Prediction of drug disposition in infants and children by means of physiologically based pharmacokinetic (PBPK) modelling: theophylline and midazolam as model drugs

Abstract

AimsTo create a general physiologically based pharmacokinetic (PBPK) model for drug disposition in infants and children, covering the age range from bir th to adulthood, and to evaluate it with theophylline and midazolam as model drugs. MethodsPhysiological data for neonates, 0.5-, 1-, 2-, 5-, 10-and 15-year-old children, and adults, of both sexes were compiled from the literature. The data comprised body weight and surface area, organ weights, vascular and interstitial spaces, extracellular body water, organ blood flows, cardiac output and glomerular filtration rate. Tissue: plasma partition coefficients were calculated from rat data and unbound fraction ( f u ) of the drug in human plasma, and age-related changes in unbound intrinsic hepatic clearance were estimated from CYP1A2 and CYP2E1 (theophylline) and CYP3A4 (midazolam) activities in vitro . Volume of distribution ( V dss ), total and renal clearance (CL and CL R ) and elimination half-life ( t 1/2 ) were estimated by PBPK modelling, as functions of age, and compared with literature data. ResultsThe predicted V dss of theophylline was 0.4-0.6 l kg -1 and showed only a modest change with age. The median prediction error (MPE) compared with literature data was 3.4%. Predicted total CL demonstrated the time-course generally repor ted in the literature. It was 20 ml h -1 kg -1 in the neonate, rising to 73 ml h -1 kg -1 at 5 years and then decreasing to 48 ml h -1 kg -1 in the adult. Overall, the MPE was -4.0%. Predicted t 1/2 was 18 h in the neonate, dropping rapidly to 4.6-7.2 h from 6 months onwards, and the MPE was 24%. The predictions for midazolam were also in good agreement with literature data. V dss ranged between 1.0 and 1.7 l kg -1 and showed only modest change with age. CL was 124 ml h -1 kg -1 in the neonate and peaked at 664 ml h -1 kg -1 at 5 years before decreasing to 425 ml h -1 kg -1 in the adult. Predicted t 1/2 was 6.9 h in the neonate and attained 'adult' values of 2.5-3.5 h from 1 year onwards.Conclusions A general PBPK model for the prediction of drug disposition over the age range neonate to young adult is presented. A reference source of physiolog ical data was compiled and validated as far as possible. Since studies of pharmacokinetics in children present obvious practical and ethical difficulties, one aim of the work was to utilize maximally already available data. Prediction of the disposition of theophylline and midazolam, two model drugs with dissimilar physicochemical and pharmacokinetic characteristics, yielded results that generally tallied with literature data. Future use of the model may demonstrate further its strengths and weaknesses. S. Björkman 69259 :6 Br J Clin Pharmacol

Physiologically based pharmacokinetic modelling: a sound mechanistic basis is needed

“…Consequently, very few clinical applications have appeared. A notable exception is the full PBPK model of Björkman, used to predict theophylline and midazolam disposition in infants and children [12]. Given the complexity of PBPK modelling, more use has been made of partial PBPK models or semimechanistic models.…”

A Workflow Example of PBPK Modeling to Support Pediatric Research and Development: Case Study with Lorazepam

“…The developmental processes involved in the creation of pediatric PBPK models has been documented by several researchers and typically include defining physiology and anatomy, protein binding, and clearance, all as a function of age (2)(3)(4). Amongst the literature, pediatric PBPK models have been utilized in several different capacities: suggesting starting doses for children of different age groups, predictions of environmental contaminant exposure, optimization of clinical drug trial design (sampling schedule, number of patients, etc.…”

Dose Selection Based on Physiologically Based Pharmacokinetic (PBPK) Approaches

“…In later drug development stages, the model can be updated with animal data specific to the therapeutic mAb to enable translation from preclinical species to human populations. We will illustrate fit- Prospective prediction of first-in-human PK in healthy volunteers (72)(73)(74) Retrospective prediction of first-in-human PK in healthy volunteers (8-11, 16,71) Prediction of PK in paediatric populations (78)(79)(80)(81)(82) Prediction of PK in patients with hepatic impairment/ liver cirrhosis (75,76) Prediction of PK in patients with renal impairment (77,86,87) Predictions of PK in other patient populations (83)(84)(85) Predictions of PK in polymorphic populations (88) for-purpose PBPK models using one example from literature and one in-house example. Adalimumab is an anti-TNF-alpha mAb approved for the treatment of rheumatoid arthritis (RA), an autoimmune disease which leads to inflammation of joints.…”