Renal Clearance in Newborns and Infants: Predictive Performance of Population‐Based Modeling for Drug Development

Abstract

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)_predicted /CL_observed 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_max ) 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.