CAPE-Lab - Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD, Italy.
Int J Pharm. 2013 Feb 28;444(1-2):25-39. doi: 10.1016/j.ijpharm.2013.01.018. Epub 2013 Jan 18.
Streamlining the manufacturing process has been recognized as a key issue to reduce production costs and improve safety in pharmaceutical manufacturing. Although data available from earlier developmental stages are often sparse and unstructured, they can be very useful to improve the understanding about the process under development. In this paper, a general procedure is proposed for the application of latent variable statistical methods to support the development of new continuous processes in the presence of limited experimental data. The proposed procedure is tested on an industrial case study concerning the development of a continuous line for the manufacturing of paracetamol tablets. The main driving forces acting on the process are identified and ranked according to their importance in explaining the variability in the available data. This improves the understanding about the process by elucidating how different active pharmaceutical ingredient pretreatments, different formulation modes and different settings on the processing units affect the overall operation as well as the properties of the intermediate and final products. The results can be used as a starting point to perform a comprehensive and science-based quality risk assessment that help to define a robust control strategy, possibly enhanced with the integration of a design space for the continuous process at a later stage.
简化制造工艺已被认为是降低生产成本和提高制药生产安全性的关键问题。尽管早期开发阶段可用的数据通常很少且是非结构化的，但它们对于提高对正在开发过程的理解非常有用。本文提出了一种将潜在变量统计方法应用于支持新连续工艺开发的一般程序，在有限的实验数据存在的情况下。所提出的程序在一个涉及对扑热息痛片剂连续生产线开发的工业案例研究中进行了测试。根据它们在解释可用数据变异性方面的重要性，确定并对作用于工艺的主要驱动力进行排序。通过阐明不同的活性药物成分预处理、不同的配方模式和处理单元的不同设置如何影响整体操作以及中间和最终产品的特性，这提高了对工艺的理解。结果可作为起点，进行全面且基于科学的质量风险评估，有助于定义稳健的控制策略，可能在以后的阶段通过整合连续工艺的设计空间来增强。
