li1, Chao Liu1, Justin Earp1, Lian Ma1, Luning Zhuang1, Yuching Yang1, Xinyuan Zhang1, Hao Zhu1, Yaning Wang1.
Abstract
The unique challenges in pediatric drug development require efficient and innovative tools. Model-informed drug development (MIDD) offers many powerful tools that have been frequently applied in pediatric drug development. MIDD refers to the application of quantitative models to integrate and leverage existing knowledge to bridge knowledge gaps and facilitate development and decision-making processes. This article discusses the current practices and visions of applying MIDD in pediatric drug development, regulatory evaluation, and labeling, with detailed examples. The application of MIDD in pediatric drug development can be broadly classified into 3 categories: leveraging knowledge for bridging the gap, dose selection and optimization, and informing clinical trial design. In particular, MIDD can provide evidence for the assumption of exposure-response similarity in bridging existing knowledge from reference to target population, support the dose selection and optimization based on the "exposure-matching" principle in the pediatric population, and increase the efficiency and success rate of pediatric trials. In addition, the role of physiologically based pharmacokinetics in drug-drug interaction in children and adolescents and in utilizing ontogeny data to predict pharmacokinetics in neonates and infants has also been illustrated. Moving forward, MIDD should be incorporated into all pediatric drug development programs at every stage to inform clinical trial design and dose selection, with both its strengths and limitations clearly laid out. The accumulated experience and knowledge of MIDD has and will continue to drive regulatory policy development and refinement, which will ultimately improve the consistency and efficiency of pediatric drug development. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.Entities:
Keywords: dose selection and optimization; informing clinical trial design; leveraging knowledge; model-informed drug development; pediatric drug development; pediatric ontogeny
Mesh:
Year: 2019 PMID: 31502691 DOI: 10.1002/jcph.1478
Source DB: PubMed Journal: J Clin Pharmacol ISSN: 0091-2700 Impact factor: 3.126