The monitoring and control of biotech processes in different phases of the product lifecycle from early development to commercial production is key for accelerated development and stringent process controls. Effective methods of monitoring are required to develop, optimize, and maintain processes at a maximum efficiency and desired product quality. In the last decade more and more research has been devoted to developing specifically designed sensors, sampling strategies and integrated data management systems to allow better and more detailed process monitoring. Especially with the Process Analytical Technology (PAT) framework published by the Food and Drug Administration (FDA) in 2004 the measurement, monitoring, modeling and control of biotech processes has become more important. This article will describe general operational aspects of sensors, sampling technologies and methods of process monitoring, advanced applications like soft sensors and metabolic controls including integrated data management and analysis. Practical examples and case studies are used to illustrate the potential of soft sensors, models and advanced sensors.

The purification of recombinant proteins and antibodies using large packed-bed columns is a key component in most biotechnology purification processes. Because of its efficiency and established practice in the industry, column chromatography is a state of the art technology with a proven capability for removal of impurities, viral clearance, and process efficiency. In general, the validation and monitoring of chromatographic operations-especially of critical process parameters-is required to ensure robust product quality and compliance with health authority expectations. One key aspect of chromatography that needs to be monitored is the integrity of the packed bed, since this is often critical to achieving sufficient separation of protein species. Identification of potential column integrity issues before they occur is important for both product quality and economic efficiency. In this article, we examine how transition analysis techniques can be utilized to monitor column integrity. A case study on the application of this method during a large scale Protein A capture step in an antibody purification process shows how it can assist with improving process knowledge and increasing the efficiency of manufacturing operations.

Chromatogram overlays are frequently used to monitor inter-batch performance of bioprocess purification steps. However, the objective analysis of chromatograms is difficult due to peak shifts caused by variable phase durations or unexpected process holds. Furthermore, synchronization of batch process data may also be required prior to performing multivariate analysis techniques. Dynamic time warping was originally developed as a method for spoken word recognition, but shows potential in the objective analysis of time variant signals, such as manufacturing data. In this work we will discuss the application of dynamic time warping with a derivative weighting function to align chromatograms to facilitate process monitoring and fault detection. In addition, we will demonstrate the utility of this method as a preprocessing step for multivariate model development.