Enhancing CHO process understanding from CHO manufacturing process data

It has been said that the biggest data can come from the smallest packages and this is certainly true in the case of CHO cells, CHO-based commercial bioprocesses can generate up to 500,000,000 data points per batch. When aggregated, organized, and analyzed, these data represent a significant, but generally underutilized, opportunity to advance product understanding and process control opportunities. Traditional approaches to CHO process analytics have typically leveraged discrete in-process control and analytical release test data for batch over batch trending to ensure that processes remain in a state of statistical control. While this methodology can be effective to ensure that a control strategy is operating as intended, it is a reactive, lagging approach to process understanding. Advances such as deployment of real time multivariate statistical process monitoring have helped to drive proactive approaches to detect weak multivariate signals within complex datasets and have been successfully utilized at Amgen to enhance monitoring controls to ensure robust performance, early detection of issues and to rapid root cause determination when process deviations occur. The ideal future state however is to truly implement an automated data infrastructure to enable the capability to allow developers, data scientists, and users within manufacturing and PD to access a variety of structured and unstructured data sources from all stages of the process lifecycle to enhance monitoring, analytics and development of predictive process models. This presentation will describe Amgen’s efforts to drive towards that ideal state through implementation of a data infrastructure that enables network wide data aggregation, predictive modeling, advanced process monitoring, and data science driven approaches to extract the knowledge within our big data

Agent-based model predictive framework to control cell culture bioreactors

Bioprocesses require unique operational conditions and highly specialized process knowledge to obtain consistent product quality and productivity. Optimization and control of these processes are challenging due to the nonlinearities and uncertainties involved, and cell-bioreactor interactions are poorly understood. Automated control of bioreactors using model predictive control (MPC) technologies is less common as translating complex process specific interactions to linear models is challenging. Accurate models of the process are needed for MPC to succeed. Due to the complexity and heterogeneity involved in the culture environment, conventional mechanistic modeling efforts are often incomplete for describing the interactions of cell physiology and environmental conditions and predicting future behavior. Agent-based computational models provide a strong tool for studying mammalian cell culture bioreactor processes where agents (cells) take action based on changing dynamics of their immediate vicinity. An ABM was previously developed to simulate individual mammalian cell behavior and dynamics of bioreactor environment. In this study, applicability of MPC using ABM has been investigated to optimize growth in mammalian cell culture bioreactors

Advancement of cell culture process understanding and control through real-time multivariate process monitoring, use of statistical process modes and deployment of process analytical technologies

Commercial bioprocess manufacturing facilities generate considerable amounts of process data which are routinely used for process monitoring. Through the analysis of batch over batch trends it is possible to ensure that the process remains in a state of continuous statistical control. Real time process data can also be used to drive rapid root cause analysis and to enable timely interventions to prevent performance parameter excursions. Finally, these process data also provide significant opportunities to further understand the interaction between operating parameters, process equipment and raw materials to enable improved process control and optimization. To use the available process data effectively, systems are required to store, aggregate, and visualize the data. These data can then be used for advanced analytics, such as development of process models or analysis of variance across similar product modalities. These advanced analytics allow for rapid identification of process excursions and identification of true special cause variation (e.g. raw material variability). These tools also enable continued process verification (CPV) as part of the lifecycle approach to process validation. This presentation will describe the deployment of advanced process monitoring approaches, the development of predictive process models using multivariate process and product data and how to leverage these tools to drive process improvements. Finally, the application of newer technologies to understand and control process performance will be discussed including a case study describing the development of closed loop feeding strategies using Raman spectroscopy. These advances will be described in the context of continued process verification and as part of a future vision for portfolio-wide approaches to process monitoring, understanding and optimization