Optimization study on periodic counter-current chromatography integrated in a monoclonal antibody downstream process

Journal of Chromatography A 1621 (2020) 461055

Contents lists available at ScienceDirect

Journal of Chromatography A
journal homepage: www.elsevier.com/locate/chroma

Optimization study on periodic counter-current chromatography
integrated in a monoclonal antibody downstream process
Joaquín Gomis-Fons a,b,∗, Niklas Andersson a, Bernt Nilsson a,b
a
b

Dept. of Chemical Engineering, Lund University, Lund, Sweden
Competence Centre for Advanced BioProduction by Continuous Processing, Royal Institute of Technology, Stockholm, Sweden

a r t i c l e

i n f o

Article history:
Received 13 January 2020
Revised 3 March 2020
Accepted 17 March 2020
Available online 19 March 2020
Keywords:
Periodic counter-current chromatography
Process optimization
Process integration
Downstream processing
Monoclonal antibody purification

a b s t r a c t
An optimization study of an integrated periodic counter-current chromatography (PCC) process in a monoclonal antibody (mAb) downstream process at lab scale, is presented in this paper. The optimization
was based on a mechanistic model of the breakthrough curve in the protein-A capture step. Productivity and resin utilization were the objective functions, while yield during the loading of the capture
column was set as a constraint. Different integration approaches were considered, and the effect of the
feed concentration, yield and the protein-A resin was studied. The breakthrough curve and the length of
the product recovery, which depended on the integration approach, determined the process scheduling.
Several optimal Pareto solutions were obtained. At 0.5 mg mL−1 and 99% yield, a maximum productivity
of 0.38 mg mL−1 min−1 with a resin utilization of 60% was obtained. On the other hand, the maximum
resin utilization was 95% with a productivity of 0.10 mg mL−1 min−1 . Due to the constraint of the process
scheduling, a lower productivity could be achieved in the integration approaches with higher recovery
time, which was more remarkable at higher concentrations. Therefore, it was shown that a holistic approach, where all the purification steps are considered in the process optimization, is needed to design a
PCC in a downstream process.
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY license. ( />
1. Introduction
The biopharmaceutical market demand is constantly changing
and there is an increasing pressure on a price reduction for a
global access to biological drugs [1,2]. Continuous bioprocessing is
a way to reduce biologics price by increasing the productivity and
diminishing the manufacturing costs [2,3]. A significant improvement has been carried out in upstream by progressively shifting
from fed-batch to perfusion bioreactors. However, the productivity in downstream has not increased accordingly and nowadays
a big proportion of the manufacturing cost are due to the product purification [4,5]. Continuous downstream processes, like periodic counter-current chromatography (PCC) [6,7], Capture SMB
[7,8] or multi-column counter-current solvent gradient purification
(MCSGP) [9,10], have gained interest in the last years. These processes offer a higher productivity and resin utilization, while keep-

∗
Corresponding author: Dept. of Chemical Engineering, Lund University, P.O. Box
124, SE-21100 Lund, Sweden.
E-mail

addresses:

(J.
Gomis-Fons),
(N. Andersson),
(B. Nilsson).

ing a similar yield than the one obtained in a batch process [7,10].
They all are based on multiple columns, in a way that a column
is loaded with the outlet of another column. In MCSGP, the eluted
impurities containing product is loaded onto another column, and
it is usually applied for polishing steps [10]. For the capture step,
the Capture SMB (2-column PCC) and the 3-column or 4-column
PCC are common alternatives [7].
In a PCC operation, two columns are interconnected and loaded
while the product is recovered in one or two more columns (depending if it is a 3-column or 4-column PCC) [6]. To be able to run
a PCC process, a feed continuity constraint must be fulfilled so that
the harvest can be continuously loaded onto the capture columns
[6]. Additional scheduling constraints can also be applied to avoid
product loss during the loading. Furthermore, to make the most
of the potential of a PCC process, resin utilization and productivity should be maximized. For those reasons, the PCC is a process
that must be carefully designed. Several authors have used empirical models of the breakthrough curve to design a PCC [6,11]. While
these models are useful to get the process conditions that make
the PCC operate, they fail in obtaining an optimal process, since
empirical models are only valid for the residence time and feed
concentration at which the experiments are run. On the contrary,

/>0021-9673/© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. ( />

2

J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

mechanistic models take into account the adsorption equilibrium
and kinetics, and the mass transfer limitations [12]. Therefore, once
the model is calibrated, an optimization over the whole range of
residence times and concentrations, and other process parameters
like loading time, can be performed to obtain one or several optimal solutions. With the help of the model, investigations around
the PCC process, like the effect of the feed concentration or the
particle diameter can be carried out [7,13].
Model-based optimization of a PCC process has been done previously [7,13]. However, in these cases only the capture step is
considered when optimizing the PCC process. But in a purification
train, the capture step is usually followed by a virus inactivation
process and several polishing steps. The integration of the capture
step with the rest of the purification steps affects the PCC optimization strongly, and depending on the integration approach, the
effect is more or less significant. This is because the time that takes
to recover the product (recovery time), which is affected by the integration approach, has to be lower than the cycle time to meet
the feed continuity constraint in PCC [6]. Therefore, if the whole
purification process is considered instead of only the capture step,
the recovery time is longer, and the process scheduling is affected.
In this work, we performed an optimization study of a PCC
integrated in a monoclonal antibody (mAb) downstream process,
for which a mechanistic model of the breakthrough curve in the
protein-A capture step was used. Three integration approaches
were evaluated and their influence on the Pareto front of optimal
solutions is presented. The effect of the chromatography resin, the
feed concentration and the yield were also studied, and the process was compared to a 1-column batch capture step. The integrated PCC process was verified experimentally at lab scale with
two different Protein-A resins.
2. Process integration alternatives
The way the PCC process in the capture step is integrated with

the rest of the purification train is not an obvious choice. In this
work, several alternatives are considered depending on the number of systems or pumps that are used, and whether a surge vessel after the PCC is used or the eluate is directly sent to the next
step (see Fig. 1). In this case, a 3-column PCC is chosen because
it provides a higher productivity and resin utilization, compared to
Capture SMB or a 4-column PCC [7]. However, the same reasoning
around the PCC and conclusions extracted from this work could be
applied for a four-column PCC or Capture SMB.
In all cases, the process consists of the same process steps corresponding to a typical mAb purification process: 1) a capture
step with a protein A resin, 2) a virus inactivation (VI) at pH
3.5 for 60 min, 3) a cation exchange chromatography (CEX) step
in bind-and-elute mode with a gradient elution, 4) an anion exchange chromatography (AEX) step in flow-through mode. A final
ultrafiltration-diafiltration step for product formulation is not included in this process because the volume of the final product
from the chromatography steps at lab scale is much lower than
the minimum starting volume in the ultrafiltration step, as shown
in [14], which would require to use much higher column volumes
in the process validation. Anyway, at larger scales, the integration
of the ultrafiltration step with the rest of the downstream process
including PCC, could easily be carried out the same way it is presented in [14]. This could affect the recovery times, but the deductions and conclusions from this work would still apply.
2.1. Integrated PCC process in one system
The first alternative to run the PCC process with the virus inactivation and the polishing steps (CEX and AEX steps) is to integrate

all steps in one chromatography system without any hold-up volume between the steps (Fig. 1A). In order to avoid hold-up
volumes, the methodology followed in [14–16], called Integrated
Column Sequence (ICS), was used. The basic principle is that the
eluate of one column is loaded directly onto the next column. That
requires synchronization between the elution of a column and the
loading of the next one. In addition, since all steps are performed
in a single chromatography system, they have to be carried out in
series. Consequently, in this case the recovery time corresponds
to the time it takes for the capture step and the polishing steps

to recover the product. The incubation time of the virus inactivation is not included because during this time, regeneration and
equilibration steps of the other columns are carried out. This is a
constraint in the minimum cycle time that limits the productivity.
On the other hand, an integrated and continuous mAb purification
process in only a chromatography system is a compact and cheap
alternative that can be specially interesting for lab-scale PCC runs.

2.2. Integrated PCC process in two systems
In this alternative, the process is integrated the same way as
in the previous one, but two chromatography systems are used instead (Fig. 1B). A system is used for the PCC process and the virus
inactivation, and another system is used for the polishing steps.
The main difference compared to the one-system alternative is that
the capture step and the polishing steps are run in parallel. That
means that the minimum cycle time is not the sum of the process times of the capture and polishing steps, but the highest of
them instead, that is, when one system is done, it waits for the
slower one. That leads to an increase of productivity due to a reduction of the process time. However, the investment cost and the
space occupied in the lab is twice as high as in the first process
alternative.

2.3. PCC process with a surge vessel in two systems
The last process choice considered in this work is the use of
a surge vessel between the capture step and the virus inactivation
step (Fig. 1C). The vessel allows for a desynchronization of the capture step respect to the polishing steps, that is, the minimum cycle
time would be only the recovery time in the capture step. Therefore, in terms of the PCC design, this alternative is equivalent to
the PCC with the capture alone, without the rest of steps. That is
an improvement respect to the previous alternative, since the capture step is usually shorter than the polishing steps, based on a
typical mAb purification process [17], thus leading to a lower minimum PCC cycle time, which means that higher productivity can
be achieved. However, this option has some drawbacks. Firstly, the
complexity of the process is significantly increased. Since the filling and the emptying of the surge vessel, both discrete operations,
are not synchronized (as it can be seen in Fig. 4), the liquid level

before emptying the vessel is different from cycle to cycle. This
means that a level sensor and a controller are needed to keep the
liquid level in the vessel the same after each cycle. In addition, this
desynchronization involves that, for certain PCC cycles the volume
in the vessel is higher than for other cycles. Therefore, the column
volumes of the polishing steps are over-designed for those cycles
with lower volume in the surge vessel. Furthermore, the presence
of a surge vessel increases the risk of product degradation and the
residence time of the product, increases the capital cost, and contributes to slowing down the scaling up time [2]. Another pitfall
of this process is the slower start-up due to the need of filling the
vessel up to a minimum level before starting the polishing steps.
Longer start-ups lead to product loss and higher cost [2].


J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

3

Fig. 1. Process alternatives for the integration of PCC with virus inactivation and polishing steps: (A) Process alternative 1: All steps in one chromatography system, (B)
Process alternative 2: the capture and the virus inactivation in a system (on the left), and the polishing steps in another one (on the right), (C) Process alternative 3: a surge
vessel is used between the capture and the virus inactivation, and two systems are used.

3. Materials and methods
3.1. Materials
Two ÄKTATM pure 150 units were used to perform all the
calibration and validation experiments. Each of the chromatography systems is equipped with the following devices: three pumps
(pumps A and B, and sample pump) with inlet valves for each of
them to be able to select different buffers, a column valve with inbuilt pressure sensors, a loop valve, an outlet valve, several versatile valves, with which different flow paths can be applied, a conductivity sensor, a pH sensor, and two UV monitors.
For the capture step, two protein A resins with different particle size were evaluated. One is mAb Select SuReTM , with 85 μm in

particle diameter, and the other one is mAb Select PrismATM , with
a particle diameter of 60 μm. The buffers and flow rates for the
capture step, except for the loading flow rate, were based on [18].
The VI was done in a 50 mL SuperloopTM provided by GE Healthcare Life Sciences (Uppsala, Sweden). The CEX resin was a CaptoTM
S Impact, and the AEX resin was a CaptoTM Adhere. The process
information regarding buffers and flow rates for these two steps,
was taken from [17]. HiTrapTM prepacked columns with a volume
of 1 mL were used for all chromatography steps. All columns and
resins, along with the chromatography systems, were provided by
GE Healthcare Life Sciences (Uppsala, Sweden). An in-line conditioning between the steps was performed by dilution. Regarding
the conditioning buffers, 100 mM acetic acid with a dilution factor
of 0.5 respect to the eluted volume, was used to set the pH at 3.5


4

J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

in the VI step, whereas a buffer with 50 mM sodium acetate and
100 mM sodium hydroxide with a dilution factor of 0.3 was used
to increase the pH after the VI for the loading of the CEX column.
The eluate of the CEX column was diluted with a factor of 1, with
a 50 mM sodium phosphate solution at a pH of 6.8. A Cleaning-InPlace (CIP) was performed after the elution of the columns, with
0.1 M NaOH for the protein-A resins and 1 M NaOH for the rest of
steps.
3.2. Methods
3.2.1. Process modelling
The breakthrough curve of the capture step was modelled in
order to simulate and optimize the PCC process, based on previous implementations of the general rate model in the research
group [19–21]. For this particular application, a modification was

introduced based on the model from Perez-Almodovar and Carta,
2009 [12]. This model assumes heterogeneous binding mechanism
with fast and slow binding sites. The concentration in the mobile
phase and in the particle are described by Eqs. (1) and (2), with
the boundary conditions in Eqs. (1a), (1b), (2a) and (2b), respectively. Eq. (3) is the description of the kinetics:

∂c
∂ 2 c v ∂ c 1 − εc 3
= Dax 2 −
−
k c − c p |r=r p
∂t
εc ∂ z
εc r p f
∂z

(1)

∂c
v
=
(c − cF ) at z = 0
∂ z εc Dax

(1a)

∂c
=0
∂z

(1b)

∂ cp
∂ cp
1 ∂
= De f f 2
r2
∂t
∂r
r ∂r
∂ cp
=0
∂r

at z = L
−

1

εp

∂ ( q1 + q2 )
∂t

at r = 0

kf
∂ cp
=
(c − c p ) at r = r p

∂r
De f f

∂ qi
= ki [(qmax,i − qi )c p − qi /K ]
∂t

(2)

(2a)

(2b)

(3)

Where c is the mobile phase mAb concentration, cF is the inlet
mAb concentration, cp is the mAb concentration inside the particle, q is the adsorbed mAb concentration Dax is the axial dispersion
coefficient, v is the superficial fluid velocity, kf is the mass transfer
coefficient in the particle layer, Deff is the effective pore diffusivity,
ɛc is the column void, ɛp is the particle porosity, rp is the particle radius, L is the column length, qmax is the maximum column
capacity, K is the Langmuir equilibrium constant, and k is the adsorption rate constant, where i can be 1 (fast kinetics) or 2 (slow
kinetics).
3.2.2. Multi-response experiments
The heterogeneous model contains several parameters that
were obtained in different ways. The column void (ε c ) and particle
porosity (ε p ) were obtained from [22] and were based on isocratic
elution experiments with dextran with molecular weights from 10
to 670 kDa. The axial dispersion coefficient Dax using the Peclet
number (Pe) correlation [23]. The mass transfer coefficient is also
obtained through a correlation based on the Sherwood, Reynolds

and Schmidt numbers [24], where the density and the viscosity are
assumed the same as for water at 20 °C.
The rest of the parameters were obtained from frontal analysis of the breakthrough experiments at different mAb concentrations and flow rates. The column volume (Vc ) was 1 mL both for

mAb Select SuRe and PrismA. Flow rates (FF ) of 0.2, 0.5, 1 and
1.5 mL min−1 (30, 75, 150 and 225 cm h−1 ) were applied at a
constant mAb concentration of 0.5 mg mL−1 . Several feed concentration values were also tested (0.25, 0.5, 1.7 and 7 mg mL−1 ) at
a constant flow rate of 0.5 mL min−1 (75 cm h−1 ). The columns
were loaded until the outlet concentration was almost as high as
the feed concentration (at t = tf ). The last part of the breakthrough
curve until reaching the feed concentration was extrapolated to
calculate the total amount of adsorbed protein per resin volume
(κ ) as follows:

κ=

tf
FF
cF t f − ∫ c|z=L dt
Vc (1 − εc )
0

(4)

With the adsorbed concentration for every corresponding mobile phase concentration, the isotherm parameters (the equilibrium
constant K and the total column capacity qmax in Eq. (5) were
obtained by fitting the data to a Langmuir adsorption isotherm
with the least-square method (Fig. S1, in Supplementary Material),
where qmax is expressed as adsorbed product per volume of resin.

κ = qmax

K cF
1 + K cF

(5)

To obtain the maximum capacity for the fast and the slow kinetics (qmax,1 and qmax,2 ), a parameter between 0 and 1 was introduced (w), where 0 meant that all sites were adsorbed with slow
kinetics, and 1 meant the opposite: qmax,1 = wmax, qmax, 2 = (1-w).
The effective diffusivity (Deff ), the kinetics constants (k1 and k2 ),
and the parameter w were obtained by running a calibration using
the MATLAB nonlinear least square curve-fitting solver lsqcurvefit. Since the sampling frequency of the UV sensor was constant,
the amount of experimental points was larger in the longer experiments. In order to have a balanced calibration with equal importance for all the experiments, the number of points was adjusted
to 200 for all curves by interpolating the raw data to obtain a new
point for each time, which resulted in identical curves as the raw
ones but with the same number of points. Before this reduction of
points, the curves were also smoothed to avoid the noise. The spatial derivatives were discretized using the Finite Volume Method,
as shown in [25], with 10 particle grid point and 30 axial grid
points. This is a considerably low number, which leads to an increased numerical dispersion, but it was shown to be enough for a
good fitting of the experimental data, and increasing this number
would have involved much longer calculation times. The resulting
ordinary differential equations were solved with MATLAB’s ode15s.
The value of all the parameters and properties used in the model
are presented in Table S1, in Supplementary Material.
3.2.3. PCC optimization
The Periodic Counter-current Chromatography (PPC) operation
requires synchronization between the columns. In a three-column
PCC in particular, two columns are loaded while the product is being recovered in the third one [6]. That means that the product
recovery is at least so long as the loading of the columns. In other
words, the cycle time (tcycle ), which is the time that takes to completely load a capture column, must be equal or higher than the

recovery time, defined as the time to recover the product in the
capture step plus any necessary waiting time, which depends on
the process alternative. Fulfilling this constraint would be enough
to make the process work [6]. However, to have an optimal process, it is desired to: maximize productivity (P), defined as adsorbed product per column volume and time (Eq. (6)), where τ
is the residence time during the loading; and maximize resin utilization (U), defined as adsorbed product divided by the maximum
adsorbed amount of product at that feed concentration, which de-


J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

pends on the Langmuir adsorption isotherm (Eq. (7)).
t

P=

tcycle cF − ∫0cycle c|z=L dt
τ (1 − εc )tcycle

(6)

t

U=

tcycle cF − ∫0cycle c|z=L dt

τ (1 − εc )qmax 1+K KcFcF

(7)

Apart from the time constraint, the yield (Y), defined as the
amount of the adsorbed product divided by the loaded product
(Eq. (8)), is also set as a constraint, in order to avoid loss of product breaking through the columns. The yield constraint was set to
99%. In a second step of the process, two columns are interconnected during the wash so that the non-adsorbed product of the
first column gets captured in the second column, and meanwhile
the third column gets loaded. Setting a yield constraint also avoids
product loss in this step. Both the objective functions and the yield
constraint were calculated at steady state, when the breakthrough
curves from the three columns were constant and equal to each
other.
t

Y =1−

∫0cycle c|z=L dt
cF tcycle

(8)

The decision variables were the residence time during the loading (τ ), which relates to the loading flow rate, and the fraction
of the breakthrough curve height respect to the maximum level
during the interconnected step (xf ), i.e. the step where the two
columns being loaded are interconnected. The lower and upper
bounds for these two variables were 0.25 and 5 min for the residence time, and 0.20 and 0.95 for the xf . The pressure drop was
also considered, but for the lower limit of the residence time (corresponding to the highest flow rate), the pressure drop was less
than the maximum, so no explicit pressure constraint was then
necessary in the optimization.
To sum up, the optimization problem consists of the following
elements:

minimize

w.r.t.

f (x ) = −[P, U ] ∈ R2
x=

τ , x f ∈ R2

s.t. 0.25 < τ < 5 min
0.20 < x f < 0.95
Y > 0.99
tcycle > trec
An optimization was run for each process alternative, where the
only difference was the last constraint (tcycle > trec ), with trec being
the recovery time. The optimization solver was gamultiobj, a function of the Global Optimization toolbox in MATLAB. This method
is based on genetics algorithms, and it allows to run constrained
multi-objective optimization problems to obtain a set of optimal
solutions, the Pareto front. The population size was 150, with a
stop criterion based on a normalized function tolerance of 10−6 , a
Pareto fraction of 0.35, and a migration faction of 0.2, which takes
place every 20th generation. The fitness and the constraint functions were computed in parallel, thus allowing to reduce the calculation time, which was around 42 h per optimization.

5

3.2.4. Experimental set-up
For the process validation, a chromatography system is used for
the capture step, with the three-column PCC, and the virus inactivation, and another system is used for the polishing steps, i.e. the
CEX and the AEX steps. The implementation of a PCC process in an
ÄKTA pure system requires the use of versatile valves, which enable the different flow paths present in this process. In Fig. 2, the

loop valve (LV) determines which column is loaded first (red solid
line), and which column goes through the recovery step (blue dotted line). Three versatile valves are used to lead the flow to the
next column, to waste or to the virus inactivation loop, which is
placed in another versatile valve. Pump A is used for wash, equilibration, elution and regeneration buffers, Sample pump is used
for the feed, and Pump B is used to dilute the eluate, being the
dilution point just before the VI loop.
The polishing steps are implemented in another ÄKTA pure system following the same flow path and process concept as in [14–
16]. The only difference in the set-up is the use of the column
valve. In this case, this valve is right after the VI loop (see Fig. 2).
By using three different positions, this valve enables the simultaneous regeneration and equilibration of the CEX and AEX column
by using the pumps A and B for the CEX column, and the Sample
pump for the AEX column. This is a way of decreasing the process
time and eventually increase the productivity. In addition, the column valve allows to empty the VI loop onto the CEX column by interconnecting the two chromatography systems. The Sample Pump
is used to increase the pH after virus inactivation and to condition
the eluate from the CEX column before being loaded onto the AEX
column. In Figure S2, in Supplementary Material, several possible
flow paths are shown for a better understanding of the process
set-up.
3.2.5. Process control
Both chromatography systems are controlled by the research
software Orbit [26]. Details about how Orbit is applied to an industrial purification case can be found in [14,15] or [16]. In this
work, the operation of Orbit is similar, but an additional feature is
included so that the two systems can communicate to each other
and synchronize. Two Orbit programs are created, one for each system, and the synchronization between them is based on flags in
form of binary communication. When one of the systems is finished with a process, the corresponding Orbit program sends a
flag to the other Orbit controlling the other system, and it remains
waiting for another flag from the other Orbit. When the other system is also finished with its task, its corresponding Orbit sends a
flag too. Once both Orbit programs have sent a flag to each other
and interpreted the other system’s flag, they are synchronized, and
the overall process can continue. This process is repeated every

time there are parallel processes, and both systems must synchronize with each other to continue to the next step.
3.2.6. Analytics
Each ÄKTA system includes one conductivity, one pH and two
UV sensors at 280 nm that measure continuously inline. In the PCC
process, a UV sensor is used after the first column that is loaded,
and the other UV sensor is used for the elution. In the polishing
steps, a UV sensor is used after each column, and the outlet of the
VI loop is also detected by one of the UV sensors in the polishing
ÄKTA system.
Additionally, the pool from the AEX column is collected every
cycle, and for the last cycle the pool from the capture step is also
collected to check the concentration, and then be able to calculate the experimental resin utilization, yield and productivity of
the capture step, and compare it with the model to validate the
obtained optimal solutions. All samples taken were measured on
an ÄKTA pure 150 system by injecting a known volume onto a


6

J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

Fig. 2. Process diagram of the PCC process integrated in a downstream process with two systems: one for PCC and virus inactivation (on the right area), and another one
for the polishing steps (on the left area). The red solid line represents the raw material being loaded onto capture columns 1 and 2 (C1 and C2). Capture column 3 (C3)
is washed, eluted and regenerated (blue dotted line). On the left, the CEX column is eluted and the product is directly loaded onto the AEX column (blue dashed line)
and collected, and the sample pump is used to dilute the stream between the two columns (green dashed line). Versatile valves (VV), a loop valve (LV), a column valve
(CV) and an outlet valve (OutV) are used to define the flow paths. Grey lines represent inactive flow paths. On the right, a simplified block diagram is shown for an easier
understanding of the flow paths. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

1 mL mAb Select PrismA column and measuring the elution chromatogram with a UV sensor at a wavelength of 280 nm. The sample concentration was calculated as the area under the eluted peak
divided by the injected volume. The extinction coefficient used was

1.4 (mg/mL)−1 cm−1 and it was taken from Maity et al., 2015 [27].
4. Results and discussion
4.1. Model calibration
The aim of using a mechanistic model was to get a good estimation of the breakthrough curves under a variety of conditions with
different mAb concentration and residence times. In Fig. 3, it can
be seen that the breakthrough curves are well fitted for both resins
except for the lowest flow rate. At higher residence times (low
flow rates), the General Rate model deviates more from the experimental breakthrough curve, something that was already shown by
Hahn et al., 2005 [28] and Perez-Almodovar and Carta, 2009 [12],
which the model used in this work is based on.
Fig. 3 also shows a good breakthrough curve fitting for all concentrations, except for the lowest concentration. Similarly to the
deviation shown with high residence times, this discrepancy between the model and the experiment corresponding to the lowest
mAb concentration has also been observed in the model calibration performed by Perez-Almodovar and Carta, 2009 [12], where
they obtained a good fitting for the early rising part of the curve,
but a big deviation for the rest of the curve. Other authors [28,29]
have obtained similar results with either higher effective diffusivity or lower binding capacities than expected for the low protein
concentration case.
Despite the deviations in the model for the lowest flow rate and
mAb concentration, it was not expected to affect the optimization

of the PCC process. On the one hand, although there are deviations at the lowest feed concentration (0.25 mg mL−1 ), the area
under the curve, which is what is used to calculate the objective
functions and the yield constraint in the optimization, is approximately the same for the simulated and the experimental data. On
the other hand, although for the lowest flow rate (0.25 mL min−1 )
there is a larger deviation, the simulation provides a more conservative solution than the reality, since the breakthrough curve appears earlier than in the experimental results. Regarding the suitability of the model for the simulation of a multi-column process
like PCC, it has been shown that model calibration from batch experiments can be used to predict the performance in a continuous
multi-column process [7,8].
4.2. PCC scheduling
The choice of the integration approach affects the product recovery time, as seen in Fig. 4, where Gantt diagrams are shown for
the three process alternatives. Process alternative 1 has the longest

recovery time because the whole downstream process is run in
one system. Therefore, due to the lack of enough pumps, all steps
must be run in series, thus making the total recovery time longer.
In process alternative 2, where two systems are used, the capture
step and the rest of the steps are run in parallel, thus reducing
the recovery time. Finally, process alternative 3 has the lowest recovery time, since the PCC and the rest of steps are de-coupled or
de-synchronized due to the presence of the surge vessel, i.e., when
the product recovery is done in the capture step, the eluate from
the protein-A resin will be hold in the surge vessel (after a pH adjustment to avoid mAb aggregation during the hold-up time), and
the next PCC cycle can then be run directly, without the need to
wait for the polishing steps to finish. When the virus inactivation


J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

7

Fig. 3. Calibration of the general rate model: (A) Breakthrough curve fitting at different flow rates and at a constant concentration of 0.5 mg mL−1 for mAb Select PrismA
(black curves), and (B) mAb Select SuRe (grey curves). (C) Breakthrough curve fitting for different mAb concentrations and at a constant flow rate of 0.5 mL min−1 for mAb
Select PrismA, and (D) mAb Select SuRe. The dots are experimental points and the solid lines are fitted curves corresponding to the general rate model.

loop is ready to be filled, which is determined by the scheduling
of the polishing steps, as shown in Fig. 4C, the surge vessel will be
emptied. Therefore, the filling frequency of the surge vessel is determined by the PCC cycle time, and its emptying depends on the
scheduling of the virus inactivation and the polishing steps.
As mentioned before, the cycle time must be higher than the
recovery time. In other words, process solutions with a PCC cycle
time lower than the recovery times stated in Fig. 4 for each process alternative, are unfeasible. That means that the PCC cycle time
must be chosen so that it is at least as high as the recovery time,
and the loading flow rate must then be adapted so that the yield

is kept within the constraint for that particular cycle time.
A possibility to reduce the recovery time could be to duplicate
the virus inactivation loop and run a semi-continuous virus inactivation where a loop is filled while the other one is emptied, like in
Pall’s CadenceTM Virus Inactivation System [30]. However, in Fig. 4,
it can be seen that the time for the polishing steps is higher than
the one for the virus inactivation. Therefore, the duplication of the
VI loop would not involve any improvement in the recovery time
in the long run, unless the polishing columns were also duplicated.
Other options include the implementation of a fully continuous
virus inactivation process with, for example a packed bed reactor
[31] or a Jig in a Box (JIB) approach [32]. The impact of this implementation on the recovery time would depend on the way of
integrating the PCC with the continuous virus inactivation, but the
length of the polishing steps would still limit the minimum overall
recovery time.

4.3. PCC optimization
The result of the two-objective optimization is a set of optimal
solutions with different values of productivity and resin utilization, the so-called Pareto front. Solutions with higher productivity
have higher loading flow rate in order to treat more material in
less time, but this makes the breakthrough curve flatter [12,13,22]
and it forces the cycle to be shorter and the resin utilization to be
lower to keep a high yield. On the contrary, solutions with high
resin utilization have higher cycle times, and lower loading flow
rate and productivity (Fig. 5). The optimization was executed for
different process conditions to evaluate the effect of several parameters of the process.
4.3.1. Effect of the integration approach
As explained before, the integration approach influences the
minimum cycle time. This means that the solutions with a lower
cycle time than the recovery time for a particular integration alternative, are unfeasible. That is what is shown in Fig. 5A. Process alternative 1 has the highest recovery time (184.1 min). This implies
that the points with higher productivity (with lower cycle time)

are unfeasible for this process alternative, and only the solutions
with high resin utilization are viable in this case. Process alternatives 2 and 3 have lower recovery time (118.4 and 60.1 min, respectively), thus the range of viable solutions is broader. It is therefore
shown that the higher the recovery time, the lower the process
flexibility and freedom to choose between high resin utilization


8

J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

Fig. 4. Gantt diagrams for the three integration alternatives: (A) Alternative 1, integrated PCC process in one system, (B) Alternative 2, integrated PCC process in two systems,
and (C) Alternative 3, PCC process with a surge vessel in two systems. (For interpretation of the references to color in the figure legend, the reader is referred to the web
version of this article.)

and high productivity solutions. The optimal Pareto solutions for
process alternative 3 are equivalent to the ones that would be obtained for an optimized capture PCC alone without the rest of the
downstream process. Therefore, as evidenced in Fig. 5A, the optimal design of a PCC process must be done in a holistic approach,
i.e., taking into account the process integration with the polishing
steps already in the optimization problem. Not doing so may lead
to optimal solutions that are feasible when running only the capture step but are unfeasible when the whole downstream process
is implemented, if no surge vessel is used.
4.3.2. Effect of the feed concentration
The inlet loading concentration affects in different ways. Firstly,
the equilibrium adsorbed product concentration (κ ) is affected
by the mobile phase concentration according to the Langmuir
isotherm (Eq. (5)). This means that at a higher concentration, the
adsorbed concentration at equilibrium is higher and therefore the
amount of product that can potentially be loaded in each cycle is
generally higher. Secondly, a higher inlet concentration increases
the productivity, because a higher amount of product is being

loaded by amount of time and volume. In addition, it can also
improve resin utilization, because a higher concentration means
that a lower flow rate can be applied to load the same amount of
product as in a low concentration process, thus making the breakthrough curve sharper, which leads to a higher resin utilization. In
Figure 5, several Pareto fronts for concentrations ranging from 0.25
to 2 mg mL−1 are shown. It is interesting to notice the decrease
in the slope of the fronts for an increasing concentration. For the
high concentration cases, a small increase in loading flow rate implies a bigger increase in productivity due to a higher amount of
product per volume being loaded, but the reduction of the resin
utilization due to this flow rate increase is small. On the contrary,

for the low concentration solutions, in order to achieve a significant rise of the productivity, a higher increase in flow rate must
be applied, with the consequent large sacrifice in the resin utilization. This behavior justifies the decrease of the slope at higher inlet
concentrations.
Another interesting fact is that the Pareto curves get increasingly flatter when approaching a very high resin utilization. In
these operating points, the flow rate and the percentage of the
unutilized resin are very low. That implies that a greater decrease of flow rate (with the corresponding drop of productivity) is
needed to get a slightly higher resin utilization. This is more pronounced at higher concentrations because this flow rate decrease
affects more the productivity than if the concentration was lower.
The lower flow rate that it is needed to apply in the process
as a result of a higher concentration, affects the choice of integration approach. As shown in Fig. 5A, at 1 mg mL−1 , only a
few operating points are feasible for process alternative 1, and at
2 mg mL−1 only process alternative 3 is feasible for the solutions
of the Pareto front. The reason the optimization method cannot
find viable points for alternatives 1 and 2 at 2 mg mL−1 is because it would require a very low loading flow rate to avoid product breakthrough with a cycle time higher than the recovery time
for these two process alternatives. But there is a low limit in the
loading flow rate in the simulation of the process set in 30 cm h−1 ,
because the model was not calibrated for lower flow rates. Simulations of the process at lower flow rates than the model was calibrated for, would have provided unreliable solutions. It should be
noticed that, despite this fact, it is possible to run process alternatives 1 and 2 at high concentrations and very low flow rates experimentally, but the simulation and prediction of these processes
would be unreliable and further model calibration for lower flow

rates would be needed.


J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

9

4.3.3. Effect of the chromatography resin
The optimization was run for two different resins: mAb Select
PrismA and mAb Select SuRe, with particle diameters of 60 μm
and 85 μm, respectively. A lower particle diameter allows for a
better mass transfer due to a shorter way from the particle surface
to the adsorption sites [28]. This is translated in a sharper breakthrough, which in turn leads to a higher resin utilization for equal
flow rate or the possibility to run at higher flow rates, thus increasing the productivity, without sacrificing the resin utilization
too much. In addition, mAb Select PrismA has a higher capacity, as
shown in Table S1. Therefore, it is expected that this resin performs
better than mAb Select SuRe. In Fig. 5A, it is confirmed that mAb
Select PrismA has a better compromise of productivity-resin utilization for all the concentrations. It is remarkable that the difference between the two resins is bigger at higher productivities. This
is due to a lower slope of the curve corresponding to mAb Select
PrismA, which is explained by the faster mass transfer in this resin.
For a certain desired increase of productivity, which is carried out
by a corresponding flow rate increase, the sacrifice in resin utilization for mAb Select PrismA is lower than for mAb Select SuRe. That
is the reason of the different slopes of the two curves, and, in turn,
of the larger difference between the two resins at operating points
with higher productivity.
Considering two operating points with the same resin utilization for both resins, the cycle time is higher for mAb Select SuRe
than for mAb Select PrismA. That means that the number of feasible solutions for the process alternatives 1 and 2 is slightly
higher with mAb Select SuRe, because these alternatives, which
have higher recovery time, benefit from an increase of the cycle
time. For example, as it can be seen in Fig. 5A, at 0.25 mg mL−1

and at a resin utilization of around 63%, an optimal solution can be
operated with process alternative 1 in the case of mAb Select SuRe,
but no solution would be feasible with the process alternative 1 at
that resin utilization with mAb Select PrismA.

Fig. 5. Pareto fronts with optimal solutions. (A) Three different integration alternatives (filled, shaded and crossed points) at four load concentrations (0.25, 0.5, 1
and 2 mg mL−1 ) for mAb Select PrismA (circles) and mAb Select SuRe (squares). (B)
Three yields: 95%, 98% and 99%, for mAb Select PrismA at a load concentration of
0.5 mg mL−1 . (C) Four operation modes: 3-column PCC, 1-column batch, 2-column
sequential batch and 3-column sequential batch, for mAb Select PrismA at a load
concentration of 0.5 mg mL−1 . The legend of the process alternatives is the same
for all the panels.

4.3.4. Effect of the yield
The optimization was run for three yields: 95%, 98% and 99%,
and the three Pareto fronts corresponding to the resin mAb Select
PrismA are shown in Fig. 5B. As expected, if the yield is lower,
the productivity and the resin utilization are higher. A process
with very high yield implies that the losses due to product breakthrough must be very low, which means that the process must be
run at a lower velocity to get a sharper breakthrough curve, thus
reducing the productivity, or finish the cycle earlier, with a corresponding drop of the resin utilization. Besides, it is shown that
the yield does not significantly affect the feasible range of optimal
points for each integration alternative, since for the three yields
there are optimal solutions that are feasible for the three process
alternatives.
Remarkably, the difference between the curves is smaller in the
solutions with higher resin utilization, whereas this difference is
more pronounced in the solutions with higher productivity. This is
due to the different slopes of the breakthrough curve in both cases.
At lower yields, a longer loading can be applied because the allowed amount of product loss is higher, and therefore higher resin

utilization can be achieved. But if the breakthrough curve is very
sharp, the loss of product will be too high at a slight increase of
the loading time. Therefore, the benefit of reducing the yield constraint does not lead to a significantly higher resin utilization in
that case. For that reason, the solutions with higher resin utilization, which have a sharper breakthrough curve, do not differ much
at different yields, while in the high productivity solutions, the difference in yield is more significant.


10

J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

Fig. 6. Chromatogram of the capture step (A) and the polishing steps (B) during a PCC run with mAb Select PrismA. The shaded areas represent the PCC cycles.

4.3.5. Comparison of PCC and batch chromatography
Periodic counter-current chromatography enables to treat a
continuous stream, but it also provides higher productivity and
resin utilization for the same yield, in comparison to batch chromatography [7,8]. For this reason, it is interesting to see the differences between the studied 3-column PCC and a traditional batch
chromatography process (Fig. 5C). In addition, two sequential batch
processes with 2 and 3 columns, respectively, are also considered,
to compare PCC with other simpler periodic processes. In a sequential batch process, one column is always being loaded, and
the product recovery is carried out in the other columns, just as
in PCC, with the difference that there is no interconnection between the columns. A multi-objective optimization was solved for
the four cases, considering there is no limitation due to the integration with the rest of the downstream processing steps, since the
effect of this limitation is already shown in Fig. 5A and discussed
in Section 4.3.1. The volume for each column was assumed to be
the same, therefore the total resin volume depended on the number of columns.
As expected, PCC provides the highest productivity and resin
utilization, since the PCC process can be run at higher flow rates,
compared to the batch processes, without compromising the yield
or the resin utilization. This is due to the interconnection of the

two columns, which avoids losing the product that is not adsorbed
in the first column. On the other hand, the 1-column batch process performs better than the sequential batch processes, as shown
in Fig. 5C. This is because the same amount of product can be
loaded in the 1-column batch process and in the sequential processes, but the number of columns is different. Since the productivity is defined by the total resin volume, and the sequential processes have more columns, the productivity is, in general, higher

for the 1-column batch process. However, it is noteworthy that the
sequential batch processes can achieve a higher productivity than
the 1-column batch process, although at a lower resin utilization,
as seen in Fig. 5C. The reason is that in the sequential processes,
the wash, elution and CIP steps are performed at the same time
as the loading, whereas in the 1-column batch process, these steps
are run after the loading. Therefore, in some of the solutions, the
processing time in the sequential processes is significantly shorter
than in the 1-column batch process, thus compensating the fact
that more columns are used.
Fig. 5C clearly shows that, in the conversion from batch to continuous capture, the choice of the process is very important. While
a PCC process may be a more complex alternative to implement
than a sequential batch process, it provides almost 5 times more
productivity at a constant resin utilization (in Fig. 5C, at a resin
utilization of approximately 60%, the productivities are circa 0.38
and 0.08 mg mL-1 min-1 , respectively for the 3-column PCC and
the 3-column sequential batch process).
4.4. Experimental validation
The process was implemented with the process set-up shown in
Fig. 2. The process was run for the two resins, and a solution with
similar resin utilization was chosen from the Pareto front of each
resin. Both processes were run at 0.5 mg mL−1 . The column volume of the protein-A resin was 1 mL, and the rest of columns were
sized according to the same procedures used in [14]. Regarding the
process integration, alternative 2 was chosen. Compared to alternative 1, where the whole downstream process is run in one system,
it was simpler and more flexible to carry out all the steps in two

systems than trying to fit every step in one system, since the num-


J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

11

Table 1
Summary of the experimental validation of a 3-column PCC integrated in a downstream process.
Parameter
Cycle time
Loading flow rate
Load per cycle
Specific buffer consumption
Capture eluate concentration
Capture yielda)
Overall yieldb)
Resin utilization
Capture productivitya)
Overall productivityb)

Units

MAb Select PrismA

MAb Select SuRe

min
mL min−1
mg cycle−1

mL mg−1
mg mL−1
%
%
%
mg mL−1 min−1
mg mL−1 min−1

Simulation
140.40
0.98
68.80
0.47
5.55
99.00
77.80
0.25
-

Simulation
165.60
0.66
54.60
0.59
4.44
99.00
77.60
0.16
-

Experimental
140.40
0.98
68.80
0.49
5.28
94.60
78.20
74.00
0.23
0.20

Experimental
165.60
0.66
54.60
0.63
4.16
93.60
75.20
72.80
0.15
0.11

Notes:
a)
Yield/productivity for the capture step. The productivity is based on the protein-A resin volume.
b)
Yield/productivity for the whole downstream process. The productivity is based on the protein-A resin volume.

ber of valves and pumps available in two systems are higher than
those in one system. In addition, as shown in Fig. 5A, it offered a
broader range of possible solutions with higher productivity. Alternative 3 was even better in this aspect, but the introduction of a
surge vessel leads to a hold-up volume in the system that implies
that process is not completely integrated anymore [2], apart from
a much more complex implementation and other disadvantages
already exposed in Section 2. In Fig. S3, in Supplementary Material, a picture of the two systems connected to each other for the
implementation of the integrated PCC process, is shown.
Fig. 6 reveals that the process gets to steady state very quickly,
already in the second cycle. This is because in the start-up of
the process, a longer loading is applied, so that approximately the
same amount of product is loaded in the start-up and the following cycles. Both the initial start-up time and the steady-state cycle time were determined by the simulation of the process. In the
panel A of Fig. 6, the absorbance of the outlet of the capture column is displayed. There are three peaks: the first one corresponds
to the washed impurities, the second one is the eluted product
that is directly loaded onto a virus inactivation loop, and a third
peak that corresponds to the strongly adsorbed impurities that get
out of the column during the cleaning-in-place (CIP). The panel B
of Fig. 6 shows the absorbance of the outlets from the CEX and the
AEX columns. The first CEX peak is linked to the loading of the CEX
column, or the emptying of the VI loop. The second CEX peak is
the eluted product that gets directly loaded onto the AEX column.
Since the AEX step is in flow-through mode, the product gets out
of this column at the same time it is being loaded (AEX peak), with
some delay corresponding to the column volume, and at a lower
concentration as a result of the dilution that is carried out before
the loading, in order to condition the product to the right pH and
salt concentration. The cycles in the polishing steps are delayed respect to the ones in the capture step due to the process scheduling,
as revealed in Fig. 4. The results shown in Fig. 6 corresponds to the
resin mAb Select PrismA. Similar results are presented in Figure S4
for mAb Select SuRe.

In Table 1, a summary of the experimental results is presented.
The loading flow rate is significantly higher for mAb Select PrismA,
and therefore the productivity is also higher. As a result of the
higher flow rate, the cycle time is lower for this resin, while
the resin utilization is similar for both processes, around 73–74%.
The amount of product treated per cycle is higher for mAb Select PrismA, so the specific buffer consumption is lower for this
resin, since all flow rates (except for the loading flow rate of the
capture step) and volumes are the same for both resins. The constraint on the capture yield was set to 99%, and the experimental one was around 94% for both processes. This difference is due
to the fact that the yield used in the optimization is a theoretical
one that only considers the product lost in the breakthrough dur-

ing the loading, and it does not take into account the losses during the elution, wash or regeneration of the capture column. The
lower overall yield relates to the product loss in the subsequent
steps (virus inactivation, CEX and AEX). The polishing steps were
not modelled, and that is why the simulated overall yield and productivity were not provided in Table 1. Overall, the process was
successfully validated since productivity and resin utilization were
similar as the simulated results, while keeping a very high yield.
5. Conclusions
The implementation of a periodic counter-current chromatography integrated in a downstream process requires an optimal design
in order to operate at maximum productivity, resin utilization and
yield. In this work, it was shown that the optimal results obtained
from an optimization of a PCC process for the capture of antibodies without considering the integration with the following steps
cannot always be used in an integrated process due to scheduling mismatches, and a holistic approach is needed to design the
PCC in a downstream process. Three different integration alternatives were presented: 1) an integrated PCC process in one system,
2) an integrated PCC process in two systems, and 3) a PCC process
with a surge vessel in two systems. The main difference lies in the
total time that takes to purify the product, the so-called recovery
time. The first two alternatives have a higher recovery time than
the third one because a synchronization between the capture step
and the following steps is required, whereas with a surge vessel

once the product is purified in the capture step, there is no need
to wait for the polishing steps to finish. Since a requirement for
feed continuity in the PCC process is that the cycle time is equal
or higher than the recovery time, the choice of integration alternative affects the optimal design of the PCC.
A multi-objective optimization based on a mechanistic model
of the protein-A capture step was carried out. Several Pareto fronts
with optimal solutions were obtained for different conditions of
feed concentration, yield and protein-A resin, and the feasibility
of the solutions depending on the integration approach was studied. The highest productivity could be achieved with the third process alternative (for example at 0.5 mg mL−1 of feed concentration, up to 0.38 mg mL−1 min−1 and 99% yield), since the minimum cycle time in this process is lower than in the other two
alternatives. In the second process alternative, with two systems
and no surge vessel, a reasonable high productivity and resin utilization was obtained, and in the integration approach with only
one system, the recovery time is high, and the process cannot operate at high productivities, but the resin utilization is very high
(up to 95%). At a higher feed concentration, both the productivity
and the resin utilization are heavily increased, and the cycle time
is in general lower, which affects the feasibility of the solutions in


12

J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055

relation to the integration approach. At the highest feed concentration (2 mg mL−1 ), only the process with surge vessel can be run at
optimal conditions, whereas the other two alternatives would have
to be operated at sub-optimal conditions.
The choice of the protein-A resin also affects the performance
of the process. A better mass transfer in mAb Select PrismA due
to a lower particle diameter, together with a higher resin capacity,
compared to mAb Select SuRe, allows for a much better compromise between productivity and resin utilization. In addition, the
effect of the yield was also studied. At a lower yield, the allowed
amount of product loss is higher, and therefore a higher flow rate

or a longer cycle can be applied, thus increasing the productivity
and/or the resin utilization. Finally, the 3-column PCC was compared to a 1-column batch process and two sequential batch processes with 2 and 3 columns, respectively. As expected, the PCC
optimization provided much better operation points in terms of
productivity and resin utilization. The 1-column batch process outperformed the sequential batch processes due to the use of less
columns. Therefore, in this work, it is shown that, in order to convert a batch process into a continuous process, the simpler alternative of running a multi-column process sequentially with no column interconnection leads to a more inefficient process. However,
the conversion to a PCC process, not only allows a continuous load
of the column, but also a significant increase of the productivity
and the resin utilization.
The optimized design was validated experimentally using the
integration approach with no surge vessel and two ÄKTA pure systems centrally controlled by the research software Orbit. The process was run at a feed concentration of 0.5 mg mL−1 and a theoretical yield of 99%. A solution with similar resin utilization was
selected from the Pareto front of both resins. A steady-state operation was achieved already in the second cycle. A productivity
in the capture step of 0.23 and 0.15 mg mL−1 min−1 was obtained
for mAb Select PrismA and mAb Select SuRe, respectively. The resin
utilization was around 73-74% in both cases, and the total recovery
yield, including the polishing steps, was in the range of 75–78%,
whereas the yield in the capture step was around 94% for both
cases. Therefore, it was shown that the optimization study can be
used to obtain an optimal design of the PCC integrated in a downstream process, which was successfully validated experimentally.

Declaration Competing of Interest
The authors declare no commercial or financial conflict of interest.

Acknowledgements
The authors acknowledge that this research is part of the Competence Centre for Advanced BioProduction by Continuous Processing (AdBIOPRO), which is co-funded by VINNOVA, the Swedish
Agency for Innovation (grant ID: 2016-05181) and the industrials
partners: Swedish Orphan Biovitrum, Cobra Biologics, BioInvent, GE
Healthcare Life Sciences, Valneva, Lab-on-a-Bead, and CellProtect
Nordic Pharmaceutical. The authors would also like to thank Andreas Castan (GE Healthcare Life Sciences) for providing the raw
material, and Dennis Bogren (Department of Chemical Engineering, Lund University) for his assistance with the calibration experiments.

Supplementary materials
Supplementary material associated with this article can be
found, in the online version, at doi:10.1016/j.chroma.2020.461055.

References
[1] S.S. Farid, Process economics of industrial monoclonal antibody manufacture,
J. Chromatogr. B 848 (2007) 8–18.
[2] K.B. Konstantinov, C.L. Cooney, White paper on continuous bioprocessing may
20–21 2014 continuous manufacturing symposium, J. Pharm. Sci. 104 (2015)
813–820.
[3] A. Jungbauer, Continuous downstream processing of biopharmaceuticals,
Trends Biotechnol. 31 (2013) 479–492.
[4] M.D. Costioli, C. Guillemot-Potelle, C. Mitchell-Logean, H. Broly, Cost of goods
modeling and quality by design for developing cost-effective processes, Biopharm. Int. 23 (2010) 26–35.
[5] O. Ötes, H. Flato, J. Winderl, J. Hubbuch, F. Capito, Feasibility of using continuous chromatography in downstream processing: comparison of costs and product quality for a hybrid process vs. a conventional batch process, J. Biotechnol.
259 (2017) 213–220.
[6] R. Godawat, K. Brower, S. Jain, K. Konstantinov, et al., Periodic counter-current chromatography – design and operational considerations for integrated
and continuous purification of proteins, Biotechnol. J. 7 (2012) 1496–1508.
[7] D. Baur, M. Angarita, T. Müller-Späth, F. Steinebach, M. Morbidelli, Comparison
of batch and continuous multi-column protein A capture processes by optimal
design, Biotechnol. J. 11 (2016) 920–931.
[8] D. Baur, M. Angarita, T. Muller-Spath, M. Morbidelli, Optimal model-based design of the twin-column CaptureSMB process improves capacity utilization and
productivity in protein A affinity capture, Biotechnol. J. 11 (2016) 135–145.
[9] N. Andersson, H.-K. Knutson, M. Max-Hansen, N. Borg, B. Nilsson, Model-based
comparison of batch and continuous preparative chromatography in the separation of rare earth elements, Ind. Eng. Chem. Res. 53 (2014) 16485–16493.
[10] L. Aumann, M. Morbidelli, A continuous multicolumn countercurrent solvent gradient purification (MCSGP) process, Biotechnol. Bioeng. 98 (2007)
1043–1055.
[11] H.-L. Jiang, Y.-C. Chou, S.-C. Chen, C.-J. Yang, I.-L. Kou, W.-K. Chi, Periodic
counter-current chromatography for continuous purification of monoclonal
antibody, Poster presented in: Biochemical and Molecular Engineering XX,

ECI Symposium Series, 2017 confintl.org/biochem_xx/51 Accessed:
December 10, 2019.
[12] E.X. Perez-Almodovar, G. Carta, IgG adsorption on a new protein A adsorbent
based on macroporous hydrophilic polymers. I. Adsorption equilibrium and kinetics, J. Chromatogr. A 1216 (2009) 8339–8347.
[13] D. Baur, J.M. Angelo, S. Chollangi, X. Xu, et al., Model assisted comparison of
Protein A resins and multi-column chromatography for capture processes, J.
Biotechnol. 285 (2018) 64–73.
[14] J. Gomis-Fons, A. Löfgren, N. Andersson, B. Nilsson, et al., Integration of a complete downstream process for the automated lab-scale production of a recombinant protein, J. Biotechnol. 301 (2019) 45–51.
[15] N. Andersson, A. Lofgren, M. Olofsson, A. Sellberg, et al., Design and control
of integrated chromatography column sequences, Biotechnol. Prog. 33 (2017)
923–930.
[16] A. Löfgren, N. Andersson, A. Sellberg, B. Nilsson, et al., Designing an autonomous integrated downstream sequence from a batch separation process − an industrial case study, Biotechnol. J. (2018).
[17] GE Healthcare Bio-Sciences AB, Continuous chromatography, Downstream Processing of a Monoclonal Antibody, 2015 Report 29170800 AA.
[18] GE Healthcare Bio-Sciences AB, Evaluation of Protein A resin lifetime during
extensive use (overloading) in continuous chromatography mode, 2017 Report
29260553 AA, Uppsala, Sweden.
[19] N. Borg, Y. Brodsky, J. Moscariello, S. Vunnum, et al., Modeling and robust pooling design of a preparative cation-exchange chromatography step for purification of monoclonal antibody monomer from aggregates, J. Chromatogr. A 1359
(2014) 170–181.
[20] M. Degerman, N. Jakobsson, B. Nilsson, Constrained optimization of a preparative ion-exchange step for antibody purification, J. Chromatogr. A 1113 (2006)
92–100.
[21] D. Karlsson, N. Jakobsson, A. Axelsson, B. Nilsson, Model-based optimization
of a preparative ion-exchange step for antibody purification, J. Chromatogr. A
1055 (2004) 29–39.
[22] T.M. Pabst, J. Thai, A.K. Hunter, Evaluation of recent Protein A stationary phase
innovations for capture of biotherapeutics, J. Chromatogr. A 1554 (2018) 45–60.
[23] S.O. Rastegar, T. Gu, Empirical correlations for axial dispersion coefficient and
Peclet number in fixed-bed columns, J. Chromatogr. A 1490 (2017) 133–137.
[24] M.C. Annesis, L. Merrelli, V. Piemonte, L. Turchetti, Mass Transfer Coefficients,
Springer-Verlag, London, 2017, pp. 26–27.
[25] B. Nilsson, N. Andersson, Simulation of Process Chromatography, in: A. Staby,

A.S. Rathore, S. Ahuja (Eds.), Preparative Chromatography for Separation of Proteins, John Wiley & Sons, Inc, Hoboken, NJ, USA, 2017.
[26] B. Nilsson, N. Andersson, J. Gomis-Fons, A. Löfgren, Supervisory control
of integrated continuous downstream processes, Poster presented in: Integrated Continuous Biomanufacturing III, ECI Symposium Series, 2017 http:
//dc.engconfintl.org/biomanufact_iii/80 Accessed: December 10, 2019.
[27] H. Maity, A. Wei, E. Chen, J.N. Haidar, et al., Comparison of predicted extinction
coefficients of monoclonal antibodies with experimental values as measured
by the Edelhoch method, Int. J. Biol. Macromol. 77 (2015) 260–265.
[28] R. Hahn, P. Bauerhansl, K. Shimahara, C. Wizniewski, et al., Comparison of protein A affinity sorbents: II. Mass transfer properties, J. Chromatogr. A 1093
(2005) 98–110.


J. Gomis-Fons, N. Andersson and B. Nilsson / Journal of Chromatography A 1621 (2020) 461055
[29] J.T. McCue, G. Kemp, D. Low, I. Quiñones-Garcıa´ , Evaluation of protein-A chromatography media, J. Chromatogr. A 989 (2003) 139–153.
[30] Pall’s
Cadence
Virus
Inactivation
System,
Single-use
virus
inactivation system for batch and continuous processing. (https:
//shop.pall.com/us/en/biotech/continuous-processing/viral-inactivation/
cadence- virus- inactivation- system- zidimmfdc1e. Accessed: December 10,
2019).

13

[31] D.L. Martins, J. Sencar, N. Hammerschmidt, B. Tille, et al., Continuous solvent/detergent virus inactivation using a packed-bed reactor, Biotechnol. J. 14
(2019).
[32] R. Orozco, S. Godfrey, J. Coffman, L. Amarikwa, et al., Design, construction, and

optimization of a novel, modular, and scalable incubation chamber for continuous viral inactivation, Biotechnol. Prog. 33 (2017) 954–965.