Rathore Anurag S, Mittal Shachi, Pathak Mili, Arora Arushi
Dept. of Chemical Engineering, Indian Inst. of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
Biotechnol Prog. 2014 Jul-Aug;30(4):967-73. doi: 10.1002/btpr.1922. Epub 2014 May 8.
Biotech unit operations are often characterized by a large number of inputs (operational parameters) and outputs (performance parameters) along with complex correlations among them. A typical biotech process starts with the vial of the cell bank, ends with the final product, and has anywhere from 15 to 30 such unit operations in series. Besides the above-mentioned operational parameters, raw material attributes can also impact process performance and product quality as well as interact among each other. Multivariate data analysis (MVDA) offers an effective approach to gather process understanding from such complex datasets. Review of literature suggests that the use of MVDA is rapidly increasing, fuelled by the gradual acceptance of quality by design (QbD) and process analytical technology (PAT) among the regulators and the biotech industry. Implementation of QbD and PAT requires enhanced process and product understanding. In this article, we first discuss the most critical issues that a practitioner needs to be aware of while performing MVDA of bioprocessing data. Next, we present a step by step procedure for performing such analysis. Industrial case studies are used to elucidate the various underlying concepts. With the increasing usage of MVDA, we hope that this article would be a useful resource for present and future practitioners of MVDA.
生物技术单元操作的特点通常是有大量的输入（操作参数）和输出（性能参数），以及它们之间复杂的相关性。一个典型的生物技术过程从细胞库的小瓶开始，以最终产品结束，并且串联有15到30个这样的单元操作。除了上述操作参数外，原材料属性也会影响过程性能和产品质量，并且它们之间也会相互作用。多变量数据分析（MVDA）提供了一种从如此复杂的数据集中获取过程理解的有效方法。文献综述表明，在监管机构和生物技术行业对设计质量（QbD）和过程分析技术（PAT）的逐渐接受的推动下，MVDA的使用正在迅速增加。QbD和PAT的实施需要对过程和产品有更深入的理解。在本文中，我们首先讨论在对生物加工数据进行MVDA时从业者需要注意的最关键问题。接下来，我们介绍进行此类分析的逐步程序。通过工业案例研究来阐明各种基本概念。随着MVDA使用的增加，我们希望本文能为MVDA的当前和未来从业者提供有用的参考。
