Population Model of Serum Creatinine as Time-Dependent Covariate in Neonates

Serum creatinine (sCr) is a commonly measured biomarker to estimate glomerular filtration rate (GFR) and therefore widely used as a covariate in population pharmacokinetic models of renally excreted drugs. In neonates, sCr dynamically changes during the first few weeks after birth. Missing covariates are a common problem in pharmacokinetic modeling of neonates due to the limited availability of blood sampling in number and volume. The objective of this work is to develop a parsimonious population model describing time courses of sCr in neonates with the intent to be incorporated into pharmacokinetic models of various drugs where sCr values are sparse or missing. The data for model development consisted of sCr measurements in 1080 newborns with a gestational age of 24-42 weeks. The model is based on a pharmacokinetic model of sCr that involves GFR, backflow of creatinine from the renal tubules, and urinary flow. Gestational age is the only covariate explaining between-subject variability of sCr. The model adequately describes distinct features of the sCr time course such as a peak and decline to a plateau. For a neonate with a GA of 35 weeks, the typical value of sCr at birth was 0.584 mg/dL, the peak (0.794 mg/dL) occurred 2.3 days after birth, to reach a plateau of 0.255 mg/dL approximately after 24.7 days. Model simulations reveal that in neonates with a similar postnatal age, sCr decreases with increasing GA. In summary, our model is designed to be a part of full random effects pharmacokinetic models where sCr is a significant covariate.

Population Pharmacokinetic Modeling in the Presence of Missing Time-Dependent Covariates: Impact of Body Weight on Pharmacokinetics of Paracetamol in Neonates

Body weight is the primary covariate in pharmacokinetics of many drugs and dramatically changes during the first weeks of life of neonates. The objective of this study is to determine if missing body weights in preterm and term neonates affect estimates of model parameters and which methods can be used to improve performance of a population pharmacokinetic model of paracetamol. Data for our analysis were obtained from previously published studies on the pharmacokinetics of intravenous paracetamol in neonates. We adopted a population model of body weight change in neonates to implement three previously introduced methods of handling missing covariates based on data imputation, likelihood function modification, and full random effects modeling. All models were implemented in NONMEM 7.4, and population parameters were estimated using the FOCE method. Our major finding was that missing body weights minimally affect population estimates of pharmacokinetic parameters but do affect the covariate relationship parameters, particularly the one describing dependence of clearance on body weight. None of the tested methods changed estimates of between-subject variability nor impacted the predictive performance of the model. Our analysis shows that a modeling approach towards handling missing covariates allows borrowing information gathered in various studies as long as they target the same population. This approach is particularly useful for handling time-dependent missing covariates.