Extrapolation potential of semi-physiological covariate models to newborns: a simulation-based study
Elisa Calvier (1), Elke H.J. Krekels (1), Catherijne A.J. Knibbe (1,2)
(1) Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands; (2) Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, the Netherlands
Objectives: Semi-physiological covariate models (SPCMs) describe the maturation of plasma clearance (CLp) for specific metabolic or elimination pathways [1,2]. These models are obtained in PK studies of model drugs that are mainly eliminated through one specific pathway. It has been suggested that SPCMs can be extrapolated between drugs sharing an elimination pathway [1,2]. In this work we investigate the extrapolation potential of SPCMs between drugs undergoing hepatic metabolism, when predicting CLp in newborns based on CLp information in adults.
Methods: In R, the dispersion model [3] was used to simulate CLp of hypothetical drugs with different unbound fractions, microsomal unbound intrinsic clearances (CLintu), and blood to plasma ratios. Regarding CLintu in newborns, two scenarios were investigated: 1) enzyme maturation completed at birth, 2) enzyme maturation not completed at birth, with changes in CLintu derived from a published SPCM [4] by retrograde calculation. Physiological parameters for scaling between adults and newborns were compiled from literature [5-9]. SPCMs were derived from the CLp simulated in adult and newborns for each hypothetical drug and scenario. Within each scenario, SPCMs were extrapolated between hypothetical drugs to predict CLp in newborns from CLp values in adults. The prediction error (PE) of the predicted CLp in newborns was computed.
Results: Patterns in PE were best summarized using extraction ratios (ER). The PE of SPCMs was higher in scenario 2 compared to scenario 1. For both scenarios, SPCMs over-predict or under-predict CLp in newborns when extrapolated to drugs with a lower or a higher ER, respectively, than the model drug from which they are derived. SPCMs developed using drugs with a low ER have the best extrapolation potential (maximum PE of -47% and -83% for scenario 1 and 2, respectively), while SPCMs developed using model drugs with a high ER have the poorest extrapolation potential (maximum PE of 89% and 475% for scenario 1 and 2).
Conclusions: SPCMs reflect not only physiological and enzymatic maturation processes, but also drug specific properties. Their predictive properties are dependent on the properties of their model drugs, while their extrapolation potential depends on the differences in properties between the model drug and the drugs the SPCM is extrapolated to, and on the extent of enzyme maturation. SPCMs developed on model drugs with low ER have the best overall extrapolation potential.
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