Introduction

The increasing need to develop new biopharmaceuticals and reduce costs have led to higher demands on processes being more versatile and efficient (Zydney, 2015). The opportunities and rationale for integrated continuous processes have been discussed in several studies (Hammerschmidt, Tscheliessnig, Sommer, Helk, & Jungbauer, 2014; Konstantinov & Cooney, 2015; Papathanasiou & Kontoravdi, 2020), and integrated continuous processes is also encouraged by the U.S. Food and Drug Administration (FDA) (Woodcock, 2014). There is also a trend towards more stratified therapies, which will lead to an increase in the demand for flexible multi-purpose facilities for smaller-scale production (Gronemeyer, Ditz, & Strube, 2014).
Upstream processes have historically been the subject of greater development in regard to integration and continuous production than downstream processes (Gronemeyer et al., 2014), despite the fact that up to 60% of the total manufacturing cost can be attributed to downstream processing (Dileo, Ley, Nixon, & Chen, 2017). Several methods have been developed to reduce this disparity, including periodic countercurrent chromatography (PCC) (Godawat et al., 2012), multicolumn countercurrent solvent gradient purification (Müller‐Späth, Aumann, Melter, Ströhlein, & Morbidelli, 2008), and simulated moving bed chromatography (Juza, Mazzotti, & Morbidelli, 2000). Integrated biomanufacturing has been found to give higher productivity and product quality than non-integrated batch processing (Andersson et al., 2017; Gomis-Fons et al., 2019; Rathore, Agarwal, Sharma, Pathak, & Muthukumar, 2015; Schügerl & Hubbuch, 2005). Karst, Steinebach, Soos, and Morbidelli (2017) suggested that the improvement in product quality was the result of a more stable cellular environment and the shorter residence time. Furthermore, the need for manual intervention is reduced, which increases the robustness and reproducibility of the process (Konstantinov & Cooney, 2015; Sonnleitner, 1997). The FDA advocates the concept of quality by design for the production of biopharmaceuticals (Rathore & Winkle, 2009). It is therefore important that the process design is retained when the process is scaled up. This is an important reason for developing integrated and continuous processes in research. Another reason for studying integrated and continuous processes is the need for flexibility to allow many similar candidates to be rapidly produced and screened. Many studies on integrated continuous biomanufacturing processes have been performed on monoclonal antibodies (Arnold, Lee, Rucker‐Pezzini, & Lee, 2019; Godawat, Konstantinov, Rohani, & Warikoo, 2015; Gomis-Fons, Schwarz, et al., 2020; Kamga, Cattaneo, & Yoon, 2018; Steinebach et al., 2017). This paper describes a general integrated continuous process for the production of various biopharmaceuticals, including a PCC operation and a truly continuous solvent/detergent virus inactivation step (Martins et al., 2019). To the best of our knowledge, this is the first time PCC and continuous virus inactivation have been integrated and run together.
Process analytical technology (PAT) is a tool that enables improved product quality and process efficiency (Fisher et al., 2019), and can be used in the quality-by-design approach. The main goal of PAT is to ensure consistent quality by both a sound understanding of the process and real-time monitoring of critical attributes (Read et al., 2010). Measurements of the attributes can be used for feedback or feedforward process control (Fisher et al., 2019). For example, Mendhe, Thukkaram, Patil, and Rathore (2015) analyzed a variety of PAT-based pooling strategies and found that the most successful application was a feedforward approach based on the retention time of a characteristic peak eluting prior to the main peak. Another example is the implementation of a strategy to control the load factor in a twin-column periodic capture step coupled with a bioreactor for the production of monoclonal antibodies, as in our previous study (Gomis-Fons, Schwarz, et al., 2020), which was achieved by online estimation of the harvest concentration and the model-based assessment of the dynamic binding capacity of the column. This type of control is necessary to avoid overloading the column when the harvest concentration increases, or to maximize the resin utilization when the harvest concentration is low. However, this approach required a very detailed and complex model, which involved substantial experimental work for its calibration. In the present study, we developed a real-time control approach in which no model is needed, and the control parameters are obtained automatically online, thus avoiding expensive and time-consuming experiments. This constitutes a very powerful PAT-based tool that can be used to improve process efficiency and facilitate the efficient integration of upstream and downstream processes, since it allows greater adaptability of the downstream process to changes in the concentration and flow rate from upstream processes.
The supervisory control and data acquisition system Orbit software (Nilsson, Andersson, Gomis-Fons, & Löfgren, 2017) was used to store the generated data, analyze them, and control the hardware. This system has been used in previous studies (Andersson et al., 2017; Gomis-Fons et al., 2019; Löfgren et al., 2018). Orbit was modified so that the real-time controller could be implemented and to allow online monitoring of process attributes.