Guk Jinju, Chae Dongwoo, Park Kyungsoo
Department of Pharmacology, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea.
Transl Clin Pharmacol. 2017 Jun;25(2):101-105. doi: 10.12793/tcp.2017.25.2.101. Epub 2017 Jun 15.
Weight is a covariate representative of body size and is known to influence drug disposition. Recently, with increased use of allometric scaling, this variable has become more significant in accounting for variability in pharmacokinetic parameters. In adults, weight can be considered as a time invariant covariate because physical development is complete. As a result, when weight is missing in data, the typical or median value (say, 70 kg) could be imputed. On the contrary, weight continuously changes with age in the pediatric population. In this case, it is more appropriate to consider different median weight for each age group. We constructed a prediction model for weight using postmenstrual age (PMA) with the data consisting of 83,014 Korean pediatric patients. Weight, PMA, and gender information were collected from electronic medical records. Sigmoid models multiplied by exponential or logistic function were tested for basic model structure. Covariate effects on model parameters were then investigated using selection criteria of p < 0.001. All analyses were performed using NONMEM 7.3.0 and R3.2.0. The sigmoid model multiplied by logistic function best described the data and there was a significant difference between boys and girls in model parameters. It is expected that the results obtained in this work can be used for imputation of missing weights in pediatrics when PMA is available. In addition, the developed model can be used for clinical studies in children under 12 years old whose weight change rapidly with age and for model building in dealing with time varying body weight as a covariate.
体重是代表身体大小的协变量,已知其会影响药物处置。最近,随着异速生长比例缩放法使用的增加,这个变量在解释药代动力学参数的变异性方面变得更加重要。在成年人中,由于身体发育已完成,体重可被视为一个随时间不变的协变量。因此,当数据中体重缺失时,可以用典型值或中位数(比如70千克)进行插补。相反,在儿科人群中,体重会随着年龄持续变化。在这种情况下,为每个年龄组考虑不同的中位数体重更为合适。我们利用月经后年龄(PMA)构建了一个体重预测模型,数据来自83014名韩国儿科患者。体重、PMA和性别信息从电子病历中收集。对乘以指数函数或逻辑函数的S形模型进行了基本模型结构测试。然后使用p < 0.001的选择标准研究协变量对模型参数的影响。所有分析均使用NONMEM 7.3.0和R3.2.0进行。乘以逻辑函数的S形模型最能描述数据,并且模型参数在男孩和女孩之间存在显著差异。预计这项工作中获得的结果可用于在有PMA时插补儿科中缺失的体重。此外,所开发的模型可用于12岁以下儿童的临床研究,这些儿童的体重随年龄快速变化,也可用于将随时间变化的体重作为协变量进行模型构建。