Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
Technical R&D, GSK Vaccines, 1330, Rixensart, Belgium.
Biotechnol J. 2020 Jan;15(1):e1900172. doi: 10.1002/biot.201900172. Epub 2019 Sep 23.
In this age of technology, the vision of manufacturing industries built of smart factories is not a farfetched future. As a prerequisite for Industry 4.0, industrial sectors are moving towards digitalization and automation. Despite its tremendous growth reaching a sales value of worth $188 billion in 2017, the biopharmaceutical sector distinctly lags in this transition. Currently, the challenges are innovative market disruptions such as personalized medicine as well as increasing commercial pressure for faster and cheaper product manufacturing. Improvements in digitalization and data analytics have been identified as key strategic activities for the next years to face these challenges. Alongside, there is an emphasis by the regulatory authorities on the use of advanced technologies, proclaimed through initiatives such as Quality by Design (QbD) and Process Analytical Technology (PAT). In the manufacturing sector, the biopharmaceutical domain features some of the most complex and least understood processes. Thereby, process models that can transform process data into more valuable information, guide decision-making, and support the creation of digital and automated technologies are key enablers. This review summarizes the current state of model-based methods in different bioprocess related applications and presents the corresponding future vision for the biopharmaceutical industry to achieve the goals of Industry 4.0 while meeting the regulatory requirements.
在这个科技时代，智能制造工厂的愿景不再是遥不可及的未来。作为工业 4.0 的前提，工业领域正在向数字化和自动化发展。尽管生物制药行业在 2017 年的销售额达到了 1880 亿美元，实现了巨大的增长，但在这一转型中明显落后。目前，面临的挑战是创新的市场颠覆，如个性化医疗，以及更快、更便宜的产品制造的商业压力不断增加。数字化和数据分析的改进已被确定为未来几年应对这些挑战的关键战略活动。此外，监管机构强调使用先进技术，通过质量源于设计（QbD）和过程分析技术（PAT）等举措宣布了这一点。在制造业中，生物制药领域具有一些最复杂和最难以理解的工艺。因此，能够将工艺数据转化为更有价值的信息、指导决策并支持数字化和自动化技术创建的过程模型是关键的推动因素。本文综述了基于模型的方法在不同生物工艺相关应用中的现状，并提出了相应的未来愿景，以实现工业 4.0 的目标，同时满足监管要求。
