Model-based optimization for mAb chromatography

Separation and Purification Technology 305 (2023) 122528
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Separation and Purification Technology
journal homepage: www.elsevier.com/locate/seppur
Model-based process optimization for mAb chromatography
Mirijam Kozorog a, Simon Caserman a, *, Matic Grom b, Filipa A. Vicente b, *, Andrej Pohar b,
Blaž Likozar b, *
a
b
Department of Molecular Biology and Nanobiotechnology, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
A R T I C L E I N F O
A B S T R A C T
Keywords:
Protein A antibody affinity chromatography
General rate mathematical model
Affinity resin properties
Process intensification and optimization
Protein A affinity chromatography is an effective method for capturing and purification of monoclonal antibodies
(mAbs), which are amongst the most important products in the biopharmaceutical industry. Being one of the
most expensive steps of downstream purification, optimization of Protein A affinity chromatography towards
higher productivity offers great potential for the reduction of production cost. Hence, this work presents the
productivity optimization through four strategies of crude harvest loading in Protein A affinity chromatography.
Loading strategies were optimized using a mathematical model and were compared on basis of their maximal
productivities. It is theoretically shown, based on computational analysis, that the performance of existing
classical batch processes can be optimized by implementing an improved loading step and fine tuning of process
parameters without any additional investment in new or modified equipment, materials or energy. This approach
offers an attractive alternative to existing capture steps and helps bridging a technological gap to new semi
continuous processes that are still lacking sufficient reliability due to technical complexity. Increased produc­
tivity leads to lower amount of affinity resin demanded to process a given amount of crude harvest or to reduce
the processing time. With a new loading strategy, less expensive affinity resins may also become an effective
alternative. Amongst four different loading strategies, the loading using flow ramp was predicted by model as the
most promising one and the mAb binding dynamic at changing loading velocity was tested experimentally on
five different affinity resins to validate model predictions.
1. Introduction
Over the last decade, there has been an increase of drug-resistant
microorganisms, diseases that are no longer responsive to common
drug therapies, individuals with more allergic reactions to drugs and the
appearance of individuals with impaired immune systems who are un­
able to respond to conventional vaccines [1]. In this sense, bio­
pharmaceuticals have revolutionized health care, allowing the
treatment and increase in the survival rate of patients with difficult
conditions and/or diseases [2]. Amongst these, monoclonal antibodies
(mAbs) are widely applied for therapeutic purposes, for instance in
vaccination and immunization as well as in the treatment of oncologic,
autoimmune, cardiovascular, inflammatory and neurological diseases
[3]. However, these applications demand high purity and high amounts.
The current process of mAbs production consists of two main steps,
namely the upstream processing, which is now quite improved and
comprises the production of antibodies by cell lines derived from
mammalian cells, and the downstream processing, which includes the
recovery, purification and isolation of mAbs from cells and cell debris,
processing medium and other impurities [1,3]. Yet, the downstream
processes have not evolved at the same pace as the upstream stage, being
the current bottleneck of the mAbs production. The mAbs downstream
process involves a multi-step approach, namely i) cells harvesting, ii)
protein A affinity chromatography, iii) ultrafiltration, iv) viral inacti­
vation, v) viral filtration, vi) ion-exchange chromatography, vii) ultra­
filtration, viii) hydrophobic chromatography and ix) ultrafiltration for
formulation [4–6]. Hence, representing up to 80% of the total
manufacturing costs, especially due to the affinity chromatography that
is the most expensive individual step of this downstream process [3].
The sole improvement of the affinity resin enables substantial reduction
in production cost. In this sense, considerable research efforts have been
directed into improving protein A resin performances, including the
overall binding capacity that is later translated in higher productivity per
volume unit; or the replacement of this step with a cheaper option.
* Corresponding authors at: National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia.
E-mail addresses: [email protected] (S. Caserman), [email protected] (F.A. Vicente), [email protected] (B. Likozar).
https://doi.org/10.1016/j.seppur.2022.122528
Received 19 August 2022; Received in revised form 21 October 2022; Accepted 28 October 2022
Available online 4 November 2022
1383-5866/© 2022 Elsevier B.V. All rights reserved.
M. Kozorog et al.
Separation and Purification Technology 305 (2023) 122528
Grilo and co-workers [4] proposed a novel purification strategy for
mAbs while replacing the need for protein A affinity chromatography.
This approach comprises a phenylboronic acid multimodal chromatog­
raphy to capture the mAbs, being followed by a polishing step with ionexchange monolithic chromatography and packed bed hydrophobic
interaction chromatography. When compared to the traditional proteinA-based process, it was verified that both processes present a similar
capital investment, though the operation cost is 20% lower for the novel
strategy. Mahajan et al. [7] also tried to improve the affinity chroma­
tography step by applying a three column periodic counter current
chromatography (PCCC) that was also compared to batch chromatog­
raphy with and without modifications, namely with recycling back to
feed tank and with increased residence time. Authors concluded that the
multi-column chromatography and the modified batch processes can
save approximately 40% in the costs considering the resin, buffer and
processing time. The main disadvantage of PCCC is that it is a more
complex chromatographic system and, consequently, can be less reli­
able. Moreover, the processing time of the chromatography with recy­
cling and with the increased residence time also resulted in longer
processes when compared to the classical batch process. Similarly,
Angarita et al. [8] reported a 10–30% reduction in resin cost by
replacing the conventional batch chromatography with a twin column
simulated moving bed (SMB) system. Although their system was a
simple SMB set-up, applying it in an already existing process would still
require significant investments.
In addition to these experimental approaches, Ng et al. [9,10] inte­
grated experimental and modeling approaches for batch and SMB pro­
cess optimization. Process productivity was optimized by varying the
column length, load, wash and elution volumes while constraining
pressure drop, purity and yield. Authors reported a productivity
improvement of 38% with SMB when compared to batch approach.
Their modeling, however, relied on simplified lumped kinetic model
instead of a pore diffusion model to describe the processes with affinity
resin particles. In modeling, only process parameters but not column
geometry can be varied to improve the performance without the need
for additional investments. Close and co-workers [11] used a similar
mathematical model as Ng et al. [9,10] to shed some light in separation
processes of closely related species. However, authors did not use their
model for process optimization. Girard and collaborators [12] used a
mathematical model to optimize batch and sequential multi-column
chromatography (SMCC) for two different affinity resins. Once more,
an increase in productivity was reported when using SMCC instead of
the classical batch process. Likewise, Baur et al. [13] optimized the
productivity and capacity use in batch and SMB affinity chromatography
with the aid of a mathematical model. Authors also confirmed the su­
perior productivity of SMB over batch process, except at low production
amounts. Herein, batch chromatography was operated in a dual flow
rate mode in loading step that was similar to Ghose et al. [14].
Overall, SMB process seems to be a way forward in view of effec­
tiveness of affinity resin utilization, process productivity and product
recovery. It however, requires more sophisticated and expensive
equipment, which is consequently less reliable than classical batch
equipment. Besides, the majority of existing production facilities still
rely on batch type equipment and personnel trained for it. Therefore, the
present work offers a few strategies for the classical batch chromatog­
raphy process with some modifications that can lead to improved per­
formance with minimum or no investment into the existing equipment.
These strategies include flow ramp and flow reversal in column loading
step. This work is based on a mathematical modeling of the process with
general rate model and compared to a lumped transport-dispersive
model. This model is more mechanistic and less dependent on empir­
ical parameters, such as the lumped mass transfer coefficient. General
rate model is standard in mathematical modeling of chromatographic
processes and has been used before by others [15–17]. Moreover, the
model here reported is simpler than the one suggested by Hahn [18],
which also includes diffusion of protein bounded to solid phase.
Economics of affinity chromatography depends on numerous factors
with usually unavailable cost data details. However, it is assumed that
affinity resin cost is the main contributor to the process cost as a whole
[19–21]. Therefore, other costs were neglected also in this study.
Improvement of affinity resin utilization is reflected in a process pro­
ductivity, which was selected as the target outcome for optimization in
this study. In this sense, the thesis behind this work is the mathematic
modeling of four loading strategies to improve the productivity of batch
affinity chromatography. As proof-of-concept, this was later experi­
mentally applied to test five different recombinant protein A resins.
2. Theory
Batch affinity chromatography operates in cyclic mode. Cycle is
composed of four basic steps: i) loading of mAb on equilibrated affinity
resin contained in the chromatographic column, ii) column washing to
remove impurities, iii) elution to desorb mAb bound on resin and iv)
equilibration/regeneration step to prepare the column with affinity
sorbent for next cycle. Process productivity (P) can be defined as mass of
mAb (mmAb) captured in given cycle time:
P=
mmAb
mmAb
mmAb
=
=
tcycle
tL + tW + tE + tR tL + tWER
(1)
where tcycle is duration time of one cycle and is composed of loading (tL),
washing (tW) elution (tE) and regeneration (tR) times. Only the loading
step was optimized in this study. Times to wash, elute and regenerate
column and their sum (tWER) and column volume were therefore held
constant. Thus, an approach based on productivity optimization was
used in this study in a similar manner to other authors [10–13], but
without normalization of productivity to column volume (CV). Opti­
mizations were constrained by arbitrary column breakthrough (BT)
limit set to 0.6% of maximum, unless it was set as an optimization
parameter:
BT =
c
;z = L
cin
(2)
Additional constraint was maximal allowed column loading velocity
as prescribed by affinity resin manufacturers. Different loading strate­
gies were also optimized with a mathematical model of affinity chro­
matography, as described below.
2.1. Mathematical model of affinity chromatography
Affinity chromatography column was modeled using general rate
model with bulk liquid mass balance:
( )
∂c
∂c
∂2 c
3
∂cp
εc = − uεc + εc Dax 2 − (1 − εc ) Deff
(3)
rp
∂t
∂z
∂z
∂r r=rp
where εc is column void fraction, u liquid interstitial velocity, z axial
coordinate, Dax axial dispersion coefficient and rp particle radius, and
particle mass balance:
( 2
)
) ∂q
(
∂c
∂ cp 2 ∂cp
εp p = Deff
+
− 1 − εp
(4)
∂t
∂r 2 r ∂r
∂t
where εp is particle porosity, τ pore tortuosity factor, Deff effective pore
diffusion coefficient, r radial coordinate and q concentration of bound
antibody.
Based on a previous work [22], it was assumed that the antibody
molecules pore diffusion was the rate determining step of mass transfer.
Therefore, the term describing the exchange of protein between liquid
and particles in Eq. (3) is proportional to the concentration gradient at
particle surface, neglecting film mass transfer resistance. Langmuir type
binding kinetics, applied amongst others [23–27], was also used in this
model:
2
M. Kozorog et al.
∂q
= ka (qmax − q)cp − kd q
∂t
Separation and Purification Technology 305 (2023) 122528
Table 1
Column loading strategies aiming to improve the productivity of affinity
chromatography.
(5)
where qmax is maximum binding capacity of affinity resin and ka and kd
adsorption and desorption rate constants. Adsorption and desorption
were found to be fast compared to pore diffusion. As soon as desorption
and adsorption rate constants (kd and ka) were high enough, they had no
influence on the process as a whole [22].
Boundary and initial conditions applied were:
cp = cp,s , r = rp
(6)
∂cp
= 0, r = 0
∂r
(7)
∂c
uc* = uc − Dax ; z = 0
∂z
(8)
∂c
= 0; z = L
∂z
(9)
c = c0 = cp0 = q0 = 0, t = 0, 0 ≤ z ≤ L
(10)
Loading
strategy
Flow ramp
Quadratic
flow function
Reversed
flow
Description
Column is
loaded with
constant
flow rate F
until BT =
0.6% at
which
loading
step ends.
Loading flow
rate starts at
value F1
decreasing
linearly to
value of F2
between times
t1 and t2 until
allowed BT =
0.6%.
Flow rate is
decreasing
with
quadratic
function of
time:
F(t) = a1 +
a2t + a3t2
(Low et al.
[21])
Optimization
parameters
Flow rate F
Flow rates F1,
F2 and times t1,
t 2,
Reasoning
Optimum
flow rate is
a balance
between
amount of
antibody
captured
per cycle
(higher
amounts
are
captured at
low flow
rates) and
time of load
(less time is
spent at
higher flow
rates).
Quadratic
function
coefficients
a1, a2, a3
Column is
loaded with
flow rate F1
until time t1
or BT =
0.6%. Then it
is loaded
from other
side of the
column with
flow rate F2
until BT =
0.6%
Unoccupied
resin is first
loaded at high
flow rate. Later
loading is
slowed down to
improve
binding
efficiency and
delay
breakthrough.
where cp,s is the pore concentration of mAb at the particle surface, u* is
the concentration at the outlet of column feed system and index 0 refers
to the initial condition values. Concentration at feed system outlet was
obtained by solving mass balances of feed system:
∂c
∂c
= − F
∂t
∂VPFR
(11)
dc*
= F(c − c* )
dt
(12)
c = c0 , t = 0, 0 ≤ x ≤ VPFR
(13)
c = cin , VPFR = 0,
(14)
VCSTR
where VCSTR and VPFR are characteristic volumes of feed system and cin is
the harvest concentration at the inlet of the feed system.
Molecular diffusivity was estimated by Polson equation [28],
whereas for the axial dispersion, the coefficient correlation found in
Hassani et al. [29] was used. Further model details are described in
Grom et al. [22].
Constant
flow rate
Similar as
flow ramp
only different
trend of flow
rate
reduction
was tested.
Flow rates
F1, F2, time t1
When preset
0.6% BR is
reached, the
flow
direction is
reversed. By
reversing
flow, fresh
harvest first
encounters
less saturated
part of
column.
varied during fitting itself. Analogously, for the “Quadratic flow func­
tion” strategy, a1 could be set at the maximum assessed value, e.g. 2.5
mL min− 1, a2 at a1/t2 (configure the “Flow ramp” strategy), whereas a3
was approximated as nil initially, subject to a simplex regression algo­
rithm as noted. For the last loading strategy, namely, “Reversed flow”,
F1, as well as t1 were taken from the optimisation of “Flow ramp”,
applying the scaling of F2 = F1, where after all employed parameters
were in turn again released, subject to regression until meeting toler­
ance. A simplex optimisation algorithm [30] was used consistently
without perturbing initial parameter values, as these seemed to be, as a
rule, realistic, even when shifting the approximations for ±20% yielding
optima/results quite evenly.”
2.2. Column loading strategies
Table 1 sums up the column loading strategies tested in this study to
improve the productivity of batch chromatography. Optimization pa­
rameters and reasoning why specific strategy was evaluated are also
explained in this table. The basic idea behind these optimization stra­
tegies is to find a balance between the binding efficiency, which is
related to antibody mass captured per cycle (nominator in Eq. (1)), and
loading time (denominator in Eq. (1)). It is well known that dynamic
binding capacity of affinity resins diminishes with increasing flow rate,
but the time needed to load the column is also shorter.
The mathematical modeling and optimization was done in Matlab
R2015a software. Unless stated otherwise, Matlab fminsearch function
was used to find minimum of inverse value of productivity corre­
sponding to its maximum value. Harvest concentration was 3.3 mg
mL− 1, 5 mL column, 8 mm internal diameter and 100 mm in length were
used in all calculations. Whereas the estimation for “Constant flow rate”
strategy was straightforward having just F as parameter, there were 4
parameters for the “Flow ramp”, namely t1, t2, F1 and F2 – the initial
value approximations were selected based on our previous work [22].
For example, F1 could be set at the maximum assessed value, e.g. 2.5 mL
min− 1, while F2 at virtually 0.0 mL min− 1, subject to regression, while t1,
as well as t2 at the 1% or 99% of breakthrough, also a subject of being
3. Materials and methods
3.1. Equipment and materials
Chromatographic experiments were performed on ÄKTA Purifier 100
chromatographic system and monitored with Unicorn 5.31, both from
GE Healthcare. 5 mL MiniChrom 8–100 columns were obtained from
Atoll. They were prepacked with 5 different recombinant protein A
resins: Eshmuno® A (Merck Millipore), CaptivA™ PriMAB (Repligen
Corporation), POROS® MabCapture A™ (Applied Biosystems), MabSe­
lect SuRe™ and MabSelect SuRe™ LX (both GE Healthcare). The resins
properties and parameters are displayed in Tables 2 and 3. HPLC Alli­
ance system (Waters) was used for eluate mAb concentration determi­
nation on Poros PA ImmunoDetection™ sensor cartridge (20 μm, 2.1 ×
3
M. Kozorog et al.
Separation and Purification Technology 305 (2023) 122528
Table 2
Properties of the different Protein A resins studied in this work [22].
Property
Kd (Mol m− 3)
qmax (Mol m− 3)
εc (/)
εp (/)
Kfit (/)
dp (μm)
Protein A resins
CaptivA™ PriMAB
MabSelect SuRe™ LX
Eshmuno® A
POROS® MabCapture A™
MabSelect SuRe™
1.47 × 10− 4
22.8
0.37
0.97
0.061
90
2.59 × 10− 4
6.03
0.61
0.76
0.093
85
3.7 × 10− 4
2.49
0.53
0.66
0.072
50
5.0 × 10− 4
2.13
0.22
0.86
0.13
45
7.7 × 10− 4
5.47
0.36
0.88
0.083
85
Nomenclature: dp - diameter of particles; εc - column void fraction; εp - particle porosity; Kd - equilibrium desorption constant; Kfit - parameter, taking resin particle pore
diameter, antibody molecule diameter and pore tortuosity into account; qmax - maximum binding capacity of affinity resin.
Table 3
Effect of parameters on DBC10 at 80% of their basic level (except k was at 60% level) [22].
Parameter
εc (/)
εp (/)
qmax (Mol m− 3)
Kd (Mol m− 3)
Kfit (/)
dp (m)
L (m)
D
k (m s− 1)
Dax (m2 s− 1)
cin (Mol m− 3)
kd (s− 1)
F (m3 s− 1)
VCSTR (m3)
VPFR (m3)
Protein A resins
CaptivA™ PriMAB
MabSelect SuRe™ LX
Eshmuno® A
POROS® MabCapture A™
MabSelect SuRe™
5.1
− 12
− 19
0.4
− 11
18
− 11
− 24
/
0.1
− 2.6
0.0
10
0.1
0.1
14
− 13
− 19
0.9
− 12
24
− 13
− 26
/
0.4
− 1.5
0.4
13
0.0
0.0
3.7
− 5.5
− 19
0.9
− 5.1
8.0
− 5.5
− 12
/
1.0
− 1.6
0.7
4.5
0.2
0.1
0.4
− 2.2
− 18
1.3
− 0.9
1.8
− 1.3
− 3.3
/
2.3
− 2.3
1.8
1.4
0.2
0.3
3.3
− 9.4
− 19
2.0
− 8.7
12
− 9.6
− 21
3.9
0.0
− 2.2
− 1
7.6
− 0.2
0.0
Nomenclature: cin - concentration at piping system inlet; D - determined diffusion coefficient for internal particle/packing transport; dp - diameter of particles; Dax axial dispersion coefficient; εc - column void fraction; εp - particle porosity; F - volume flow rate; k - film mass transfer coefficient; Kd - equilibrium desorption constant;
Kfit - parameter, taking resin particle pore diameter, antibody molecule diameter and pore tortuosity into account; L - length of column; qmax - maximum binding
capacity of affinity resin; VCSTR - volume of perfectly mixed elements; VPFR - volume of plug flow elements.
30 mm, Applied Biosystems). Protein aggregate content was monitored
with Acquity UPLC (Waters) using ACQUITY UPLC BEH200 SEC 1.7 μm
4.6 × 300 mm column. CHO HCP ELISA kit, 3G was obtained from
Cygnus technologies. BSA was from Serva and sodium phosphate dibasic
and L-arginine were from Sigma. The remaining chemicals were ac­
quired at Merck. Crude cell culture supernatant (harvest) from an
antibody-producing CHO cell line was provided from Lek d.d. mAbs in
harvest were IgGs with molecular weight of 145 kDa. All buffers and cell
culture supernatant were filtered through 0.22 μm PES Membrane filters
(TPP) before separation or analysis.
phosphoric acid until pH 3.8 for virus inactivation. pH was raised again
to 6.0 with 1 M TRIS/HCl, pH 8.0 after an hour of incubation. Inacti­
vated eluate was filtered and analyzed for impurities. After every sep­
aration, the columns were cleaned with 1 M acetic acid with 25 min
contact time and regenerated with loading buffer. The columns were
sanitized with 2 CV of 0.1 M NaOH. Protein detection was monitored by
measuring UV absorbance in effluent at 280 nm. Pressure and conduc­
tance were also monitored.
3.2. Protein A affinity chromatography
mAb concentration in column effluent was determined using
analytical Protein A liquid chromatography analysis as described in
McCaw [24] and Grom et al. [22]. Samples were diluted in 25 mM so­
dium phosphate, 100 mM NaCl, 10 mg mL− 1 sucrose, 30 mM L-arginine,
0.5 mg mL− 1 BSA, pH 6.3. Applied Biosystems™ Poros™ Prepacked
Protein A affinity column was equilibrated with 10 mM NaH2PO4, 150
mM NaCl, pH 7.5. After sample loading, mAbs were eluted with 10 mM
HCl, 150 mM NaCl, pH 2. HPLC Alliance system (Waters) was used for
analysis and UV detection was carried out at 280 nm. Linear regression
was calculated using generic mAb reference standard from Sandoz
(H2015.1PST).
Host cell proteins (HCPs) in each inactivated eluate sample were
determined according to manufacturer’s ELISA kit protocol. HCPs con­
centrations determination was based on calibration curve of standards,
provided in the kit and HCP levels were calculated per product amount
in inactivated eluate.
For mAb aggregates quantification in virus-inactivated eluates, the
samples were diluted in mobile phase buffer (150 mM potassium
3.3. Analysis
Protein A resins were equilibrated in loading buffer (20 mM sodium
phosphate, pH 7.2) at constant flow of 2 mL min− 1. The harvest con­
taining 3.3 mg mL− 1 of mAb was loaded on the columns according to
optimum predicted flow ramp protocol. Loading flow rates and harvest
loading times varied between the columns according to computational
model-based predictions. Since the mAb concentration in desired final
0.6% BT is too low for its detection with analytical liquid chromatog­
raphy method (described below) additional 3.5 mL of harvest was
loaded on each column in order to test whether the computational
predictions allow maximum column saturation at calculated loading
flow rates and loading times without the product loss in the BT fractions.
After harvest loading, the columns were rinsed with loading and wash
buffer (100 mM sodium citrate, pH 5.5), followed by mAb elution from
the column with elution buffer (100 mM sodium acetate, pH 3.6.).
Column eluate fractions were collected and 1 mL was stored for mAb
content determinations. The remaining eluate was treated with 0.3 M
4
M. Kozorog et al.
Separation and Purification Technology 305 (2023) 122528
phosphate, pH 6.5) prior size-exclusion analytical chromatography. The
column effluent was monitored at ʎ = 210 nm.
rate with the highest productivity and corresponding residence time by
sweeping amongst residence times differing by 0.25 min was considered
less time consuming than searching for best residence time by Matlabs
fminsearch function. Less time consuming feed system and column model
evaluations were needed in sweep mode than compared to real opti­
mization mode. Results of computational model-based residence time
optimization in batch chromatography at constant loading flow rates for
all five affinity resins studied are shown in Fig. 1.
At high flow rates, the residence time of load fluid on chromato­
graphic column is short. It limits the time that is available for diffusion of
the antibody molecules into the resin particles pores and further to
binding sites. Therefore, when product breakthrough is reached,
considerable portion of affinity resin in the column remains unoccupied
by antibody. This especially applies for central part of particles, with the
longest diffusion time to be reached by antibody molecules. However, a
low cycle time cannot compensate for low amount of antibody bound per
cycle. By increasing residence time through decreasing the loading flow
rate, both binding efficiency and cycle time increase. At first, the anti­
body mass captured per cycle is increasing at higher rate than the cycle
time, leading to an increase of process productivity. At certain extension
of residence time, which is different for every resin type used here, the
rates become reversed, i.e. the process time is prolonged more than the
antibody amount being bound, leading to a decrease in productivity. At
optimum residence time (optimum flow rate), the effects on antibody
binding and cycle time are of equal importance.
Table 4 sums the results of loading flow optimization, calculated for
three different tWER. The productivity curves at different residence times
and tWER are presented in Fig. 1. Optimum residence times and pro­
ductivities are shown on graph as the highest curve points. Productivity
curves calculated for three different tWER times are shifted as
4. Results and discussion
Model based optimizations of loading step were made for all strate­
gies with the five different affinity resins. The affinity resins used in this
study were the same as in Grom et al. [22], nonetheless, the most
important properties and parameters have been displayed in Tables 2
and 3 in Methods section, and are later discussed in the manuscript to
further explain the obtained results. Model-based prediction of optimum
loading flow rate strategy to ensure maximum harvest loading was
experimentally validated for all resins.
4.1. Batch process with constant flow rate optimization
The column residence times (RTs) between 2 and 10 min, where
optimal productivity was expected, were divided into stepwise time
increments of 0.25 min. Each residence time uniquely determines the
flow rate for selected column volume:
F=
CV
;
RT
(16)
Process productivity was calculated for each residence time, corre­
sponding to a specific value of flow rate. Is should be noted that even
though the times needed for washing, elution and column regeneration
affect the productivity rate, these were not a subject of optimization in
this study. For calculations, three different arbitrary sums of tWER were
used: 85, 100 and 115 min, which were estimated based on the purifi­
cation separation protocol here described. Searching for loading flow
Fig. 1. Dependence of productivity on residence time at loading step.
5
Separation and Purification Technology 305 (2023) 122528
M. Kozorog et al.
Table 4
Optimum residence times, loading times and productivities for constant flow rate loading.
Affinity resin
MabSelect SuRe™
CaptivA™ PriMAB
Eshmuno® A
Parameter
RT
tL
P
RT
tL
P
RT
tL
Unit
tWER = 85 min
tWER = 100 min
tWER = 115 min
min
5.0
5.3
5.5
min
55
60
65
mg min− 1
1.26
1.14
1.04
min
4.8
5.0
5.3
min
54
58
63
mg min− 1
1.33
1.20
1.10
min
3.8
4.0
4.0
min
44
48
48
productivity is lower at higher tWER due to increased cycle time. More­
over, the optimal residence time depends on tWER value, which increased
along with the tWER. At optimal conditions, presented in Table 4, these
17 or 35% increase in washing, elution and regeneration time results in
9.5 to 17.5% decrease in productivity. Increasing residence time is
extending the cycle time at lower rate, when tWER is higher, moving also
the optimum productivity to lower antibody binding rates at higher
resin saturation. Optimization of other steps in affinity chromatography
is beyond the scope of this work, however, they are also highly impor­
tant to reduce cycle time at preserved quality and yield of purified
antibody. Amongst the different affinity resins included in modeling and
residence time optimization, Eshmuno® A and MabSelect SuRe™ LX
showed the best predicted productivities, with the first one being
slightly better at short cycle times while the second was a little superior
at longer cycle times.
MabSelect SuRe™ LX
POROS® MabCapture A™
P
RT
tL
P
RT
tL
P
mg min− 1
1.44
1.29
1.18
min
5.0
5.5
5.5
min
63
75
75
mg min− 1
1.43
1.31
1.20
min
2.8
3.0
3.0
min
25
28
28
mg min− 1
1.18
1.04
0.93
and flow ramp scenario also speaks in favor of the latter. While tWER was
kept constant in both scenarios and in all resins, the differences in buffer
consumption depended exclusively on tL and fL. Using the flow rate
scenario, the loading steps resulted in buffer consumption of 63.0, 55.0,
58.5, 56.7 and 45.5 mL for MabSelect SuRe™ LX, MabSelect SuRe™,
Eshmuno® A, CaptivA™ PriMAB and POROS® MabCapture A™,
respectively, compared to 71.4, 60.5, 65.0, 63.3 and 51.4 mL, respec­
tively, when operating the process at optimal constant flow rate.
4.3. Process with quadratic dependence of flow on time
The improvement in productivity using flow ramp loading may not
be maximal as linear reduction in loading rate does not fully resemble
graduate resin saturation. Therefore, a strategy with quadratic function
in reduction of loading was applied. Productivity improvement with
different shapes yet still similar to the ramp flow curve was attempted.
Results of this optimization and comparison to constant loading flow are
presented in Table 6. Sum of washing, elution and regeneration times
was hold at 85 min. Productivity increased 9–21% when compared to
constant loading flow, and is overall comparable to the productivity
increase of flow ramp loading. It is however, much easier to set a flow
ramp on chromatographic control system than a quadratic dependence
of flow vs. time. Thus, since the flow control using the quadratic flow
function is quite complex and it is expected little improvements in the
mAbs productivity, this study was excluded from experimental valida­
tion. Application of more complex time dependence of loading flow
would be justified if significantly higher productivity improvements
could be obtained. Perhaps some higher polynom, with more parameters
subject to optimization, could solve that.
4.2. Process with flow ramp optimization
The flow ramp indicates a continuous and linear decrease of affinity
column loading flow rate. Reduced flow rate compensates for the
reduced binding capacity of the resin getting progressively saturated.
Table 5 shows the optimum flow rates at the start and end of flow ramp,
optimum start and end times of the ramp, optimum loading times and
productivities for the flow ramp loading strategy. Flow ramp optimiza­
tion was done only with tWER of 85 min, which was assumed to be the
most probable to be applied in practice for 5 mL columns. Comparison of
productivity of flow ramp scenario to productivity of optimum constant
loading flow batch chromatography is also shown in Table 5. Depending
on the affinity resin used, a 12 to 22% productivity enhancement can be
obtained by applying a flow ramp instead of a constant flow. Higher
efficiency of flow ramp over constant flow scenario is likely gained by
applying higher flow rates at the beginning of the loading step, when the
column has sufficient capacity to absorb antibodies from the crude
harvest. Afterwards, the flow rate is gradually reduced to lower values
once the column gets more saturated, thus giving the antibody more
time to diffuse deeper into the resin particles. With the optimized con­
stant loading flow strategy, Eshmuno® A and MabSelect SuRe™ LX
showed a superior productivity performance.
The comparison of buffer consumption between constant flow rate
4.4. Process with reversed flow
Table 7 shows the results of the optimization in the reversed loading
flow strategy. Sum of washing, elution and regeneration times was held
at 85 min. An improvement in range from 8 to 18% compared to bench
mark of constant flow loading was obtained.
As presented in Table 7, loading times are in some cases shorter than
duration of forward flow. The column loading calculation was stopped
when two criteria were fulfilled: i) breakthrough exceeded prescribed
Table 5
Optimum parameters of flow ramp loading strategy.
Parameter
Unit
Protein A resins
MabSelect
SuRe™
Start flow rate F1
Start of ramp t1
End flow rate F2
End of ramp t2
Loading time tL
Productivity P
Productivity increase compared to constant flow loading at
tWER = 85 min
mL
min− 1
min
mL
min− 1
min
min
mg
min− 1
%
CaptivA™
PriMAB
Eshmuno®
A
MabSelect SuRe™
LX
POROS® MabCapture
A™
3.18
2.51
4.19
2.60
5.03
0
6.47
1.86
0
1.89
0.77
0.72
0.97
0.69
0.72
18.93
49.84
24.85
49.02
15.03
39.20
32.16
59.11
15.56
19.29
1.42
1.54
1.66
1.66
1.44
12
16
15
16
22
6
M. Kozorog et al.
Separation and Purification Technology 305 (2023) 122528
Table 6
Optimum parameters of quadratic flow function loading strategy.
Parameter
Coefficient a1
Coefficient a2
Coefficient a3
Loading time tL
Productivity P
Productivity increase compared to constant loading
flow
Unit
Protein A resins
mL
min− 1
mL
min− 2
mL
min− 3
min
mg
min− 1
%
MabSelect
SuRe™
CaptivA™
PriMAB
Eshmuno® A
MabSelect SuRe™
LX
POROS® MabCapture
A™
3.3
1.8
3.6
2.4
4.1
− 0.17
− 1.5•10− 2
− 0.14
− 5.3•10− 2
3.1•10
− 3
5.1•10
− 5
1.7•10
− 3
3.9•10
− 3
− 0.20
2.5•10− 3
36
58
40
61
27
1.39
1.45
1.74
1.68
1.38
10
9
21
17
17
Table 7
Optimum parameters for reverse flow loading strategy.
Parameter
Unit
Protein A resins
MabSelect
SuRe™
Forward flow rate
Duration of F1 flow t1
Reverse flow rate F2
Loading time tL
Productivity P
Productivity increase compared to forward only constant
flow loading
mL
min− 1
min
mL
min− 1
min
mg
min− 1
%
CaptivA™
PriMAB
Eshmuno®
A
MabSelect SuRe™
LX
POROS® MabCapture
A™
2.3
1.1
1.2
1.5
1.1
51
50
46
54
20
3.2
2.3
3.4
1.3
4.2
49
50
40
62
21
1.41
1.48
1.61
1.55
1.39
12
11
12
8
18
0.6% and ii) time after flow reversal was more than two residence times.
This additional time criteria were prescribed to prevent calculation to
stop too early when meeting just breakthrough criteria. Discrepancy
between loading time and duration of forward flow is a consequence of
this second time not being accounted for. Nevertheless, improvement of
flow reversal strategy compared to basic case is not higher than in the
aforementioned linear and quadratic ramp strategies. Not to mention
that additional equipment, e.g. valves, would be required to implement
such a strategy. Hence, it was decided not to proceed with this strategy
to experimental phase as well.
continuously in a linear way, the even step-wise decrease in flow rate
was applied between the predicted highest and lowest flow rate.
Therefore, since the predicted start of the ramp (t1), every minute the
flow rate was lowered for 0.1 (Mab Select SuRe™ / LX, CaptivA™ Pri­
MAB), 0.25 (Eshmuno® A) or 0.3 mL min− 1 (POROS® MabCapture A™)
until t2 (the predicted time of the end of flow ramp, defined for each
resin), where the predicted flow rate is kept until the end of the loading
time (tL) (Fig. 2). On each tested resin, additional 3.5 mL of harvest was
loaded in order to observe the accuracy of predicted 0.6% of product BT
at tL, defined in Table 5. At the end of loading step, the loading buffer
was run on the column with uniform flow of 2 mL min− 1 until a steady
baseline was reached.
As seen from Fig. 2, the flow ramp loading strategy was successfully
implemented experimentally. Flow rates were gradually decreased on
regard of loading times for all tested resins and are in accordance with
predictions. There was no product in the BT fractions until the very end,
when additional 3.5 mL of harvest was loaded on the column at final
flow rates f2. Only then it was observed an absorbance increase in the BT
curves. In the case of POROS® MabCapture A™, a steep increase of mAb
content in the BT was detected, reaching 39.4% BT, which can be
explained by the resin’s lower porosity (cf. Tables 2 and 3). The increase
was consistent with steep BT curves observed in column saturation
studies at various constant flow rates [22]. For MabSelect SuRe™ and
Eshmuno® A, the determined BT was 2.4% and for MabSelect SuRe™ LX
BT, it was 12.5%. The BT is seen in chromatograms as a slight increase of
BT curves during loading of additional harvest volume (Fig. 2). The
lowest BT was detected for CaptivA™ PriMAB (0.9%) as a result of the
resin’s higher porosity (cf. Tables 2 and 3). Taken together, the BT data
for all resins show good accuracy of the mathematical model, leading to
no product loss in BT during the flow ramp protocol. The low BT values
emerged only when a small extra volume of harvest was loaded, showing
high column saturation at model–predicted flow ramp loading
4.5. Experimental results
The mathematical model for affinity chromatography predicted the
processes and parameters that ensure the highest mAb binding and flow
rate combination during loading step that guarantees the highest process
productivity. By computer simulation and optimization, it was found out
that Protein A harvest loading strategies like flow ramp loading
approach, quadratic flow function loading and reveres flow loading
could give better results compared to loading with optimized constant
flow rate. Amongst those three loading strategies, flow ramp was
selected for experimental validation at optimized loading step parame­
ters, due to the reasons previously mentioned. This loading strategy was
the simplest to implement. It required no mechanical changes to the
existing equipment and has been previously shown to significantly
improve the process throughput [14]. The goal was to experimentally
evaluate the accuracy of model predictions regarding the mAb binding
dynamics for the tested resins at model optimized loading conditions.
The accuracy was estimated in respect of reached BT levels. Chro­
matographic separations were done as described by Grom et al. [22].
Loading flow rates and times based on the model were as presented in
Table 5. However, since the flow ramp cannot be decreased
7
M. Kozorog et al.
Separation and Purification Technology 305 (2023) 122528
Fig. 2. HPLC chromatograms of model-based predicted flow ramp harvest loading strategy on five different resins. Black line: eluate UV absorbance at 280 nm; blue
line: applied flow according to predicted optimal loading protocol. (For interpretation of the references to colour in this figure legend, the reader is referred to the
web version of this article.)
conditions. Such maximization of column saturation vs. cycle time ratio
results in higher process productivities [31].
Overall, these results are considerably influenced by the resin’s
binding capacity, for which MabSelect SuRe™ LX displayed the highest
binding capacity at full column saturation (DBC100 = 78 mg mL− 1),
which was reached at 10 min. At shorter residence times (2 min, highest
flow rate), the DBC100 followed the trend MabSelect SuRe™ LX >
MabSelect SuRe™ ≈ Eshmuno ® A > POROS® MabCapture A™ ≈
CaptivA™ PriMAB. Yet, at residence times of at least 4 min, CaptivA™
PriMAB resin bound an almost two times higher amount of mAbs, with
its DBC100 surpassing the remaining resins, with the exception of Mab­
Select SuRe™ LX.
One of the main reasons for choosing protein A affinity
chromatography as the predominant capture step in the mAb purifica­
tion process is its high selectivity for the product. Therefore, to further
evaluate and compare the optimized chromatographic processes, the
product purity levels were checked after the chromatographic step. mAb
fractions in virus inactivated eluates, purified on five different affinity
resins following the described flow ramp loading strategies contained
encouraging low levels of remaining host cell proteins, namely below
200 ppm when purified on MabSelect SuRe™ LX or CaptivA™ PriMAB
resins, due to their higher DBC100, and between 300 and 500 ppm for
POROS® MabCapture A™, MabSelect SuRe™ and Eshmuno® A (Fig. 3,
left). The virus inactivated eluates were further checked for aggregate
levels. Their presence was similar for all tested resins – around 2.5% at
presented conditions (Fig. 3, right) as is comparable to previously
Fig. 3. Impurities presence in virus inactivated eluates, purified on five different resins following model-based predicted flow ramp harvest loading strategy. Left:
HCP levels, calculated per mAb product content; right: mAb aggregates content.
8
M. Kozorog et al.
Separation and Purification Technology 305 (2023) 122528
published data [32]. The impurity analysis results altogether show high
HCP clearance when flow ramp strategy is implied however, the
aggregate levels should be also taken into consideration in further pu­
rification steps.
P2-0152).
CRediT authorship contribution statement
Mirijam Kozorog: Data curation, Formal analysis, Writing – original
draft. Simon Caserman: Data curation, Formal analysis. Matic Grom:
Data curation, Formal analysis, Investigation, Methodology, Software,
Writing – original draft. Filipa A. Vicente: Validation, Visualization,
Writing – review & editing. Andrej Pohar: Investigation, Methodology,
Software, Supervision, Validation, Visualization. Blaž Likozar:
Conceptualization, Funding acquisition, Project administration, Super­
vision, Writing – review & editing.
5. Conclusions
Protein A affinity chromatography has been identified as the most
expensive and significantly time-consuming step in mAb purification
processes. Therefore, its optimization enables improvements in terms of
cost, production time and process productivity. In this respect, mathe­
matical model of affinity chromatography was used to predict the effi­
ciency of three different loading strategies and optimize the loading
parameters while aiming at improving the process productivity. Five
different affinity resins were evaluated in this study and the constant
flow rate loading strategy with optimized flow rate was used as bench­
mark. Amongst the proposed loading strategies, the flow ramp approach
was the easiest to implement, requiring no modifications of the existing
equipment. This is assumed to be superior to constant flow loading since
it uses high flow rate at the beginning of loading step when resin is
unoccupied with antibody molecules and low flow rate at the end of
loading step when longer diffusion times are needed for the antibody
molecules to reach unoccupied central parts of the resin particles. By
applying the flow ramp, a higher level of column saturation is achieved.
Experimental results showed good correlation with model-based pre­
dictions of mAb binding to Protein A affinity columns. The predicted
decrease in flow rates enabled high column saturations and maximum
product binding at optimal loading time, displaying an increasing col­
umn BT trend of CaptivA™ PriMAB (0.9%) > MabSelect SuRe™ (2.4%)
≈ Eshmuno® A (2.4%) > MabSelect SuRe™ LX (12.5%) > POROS®
MabCapture A™ (39.4%). Moreover, under the optimized conditions,
the impurity levels were very low, namely 200 ppm when mAb were
purified with MabSelect SuRe™ LX and CaptivA™ PriMAB resins, as a
result of their higher DBC100, and 300–500 ppm for the remaining resins.
Therefore, flow ramp loading strategy offers the possibility to improve
the process productivity in already existing industrial processes without
the need for further investment into equipment modifications. The only
requirement is a process control system to allow variation of flow rate
during loading step, e.g. by linear flow ramp. The work beyond our
present research could consider combining various loading strategies,
for example, the quadratic flow function with reverse flow switching, as
well as effluent recycling strategies, resulting in a tanks in series
dispersion model, which minimises losses. Indeed, present research re­
sults were implemented for a robust process optimisation at a Novartis
biopharmaceutical manufacturing site, whereas they proved to be
robust, when it comes to incremental yield improvement, but main­
taining high general throughput. What is more, our (also modified)
detailed general rate model can be (and was) a subject of the optimi­
sation for a quasi-continuously simulated moving bed operation,
whereas elution/washing dead times were taken as variable tuneable
parameters additionally. While it might be of general academic interest
to mechanistically describe those as well, it more or less turns out that
the contribution of having more complex functions (of a general rate
model formulation type) to describe those only yields incremental pro­
ductivity improvement, often within experimental error margins or bias.
What also our present study hints at is potential bed structuring –
indeed, having an “by design” packing gradation might be an answer for
even further optimisation, especially when similar harvest quality is
continuously foreseen.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
All data generated or analysed during this study are included in this
published article.
Acknowledgements
Feedstock and reference mAb material was kindly provided by Lek
Pharmaceuticals d.d.
References
[1] E.V. Capela, M.R. Aires-Barros, M.G. Freire, A.M. Azevedo, Monoclonal
antibodies—addressing the challenges on the manufacturing processing of an
advanced class of therapeutic agents, Front. Clini. Drug. Res. Anti. Infect. 4 (2017)
142.
[2] G. Walsh, Biopharmaceuticals: Biochemistry and Biotechnology, John Wiley &
Sons, 2013.
[3] E.V. Capela, A.E. Santiago, A.F.C.S. Rufino, A.P.M. Tavares, M.M. Pereira,
A. Mohamadou, M.R. Aires-Barros, J.A.P. Coutinho, A.M. Azevedo, M.G. Freire,
Sustainable strategies based on glycine–betaine analogue ionic liquids for the
recovery of monoclonal antibodies from cell culture supernatants, Green Chem. 21
(2019) 5671–5682, https://doi.org/10.1039/C9GC02733E.
[4] A.L. Grilo, M. Mateus, M.R. Aires-Barros, A.M. Azevedo, Monoclonal antibodies
production platforms: an opportunity study of a non-protein-A chromatographic
platform based on process economics, Biotechnol. J. 12 (2017) 1700260, https://
doi.org/10.1002/biot.201700260.
[5] S.-Y. Jing, J.-X. Gou, D. Gao, H.-B. Wang, S.-J. Yao, D.-Q. Lin, Separation of
monoclonal antibody charge variants using cation exchange chromatography:
resins and separation conditions optimization, Sep. Purif. Technol. 235 (2020),
116136, https://doi.org/10.1016/j.seppur.2019.116136.
[6] W. Chen, X. Li, M. Guo, F.J. Link, S.S. Ramli, J. Ouyang, I. Rosbottom, J.Y.Y. Heng,
Biopurification of monoclonal antibody (mAb) through crystallisation, Sep. Purif.
Technol. 263 (2021), 118358, https://doi.org/10.1016/j.seppur.2021.118358.
[7] E. Mahajan, A. George, B. Wolk, Improving affinity chromatography resin
efficiency using semi-continuous chromatography, J. Chromatogr. A 1227 (2012)
154–162, https://doi.org/10.1016/j.chroma.2011.12.106.
[8] M. Angarita, T. Müller-Späth, D. Baur, R. Lievrouw, G. Lissens, M. Morbidelli,
Twin-column CaptureSMB: a novel cyclic process for protein A affinity
chromatography, J. Chromatogr. A 1389 (2015) 85–95, https://doi.org/10.1016/j.
chroma.2015.02.046.
[9] C.K.S. Ng, H. Osuna-Sanchez, E. Valéry, E. Sørensen, D.G. Bracewell, Design of high
productivity antibody capture by protein A chromatography using an integrated
experimental and modeling approach, J. Chromatogr. B 899 (2012) 116–126,
https://doi.org/10.1016/j.jchromb.2012.05.010.
[10] C.K.S. Ng, F. Rousset, E. Valery, D.G. Bracewell, E. Sorensen, Design of high
productivity sequential multi-column chromatography for antibody capture, Food
Bioprod. Process. 92 (2014) 233–241, https://doi.org/10.1016/j.fbp.2013.10.003.
[11] E.J. Close, J.R. Salm, D.G. Bracewell, E. Sorensen, Modelling of industrial
biopharmaceutical multicomponent chromatography, Chem. Eng. Res. Des. 92
(2014) 1304–1314, https://doi.org/10.1016/j.cherd.2013.10.022.
[12] V. Girard, N.-J. Hilbold, C.K.S. Ng, L. Pegon, W. Chahim, F. Rousset, V. Monchois,
Large-scale monoclonal antibody purification by continuous chromatography,
from process design to scale-up, J. Biotechnol. 213 (2015) 65–73, https://doi.org/
10.1016/j.jbiotec.2015.04.026.
[13] D. Baur, M. Angarita, T. Müller-Späth, M. Morbidelli, Optimal model-based design
of the twin-column CaptureSMB process improves capacity utilization and
Funding
This work was funded by the European Union’s Horizon 2020
research and innovation programme under grant agreement No 635557
(nextBioPharmDSP project). The authors acknowledge the financial
support from the Slovenian Research Agency (research core funding No.
9
M. Kozorog et al.
Separation and Purification Technology 305 (2023) 122528
[23] H.A. Chase, Prediction of the performance of preparative affinity chromatography,
J. Chromatogr. A 297 (1984) 179–202, https://doi.org/10.1016/S0021-9673(01)
89041-5.
[24] E.M. Martín del Valle, M.A. Galán, Specific and nonspecific adsorption in affinity
chromatography. Part II. Kinetic and mass transfer studies, Ind. Eng. Chem. Res. 40
(2001) 377–383, https://doi.org/10.1021/ie000402f.
[25] T.R. McCaw, E.K. Koepf, L. Conley, Evaluation of a novel methacrylate-based
protein a resin for the purification of immunoglobulins and Fc-fusion proteins,
Biotechnol. Prog. 30 (2014) 1125–1136, https://doi.org/10.1002/btpr.1951.
[26] F.H. Arnold, H.W. Blanch, Analytical affinity chromatography: II. Rate theory and
the measurement of biological binding kinetics, J. Chromatogr. A 355 (1986)
13–27, https://doi.org/10.1016/S0021-9673(01)97300-5.
[27] L. Leickt, A. Månsson, S. Ohlson, Prediction of affinity and kinetics in biomolecular
interactions by affinity chromatography, Anal. Biochem. 291 (2001) 102–108,
https://doi.org/10.1006/abio.2001.5019.
[28] A. Polson, The some aspects of diffusion in solution and a definition of a colloidal
particle, J. Phys. Colloid Chem. 54 (1950) 649–652, https://doi.org/10.1021/
j150479a007.
[29] D. Hassani, S. Hanini, K. Daoud, E. Mauret, Application of the neuronal method for
calculating the axial dispersion in the beds fixed of the linings parallelepipedic,
J. Appl. Sci. 8 (2008) 3380–3388, https://doi.org/10.3923/jas.2008.3380.3388.
[30] J.C. Lagarias, J.A. Reeds, M.H. Wright, P.E. Wright, Convergence properties of the
Nelder–Mead simplex method in low dimensions, SIAM J. Optim. 9 (1998)
112–147, https://doi.org/10.1137/S1052623496303470.
[31] N. Tugcu, D.J. Roush, K.E. Göklen, Maximizing productivity of chromatography
steps for purification of monoclonal antibodies, Biotechnol. Bioeng. 99 (2008)
599–613, https://doi.org/10.1002/bit.21604.
[32] Z. Liu, S.S. Mostafa, A.A. Shukla, A comparison of protein A chromatographic
stationary phases: performance characteristics for monoclonal antibody
purification, Biotechnol. Appl. Biochem. 62 (2015) 37–47, https://doi.org/
10.1002/bab.1243.
productivity in protein A affinity capture, Biotechnol. J. 11 (2016) 135–145,
https://doi.org/10.1002/biot.201500223.
[14] S. Ghose, D. Nagrath, B. Hubbard, C. Brooks, S.M. Cramer, Use and optimization of
a dual-flowrate loading strategy to maximize throughput in protein-A affinity
chromatography, Biotechnol. Prog. 20 (2004) 830–840, https://doi.org/10.1021/
bp0342654.
[15] T. Gu, Y. Zheng, A study of the scale-up of reversed-phase liquid chromatography,
Sep. Purif. Technol. 15 (1999) 41–58, https://doi.org/10.1016/S1383-5866(98)
00083-5.
[16] R. Hahn, P. Bauerhansl, K. Shimahara, C. Wizniewski, A. Tscheliessnig,
A. Jungbauer, Comparison of protein A affinity sorbents: II. Mass transfer
properties, J. Chromatogr. A 1093 (2005) 98–110, https://doi.org/10.1016/j.
chroma.2005.07.050.
[17] E. von Lieres, J. Andersson, A fast and accurate solver for the general rate model of
column liquid chromatography, Comput. Chem. Eng. 34 (2010) 1180–1191,
https://doi.org/10.1016/j.compchemeng.2010.03.008.
[18] R. Hahn, Methods for characterization of biochromatography media, J. Sep. Sci. 35
(2012) 3001–3032, https://doi.org/10.1002/jssc.201200770.
[19] B. Kelley, Very large scale monoclonal antibody purification: the case for
conventional unit operations, Biotechnol. Prog. 23 (2007) 995–1008, https://doi.
org/10.1021/bp070117s.
[20] P.A.J. Rosa, A.M. Azevedo, M.R. Aires-Barros, Application of central composite
design to the optimisation of aqueous two-phase extraction of human antibodies,
J. Chromatogr. A 1141 (2007) 50–60, https://doi.org/10.1016/j.
chroma.2006.11.075.
[21] D. Low, R. O’Leary, N.S. Pujar, Future of antibody purification, J. Chromatogr. B
848 (2007) 48–63.
[22] M. Grom, M. Kozorog, S. Caserman, A. Pohar, B. Likozar, Protein a affinity
chromatography of Chinese hamster ovary (CHO) cell culture broths containing
biopharmaceutical monoclonal antibody (mAb): experiments and mechanistic
transport, binding and equilibrium modeling, J. Chromatogr. B 1083 (2018)
44–56, https://doi.org/10.1016/j.jchromb.2018.02.032.
10