Abstract
The scale-up of bioprocesses is still one of the major obstacles in
biotechnological industry.
Scale-down bioreactors were identified as valuable tools to investigate
the heterogeneities observed in large-scale tanks in laboratory-scale.
Additionally, computational fluid dynamics (CFD) simulations can be used
to gain information about fluid flow in tanks used for production.
Here we present the rational design and comprehensive characterization
of a scale-down setup, in which a flexible and modular plug-flow reactor
is connected to a stirred tank bioreactor. With the help of CFD the
mixing time difference between differently scaled bioreactors were
evaluated and used as scale-down criterium. Additionally, it was used to
characterize the setup at conditions were experiments could technically
not be performed. This was the first time a scale-down setup was tested
on high cell density Escherichia coli cultivations to produce
industrial relevant antigen-binding fragments (Fab). Reduced biomass and
product yields were observed during the scale-down cultivations.
Additionally, the intracellular Fab fraction was increased by using the
setup.
The results show that including CFD in the design and characterization
of a scale-down reactor can help to keep a connection to production
scale and also gain intensive knowledge about the setup, which enhances
usability.
Introduction
Scale-up is still one of the major challenges in biotechnological
production.[1] The main reason is, that when using
the classical scale-up criteria considering, e.g. constant volumetric
power input or impeller tip speed during the increase of bioreactor
volume, it is not possible to keep at the same time also the mixing time
constant.[2, 3] As a consequence, gradients of
substrate, dissolved oxygen and other parameters develop in large scale
tanks.[4, 5] Accordingly, the living organisms
used in fermentation processes respond to these gradients, which can
have several drawbacks on the bioprocess including a reduced biomass
yield or increased side product formation.[4, 6,
7]
An approach to tackle this problem is the development of different
scale-down setups, which simulate the heterogeneous conditions observed
in large-scale using laboratory-scale
systems.[8-10] Commonly applied techniques are
pulse feeding of substances, like substrate into a stirred tank reactor
(STR), [11, 12] or the use of two-compartment
reactors. For the latter, different setups exist, where either two STRs
are connected,[13, 14] or a STR is combined with a
plug-flow reactor (PFR).[15-18] A comprehensive
review about the use of several different scale-down setups can be found
in Neubauer, et al.[19] In literature there are
also reports, where more than two compartments can be used for such
experiments.[20]
The flow in these devices is usually characterized by tracer pulse
experiments with water, but these do not take into account the change of
fermentation broth viscosity during cultivation.[16,
21] The achievement of direct linkage between the production-scale
and the scale-down setup remains difficult as well, as experimental data
about industrial production equipment is commonly
rare.[1, 22] Computational fluid dynamics (CFD)
modelling is considered to be a suitable tool to close this
gap.[22, 23] Several studies were performed, where
mixing inside large-scale tanks was described by using
CFD[23-25]
Our aim was to develop a flexible and modular plug-flow reactor for
scale-down purposes, which could be easily connected to a
laboratory-scale bioreactor used for process development and
optimization. The development was done in coordination with a production
scale (4 m3) bioreactor. The main scale-down criterion
was based on the mixing time difference between a laboratory- and the
production-scale bioreactor. The difference was used as mean residence
time (RT) the cells spend inside the PFR. CFD simulation was not only
used to calculate the mixing time in the two different scaled
bioreactors, it was also used for detailed characterization of the PFR.
RT distributions inside the main part of the plug-flow compartment were
calculated taking different flow rates, fluid viscosities and the
presence of static mixers (SM) into account. To the authors knowledge
this is the first study where CFD was used to characterize and optimize
the second compartment of a scale-down setup. Tracer-pulse experiments
were done to compare and validate the CFD simulations.
To test the setup for an industrially relevant process, high cell
density Escherichia coli (E. coli ) cultivations producing
antigen-binding fragments (Fab) were performed. Fabs have high potential
for biopharmaceutical industry due to their less complex structure and
the potential to be produced in cost efficient microbial cultivations
compared to full length antibodies.[26, 27]Nevertheless, Fab production in E. coli is still
challenging.[28, 29] This provides an optimal
starting point to investigate scale effects on process efficiency by
means of using the designed scale-down reactor.
Material and Methods
Design of the scale-down reactor
The STR (Bioengineering, Switzerland) used in this study had a maximal
working volume of 20 L and was equipped with two six-blade Rushton type
impellers. It was a stainless-steel tank with a height to diameter ratio
of 2.8 and standard 25 mm Ingold ports for connection of sensors, as
well as other equipment. For online monitoring and control a pH sensor
(Easyferm Plus PHI ARC 120, Hamilton Bonaduz AG, Switzerland) and a
dissolved oxygen (DO) sensor (Visiferm DO ARC 120, Hamilton Bonaduz AG,
Switzerland) were used.
The authors are aware that plug-flow indicates an axial dispersion of
0,[30] which is hardly achievable in reality.
Nevertheless, it is common practice in relevant
literature[16, 20, 31, 32] to use the term PFR as
synonym for tubular reactor. Therefore, the authors apply this practice
in this work as well. The whole scale down setup can be seen in Figure 1
and Figure S 1, the following labeling is according to these figures.
The main part of the PFR consisted of four insulated straight
stainless-steel DN25 DIN tubes (7) (Bilfinger Industrietechnik Salzburg
GmbH, Austria) connected via three stainless steel bows DN25 DIN (8)
(Bilfinger Industrietechnik Salzburg GmbH, Austria). At the beginning
and the end of this main part a sampling device (5 + 10) and a sensor
device (6 + 9) (both SIBA Sonderanlagen GmbH, Austria) were located. In
the sampling device up to three sensors could be mounted. The connection
of the PFR to the STR was done with connectors aligned to two-way valves
(2 + 13), which could be mounted in standard 25 mm Ingold ports. The
transfer pipes were DN20 Pharmaline PTFE tubes (4) (Tecno Plast
Industrietechnik GmbH, Germany) to withstand the sterilization and
cleaning in place (CIP) procedure. For adding feed solution inside the
PFR a T-shaped adapter could be mounted (Figure 1 B). For recirculation
of the fermentation broth a peristaltic pump (3) (Masterflex I/P, with
Masterflex HP pump head, Cole-Parmer, USA) was used. To compensate for
variation of the flow rate due to abrasion of the pump hose (Masterflex
Norprene Food, Cole-Parmer, USA) and change of fluid properties during
the bioprocess, the flow inside the PFR was controlled via a
sterilizable magnetic-inductive flowmeter (12) (Promag H300,
Endress+Hauser, Austria). To enhance radial mixing inside the PFR,
stainless-steel SM (Figure 1 C) (Stamixco AG, Switzerland) could be
inserted inside the straight tube compartments. Their impact was studied
by performing the experiments with and without the SM. Sterilization was
done for 40 minutes with hot steam at 121 °C and 1.2 barg. For CIP the
PFR was rinsed with sodium hydroxide, phosphoric acid and deionized
water. Pressure was monitored via an inline pressure sensor (11) (Labom
Mess- und Regeltechnik GmbH, Germany) and temperature (T) was measured
with a resistance thermometer (Labom Mess- und Regeltechnik GmbH,
Germany). At the two measurement points (MP) 1 and 2 pH (Easyferm Plus
PHI ARC 120, Hamilton Bonaduz AG, Switzerland) and DO sensors (Visiferm
DO ARC 120, Hamilton Bonaduz AG, Switzerland) were mounted. The volume
of PFR was equal to 20.8% of the maximal working volume of the STR.
CFD characterization of STR and PFR
To characterize the flow field and intensity of the mixing in the STR
and the PFR we performed CFD simulations of both by using the program
Ansys Fluent v. 2021. The sketches of STR and PFR together with their
geometries are presented in Figure 1 B and Table S 1. To reflect the
measurement of the mixing time where no sparging was used, the CFD
simulations were realized using single phase with water as the working
fluid, having viscosity and density equal to 0.72 mPa.s and 994
kg/m3, respectively. The flow in the STR was simulated
using the realizable k -ε model [33]combined with the standard wall function to describe the flow in the
boundary layer near the solid walls. Impeller rotation was modelled
through sliding mesh approach. The simulations were performed for
stirring speed ranging from 800 to 1500 revolutions per minute (rpm) to
cover typical cultivation conditions. To describe the flow by taking the
complex internal structure of the impeller and all used probes into
account, a mesh consisting of 4.7 million hexahedral elements was used.
Mesh independence study confirmed no impact of further mesh refinement
on the flow pattern. Since only small vortex formation was
experimentally observed at the gas-liquid interface, the top interface
was simulated via symmetry boundary conditions. The mixing time was
simulated using time dependent evolution of tracer inside the STR.
Due to the complexity of the flow in the peristaltic pump, used to drive
the fluid through the scale-down setup, only the main body of the PFR
ranging from (5) to (10) in Figure 1 B was used for CFD modelling of the
flow field and the tracer mixing time. Two geometries of PFR were
considered. For the first one four SM were placed in the straight parts
of the PFR (Figure 1 B), while in the second geometry the SM were
omitted. The flow in the PFR was modelled via SST k -ωturbulence model.[33] To resolve the near-wall
region the mesh was build such that more elements were located in this
region resulting in total of 3.2 million polyhedral elements. Similar to
the STR also in this case only single phase was considered for CFD
simulations. This choice was supported by the positioning of the
connector between STR and PFR at the bottom of the STR (Figure S 1). Due
to this measure, only liquid was entering the PFR during cultivation
process. In contrast to the STR, several variations of the fluid
properties were considered in the simulations. In particular, we
performed two simulations using the flow rates of 1.37 L/min and 4.11
L/min, which were compared with the experimentally measured time
evolution of a tracer. After CFD model validation, we performed further
simulations considering a faster flow rate, which was actually used
during cultivation experiments. Additionally, two scenarios of fluid
viscosity were considered as well. Firstly, 1 mPa.s representing the
viscosity of the media at fermentation start. In the second case, we
considered properties of fermentation broth, which was characterized at
the end of the fermentation process. It was found that it changed its
rheological properties from Newtonian to non-Newtonian with shear
thinning behavior. To take this into consideration we adopted in our CFD
simulation the viscosity dependency on shear rate as measured by
Rheometer (viscosity [mPa.s] = 2.6+(6e6/(1+70000*shear rate
[1/s]))). Under these conditions the viscosity was varying from 2.6
mPa.s up to 10 mPa.s in the PFR. Tracer viscosity in both cases was
equal to 18 mPa.s, and thus reflecting high glucose concentration in the
feed.
Tracer-pulse experiments
To validate the CFD simulation results of the mixing inside the 20 L
STR, pulse experiments with a salt tracer were
performed.[34] 20 mL of 4 M
NH4SO4 solution were pulsed into the
reactor filled with 20 L deionized water and tempered to 37 °C. A
conductivity sensor (inLab 710 together with SevenExcellence
conductivity meter, Mettler Toledo, Switzerland) was used to track the
pulse response and the mixing time was determined when 95% homogeneity
was reached.[22, 35, 36] The experiments were
performed in three independent replicates for 800 rpm and 1200 rpm
stirrer speed. Data acquisition was done with LabX direct pH3 software
(Mettler Toledo, Switzerland).
In the case of PFR, RT experiments were performed using the setup with
and without SM. A pulse injection of 20 mL 4 M
NH4SO4 solution was done via the
T-shaped feed addition point positioned directly after the first
connector (Figure 1 B and Figure S 1). A conductivity sensor (inLab 710
together with SevenExcellence conductivity meter, Mettler Toledo,
Switzerland) was used to monitor the resulting tracer peaks at the two
measurement points MP1 and MP2 (Figure 1 B). The flow rate inside the
plug-flow compartment was adjusted either to 1.37 L/min or 4.11 L/min.
Due to short tracer RT and slow response of the conductivity probe,
measurements at higher flow rates were not possible. Each experiment was
performed in four replicates. Each data set was normalized between 0 and
1 prior to further evaluation. Evaluation was done with the program Peak
fit (Systat Software Inc., USA), by fitting the exponential modified
gaussian (EMG) function to the experimental data and obtaining the mean
RT and the variance by calculation of the first and the second moment of
the function. Additionally, to compare the shape of the resulting peaks,
the peak asymmetry at 10% peak height (Asymmetry 10) was obtained as
well. The obtained mean RTs were compared to the theoretical ones and
the one calculated via CFD. The characterization of the flow behavior
inside the PFR was done via a dimensionless number calculated according
to Levenspiel,[30] taking the axial dispersion
into account. This number is considered as altered form of the
Bodenstein number (Bo) by several different authors in this
field[16, 21, 32] and is defined as:
\begin{equation}
Bo=\ \frac{2*\tau^{2}}{{\sigma_{\tau}}^{2}}\nonumber \\
\end{equation}Equation 1
where τ is the mean residence time and στ is the
variance of the residence time distribution. If the value of Bo is
bigger than 10, the flow is considered to be plug-flow
like.[32]
Strain and culture conditions
E. coli BL21(DE3) (New England Biolabs GmbH, USA) was used for
this study. The integration of the Fab FTN2, which targets Tumor
necrosis factor α (TNFα), into the production host’s genome is described
in detail in a previous publication.[37]
For cultivation the 20 L stainless-steel fully automated bioreactor
already described above was used. As preculture, cells from glycerol
cell banks were grown in 200 mL semi-synthetic media (SSM) in 2 L
baffled flasks, at 37 °C and 180 rpm shaking frequency. The media
composition was the same as described by Fink et
al.[29] Approximately 280 mg of cell dry mass
(CDM) were used to inoculate the bioreactor. During batch phase, the
reactor contained 10 L media with following components calculated per g
CDM: 94.1 mg/g KH2PO4, 31.8 mg/g 85%
H3PO4, 150 mg/g yeast extract, 41.2 mg/g
Na3-Citrate*2 H2O, 46.0 mg/g
MgCl2*6 H2O, 20.2 mg/g
CaCl2*2 H2O, 45.3 mg/g
NH4SO4, 50 µL/g trace element solution
with the same composition as used by Marisch et
al.[38]. Glucose*H2O was added to
achieve 120 g CDM by assuming a yield coefficient (YX/s)
of 0.303 g/g. Additionally, 10 mL PPG 2000 were added to the batch
medium as anti-foam agent. The feed solution consisted of the same
composition as the batch medium, except for omitting yeast extract,
NH4SO4 and PPG 2000. The amount of
NH4SO4 required for the feed medium was
additionally added to the batch medium. In the first feed phase the
cells were grown with an exponential growth rate (µ) of 0.17
h-1 for 2.21 generations. This was followed by a
second exponential growth phase with µ of 0.05 h-1.
With this feed profile 1506 g CDM should be achieved at fermentation
end. Pulse induction was done 19 h after feed start with 1 µmol IPTG per
g CDM calculated for the planned biomass at fermentation end. During
batch phase temperature was set to 37 °C and was shifted to 30 °C at
feed start. The DO was set to a minimum of 40% and was controlled by
stirrer speed (800 rpm – 1200 rpm) and manual variation of aeration
rate (5 standard liter (sL)/min – 25 sL/min), as well as variation of
the headspace pressure (0.5 barg – 1.2 barg). During the whole process,
the pH was maintained at 7.0±0.2 with the addition of 25% ammonia. The
pH probes were calibrated with pH 4 and pH 7 buffer solution (Hamilton
Bonaduz AG, Switzerland). For the DO sensors, a 2-point calibration was
performed in Batch media at 37 °C, 0.25 barg, 5 sL/min aeration (100%)
and after sparging with nitrogen (0%). For scale-down experiments
performed with the combination of STR and PFR, the PFR was connected at
feed start. For the cultivations without the PFR (reference), the feed
solution was added directly in the STR. For the scale-down cultivations
the feed was injected in the beginning of the PFR via the feed addition
point (Figure 1 B). To investigate the effect of the SM on the
cultivation, fermentations with and without the mixers were performed as
well. To compensate for the SM-volume and to achieve the same mean RT,
the flow rate was adapted accordingly. Each cultivation was performed in
duplicates.
Off-line fermentation analysis
During reference cultivations and scale-down cultivations several
samples were taken at the same time points for comparison. The
description of gravimetrical CDM analysis and OD 600
measurements[38] as well as the sampling for
product analysis[29] were already described in
literature.
The cell lysis protocol using lysozyme and the Fab quantification by a
sandwich enzyme-linked immunosorbent assay (ELISA) was done as described
by Fink et.al [37]. Due to discontinuation
of Anti-human IgG mouse antibody [2A11] (Abcam, Cambridge, UK), it
was replaced for this study by Mouse Anti-human IgG Fab secondary
antibody [SA1-19255] (Thermo Fisher Scientific Inc., USA), which was
diluted 1:800. For quantification of the intracellular Fab fraction the
lysed cell pellet was analyzed, for the extracellular fraction the
thawed supernatant was used. The sum of both fractions corresponds to
the total amount of Fab produced.
Results
Mixing time in the stirred tank bioreactor (STR)
As the mixing time was our scale-down criterion, it was important to
determine the mixing time in the 20 L STR. Therefore, a CFD model was
created to calculate the mixing times at various stirring speeds. The
operating conditions for the CFD simulation are summarized in Table 1.
According to the calculated impeller Reynolds number (Re) , which
cover the range from 145967 to 273689, the flows in the STR were
turbulent under all studied conditions. Despite the complex STR internal
parts, the flow generated by the two Rushton impellers is dominated by
strong radial pumping from the impeller blades towards the vessel
periphery, followed by the formation of two circulation zones above and
below each impeller (Figure S 2).[39] The radial
pumping zone is characterized by the highest values of the turbulent
kinetic energy (k ) and turbulent energy dissipation rate
(ε ) (Figure S 2 C and D). Summary of the vessel averaged
〈ε 〉 together with the obtained Power number
(Po )[40, 41] calculated from the torque
acting on the impeller surface is presented in Table 1.
To get the mixing time, simulations of tracer mixing inside the
considered system were performed. The example of the time evolution of
normalized tracer mass fractions at four probe positions (Figure 1 A) is
presented in Figure S 3. Due to the closer distance of the top probes to
the point of tracer addition and formation of mixing zones by the action
of Rushton impellers,[41] tracer mass fraction was
the highest at the top part of the vessel, while the tracer mass
fraction at the bottom of STR was gradually rising. Mixing time was
determined when the normalized tracer concentration reached 95% vessel
homogeneity at the last probe. The mixing times plotted as a function of
〈ε 〉 obtained for all simulated conditions in STR, follow a
power-law scaling with the slope close to that measured experimentally
by Nienow et al (Figure S 4).[41, 42] This scaling
was used to determine the mixing time in the 20 L vessel at the minimum
energy input used for the 4 m3 bioreactor.
The obtained results were validated by tracer-pulse experiments. Even
though experimental values are slightly lower than the calculated ones,
they closely follow the same trend with decreasing the mixing time by
increasing 〈ε 〉.[41, 42] Therefore, the
difference between mixing time in laboratory scale STR and the
large-scale STR at lowest energy input during operation was used as
basis for defining the RT of the E. coli cells in the PFR.
Design of the scale-down reactor
The PFR (Figure 1 and Figure S 1) was designed to contain approximately
20 % (finally achieved 20.8 %) of the maximal working volume of the 20
L working volume STR, as this is considered to be a suitable value also
in other studies.[16, 21, 31] To keep the
connection to the large scale systems, we adjusted the RT of the cells
inside the PFR to be equal to the mixing time difference between the 20
L and a 4 m3 STR at minimum energy input during
operation, which was in our case equal to 37 s. By taking the PFR volume
and the mixing time difference into account, it resulted in a flow rate
of 6.66 L/min. To handle this flow-rate the PFR was built from
stainless-steel pipes in combination with rigid PTFE transfer tubes.
This measure had also the advantage that sterilization and cleaning
procedures could be implemented easily. To keep the setup still flexible
and modular the connections between the different parts (tubes, bows,
etc.) were done with standard Tri-Clamp connections. This enabled us to
move the sensor, sampling and feed ports to various positions along the
PFR. Additionally, the variation of the STR to PFR volume ratio could be
easily adjusted by removing or adding pipe segments to the setup. The
high flow rate made it necessary to acquire a powerful pump. For
sterility purposes we used a peristaltic pump, with which we could vary
the mean RT of the cells inside the PFR between 15 s and 8.3 minutes.
This enabled us to achieve the planned 37 s RT, but also added further
flexibility to our setup. Stainless-steel SM could be inserted inside
the straight stainless-steel pipes, which will enhance the radial mixing
inside PFR and reduce axial dispersion, thus promote plug-flow behavior
inside the reactor. The actual influence of the SM on the fluid flow
behavior inside the tubular reactor, as well as the influence onE. coli high cell density processes is evaluated in this study.
Gradients of substrate, pH and DO were built up during cultivation by
adding feed solution at the entrance of the PFR. Temperature gradients
were avoided by insulation of the pipes.
Characterization of the scale-down reactor
To verify, if the flow inside the tubular reactor can be considered as
plug-flow and to analyze the RT distribution inside the plug-flow
compartment, tracer-pulse experiments at two different flow rates were
performed. Due to technical limitation of our conductivity probe, the
flow rates were chosen to result in mean RT equal to 1 and 3 minutes,
corresponding to laminar and nearly fully turbulent conditions (Table
2). The measurements were performed at MP1 and MP2 (Figure 1 B) using
the PFR with and without the SM. In Table 2 the theoretical RT is
compared with the mean RT of the resulting peaks. The variance and the
Asymmetry 10 were shown and gave information about the peak width and
shape. As Bo was in every case bigger than 10, the presented results
confirmed the plug-flow-like behavior inside the tubular
reactor.[32] The experimentally determined mean RT
at 4.11 L/min fit very well to the theoretical value, additionally the
peak variance got smaller at higher flow rate as expected. In contrast,
at the low flow rate non-ideal reactor behavior existed as the peak was
passing the second measurement point MP2 some seconds earlier as
expected. The data also showed that the SM reduced the axial dispersion
and thus kept the RT distribution narrow. This can be seen when
comparing the variance, the experimental error, the Asymmetry 10 and the
Bo number. Without SM a much more pronounced peak tailing was observed,
especially at low Re number.
Since the slow response of the conductivity probe prevented the
execution of tracer pulse experiments at the flow rate used in
scale-down cultivations, a CFD simulation of the PFR was performed
instead. By validation of the CFD simulation for the above discussed
flow rates, the model could be used to extrapolate for the higher flow
rate. As the experimental and the modelling setup were different (we did
not consider to model the transfer tubes and the peristaltic pump), the
comparison was done by evaluation of the time when the tracer passed
between MP1 and MP2 (Table 2 B). Furthermore, the tracer signal obtained
at position MP2 was used to evaluate the variance of the distribution
for both the model and the experiments.
The mean RT between MP1 and MP2 fit very well between the theoretical,
experimental and modelled values at the higher flow rate. For the lower
flow rate the tracer passed earlier than the theoretical and CFD
modelled values would suggest. However, as the experimental error for
these conditions was quite large, the relative deviation was considered
to be acceptable. Please note, that the absolute values for variance
cannot be compared directly as the tracer in the experimental setup had
to pass a longer distance, but the trend was the same. Variance
decreased significantly by using higher flow rate and SM, as it was
determined experimentally. Therefore, these results confirm that the CFD
model could be used to predict the flow behavior inside the PFR
especially for higher flow rates.
Additionally, an example of the contour plot of velocity magnitude,
turbulent kinetic energy and ε obtained for above mentioned flow
rates is presented in Figure S 5, Figure S 6 and Figure S 7. The impact
of the SM on the radial mixing inside PFR was clearly seen, particularly
in the horizontal parts of the tubular reactor. This mixing enhancement
is further documented in Figure S 8 A and B, where the time evolution of
the tracer mass fraction at MP1 and MP2 is presented. While tracer
distribution at the tubular reactor inlet was rather narrow, the axial
dispersion increased along the flow direction resulting in broader
tracer distribution at the MP2. Furthermore, this distribution became
even broader with tailing for PFR without SM.
CFD for flow extrapolation
Once validated, the model was used for an extrapolation of the flow rate
with 6.66 L/min, but also to study the effect of varying fluid
viscosity. Two limiting cases of fluid viscosities, covering constant
viscosity of media with a value of 1 mPa.s and shear rate dependent
viscosity at fermentation end, were used. Comparison of the tracer mass
fraction as a function of time is shown in Figure 2. It can be seen,
that while for low viscosity of the fluid there was only small
difference in the tracer mass fraction profile at MP2, at higher fluid
viscosity there was a big improvement of the mixing when the SM was
used. This is indicated by the more symmetric and narrower peak that
occurs when using SM.
Scale-down and reference cultivations
To test the performance of the scale-down setup under production
conditions, high cell density E. coli cultivations to produce Fab
were performed with (scale-down) and without (reference) the tubular
reactor connected to the STR. Additionally, the influence of an altered
RT distribution on the bioprocess was evaluated in experiments with and
without SM.
The use of the scale-down setup with SM in comparison to the standard
laboratory scale cultivation using only the STR (reference) lead to a
biomass yield (Yx/s) reduction of 11% (Figure 3 A).
Only 65 g/L CDM were reached instead of 73 g/L. The omittance of the SM
led to a further decrease of 2%. Nevertheless, as this change was in
the range of the experimental error, it could be concluded that a
broader RT distribution did not influence the biomass yield to a high
extent
To investigate the influence of the scale-effects on the recombinant Fab
production, we used the intracellular and extracellular Fab yield. The
total amount of Fab was the sum of both fractions. When using the
scale-down setup, the total specific Fab yield was reduced by 20%
(Figure 3 B). In fact, accounting also for the biomass reduction a
volumetric total Fab yield reduction of 28% compared to the reference
cultivation without the PFR was observed. The broader RT distribution
induced by the removal of the SM decreased the yield further, but again
just marginally. Interestingly, the extracellular Fab fraction was
larger during reference cultivation compared to the scale-down setup
(Figure 3 D). For the reference, 26% of the specific total Fab was
found in the extracellular fraction, whereas for the scale-down
cultivations it was only around 1%.
Interesting insights in the heterogeneities induced by the PFR were
gained by the online data of pH and DO at different positions along the
scale-down setup (Figure 3 D and E). 9 h after feed start, the feed
profile was changed from an exponential growth rate of µ = 0.17
h-1 to µ = 0.05 h-1. This switch was
reflected by a sharp increase in both pH and DO online data. At the end
of the first growth phase, an acidification of 0.3 pH units were seen
after 33 s (MP2) in the PFR. In the second growth phase, the pH
continuously decreased as the fermentation proceeded and reached a value
of 6.9 at fermentation end.
At the end of the first feed phase, the DO was consumed in the PFR
already after 5 s mean RT (MP1). By switching to a lower growth rate,
the oxygen consumption in the PFR was lowered as well. The oxygen
concentration was decreasing until the end of the cultivation. At MP2,
the dissolved oxygen concentration in the PFR was 0% along the whole
cultivation course.
Discussion
In this work, we designed and constructed a flexible and modular
plug-flow type scale-down reactor that can be easily adapted to various
conditions and research questions. The relative long oxygen limited zone
(Figure 3 F) mimics the feeding zone in large industrial scale reactors,
where oxygen depletion can occur, especially at high cell
densities.[2, 5, 19] The size of this zone can
also vary with the feed rate, as more substrate leads to higher
metabolic activity, higher oxygen consumption and therefore bigger
oxygen depleted zones. This mechanism can be mimicked by our scale-down
setup as well. Additionally, a pH gradient along the PFR was reported
(Figure 3 E). The observed acidification could be explained by the
production of organic acids and CO2.
In contrast to other publications, we also investigated the influence of
scale-effects on an industrially relevant Fab production process. In
these experiments we could demonstrate a reduction in
YX/s of 11%. This is in accordance with Bylund et
al.[4] who reported a biomass reduction of 15%-
20% by scaling up a 30 g/L E.coli process from 15 L to 8
m3. We could show that the total Fab production was
reduced by the scale down setup and that the distribution between intra-
and extracellular Fab was altered. The reduction of recombinant protein
production caused by scale-effects was already reported in
literature.[13] Nevertheless, the shift to higher
intracellular product when using a scale-down setup is a notable
finding. It could be explained by the higher productivity during the
reference cultivations and the resulting higher metabolic load for the
cells, that lead to the release of product from the periplasm by cell
lysis. However, it could also be attributed to an increase in cell
robustness originating from varying conditions in the scale-down setup.
Existence of this effect was reported previously by several authors
performing flow cytometry with different staining methods duringE.coli cultures.[17, 43, 44] Brognaux et
al[44] linked this behavior to a higher membrane
permeability during well-mixed fed-batch cultivations. With respect to
product purification, the increase of cell robustness could lead to
beneficial effects, as almost the whole amount of product is then
located in one compartment. The absence of cell lysis would further
facilitate subsequent downstream operations.
The CFD model of the STR is in closed agreement with Rutherford et al.[39] , who experimentally measured Po for
two Rushton impellers with parallel flow configuration, supporting the
turbulent conditions. Additionally, the determined mixing time
difference of 37 s between the different scales is in a reasonable range
as mixing times (t95) of approximately 50 s for a 12
m3 and 150 s for a 30 m3 scale STR
with Rushton impellers at comparable specific power input were
reported.[45]
To characterize the scale-down setup even further we did not only use
standard tracer-pulse experiments, but also CFD simulation to model a
flow rate, where experiments could not be performed due to technical
limitations. Furthermore, the influence of SMs were investigated, as
well as changes in fluid viscosity were addressed, which was not
considered in previous work. The omittance of the SM lead to asymmetric
peaks, suggesting strong segregation of the flow. This is documented by
the contour plot of the tracer mass fraction presented in Figure S 9 and
Figure S 10. While nearly no difference in tracer profile was observed
for low viscosities there was clear segregation of the flow in PFR
without static mixer, under high viscosity conditions. This not optimal
behavior at low Re numbers could also lead to dead zone
development, which would be an explanation for the earlier tracer
passing observed during the experiments with the lower flow rate. These
results do not only provide valuable information on the setup at real
cultivation conditions, they can also be used to further optimize the
design of the scale-down reactor. In particular, Figure S 10 indicates
possible improvement of the bow geometry to reduce the segregation for
highly viscose media. Particularly, a more radial shape of the bows or
additional mixers in this section would avoid this not optimal behavior
of tracer by-passing especially at high viscosities and low Re numbers.
With the extensive knowledge gained about the setup, we were able to
investigate how a broader RT distribution would influence the
bioprocess, by removing the SM for cultivation. As reported by Limberg
et al.,[31] who compared a STR-STR with a STR-PFR
scale down setup, no significant impact of the RT distribution on
biomass and product formation could be shown. In our experiments a
possible explanation for this behavior could be that the viscosity of
the fermentation broth is increasing over time and that the broader RT
distribution will develop only at a later stage of the cultivation,
which might be too short to make a difference. Another more general
explanation could be that we and Limberg et
al.[31] followed a bulk approach, as we analyzed
samples from the whole fermentation broth. This means that on average
all the cells were the same time inside the PFR and no effect on
population average dependent parameters just as biomass yield can be
seen. Nevertheless, the heterogeneity on microbial level could still be
altered.[46] More experiments need to be done to
answer this question.
More knowledge about the conditions in industrial large-scale
bioreactors is needed as well, to adapt the scale-down setup even
further. This is pointed out in a recent review from Nadal-Rey et
al.[47] as well, where also the importance of
computational methods in this field is mentioned. We have shown that
computational methods are not only necessary to gain information about
the large scale itself, but also about the tools that should be used to
mimic these conditions. This knowledge can enable engineers and
scientists to optimize scale-down tools further. In the future even more
collaborations between industry and academia will be necessary to solve
the problems that occur during scale-up. This close connection will make
it possible to mimic the inhomogeneous conditions in industrial scale
and speed up bioprocess development, especially scale-up. The broad
flexibility offered by the introduced scale-down setup can make it to a
powerful tool for further studies in this direction.
Acknowledgement
The authors want to thank the Austrian Federal Ministry of Science,
Research and Economy, the National Foundation of Research, Technology
and Development and Boehringer Ingelheim RCV GmbH & Co KG for financial
support in the course of the Christian Doppler Laboratory for
“Production of next level biopharmaceuticals in E. coli ”. The
scientific input and support from our colleagues at the University of
Natural Resources and Life sciences as well as from our company partner
Boehringer Ingelheim RCV GmbH & Co KG is gratefully acknowledged.
Conflict of interest statement
The authors declare no commercial or financial conflict of interest.
Data availability statement: Data available in article
supplementary material
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Tables
Table 1: Summary of operating
conditions of the STR including also the mixing times determined by CFD
simulation and tracer-pulse experiments. The deviations indicate the
experimental uncertainty.