Towards real-time monitoring, control and real-time
release
Real-time monitoring provides information if the process runs within the
specified operating range. This is useful information and can be linked
to release criteria, but it could be further exploited for process
control. Then raw material variations, fluctuations in process
parameters could be controlled and a much more robust process can be
obtained. This is then also the step towards automation, because human
intervention could be minimized. In bioreactors real-time monitoring is
used to control the process to deliver optimal nutrients to reach a high
titer or product quality. A typical example is the addition to manganese
ions to trigger the glycosylation of antibodies (Tharmalingam et al.,
2015) addition of amino acids or control the level of glucose at a
minimum level. Bioreactor control is much more advanced compared to
downstream processing. Besides pooling there are not a lot of
opportunities to control batch chromatography. An interesting approach
is to control the gradient shape in batch chromatography to optimize
resolution and binding capacity (Sellberg et al., 2017). The authors
named it salt trajectory. An optimizer adapts the shape of the gradient
according to the retention behavior of product and impurities and the
salt gradient is modulated. The wider application of such a controls
system would require a fast-real-time monitoring of the effluent stream
of the chromatography column, because they still used off-line
measurements of the column effluent. Such a system maximizes
productivity while resolution is not affected. Usually increasing
productivity will be obtained at the expense of reduced resolution.
Implementation of soft sensors such as the (Walch et al., 2019) into a
column chromatography with a gradient optimizer would lead to an optimal
system where product quality and quantity is monitored, and the
information is further utilized to control the system and operate under
high resolution.
Another dilemma is the optimization of dynamic binding capacity. When
chromatography is operated with constant velocity an optimum of
productivity at a certain residence time is achieved. At this residence
time the column utilization is rather low, but by increasing the column
utilization the productivity is reduced. At high velocity/low residence
time the breakthrough curve is shallow and at low velocity steep,
therefore the dynamic binding capacity and column utilization is higher
at low velocity (Carta et al., 2010; Eslami et al., 2022a; Eslami et
al., 2022b). One solution is countercurrent chromatography, which allows
maximizing column utilization and productivity, but on expense
equipment/instrument complexity(Heeter et al., 1996). Countercurrent
loading or periodic counter current chromatography is a way to render
batch chromatography into continuous chromatography and in addition
column utilization is improved. In its simplest form a column is
overloaded, and the breakthrough loaded on the second column.
Concurrently the third column is washed, eluted, and regenerated. This
concept has been extensively used for purification of recombinant
antibodies with protein A affinity chromatography(Badr et al., 2021;
Davis et al., 2021; Farid et al., 2015; Gerstweiler et al., 2021;
Gomis-Fons et al., 2020; Rathore et al., 2022b; Scheffel et al., 2022;
Sun et al., 2022; Vetter et al., 2021; Vogg et al., 2018). In order to
control the overloading of the first column a control algorithm using
absorbance at 280 nm has been implemented. First a breakthrough of the
impurities is observed and when the column is close saturation a second
breakthrough is caused by the product. This signal can be used as a
control algorithm for switching columns (Chmielowski et al., 2017;
Gerstweiler et al., 2022; Godawat et al., 2012). This will only work
when rather pure feedstock such as an antibody produced in chemical
defined media is purified. Crude feedstocks would fully saturate the UV
sensor and the difference between the signals of the impurities and
product might be too low in order to get useful information to control
the loading. In this case a soft sensor such as the Walch et. al.
approach (Walch et al., 2019) will be a general solution to control
loading in PCC. Near infrared spectroscopy (NIRS) has been also placed
at the column entrance and exit and calibrated with a chemometrics
approach (Thakur et al., 2020). It is not yet clear if this sensor alone
would work for crude solutions or if only the concentration range is
wider compared to a UV-cell. If only a wider concentration range must be
covered flow VP is a good solution.
Another way to solve the problem of productivity and column utilization
is to change the flow rate during loading. A dual flowrate during
loading has been proposed by Lacki and coworkers (Ghose et al., 2004a;
Ghose et al., 2004b) and then extend to either multiple steps
(Ramakrishna et al., 2022) or a linear flow-rate gradient (Chen et al.,
2021)or a model predicted gradient (Eslami et al., 2022a; Eslami et al.,
2022b; Gomis-Fons et al., 2021; Sellberg et al., 2018)). The loading is
started with a high velocity and then subsequently reduced. In order to
establish an optimized system an optimizer and controller are necessary.
Such a model predictive control results in high productivity and high
column utilization but with less complex equipment. In order to run such
a controlled process a soft sensor is required at the column in-let and
outlet when crude feedstocks are purified and UV280 is not sufficient to
monitor product concentration (Eslami et al., 2022a; Eslami et al.,
2022b; Sellberg et al., 2018). Such a controller has been implemented in
the XAMIris (Evon, Austria) (Figure 8).