Model based strategies towards protein A resin lifetime optimization and supervision.

The high cost of protein A resins drives the biopharmaceutical industry to maximize its lifetime, which is limited by several processes, usually referred to as resin aging. In this work, two model based strategies are presented, aiming to control and improve the resin lifetime. The first approach, purely statistical, enables qualitative monitoring of the column state and prediction of column performance indicators (e.g. yield, purity and dynamic binding capacity) from chromatographic on-line data (e.g. UV signal). The second one, referred to as hybrid modeling, is based on a lumped kinetic model, which includes two aging parameters fitted on several resin cycling experimental campaigns with varying cleaning procedures (CP). The first aging parameter accounts for binding capacity deterioration (caused by ligand degradation, leaching, and pore occlusion), while the second accounts for a decreased mass transfer rate (mainly caused by fouling). The hybrid model provides important insights into the prevailing aging mechanism as a function of the different CPs. In addition, it can be applied to model based CP optimization and yield forecasting with the capability of state estimation corrections based on on-line process information. Both approaches show promising results, which could help to significantly extend the resin lifetime. This comes along with increased understanding, reduced experimental effort, decreased cost of goods, and improved process robustness.

Hybrid-EKF: Hybrid model coupled with extended Kalman filter for real-time monitoring and control of mammalian cell culture.

In a decade when Industry 4.0 and quality by design are major technology drivers of biopharma, automated and adaptive process monitoring and control are inevitable requirements and model-based solutions are key enablers in fulfilling these goals. Despite strong advancement in process digitalization, in most cases, the generated datasets are not sufficient for relying on purely data-driven methods, whereas the underlying complex bioprocesses are still not completely understood. In this regard, hybrid models are emerging as a timely pragmatic solution to synergistically combine available process data and mechanistic understanding. In this study, we show a novel application of the hybrid-EKF framework, that is, hybrid models coupled with an extended Kalman filter for real-time monitoring, control, and automated decision-making in mammalian cell culture processing. We show that, in the considered application, the predictive monitoring accuracy of such a framework improves by at least 35% when developed with hybrid models with respect to industrial benchmark tools based on PLS models. In addition, we also highlight the advantages of this approach in industrial applications related to conditional process feeding and process monitoring. With regard to the latter, for an industrial use case, we demonstrate that the application of hybrid-EKF as a soft sensor for titer shows a 50% improvement in prediction accuracy compared with state-of-the-art soft sensor tools.

Bioprocessing in the Digital Age: The Role of Process Models.

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.