Vann Lucas, Sheppard John
North Carolina State University, Raleigh, NC, USA.
J Ind Microbiol Biotechnol. 2017 Dec;44(12):1589-1603. doi: 10.1007/s10295-017-1984-2. Epub 2017 Oct 25.
Control of biopharmaceutical processes is critical to achieve consistent product quality. The most challenging unit operation to control is cell growth in bioreactors due to the exquisitely sensitive and complex nature of the cells that are converting raw materials into new cells and products. Current monitoring capabilities are increasing, however, the main challenge is now becoming the ability to use the data generated in an effective manner. There are a number of contributors to this challenge including integration of different monitoring systems as well as the functionality to perform data analytics in real-time to generate process knowledge and understanding. In addition, there is a lack of ability to easily generate strategies and close the loop to feedback into the process for advanced process control (APC). The current research aims to demonstrate the use of advanced monitoring tools along with data analytics to generate process understanding in an Escherichia coli fermentation process. NIR spectroscopy was used to measure glucose and critical amino acids in real-time to help in determining the root cause of failures associated with different lots of yeast extract. First, scale-down of the process was required to execute a simple design of experiment, followed by scale-up to build NIR models as well as soft sensors for advanced process control. In addition, the research demonstrates the potential for a novel platform technology that enables manufacturers to consistently achieve "goldenbatch" performance through monitoring, integration, data analytics, understanding, strategy design and control (MIDUS control). MIDUS control was employed to increase batch-to-batch consistency in final product titers, decrease the coefficient of variability from 8.49 to 1.16%, predict possible exhaust filter failures and close the loop to prevent their occurrence and avoid lost batches.
生物制药过程的控制对于实现一致的产品质量至关重要。由于将原材料转化为新细胞和产品的细胞具有极其敏感和复杂的性质，生物反应器中的细胞生长是最难控制的单元操作。目前的监测能力在不断提高，然而，现在的主要挑战是如何有效地利用所生成的数据。造成这一挑战的因素有很多，包括不同监测系统的集成以及实时进行数据分析以生成过程知识和理解的功能。此外，缺乏轻松生成策略并闭环反馈到过程中以实现先进过程控制（APC）的能力。当前的研究旨在展示使用先进的监测工具以及数据分析来在大肠杆菌发酵过程中生成过程理解。近红外光谱用于实时测量葡萄糖和关键氨基酸，以帮助确定与不同批次酵母提取物相关的失败原因。首先，需要缩小过程规模以执行简单的实验设计，然后扩大规模以建立近红外模型以及用于先进过程控制的软传感器。此外，该研究展示了一种新型平台技术的潜力，该技术使制造商能够通过监测、集成、数据分析、理解、策略设计和控制（MIDUS控制）持续实现“黄金批次”性能。采用MIDUS控制来提高最终产品效价的批次间一致性，将变异系数从8.49%降低到1.16%，预测可能的排气过滤器故障并闭环以防止其发生并避免批次损失。
