Digitalization platform and supervisory control of a continuous integrated bioprocess based on Raman spectroscopy

In the last years, the implementation of Raman spectroscopy, multivariate data analysis (MVDA) and advanced control algorithms gained increasing interest in the biopharmaceutical industry through the PAT initiative. However, there is still a huge gap towards an efficient implementation of modern process analyzers, a centralized data mining combined with online use of MVDA and the integration of process knowledge into a supervisory control frame. To bridge this gap, a digitalization platform for a fully continuous integrated manufacturing bioprocess was developed in collaboration with leading companies for process digitalization solutions, advanced monitoring sensors and cell cultures (Siemens, Kaiser Optical Systems and Merck). The potential of online Raman spectroscopy in upstream and downstream was tested to gain as much as possible process information. Different media, products and cell lines were monitored and diverse spiking strategies and advanced modeling algorithms were investigated to improve the robustness and predictive power of the models. Finally, dedicated runs were performed to develop and tune control algorithms. The developed IT platform facilitates the efficient collection and centralized storing of all process data. In addition, it is able to interact with the control systems of each process unit and close the control loop. Advanced multivariate statistical and mechanistic models as well as process control and optimization tools, can be integrated. In particular, the possibility to decently predict the dynamic evolution of central process variables including glucose, viable cell density and product titer, all amino acids and even quality attributes (aggregates and glycosylation patterns), outlines the important role of online Raman spectroscopy in the supervisory control. The hierarchical control system enables the handling of process perturbations and optimization of diverse objectives such as productivity, efficiency and product quality. The efficient implementation of Raman spectroscopy, facilitated by the IT platform, and the innovative control system provides a very important basis to intensify the main advantages of continuous integrated manufacturing and fully follows the trend of industry 4.0. Please click Additional Files below to see the full abstract

Combining Mechanistic Modeling and Raman Spectroscopy for Monitoring Antibody Chromatographic Purification

Chromatography is widely used in biotherapeutics manufacturing, and the corresponding underlying mechanisms are well understood. To enable process control and automation, spectroscopic techniques are very convenient as on-line sensors, but their application is often limited by their sensitivity. In this work, we investigate the implementation of Raman spectroscopy to monitor monoclonal antibody (mAb) breakthrough (BT) curves in chromatographic operations with a low titer harvest. A state estimation procedure is developed by combining information coming from a lumped kinetic model (LKM) and a Raman analyzer in the frame of an extended Kalman filter approach (EKF). A comparison with suitable experimental data shows that this approach allows for the obtainment of reliable estimates of antibody concentrations with reduced noise and increased robustness

Process-wide control and automation of an integrated continuous manufacturing platform for antibodies

Integrated continuous manufacturing is entering the biopharmaceutical industry. The main drivers range from improved economics, manufacturing flexibility, and more consistent product quality. However, studies on fully integrated production platforms have been limited due to the higher degree of system complexity, limited process information, disturbance, and drift sensitivity, as well as difficulties in digital process integration. In this study, we present an automated end-to-end integrated process consisting of a perfusion bioreactor, CaptureSMB, virus inactivation (VI), and two polishing steps to produce an antibody from an instable cell line. A supervisory control and data acquisition (SCADA) system was developed, which digitally integrates unit operations and analyzers, collects and centrally stores all process data, and allows process-wide monitoring and control. The integrated system consisting of bioreactor and capture step was operated initially for 4 days, after which the full end-to-end integrated run with no interruption lasted for 10 days. In response to decreasing cell-specific productivity, the supervisory control adjusted the loading duration of the capture step to obtain high capacity utilization without yield loss and constant antibody quantity for subsequent operations. Moreover, the SCADA system coordinated VI neutralization and discharge to enable constant loading conditions on the polishing unit. Lastly, the polishing was sufficiently robust to cope with significantly increased aggregate levels induced on purpose during virus inactivation. It is demonstrated that despite significant process disturbances and drifts, a robust process design and the supervisory control enabled constant (optimum) process performance and consistent product quality

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

Understanding mAb aggregation during low pH viral inactivation and subsequent neutralization

ISSN:0006-3592ISSN:1097-029

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.Fil: Feidl, Fabian. Institute for Chemical and Bioengineering; SuizaFil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Podobnik, Matevz. Institute for Chemical and Bioengineering; SuizaFil: Vogg, Sebastian. Institute for Chemical and Bioengineering; SuizaFil: Angelo, James. No especifíca;Fil: Potter, Kevin. No especifíca;Fil: Wiggin, Elenore. No especifíca;Fil: Xu, Xuankuo. No especifíca;Fil: Ghose, Sanchayita. No especifíca;Fil: Li, Zheng Jian. No especifíca;Fil: Morbidelli, Massimo. Politecnico di Milano; ItaliaFil: Butté, Alessandro. No especifíca