Trismaleimide Dendrimers: Helix-to-Superhelix Supramolecular Transition Accompanied by White-Light Emission

Article abstract of DOI:10.1002/anie.201908837

Reported here are unprecedented fluorescent superhelices composed of primary, supramolecular polymers of the opposite helical twist. A new class of functional dendrimers was synthesized by amino-ene click reactions, and they demonstrate an alternating OFF/ON fluorescence with generation growth. A peripherally alkyl-modified dendrimer displays helix-sense-selective supramolecular polymerization, which predominantly forms right-handed (or left-handed) helical supramolecular polymers in the solution containing chiral solvents. With increasing the concentration, these primary helical supramolecular polymers spontaneously twist around themselves in the opposite direction to form superhelical structures. Atomic force microscopy and circular dichroism measurements were used to directly observe the helix-to-superhelix transition occurring with a reversal in the helical direction. Exceptional white-light emission was observed during superhelix formation.

Full text of DOI:10.1002/anie.201908837

Received: 2 June 2018
DOI: 10.1002/bit.26914
Revised: 4 December 2018
Accepted: 9 January 2019
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A R T I C L E
ControlofIgGglycosylationbyinsituandreal‐timeestimation
of specific growth rate of CHO cells cultured in bioreactor
Meng‐Yao Li1
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Bruno Ebel1
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Fabrice Blanchard1
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Cédric Paris2
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Emmanuel Guedon1
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Annie Marc1
1Laboratoire Réactions et Génie des Procédés,
Université de Lorraine, CNRS, LRGP,
Vandœuvre‐lès‐Nancy, France
2Structural and Metabolomics Analyses
Platform, SF4242, Université de Lorraine,
EFABA, Vandœuvre‐lès‐Nancy, France
Abstract
The cell‐specific growth rate (µ) is a critical process parameter for antibody
production processes performed by animal cell cultures, as it describes the cell
growth and reflects the cell physiological state. When there are changes in these
parameters, which are indicated by variations of µ, the synthesis and the quality of
antibodies are often affected. Therefore, it is essential to monitor and control the
variations of µto assure the antibody production and achieve high product quality. In
this study, a novel approach for on‐line estimation of µ was developed based on the
process analytical technology initiative by using an in situ dielectric spectroscopy.
Critical moments, such as significant µ decreases, were successfully detected by this
method, in association with changes in cell physiology as well as with an accumulation
of nonglycosylated antibodies. Thus, this method was used to perform medium
renewals at the appropriate time points, maintaining the values of µ close to its
maximum. Using this method, we demonstrated that the physiological state of cells
remained stable, the quantity and the glycosylation quality of antibodies were
assured at the same time, leading to better process performances compared with the
reference feed‐harvest cell cultures carried out by using off‐line nutrient
measurements.
Correspondence
Emmanuel Guedon, Laboratoire Réactions et
Génie des Procédés, Université de Lorraine,
CNRS, LRGP, F‐54518 Vandœuvre‐lès‐Nancy,
France.
Email: emmanuel.guedon@univ-lorraine.fr
Funding information
French Ministry of Research; French National
Research Agency
K E Y W O R D S
antibody glycosylation, cell culture, in situ dielectric spectroscopy, real‐time monitoring, specific
cell growth rate
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1
INTRODUCTION
quality attribute (CQA) for mAb production processes that should be
taken into consideration in all manufacturing steps, as outlined in the
The need for biopharmaceutical protein products, in particular
recombinant monoclonal antibodies (mAbs), has been constantly
increasing in the last decade (Walsh, 2014). Chinese hamster ovary
(CHO) cells are the most commonly used cell lines for mAb
production due to their capability of performing human‐like
posttranslational modifications (PTMs; Cole, Demont, & Marison,
2015). Glycosylation is one of the most important PTMs for mAbs, as
it impacts effector functions, pharmacokinetics, antigenicity, safety,
stability, and solubility of the mAbs produced (Tharmalingam, Wu,
Callahan, & T. Goudar, 2015). Therefore, glycosylation is a critical
quality by design (QbD) approach initiated by the Food and Drug
Administration (FDA) in 2004(ICH, 2017). The QbD initiative
emphasizes the importance of using process analytical technologies
(PAT) to monitor and control in real‐time the critical process
parameters (CPPs) which may influence the product CQAs in
biopharmaceutical production processes.
Among various CPPs of mAb production bioprocesses, viable cell
density (VCD) is undoubtedly one of the most important, as it is often
closely related to the cell growth, the mAb production, and overall
process performances (Lee, Carvell, Brorson, & Yoon, 2015). With
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Biotechnology and Bioengineering. 2019;116:985-993.
wileyonlinelibrary.com/journal/bit
© 2019 Wiley Periodicals, Inc.
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increasing production potential of these bioprocesses, reliable in‐line
and real‐time measurements of VCD are more and more required.
Various PAT tools have been proposed to monitor VCD in real‐time,
including dielectric spectroscopy (Ansorge, Esteban, & Schmid, 2007;
Cannizzaro, Gügerli, Marison, & von Stockar, 2003; Courtès, Ebel,
Guédon, & Marc, 2016; Opel, Li, & Amanullah, 2010; Yardley, Kell,
Barrett, & Davey, 2000), near‐infrared and Raman spectroscopies (Abu‐
Absi et al., 2011; Cervera, Petersen, Lantz, Larsen, & Gernaey, 2009), in
situ microscopy (Guez, Cassar, Wartelle, Dhulster, & Suhr, 2004),
acoustic resonance densitometry (Kilburn, Fitzpatrick, Blake‐Coleman,
Clarke, & Griffiths, 1989), and soft‐sensor‐based approaches (Kiviharju,
Salonen, Moilanen, & Eerikäinen, 2008). Among these technologies,
dielectric spectroscopy is probably one of the most reliable methodol-
ogies to monitor VCD due to its simplicity, robustness, and its capability
of in situ fast signal acquisition which is noninvasive and nondestructive
for cell cultures. The dielectric spectroscopy is also insensible to air
bubbles and cell debris which often cause problems for other methods
(Justice et al., 2011). Dielectric spectroscopy is based on measurements
of the ability of viable cells to store electrical charge as a function of the
frequency of the applied electrical field. The basic theoretical back-
ground of dielectric properties of biological cells was described
elsewhere (Yardley et al., 2000). However, on‐line monitoring of VCD
alone gives an incomplete vision of cell growth and cell physiological
state. To date, it remains challenging to monitor in real‐time the cellular
physiological state due to the complexity of animal cell system (Henry,
Kamen, & Perrier, 2007).
important process parameter allowing to detect early cell physiological
state changes. On‐line estimation of µ by using dielectric spectroscopy
leads to better process control for the feed‐harvest cultures, and as a
result, the antibody quantity and the quality concerning the glycosylation
were assured.
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2
MATERIALS AND METHODS
Cell culture
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2.1
A genetically modified DG44 CHO cell line (CHO M250‐9) kindly
provided by Bioprocessing Technology Institute (Singapore) was used in
this study. The culture medium used during this study was a protein‐
free medium mixture consisting of a 1:1 ratio of PF‐CHO (GE
Healthcare, Velizy‐Villacoublay, France) and CD‐CHO (Thermo Fisher
Scientific, Villebon‐sur‐Yvette) supplemented with 4 mM L‐glutamine
(Sigma‐Aldrich, Saint‐Quentin Fallavier), and 0.1% Pluronic F‐68
(Sigma‐Aldrich). Cells were initially cultured in shake flasks (Thermo
Fisher Scientific) and were incubated in an agitated incubator (Kuhner,
Birsfelden, Switzerland) at 37°C, 5% CO2, 80% humidity with an
agitation rate of 70 rpm. Bioreactor cell culture of CHO cell were
performed in 2 L bench‐top bioreactors (Pierre Guérin, Mauzé‐sur‐le‐
mignon, France). The exponentially growing cells were seeded at about
3 × 105 cell/ml in 1.5 L of working volume. Culture temperature was
maintained at 37°C and the pH was regulated at 7.2 using 0.5M sodium
hydroxide and CO2. Dissolved oxygen was controlled at 50% of air
saturation by delivering oxygen through the sparger. Agitation rate was
fixed at 90 rpm throughout the culture.
The specific cell growth rate (µ) is one of the most direct indicators of
cell physiological state. When cells are in their active growth stage (µ
close to its maximum, µmax), they are able to produce enough energy for
energy‐consuming actions such as cell division and recombinant protein
synthesis. On the contrary, under nutrients depletion, toxins accumula-
tion, or when other physicochemical stresses appear, energy production
of cells may be negatively influenced, resulting in a decrease in µ (Kondo
et al., 2000). In this case, not only the quantity but also the quality of the
recombinant proteins produced could be affected, since noncorrectly
performed PTMs associated to mAb production could appear during the
process (Kochanowski et al., 2008). Therefore, µ appears as an essential
parameter for mAb production bioprocesses, which should be carefully
monitored and controlled throughout the process to ensure the quantity
and the quality of the mAb synthesized. Classically, µ is often calculated
indirectly from off‐line measurements of cell densities, or by macroscopic
kinetic modeling approaches, which could be inaccurate or provide only
estimated values, resulting in delayed information which is not adequate
for control strategies (Xiong, Guo, Chu, Zhuang, & Zhang, 2015).
In the present work, a novel approach of real‐time estimation of µ
was developed based on on‐line monitoring of VCD by using in situ
dielectric spectroscopy and mass balance equations. This approach was
then applied on a recombinant immunoglobulin G (IgG) production
process performed by CHO cells cultured in a bioreactor in feed‐harvest
mode to investigate its potential for the improvement of the process
control. Quality of the IgG produced was assessed by analyzing the
macroheterogeneity of IgG glycosylation which is the glycosylation site
occupancy. Overall, the results of this study demonstrate that µ is an
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2.2
Off‐line analysis
Samples were taken 3–4 times per day. VCD, viability, and average
diameter of cells were measured by Vi‐CELL™ cell counter (Beckman
Coulter, Villepinte) on the basis of trypan blue dye exclusion of the
viable cells. An automated photometric analyzer Gallery™ (Thermo
Fisher Scientific) was used to measure glucose, lactate, glutamine,
ammonium ions, and IgG titer.
For glycosylation analysis, IgGs were precipitated by cold acetone
and were incubated at −20°C for 2 hr. Centrifugation for 5 min at
13,000g was carried out to recover the IgGs. Recovered IgGs were
dissolved in 500 µl of 50 mM ammonium hydrogen carbonate buffer
and were then heated to 95°C for 10 min to be denatured. Next,
20 µl of prepared trypsin solution (Sigma‐Aldrich) were added in the
cooled IgG samples at a concentration of 1 g/L. Samples were
incubated overnight at 37°C, and the reaction was stopped by adding
15 µl of formic acid (98%; Sigma‐Aldrich). Analysis of peptides
compounds from IgG hydrolysate was performed on a UHPLC‐MS
system (Thermo Fisher Scientific) connected to a photodiode array
detector and a linear trap quadrupole mass spectrometer equipped
with an atmospheric pressure ionization interface operating in
positive electrospray mode (ESI+). Details of the mass spectrometer
(MS) analyses have been described previously (Li et al., 2018).

LI ET AL.
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The abundance of nonglycosylated IgG form was obtained by
dielectric spectroscopy; the average cell diameter d (m) measured
off‐line by Vi‐CELL™ cell counter.
dividing its MS peak integral to the total MS glycopeptide signal.
Concentration of nonglycosylated IgGs was calculated by multiplying
the percentage of nonglycosylated form of digested IgGs by total IgG
titer measured by Gallery™ device.
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2.4
Statistical analysis
The accuracy of the developed method was evaluated by the root
mean square error (RMSE), calculated as follows:
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2.3
In situ dielectric spectroscopy
n
i=1
2
ˆ
∑
(yi − yi)
The cell culture permittivity was measured in real‐time using a
sterilizable capacitance probe connected to an Evo 200 iBiomass
system (Hamilton, Bonaduz, Switzerland) at a working frequency of
1,000 kHz. Measurements were performed every 12 min and the
baseline was set by recording the permittivity of the cell‐free
medium, before cell seeding. The permittivity can be related to VCD
using a linear relationship (Courtès et al., 2016):
RMSE =
.
(7)
n
ˆ
where yi is the off‐line measured values (VCD or µ), yi is the on‐line
n
predicted values (VCD or µ), and the number of samples under
consideration.
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3
RESULTS AND DISCUSSION
(1)
ε = α × VCD + β.
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3.1
Cell growth, metabolism, and IgG production
α
In this study equals to 13 and β equals to 3.
kinetics in batch culture
Based on VCD on‐line estimation and mass balance equation, µ
can be obtained in real‐time by calculating the slope of a moving
window linear regression on the logarithmic values of VCD in an
interval of time (Equation (2)).
To assess the influence of cell growth on IgG glycosylation during
batch cultures, CHO cells were cultured in a 2‐L bioreactor, and cell
density, viability, nutrients and by‐products concentration, IgG
production, and IgG glycosylation were monitored off‐line. In addition,
the off‐line values of µ were calculated at the end of the culture using
VCD values measured off‐line. Although cells displayed excellent
viabilities (>95%) until 127 hr, the value of µ started to decrease after
about 100 hr of culture (Figure 1a). This decrease of µ is probably due
to the glutamine depletion (Figure 1b), since glutamine, in addition to
its energetic substrate function, is proved to be one of the most
important key‐precursors in several biosynthetic pathways of CHO
cells. Its availability can affect the purine and pyrimidine synthesis rate
(Hayter et al., 1991). Alternatively, it is possible that the accumulation
of lactate and ammonia inhibits the growth of cells according to other
authors (Glacken, Fleischaker, & Sinskey, 1986; Hassell, Gleave, &
Butler, 1991; Yang & Butler, 2000). However, in our case, the
concentration of ammonium ions and lactate were relatively low (<5
and <20 mM, respectively; Figure 1b), which were shown to have little
or negligible effect on the growth of cells (Hayter et al., 1991; Lao &
Toth, 1997). The intracellular conductivity (σi), which measures the
ability of the cell’s intracellular environment to conduct electrical
current, was relatively stable during the first 95 hr of culture, followed
by a light decrease until 103 hr before a significant increase after this
period. This observation indicates potential changes in cell physiolo-
gical state, which is demonstrated by the variations of σi (Figure 1c). As
suggested by some authors, the decrease of this parameter often
corresponds to nutrients limitations and depletions (Ansorge, Esteban
& Schmid, 2009; Opel et al., 2010). In our case, the decrease of σi is
associated with glutamine exhaustion. In addition, the final increase of
σi was reported to be linked to cell death since alteration in cell
membrane integrity could result in the entry of medium ions into cells,
leading to significant changes in the intracellular environment (Opel
et al., 2010). As for IgG production, Figure 1d shows that almost all the
dVCD
dt
1
∆ln (VCD)
∆t
µ =
×
=
.
(2)
VCD
To reduce the noise of data collected from dielectric spectro-
scopy, moving average filters were used to have a more stable on‐line
µ estimation. Meanwhile, the distance residual (R) was calculated in
real‐time to evaluate the variations of the µ values with respect to
the value of µmax (Equation (3)).
R = (µmax − µt)2 .
(3)
Here µmax is a predefined value of the maximum value of µ at the
exponential growth phase obtained from previous cultures, and the µt
is the on‐line estimation of µ at the instant t.
Moreover, using multifrequency dielectric measurements and off‐line
cell diameter measurements, a cell‐specific dielectric property parameter,
the intracellular conductivity (σi) can be calculated with Equations (4-6):
3
4
3
d
2
⎛
⋅π ⋅
⎝
⎞
⎠
pm
=
⋅VCD.
(4)
(5)
σ
σa =
.
(1−pm)1.5
8⋅π⋅fc⋅∆ε⋅σa
σ =
i
.
(6)
9 ⋅pm⋅σa−4⋅π ⋅f ⋅∆ε
c
Where pm is the viable biovolume fraction, and σa (S/m) is the
conductivity of the medium. The values used in these calculations are
the critical frequency fc (Hz), the magnitude of the β‐dispersion drop
∆ε (F/m), and the suspension conductivity σ (S/m) measured by the

988
LI ET AL.
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(a)
(c)
(b)
(d)
FIGURE 1 (a) Kinetics of cells growth: viable cell density (●), viability (◇), and specific growth rate (µ; △). (b) Nutrients consumption and
by‐products accumulation: glucose ( ), glutamine (■), lactate (○), and ammonium ions (□). (c) Cell physiological state: intracellular conductivity
(σi; ). (d) IgG production: total IgG (▲), glycosylated IgG ( ), nonglycosylated IgG ( ) throughout a representative batch culture of CHO cell
in bioreactor. IgG: immunoglobulin G
IgGs produced were fully glycosylated until 100 hr of culture, then
nonglycosylated IgGs began to accumulate in cell culture and reached
about 30% of the total IgGs, resulting in an important product quality
decrease.
was measured every 12 min during cell cultures, and the on‐line VCD
was calculated from permittivity measured, by using linear
a
correlation. Estimation of on‐line VCD is presented alongside with
off‐line VCD measurements in Figure 2a. The values of VCD
predicted were in good agreement with the off‐line VCD values
(RMSE = 3.2 × 105 cell/ml).
It seems that the diminution of µ coincided with the accumulation
of nonglycosylated IgG form, which could be explained by an
insufficient energy production of CHO cells due to the glutamine
exhaustion after about 100 hr, as suggested by Xie and Wang (1996).
This decrease of µ led to cell physiological state changes as showed
by σi calculations. Consequently, the recombinant protein production
was also affected, resulting in a loss of the ability for cells to perform
correctly the PTMs for the proteins produced because of less
available energy (Hayter et al., 1993). In this study, we demonstrated
that the global glycosylation of IgGs was influenced by the growth of
cells, since the nonglycosylated IgG level increased abundantly after
the decrease of µ, leading to a significant decrease in the final
product quality at the end of the culture.
To calculate the value of µ, VCD predicted was first converted to
a logarithmic form (ln(VCD)), then the slope of the moving window
linear regression of ln(VCD) as a function of culture time was
calculated to obtain the value of µ, as illustrated by Equation (2).
However, data collected from capacitance probe presented high level
of noise, so smoothing filters were applied to make data more stable
for µ estimation. In this study, two moving average filters were
implemented to smooth the VCD predicted and the ln(VCD)
calculated. The on‐line values of µ estimated are presented in
Figure 2b, which are validated by the off‐line values of µ calculated at
the end of the culture, with a RMSE equal to 0.008 hr−1, indicating
that the order of magnitude and the general trends of µ were
correctly predicted, which allows validating our developed method.
Variations of µ, with respect to µmax, estimated in real‐time were
Therefore, as a direct indicator of the cell physiological state, µ is
a key parameter of cell culture processes and should be accurately
monitored and controlled as rapidly as possible, to limit the
physiological changes of cells, and thus make sure that the
glycosylation of IgGs produced is not altered.
highlighted by the calculation of
R (Equation (3); Figure 2b).
Nevertheless, these variations of µ could be caused by physiological
changes of cells and also signal instabilities. The signal instabilities
were evaluated during the middle stage of exponential cell growth
phase (between 40 and 70 hr in this study). The average values and
the derivatives of R were calculated and were defined as the
threshold sinceµ was supposed to be constant during this phase;
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3.2
In situ and real‐time estimation of µ
To estimate in real‐time the value of µ, an in situ capacitance probe
connected with dielectric spectroscopy was used. The permittivity

LI ET AL.
989
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(a)
(b)
FIGURE 2 (a) On‐line VCD prediction: VCD predicted ( ) versus VCD measured ( ). (b) On‐line µ estimation: µ estimated (•) versus µ
calculated off‐line ( ), on‐line calculation of R ( ), and on‐line critical moment ( ) detection (blue dotted line). VCD: viable cell density [Color
figure can be viewed at wileyonlinelibrary.com]
therefore, the variations of
R
were mainly caused by signal
decrease in cell energy production, or in changes in cell physiological
states, that may affect the culture process and the production of
antibodies as well as their glycosylation.
instabilities. When both the average values and the derivatives of R
exceeded the threshold value during at least 36 min (three recorded
dielectric signals), the critical moment when the cell growth begins to
slow down due to cell physiological changes can be determined. Here,
the critical moment was detected at 102 hr (blue dotted line). It
should be noticed that after the critical moment has been identified,
the culture, and in particular the physiological state of cells, could be
altered as long as no interventions have been carried out.
These results demonstrated the potential of using on‐line
estimation of µ to have better understandings of animal cell culture
bioprocesses, which could lead to a better process control by rapid
interventions at the critical moment to maintain an appropriate cell
physiological state.
Three feed‐harvest cell cultures were carried out in this study
using different strategies for medium renewals. The first cell culture
using an off‐line strategy was designed to simulate late medium
renewals after total glucose and glutamine depletion (Figure 3b, left).
For the second strategy, medium renewals were performed at the
time when the concentration of glucose or glutamine reached their
threshold (5 and 1 mM, respectively; Figure 3b, middle), to simulate
early medium renewals. These concentrations were set up as more
than 10 times of their Monod’s half rate constants for the cell line
studied (Xing, Bishop, Leister, & Li, 2010). The last cell culture was
performed using real‐time estimations of µ, the medium renewals
were carried out each time when a critical moment was detected by
the on‐line developed method (Figure 3, right).
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3.3
Application of the on‐line estimation of µ in
feed‐harvest cell cultures
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3.3.1
Feed‐harvest cell culture using off‐line
To demonstrate the interest of using on‐line estimations of µ in the
animal cell process control, the approach described previously in this
study was used to control feed‐harvest cell cultures. This culture mode
could extend the duration of the cell culture, increase the antibody
production level, and could also maintain an appropriate cells physiolo-
gical state associated with fully glycosylated IgG production by restoring
the nutrients and removing the cell wastes at appropriate moments. In
this study, the feedings were performed by punctual medium renewals of
two‐thirds of total medium volume each time. Classically, medium
renewals are usually carried out at fixed time points or by off‐line
measurements of nutrients (glucose and glutamine) with little considera-
tions of cell physiological states. However, without a perfect knowledge
of cell metabolism, it is extremely difficult to predict the exact moment
when medium renewals are needed. Although using off‐line measure-
ments could help in understanding cell metabolic behaviors, there would
still be risks of missing the moment for efficient medium renewals, as off‐
line measurements are performed only 2–3 times per day and the
kinetics of nutrients consumption could vary rapidly especially at high cell
concentrations. Consequently, when medium renewals are not performed
at the right time, there could be either nutrients depletion, resulting in a
strategy with late medium renewals
In the first cell culture process, two medium renewals were
performed at 159 and 215 hr (red lines), when off‐line measurements
indicated that there was no more glucose, nor glutamine in the
culture medium (Figure 3, left), leading to
a total cumulative
production of 1 g IgG at the end of the culture (312 hr; Figure 4).
However, on‐line monitoring of µ and the calculation of R showed
that the critical moments (blue dotted lines) were 130, 200, and
250 hr of culture, respectively, (Figure 3, left). As highlighted by the
calculation of R, the value of µ began to decrease significantly after
every critical moment, as nutrients depletions could cause nonsuffi-
cient energy production in cells to support the cell exponential
growth and an appropriate cell physiological state for fully
glycosylated IgG production. Indeed, as showed in Figure 5a, the σi
of the cells increased after each critical moment, and decreased after
the medium renewals, indicating that changes have appeared in cell
physiological state. Moreover, the nonglycosylated form of IgG
increased dramatically after the critical moments (Figure 6a). Similar
phenomenon has been observed in many other studies, where

990
LI ET AL.
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(a)
(a)
(b)
(a)
(b)
(b)
(c)
(d)
(c)
(d)
(c)
(d)
FIGURE 3 Kinetics of feed‐harvest cell cultures using off‐line late (left), off‐line early (middle), and on‐line (right) medium renewal strategies.
(a) Viable cell density (●) and IgG titer (△). (b) Glucose ( ) and glutamine (■) concentration. (c) On‐line µ estimation (•) and off‐line µ calculated
(
). (d) On‐line R calculation ( ) and critical moments detected ( ). Blue dotted lines indicate critical moments and red solid lines indicate
moments of medium renewals. IgG: immunoglobulin G [Color figure can be viewed at wileyonlinelibrary.com]
2008). Therefore, when there were alterations in cell physiological
state as indicated by the decreases of µ and confirmed by the
variations of σi, the nonglycosylated antibody level increased,
although this level was reduced slowly after medium renewals.
|
3.3.2
Feed‐harvest cell culture using off‐line
strategy with early medium renewals
In another feed‐harvest cell culture, three medium renewals were
performed when the concentration of glucose or glutamine reached 5 or
1 mM, respectively. As indicated by the on‐line calculation of µ, there
was no critical moment detected until about 365 hr of cell culture,
suggesting that the medium renewals were performed too early, before
the critical moments when they become necessary. Although the values
of µ remained relatively stable all along the cell culture, short lag phases
can be observed each time after the medium renewal. Consequently,
the values of µ dropped immediately after medium renewals and
regained its maximum value about 10 hr after (Figure 3c, middle),
indicating that this phenomenon could be considered as a sign of an
instable cell physiological state during these periods. This hypothesis
can be confirmed by the decreases of σi every time when a medium
renewal was performed (Figure 5b). Concerning the IgG production,
whereas there was no negative effect for the IgG glycosylation as the
level of nonglycosylated IgG remained low all along the cell culture, IgG
produced in this culture appeared much lower in quantity and
FIGURE 4 Cumulated antibody production in feed‐harvest cell
cultures using off‐line late ( ), off‐line early ( ), and on‐line (
medium renewal strategies. IgG: immunoglobulin G [Color figure can
be viewed at wileyonlinelibrary.com]
)
changes in protein glycosylation occurred at the end of the
exponential cell growth phase (Castro, Ison, Hayter, & Bull, 1995;
Hooker et al., 1995; Liu et al., 2014). They suggested that the
depletion in nutrients or in glycosylation precursors in the medium
could be responsible for the change in the glycosylation pattern.
However, another study suggested that protein glycosylation was not
dependent on limitation in glucose, nor glutamine, but highly
dependent on the energetic status of the cells (Kochanowski et al.,

LI ET AL.
991
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FIGURE 5 Profile of cell intracellular conductivity (σi) in feed‐harvest cell cultures using off‐line late (a), off‐line early (b), and on‐line (c)
medium renewal strategies. Blue dotted lines indicate critical moments and red solid lines indicate moments of medium renewals [Color figure
can be viewed at wileyonlinelibrary.com]
accumulated in a longer period, with a total of 0.74 g produced in
422 hr. This could be explained by the fact that the medium renewals
were performed too early, and cells did not have enough time for cell
division before being diluted, which may cause the lag phases.
Therefore, with less cells and longer lag phases, the IgG productions
were negatively affected in these experimental conditions.
importantly, the quality of the produced IgG was maintained, as the
level of nonglycosylated form of IgG produced was kept low (~5%)
throughout the culture using this on‐line strategy (Figure 6c).
Moreover, it is noteworthy that the critical moment would also
be a good indicator for final culture harvesting, as it allows to harvest
the cell culture before the accumulation of nonglycosylated
antibodies due to the final cell death stage. Indeed, for the cell
culture using on‐line strategy if the harvest is performed at that
moment, then the final level of the nonglycosylated IgG would be
about 2% of the total IgG produced, leading to 0.79 g IgG correctly
glycosylated. However, for cell cultures using off‐line strategies, the
final harvest could only be done based on off‐line measurements of
the cell viability. Therefore, for cell cultures using off‐line late and
early strategies, the final harvest would be performed at 274 and
382 hr when cell viability was inferior to 80%, which would lead to
only 0.69 and 0.55 g correctly glycosylated IgG, respectively.
Using on‐line estimation of µ, these results suggest that when the
growth rate of cells is kept stable by medium renewals, correctly and
timely performed, both the quantity and the quality of the antibody
produced can be assured, leading to better process performances
with minimum operator intervention.
|
3.3.3
Feed‐harvest cell culture using on‐line
estimation of µ
The developed approach of on‐line estimation of µ was used in a
feed‐harvest culture to perform medium renewals at critical
moments, at about 110, 160, and 200 hr of the culture, as indicated
by the on‐line calculation of R (Figure 3b, right). By using this on‐line
strategy, the decreases of µ were limited by timely performed
medium renewals, and the value of µ remained nearly constant during
the whole culture process before the final cell death phase.
Moreover, there was no observation of lag phases after each medium
renewal, and the values of µ remained close to its maximum.
Consistently, a constant value of σi was maintained all along the cell
culture before the final death phase (Figure 5c), indicating that cell
physiological state remained stable, which led to better process
performance regarding antibody quantity as well as antibody quality.
As demonstrated in Figure 4, the total cumulative production of IgG
was 0.96 g in 310 hr, which was more important than that produced
in the cell cultures using off‐line (early) strategy, and similar to that
produced in the cell culture using off‐line (late) strategy. Most
|
3.3.4
Statistical analysis
As for statistical analysis, the RMSE of VCD prediction for the three
feed‐harvest strategies (off‐line late, off‐line early, and on‐line) was
3.4, 2.7, and 2.6 × 105 cell/ml, respectively. These low relative errors
(a)
(b)
(c)
FIGURE 6 Relative abundance of nonglycosylated IgG produced in feed‐harvest cell cultures using off‐line late (a), off‐line early (b), and on‐
line (c) medium renewal strategies. Blue dotted lines indicate critical moments and red solid lines indicate moments of medium renewals. IgG:
immunoglobulin G [Color figure can be viewed at wileyonlinelibrary.com]

992
LI ET AL.
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parameters in mammalian cell culture bioreactors using an in‐line
Raman spectroscopy probe. Biotechnology and Bioengineering, 108(5),
1215–1221.
showed good prediction capability of dielectric spectroscopy for the
VCD values. The RMSE of the on‐line estimation of µ was 0.01, 0.007,
and 0.015 hr−1 for cell cultures using off‐line late, off‐line early, and
on‐line strategies, respectively. The general trends of the on‐line and
off‐line values of µ were in agreement, allowing the identification and
the confirmation of the critical moments.
Ansorge, S., Esteban, G., & Schmid, G. (2007). On‐line monitoring of
infected Sf‐9 insect cell cultures by scanning permittivity measure-
ments and comparison with off‐line biovolume measurements.
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Ansorge, S., Esteban, G., & Schmid, G. (2009). Multifrequency permittivity
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1002/btpr.347
|
4
CONCLUSION
Cannizzaro, C., Gügerli, R., Marison, I., & von Stockar, U. (2003). On‐line
biomass monitoring of CHO perfusion culture with scanning dielectric
spectroscopy. Biotechnology and Bioengineering, 84(5), 597–610.
https://doi.org/10.1002/bit.10809
Cell‐specific growth rate (µ) is a key parameter for antibody‐
producing animal cell culture processes which reflects the cell
physiological state. To optimize the antibody production, and
especially, to ensure the quality of the final product, µ should be
carefully monitored and controlled in real‐time. In this study, a
method of on‐line estimation of µ was developed based on in situ
dielectric spectroscopy measurements. As a result, the variations of µ
were monitored in real‐time using various mathematical methods to
process these data, allowing rapid detection of the critical moment
when µ decreased significantly. This on‐line detection of the critical
moment was successfully implemented in feed‐harvest cell cultures,
and µ was maintained around its maximum value by performing
medium renewals at critical moments. This on‐line strategy allowed
to maintain an appropriate cell physiological state. Consequently, a
better process performance was obtained with both the quantity and
the quality (concerning the glycosylation) assured for the antibody
produced. When harvested at a critical moment, the nonglycosylated
antibody level was kept minimum, leading to the highest recovery of
the antibody correctly glycosylated compared with the cell cultures
performed with off‐line strategies. The method of on‐line estimation
of µ shows great potentials for antibody production bioprocesses,
and future efforts should be aimed at the implementation of this on‐
line method to fully automated feedback control scheme in animal
cell culture processes for antibody quality control.
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ACKNOWLEDGMENT
The authors thank the French Ministry of Research and the French
National Research Agency (ProCell‐In‐Line Project) for funding
Mengyao Li.
Hayter, P. M., Curling, E. M. A., Gould, M. L., Baines, A. J., Jenkins, N.,
Salmon, I., …, Bull, A. T. (1993). The effect of the dilution rate on CHO
cell physiology and recombinant interferon‐γ production in glucose‐
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CONFLICTS OF INTEREST
Henry, O., Kamen, A., & Perrier, M. (2007). Monitoring the physiological
state of mammalian cell perfusion processes by on‐line estimation of
intracellular fluxes. Journal of Process Control, 17(3), 241–251. https://
doi.org/10.1016/j.jprocont.2006.10.006
The authors declare that there are no conflicts of interest.
ORCID
Hooker, A. D., Goldman, M. H., Markham, N. H., James, D. C., Ison, A. P.,
Bull, A. T., …, Jenkins, N. (1995). N‐glycans of recombinant human
interferon‐γ change during batch culture of chinese hamster ovary
cells. Biotechnology and Bioengineering, 48(6), 639–648. https://doi.org/
10.1002/bit.260480612
Emmanuel Guedon
http://orcid.org/0000-0001-8758-3779
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How to cite this article: Li M‐Y, Ebel B, Blanchard F, Paris C,
Guedon E, Marc A. Control of IgG glycosylation by in situ and
real‐time estimation of specific growth rate of CHO cells
cultured in bioreactor. Biotechnology and Bioengineering.
2019;116:985–993. https://doi.org/10.1002/bit.26914
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