Office of Drug Evaluation IV, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Department of Pediatrics, Division of Clinical Pharmacology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Clin Pharmacol Ther. 2019 Jun;105(6):1462-1470. doi: 10.1002/cpt.1332. Epub 2019 Feb 10.
The objective of this study was to evaluate the predictive performance of population models to predict renal clearance in newborns and infants. Pharmacokinetic (PK) data from eight drugs in 788 newborns and infants were used to evaluate the predictive performance of the population models based on postmenstrual age (PMA), postnatal age, gestational age, and body weight. For the PMA model, the average fold error for clearance (CL) /CL was within a twofold range for each drug in all subgroups. For drugs with > 90% renal elimination, the prediction bias ranged from 0.7-1.3. For drugs with 60-80% renal elimination, the prediction bias ranged 0.6-2.0. Our results suggest that PMA-based sigmoidal maximum effect (E ) model, in combination with bodyweight-based scaling and kidney function assessment, can be used in population PK (PopPK) modeling for drugs that are primarily eliminated via renal pathway to inform initial dose selection for newborns and infants with normal renal function in clinical trials.
本研究旨在评估基于人群的模型预测新生儿和婴儿肾清除率的性能。利用 788 例新生儿和婴儿 8 种药物的药代动力学（PK）数据，评估了基于胎龄后时间（PMA）、出生后时间、胎龄和体重的人群模型的预测性能。对于 PMA 模型，对于所有亚组中的每种药物，CL/CL 的清除率的平均折叠误差都在两倍范围内。对于>90%经肾消除的药物，预测偏差范围为 0.7-1.3。对于 60-80%经肾消除的药物，预测偏差范围为 0.6-2.0。我们的结果表明，基于 PMA 的 sigmoidal 最大效应（E）模型，结合体重基础的比例和肾功能评估，可用于主要经肾脏途径消除的药物的群体药代动力学（PopPK）建模，以告知临床试验中肾功能正常的新生儿和婴儿的初始剂量选择。
