Development of an In Vivo Predictive Dissolution Methodology of Topiroxostat Immediate-Release Tablet Using In Silico Simulation.
AAPS PharmSciTech
; 22(3): 132, 2021 Apr 13.
Article
em En
| MEDLINE
| ID: mdl-33851275
ABSTRACT
The main objective of this study was to develop an in vivo predictive dissolution (IVPD) model for topiroxostat immediate-release (IR) formulation by the combination of mechanistic absorption model (MAM) deconvolution method with time shifting factor (TSF) adjustment. The in vitro dissolution profiles in different biorelevant dissolution media containing different concentrations of sodium lauryl sulfate (SLS) were obtained from dissolution testing with the paddle method of the US Pharmacopeia, while the human pharmacokinetic profile was taken from the published experimental results. The GastroPlus™ software was used to observe the linear relationship between in vitro drug dissolution and in vivo absorption. The pharmacokinetic profile of topiroxostat IR tablet was first deconvoluted through the MAM method to obtain the fraction absorbed in vivo. Next, Levy plot was constructed to estimate the TSF, and the time scale for both processes of dissolution and absorption was then adjusted to be superimposable. The IVPD modelling was subsequently established with data between in vitro dissolution profiles and fraction absorbed in vivo. Finally, the dissolution profiles of topiroxostat IR tablet were translated into a pharmacokinetic curve in terms of convolution method. The comparison between translated and observed pharmacokinetic data will validate the performance of the developed IVPD model. This new linear IVPD model with high predictive power for the tablet can predict the in vivo pharmacokinetic differences through in vitro dissolution data, and it can be utilized as a risk-control tool for the formulation development of the topiroxostat IR tablet and the quality control of product batches.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Piridinas
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Simulação por Computador
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Modelos Biológicos
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Nitrilas
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article