Infection, Inflammation and Rheumatology Section, UCL Great Ormond Street Institute of Child Heath, University College London, London, UK.
Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, PO Box 591, 751 24, Uppsala, Sweden.
Clin Pharmacokinet. 2019 Jan;58(1):39-52. doi: 10.1007/s40262-018-0659-0.
Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies.
药代动力学/药效学（PKPD）建模在儿童临床药理学研究的设计和实施中非常重要。在药物开发过程中，PKPD 建模和模拟应支持合理的试验设计，并有助于推断疗效和安全性。PKPD 建模在优化给药建议和治疗药物监测中的应用也在增加，基于 PKPD 模型的个体化剂量将成为个性化医学的核心特征。在儿科 PK 建模取得广泛进展之后，现在需要更加重视 PD 建模，以了解药物作用与年龄相关的变化。本文讨论了儿科药物开发背景下 PKPD 建模的原理，总结了清除率（CL）等重要 PK 参数如何随体型和年龄而变化，并强调了儿童 CL 缩放的标准化方法。一种标准的缩放方法将有助于比较多个研究中的 PK 参数，从而提高现有 PK 模型的实用性，并促进新研究的最佳设计。
