Silva A F, Sarraguça M C, Fonteyne M, Vercruysse J, De Leersnyder F, Vanhoorne V, Bostijn N, Verstraeten M, Vervaet C, Remon J P, De Beer T, Lopes J A
Laboratory of Pharmaceutical Process Analytical Technology, Ghent University, Ghent, Belgium; LAQV/REQUIMTE, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal.
LAQV/REQUIMTE, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal.
Int J Pharm. 2017 Aug 7;528(1-2):242-252. doi: 10.1016/j.ijpharm.2017.05.075. Epub 2017 Jun 2.
A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma™-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control.
开发了一种多变量统计过程控制(MSPC)策略,用于监测ConsiGma™-25连续片剂生产线。使用涵盖三个主要单元(双螺杆高剪切制粒机、流化床干燥机和产品控制单元)的35个记录变量来监测该过程。MSPC策略基于在正常操作条件下通过一系列四次过程运行获取的数据进行主成分分析。利用干燥机气流和温度、制粒机料筒温度、速度和液体质量流量以及粉末计量单元质量流量中施加干扰的运行来评估模型的监测性能。还使用霍特林T和Q残差统计控制图评估施加偏差对过程连续性的影响。通过分析特定时间点的贡献图来评估各个过程变量的影响。结果表明,在两个控制图中都检测到了施加的干扰。总体而言,成功开发并应用了MSPC策略。此外,还检测到了与施加变化无关的偏差,主要出现在制粒机料筒温度控制方面。