De Cock Roosmarijn F W, Allegaert Karel, Sherwin Catherine M T, Nielsen Elisabet I, de Hoog Matthijs, van den Anker Johannes N, Danhof Meindert, Knibbe Catherijne A J
Division of Pharmacology, LACDR, Leiden University, Leiden, The Netherlands.
Pharm Res. 2014 Mar;31(3):754-67. doi: 10.1007/s11095-013-1197-y. Epub 2013 Sep 25.
Recently, a covariate model characterizing developmental changes in clearance of amikacin in neonates has been developed using birth bodyweight and postnatal age. The aim of this study was to evaluate whether this covariate model can be used to predict maturation in clearance of other renally excreted drugs.
Five different neonatal datasets were available on netilmicin, vancomycin, tobramycin and gentamicin. The extensively validated covariate model for amikacin clearance was used to predict clearance of these drugs. In addition, independent reference models were developed based on a systematic covariate analysis.
The descriptive and predictive properties of the models developed using the amikacin covariate model were good, and fairly similar to the independent reference models (goodness-of-fit plots, NPDE). Moreover, similar clearance values were obtained for both approaches. Finally, the same covariates as in the covariate model of amikacin, i.e. birth bodyweight and postnatal age, were identified on clearance in the independent reference models.
This study shows that pediatric covariate models may contain physiological information since information derived from one drug can be used to describe other drugs. This semi-physiological approach may be used to optimize sparse data analysis and to derive individualized dosing algorithms for drugs in children.
最近，利用出生体重和出生后年龄建立了一个表征新生儿阿米卡星清除率发育变化的协变量模型。本研究的目的是评估该协变量模型是否可用于预测其他经肾脏排泄药物清除率的成熟情况。
有五个关于奈替米星、万古霉素、妥布霉素和庆大霉素的不同新生儿数据集。使用经过广泛验证的阿米卡星清除率协变量模型来预测这些药物的清除率。此外，基于系统的协变量分析开发了独立的参考模型。
使用阿米卡星协变量模型开发的模型的描述性和预测性属性良好，并且与独立参考模型相当相似（拟合优度图，NPDE）。此外，两种方法获得的清除率值相似。最后，在独立参考模型中，在清除率方面确定了与阿米卡星协变量模型中相同的协变量，即出生体重和出生后年龄。
本研究表明，儿科协变量模型可能包含生理信息，因为从一种药物获得的信息可用于描述其他药物。这种半生理方法可用于优化稀疏数据分析，并为儿童药物推导个体化给药算法。
