Population <scp>PBPK</scp> modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers
J. R. Wedagedera, Anthonia Afuape, Siri Kalyan Chirumamilla, Hiroshi Momiji, Robert Leary, Mike Dunlavey, Richard Matthews, Khaled Abduljalil, Masoud Jamei, Frédéric Y. Bois
Abstract
Physiologically-based pharmacokinetic (PBPK) models usually include a large number of parameters whose values are obtained using in vitro to in vivo extrapolation. However, such extrapolations can be uncertain and may benefit from inclusion of evidence from clinical observations via parametric inference. When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics inferential framework to estimate unknown or uncertain parameters. Several approaches are available for that purpose, but their relative advantages for PBPK modeling are unclear. We compare the results obtained using a minimal PBPK model of a canonical theophylline dataset with quasi-random parametric expectation maximization (QRPEM), nonparametric adaptive grid estimation (NPAG), Bayesian Metropolis-Hastings (MH), and Hamiltonian Markov Chain Monte Carlo sampling. QRPEM and NPAG gave consistent population and individual parameter estimates, mostly agreeing with Bayesian estimates. MH simulations ran faster than the others methods, which together had similar performance.