Changes in individual drug-independent system parameters during virtual paediatric pharmacokinetic trials: introducing time-varying physiology into a paediatric PBPK model

Abstract

Although both POPPK and physiologically based pharmacokinetic (PBPK) models can account for age and other covariates within a paediatric population, they generally do not account for real-time growth and maturation of the individuals through the time course of drug exposure; this may be significant in prolonged neonatal studies. The major objective of this study was to introduce age progression into a paediatric PBPK model, to allow for continuous updating of anatomical, physiological and biological processes in each individual subject over time. The Simcyp paediatric PBPK model simulator system parameters were reanalysed to assess the impact of re-defining the individual over the study period. A schedule for re-defining parameters within the Simcyp paediatric simulator, for each subject, over a prolonged study period, was devised to allow seamless prediction of pharmacokinetics (PK). The model was applied to predict concentration-time data from multiday studies on sildenafil and phenytoin performed in neonates. Among PBPK system parameters, CYP3A4 abundance was one of the fastest changing covariates and a 1-h re-sampling schedule was needed for babies below age 3.5 days in order to seamlessly predict PK (<5% change in abundance) with subject maturation. The re-sampling frequency decreased as age increased, reaching biweekly by 6 months of age. The PK of both sildenafil and phenytoin were predicted better at the end of a prolonged study period using the time varying vs fixed PBPK models. Paediatric PBPK models which account for time-varying system parameters during prolonged studies may provide more mechanistic PK predictions in neonates and infants.

Fig. 1

Relative expression of human drug-metabolising enzymes at different ages. Lines represent simulated ontogeny profiles based on ‘best fit’ equations to the original data (see “Discussion”)

Fig. 2

Percent change of CYP3A4 for different sampling periods for different ages and the derived age cut off points for default re-sampling schedule within Simcyp paediatric

Fig. 3

Simulated profiles for theophylline in a virtual full-term newborn population after administration of 4.8 mg/kg iv theophylline followed by 2 mg/kg iv every 12 h. The therapeutic window (8–12 ug/ml) is bound by the two black lines. The dashed line shows the higher level predicted when re-defining individuals in the paediatric-PBPK model was not consider (baseline model) and the solid line shows the achievement of therapeutic level by using the re-defining approach (time-changing physiology paediatric-PBPK model)

Fig. 4

Simulated mean (solid lines) and 95% predictive interval values (dashed lines) of sildenafil plasma concentration over time for three representative subjects using both baseline and time-based changing physiology in the p-PBPK model. Filled circles are the observations from each subject as reported in Mukherjee et al. 2009

Fig. 5

Hepatic intrinsic clearance of sildenafil over time for CYP3A4 (upper panel) and CYP2C9 (lower panel) for representative subject 1 in Fig. 4

Fig. 6

Simulated mean values of plasma concentration of phenytoin over time. Filled circles and diamonds are the observations from individual U and V reported in Loughnan et al., 1977