Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Mais filtros











Base de dados
Assunto principal
Intervalo de ano de publicação
1.
CPT Pharmacometrics Syst Pharmacol ; 11(6): 755-765, 2022 06.
Artigo em Inglês | MEDLINE | ID: mdl-35385609

RESUMO

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.


Assuntos
Modelos Biológicos , Teorema de Bayes , Humanos , Cadeias de Markov , Método de Monte Carlo , Incerteza
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA