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.