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PAT Series: Predictive monitoring and control approaches in biopharmaceutical manufacturing

Posted: 3 September 2015 |

Predictive monitoring is a key feature of biopharmaceutical manufacturing; making predictions about the key process end points such as process performance indicators or quality attributes using a process model offers the unique advantages of process improvement and optimisation, and helps give insights into variability.

PAT Series: Predictive monitoring and control approaches in biopharmaceutical manufacturing

However, whilst model-predictive monitoring is advantageous, it is also desirable to apply model predictions for closed loop control of biologics manufacturing using various process analytical technology (PAT) tools. We summarise some of our experiences with predictive monitoring, closed loop control using in situ Raman spectroscopy and state-space methods for model predictive control of cell culture bioreactors.

Introduction

As in other process industries, biopharmaceutical manufacturing processes generate a lot of data during the clinical and commercial production runs collected over many stages on many different variables. It is imperative to monitor these variables across the stages and batches in real-time to detect any developing trends that may affect batch performance or lead to loss of a batch. It also is of great interest to understand trends in process variables that may impact on product quality attributes (PQA) and key performance indicators (KPI). Regulatory Agency guidelines also promote the lifecycle concept linking product and process development with the commercial manufacturing process. We have previously discussed and presented very powerful PATs such as application of real-time multivariate statistical process monitoring in the open-loop mode of operation where many variables across many stages in a biopharmaceutical manufacturing process are monitored and weak signals are detected.

While detecting weak signals is important to enable engagement in process monitoring and troubleshooting, it is also highly desirable to control the process performance and product quality via closed-loop approaches using model-based predictive techniques. As described in the PAT guidance, some of these control applications may involve process chemistry tools and analysers, whilst others may depend on soft-sensors and other predictive technologies. This aligns with a quality-by-design approach and ensures the target product profile is met by establishing an effective control strategy. Ample literature exists in closed loop and model predictive control in various industries including fermentation technologies. However, actual industrial applications of model-based or model-predictive control are not yet very common in biopharmaceuticals manufacturing…

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