Fayed Mohamed H, Alalaiwe Ahmed, Almalki Ziyad S, Helal Doaa A
Department of Pharmaceutics, Faculty of Pharmacy, Fayoum University, Fayoum 63514, Egypt.
Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Pharmaceutics. 2022 Jul 15;14(7):1471. doi: 10.3390/pharmaceutics14071471.
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 3 full-factorial design was performed to explore the effect of water amount (X; 1-6% /) and spray rate (X; 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 X and X significantly affect ( ≤ 0.05) the CQAs of granules and tablets. Particularly, X 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 X and X at high (5.69% /) and low (2 g/min) levels, respectively, demonstrated the optimum operating conditions. By optimizing X and X, 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.
在制药行业,采用质量源于设计(QbD)方法对工艺变量进行系统优化具有高精度、经济性,并能确保产品质量。当前研究展示了一种基于实验设计(DoE)的QbD方法在绿色流化床制粒(GFBG)工艺关键工艺变量优化中的应用。进行了三因素全因子设计,以探究水量(X₁;1 - 6% /)和喷雾速率(X₂;2 - 8 g/min)作为关键工艺变量对颗粒剂和片剂关键质量属性(CQAs)的影响。回归分析表明,改变X₁和X₂的水平会显著影响(p ≤ 0.05)颗粒剂和片剂的CQAs。特别是,发现X₁对CQAs有显著影响。对GFBG工艺进行了优化,并通过数值优化构建了设计空间(DS)。结果发现,分别在高(5.69% /)和低(2 g/min)水平的X₁和X₂展示了最佳操作条件。通过优化X₁和X₂,GFBG可以提高含难溶性药物片剂的崩解和溶出度。相关响应的预测误差值小于5%,这证实了所生成的DS在GFBG优化中的有效性、稳健性和准确性。