Division of Pharmacology, Leiden Academic Center for Drug Research, Gorlaeus Laboratories, Einsteinweg 55, 2333 CC Leiden, The Netherlands.
Department of Pediatrics, Division of Neonatology, Erasmus MC - Sophia Children's Hospital, Rotterdam, The Netherlands; Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Pharmacy, Erasmus Medical Center, Rotterdam, The Netherlands.
Eur J Pharm Sci. 2017 Nov 15;109S:S90-S97. doi: 10.1016/j.ejps.2017.05.026. Epub 2017 May 12.
Particularly in the pediatric clinical pharmacology field, data-sharing offers the possibility of making the most of all available data. In this study, we utilize previously collected therapeutic drug monitoring (TDM) data of term and preterm newborns to develop a population pharmacokinetic model for phenobarbital. We externally validate the model using prospective phenobarbital data from an ongoing pharmacokinetic study in preterm neonates.
TDM data from 53 neonates (gestational age (GA): 37 (24-42) weeks, bodyweight: 2.7 (0.45-4.5) kg; postnatal age (PNA): 4.5 (0-22) days) contained information on dosage histories, concentration and covariate data (including birth weight, actual weight, post-natal age (PNA), postmenstrual age, GA, sex, liver and kidney function, APGAR-score). Model development was carried out using NONMEM 7.3. After assessment of model fit, the model was validated using data of 17 neonates included in the DINO (Drug dosage Improvement in NeOnates)-study.
Modelling of 229 plasma concentrations, ranging from 3.2 to 75.2mg/L, resulted in a one compartment model for phenobarbital. Clearance (CL) and volume (V) for a child with a birthweight of 2.6kg at PNA day 4.5 was 0.0091L/h (9%) and 2.38L (5%), respectively. Birthweight and PNA were the best predictors for CL maturation, increasing CL by 36.7% per kg birthweight and 5.3% per postnatal day of living, respectively. The best predictor for the increase in V was actual bodyweight (0.31L/kg). External validation showed that the model can adequately predict the pharmacokinetics in a prospective study.
Data-sharing can help to successfully develop and validate population pharmacokinetic models in neonates. From the results it seems that both PNA and bodyweight are required to guide dosing of phenobarbital in term and preterm neonates.
在儿科临床药理学领域，数据共享提供了充分利用所有可用数据的可能性。在这项研究中，我们利用先前收集的足月和早产儿的治疗药物监测（TDM）数据，为苯巴比妥建立群体药代动力学模型。我们使用正在进行的早产儿药代动力学研究中的前瞻性苯巴比妥数据来验证该模型。
53 名新生儿（胎龄（GA）：37（24-42）周，体重：2.7（0.45-4.5）kg；新生儿期后年龄（PNA）：4.5（0-22）天）的 TDM 数据包含了剂量史、浓度和协变量数据（包括出生体重、实际体重、新生儿期后年龄（PNA）、胎龄、性别、肝肾功能、APGAR 评分）。模型开发使用 NONMEM 7.3 进行。在评估模型拟合度后，使用 DINO（新生儿药物剂量改善）研究中纳入的 17 名新生儿的数据对模型进行验证。
对 229 个浓度范围为 3.2 至 75.2mg/L 的血浆浓度进行建模，得到苯巴比妥的单室模型。对于出生体重为 2.6kg、PNA 第 4.5 天的儿童，清除率（CL）和体积（V）分别为 0.0091L/h（9%）和 2.38L（5%）。出生体重和 PNA 是 CL 成熟的最佳预测因子，出生体重每增加 1kg，CL 增加 36.7%，新生儿期后每天增加 5%。预测 V 增加的最佳指标是实际体重（0.31L/kg）。外部验证表明，该模型能够充分预测前瞻性研究中的药代动力学。
数据共享有助于在新生儿中成功开发和验证群体药代动力学模型。从结果来看，在指导足月和早产儿苯巴比妥的剂量时，均需要考虑 PNA 和体重。
