Your browser doesn't support javascript.
loading
Design Space Approach for the Optimization of Green Fluidized Bed Granulation Process in the Granulation of a Poorly Water-Soluble Fenofibrate Using Design of Experiment.
Fayed, Mohamed H; Alalaiwe, Ahmed; Almalki, Ziyad S; Helal, Doaa A.
Afiliação
  • Fayed MH; Department of Pharmaceutics, Faculty of Pharmacy, Fayoum University, Fayoum 63514, Egypt.
  • Alalaiwe A; Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
  • Almalki ZS; Department of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
  • Helal DA; Department of Pharmaceutics, Faculty of Pharmacy, Fayoum University, Fayoum 63514, Egypt.
Pharmaceutics ; 14(7)2022 Jul 15.
Article em En | MEDLINE | ID: mdl-35890366
ABSTRACT
In the pharmaceutical industry, the systematic optimization of process variables using a quality-by-design (QbD) approach is highly precise, economic and ensures product quality. The current research presents the implementation of a design-of-experiment (DoE) driven QbD approach for the optimization of key process variables of the green fluidized bed granulation (GFBG) process. A 32 full-factorial design was performed to explore the effect of water amount (X1; 1-6% w/w) and spray rate (X2; 2-8 g/min) as key process variables on critical quality attributes (CQAs) of granules and tablets. Regression analysis have demonstrated that changing the levels of X1 and X2 significantly affect (p ≤ 0.05) the CQAs of granules and tablets. Particularly, X1 was found to have the pronounced effect on the CQAs. The GFBG process was optimized, and a design space (DS) was built using numerical optimization. It was found that X1 and X2 at high (5.69% w/w) and low (2 g/min) levels, respectively, demonstrated the optimum operating conditions. By optimizing X1 and X2, GFBG could enhance the disintegration and dissolution of tablets containing a poorly water-soluble drug. The prediction error values of dependent responses were less than 5% that confirm validity, robustness and accuracy of the generated DS in optimization of GFBG.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Pharmaceutics Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Pharmaceutics Ano de publicação: 2022 Tipo de documento: Article