Zhao Jie, Tian Geng, Qu Haibin
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Biomedicines. 2023 Jul 19;11(7):2030. doi: 10.3390/biomedicines11072030.
The continuous twin-screw wet granulation (TSWG) process was investigated and optimized with prediction-oriented I-optimal designs. The I-optimal designs can not only obtain a precise estimation of the parameters that describe the effect of five input process parameters, including the screw speed, liquid-to-solid (L/S) ratio, TSWG feed rate, and numbers of the 30° and 60° mixing elements, on the granule quality in a TSWG process, but it can also provide a prediction of the response to determine the optimum operating conditions. Based on the constraints of the desired granule properties, a design space for the TSWG was determined, and the ranges of the operating parameters were defined. An acceptable degree of prediction was confirmed through validation experiments, demonstrating the reliability and effectiveness of using the I-optimal design method to study the TSWG process. The I-optimal design method can accelerate the screening and optimization of the TSWG process.
采用面向预测的I-最优设计对连续双螺杆湿法制粒（TSWG）工艺进行了研究和优化。I-最优设计不仅能够精确估计描述五个输入工艺参数（包括螺杆转速、液固比（L/S）、TSWG进料速率以及30°和60°混合元件数量）对TSWG工艺中颗粒质量影响的参数，还能对响应进行预测以确定最佳操作条件。基于所需颗粒特性的约束条件，确定了TSWG的设计空间，并定义了操作参数的范围。通过验证实验证实了可接受的预测程度，证明了使用I-最优设计方法研究TSWG工艺的可靠性和有效性。I-最优设计方法能够加速TSWG工艺的筛选和优化。
