Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation

Abstract

Objectives: The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset.

Methods: Nonlinear mixed-effects models were constructed from paracetamol concentration-time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors.

Results: The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1-14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1-28.1).

Conclusions: Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations.

Conflict of interest statement

Conflicts of interest Sarah Cook, Jessica Roberts, Samira Samiee-Zafarghandy, Chris Stockmann, Amber King, Nina Deutsch, Elaine Williams, Karel Allegaert, Diana Wilkins, Catherine Sherwin, and John van den Anker have no potential conflicts of interest to declare.

Figures

Fig. 1

Diagnostic plots for the final covariate model. Observed versus a population-predicted and b individual-predicted paracetamol plasma concentrations. The solid black lines depict the lines of identity (y = x), and the dashed red lines depict the LOESS fits of the data

Fig. 2

Diagnostic plots for the final covariate model. Conditional weighted residuals of paracetamol plasma concentrations versus a time since previous dose and b population-predicted paracetamol concentrations. The solid black lines depict y = 0

Fig. 3

NPDEs of paracetamol plasma concentrations from the model-building dataset (a, c, e, g) and the external evaluation dataset (b, d, f, h). Density histograms of NPDEs (a, b) with overlaid solid black curves depicting standard normal distributions for comparison. NPDEs versus time since previous dose (c, d), population-predicted paracetamol concentration (e, f), and current body weight (g, h). NPDEs normalized prediction distribution errors

Fig. 4

Visual predictive checks of the final covariate model for a the model-building dataset and b the external evaluation dataset. The solid black lines depict the observed 50th percentiles, and the dashed black lines depict the observed 5th and 95th percentiles. The shaded gray regions depict the 95 % confidence intervals surrounding the predicted 5th, 50th, and 95th percentiles. Individual observations are depicted as gray dots. Individual observations were omitted from b because the density of points would obscure the percentile lines and prediction intervals