Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Br J Clin Pharmacol. 2021 Jul;87(7):2847-2854. doi: 10.1111/bcp.14694. Epub 2021 Jan 11.
Simulations are an essential tool for investigating scenarios in pharmacokinetics-pharmacodynamics. The models used during simulation often include the effect of highly correlated covariates such as weight, height and sex, and for children also age, which complicates the construction of an in silico population. For this reason, a suitable and representative patient population is crucial for the simulations to produce meaningful results. For simulation in paediatric patients, international growth charts from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) provide a reference, but these may not always be representative for specific populations, such as malnourished children with HIV or acutely unwell children.
We present a workflow to construct a virtual paediatric patient population using WHO and CDC growth charts, suggest piecewise linear functions to adjust the median of the growth charts by sex and age, and suggest visual diagnostics to compare with the target population. We applied this workflow in a population of 1206 HIV-positive African children, consisting of 19 742 observations with weight ranging from 3.8 to 79.7 kg, height from 55.5 to 180 cm, and an age between 0.40 and 18 years.
Before adjustment, the WHO and CDC charts produced weights and heights higher compared to the observed data. After applying our methodology, we could simulate weight, height, sex and age combinations in good agreement with the observed data.
The methodology presented here is flexible and may be applied to other scenarios where WHO and CDC growth standards might not be appropriate. In addition we provide R scripts and a large ready-to-use paediatric population.
模拟是研究药代动力学-药效学中各种场景的重要工具。模拟过程中使用的模型通常包含体重、身高和性别等高度相关的协变量的影响,对于儿童还包括年龄,这使得构建虚拟人群变得复杂。因此,合适且具有代表性的患者群体对于模拟产生有意义的结果至关重要。对于儿科患者的模拟,世界卫生组织(WHO)和疾病控制与预防中心(CDC)的国际生长图表提供了一个参考,但这些图表并不总是能代表特定人群,例如感染艾滋病毒的营养不良儿童或病情严重的儿童。
我们提出了一种使用 WHO 和 CDC 生长图表构建虚拟儿科患者群体的工作流程,建议使用分段线性函数来调整按性别和年龄划分的生长图表的中位数,并建议使用视觉诊断方法来与目标群体进行比较。我们将此工作流程应用于 1206 名感染艾滋病毒的非洲儿童群体中,该群体共有 19742 个体重范围在 3.8 至 79.7 公斤、身高范围在 55.5 至 180 厘米、年龄在 0.40 至 18 岁的观察结果。
在调整之前,WHO 和 CDC 图表生成的体重和身高与观察数据相比更高。在应用我们的方法后,我们可以很好地模拟与观察数据一致的体重、身高、性别和年龄组合。
本文提出的方法具有灵活性,可以应用于其他不太适用 WHO 和 CDC 生长标准的情况。此外,我们还提供了 R 脚本和一个大型现成的儿科患者群体。