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.

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.