The use of PBPK modeling across the pediatric age range using propofol as a case

Robin Michelet1, Jan Van Bocxlaer2, Karel Allegaert3,4

  • 1Laboratory of Medical Biochemistry and Clinical Analysis, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium. robin.michelet@ugent.be.

Insights

Physiologically based pharmacokinetic (PBPK) models accurately predicted drug pharmacokinetics (PK) in neonates and children without needing pediatric data. This approach enhances drug research safety and efficacy for pediatric populations.

Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Pediatric Pharmacology
  • Computational Modeling

Background:

  • Drug research in children requires specific guidelines, utilizing both bottom-up and top-down approaches.
  • Propofol, a lipophilic drug extensively metabolized in the liver and kidney via glucuronidation, serves as a model compound.
  • Previous studies employed physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (popPK) models separately to describe propofol's pharmacokinetics (PK).

Purpose of the Study:

  • To evaluate the combined performance of PBPK and popPK approaches in describing propofol's PK in pediatric and neonatal populations (term and preterm).
  • To develop and qualify PBPK models for propofol, extrapolating adult models to pediatric populations using ontogeny functions.
  • To assess the predictive accuracy of PBPK models for pediatric drug pharmacokinetics.

Main Methods:

  • In vitro studies using human liver microsomes and recombinant enzymes to develop an adult PBPK model (Simcyp®).
  • Calculation of activity adjustment factors (AAFs) to reconcile in vitro and in vivo enzyme activities.
  • Analysis of clinical data using a 3-compartment popPK model (NONMEM) to construct and qualify retrograde PBPK models.
  • Extrapolation of the adult PBPK model to pediatric populations using default and in vivo derived ontogeny functions.
  • Model qualification by comparing predicted PK parameters and concentration-time profiles with published data and clinical observations.

Main Results:

  • PBPK models accurately predicted in vivo clearance, particularly when compared to trials with long-term sampling.
  • Predicted volume of distribution was lower compared to typical popPK model predictions.
  • Concentration-time profiles were well-predicted up to and including the preterm neonatal population.

Conclusions:

  • Physiologically based pharmacokinetic (PBPK) modeling can effectively predict drug pharmacokinetics (PK) in pediatric and preterm neonatal populations without requiring pediatric in vivo data.
  • The study highlights the potential of PBPK modeling for advancing pediatric drug research and guideline development.
  • Further development of PBPK models is recommended, focusing on improving distribution modeling and incorporating in vivo derived ontogeny functions.

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