Castilho Leda R., Moraes Angela Maria (Ed.) Animal Cell Technology: From Biopharmaceuticals to Gene Therapy
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9.7 Economic aspects relevant to bioreactor selection: the
productivity factor
Most proteins produced by animal cell culture on a large scale are
innovative biopharmaceuticals, protected by patents, that have a selling
price several times higher than the production costs. Those prices are
justified by the large investments in research and development for devel-
oping a new biopharmaceutical, which are calculated to be in the range of
2 to 250 million US dollars (Savage, 2000).
The complex production facilities required for the cultivation of animal
cells, with sophisticated auxiliary services that should avoid contamination
and toxicity in the cultures, and fitted with highly automated and valida-
table equipment, are the reasons why the main cost component of these
processes is associated with equipment and other technical components
(Petrides, 2000).
That is why the main economic consequence of animal cell process
optimization is the decrease of the scale of equipment needed to meet a
given product demand. This can reduce the investment required to build a
production facility, which varies in the range of 10 to 300 million US
dollars (Petrides, 2000).
Volumetric productivity of a process is a parameter that determines the
size of the facilities needed to obtain a given annual production, thus
determining the economic efficiency of protein production based on
animal cells. Volumetric productivity, as well as the annual production,
can be calculated from Equations 19 to 22:
P
acc
¼ Vq
p
ð
X
v
dt (19)
t
proc
¼ t
prep
þ t
growth
þ t
prod
(20)
P
V
¼
P
acc
t
proc
V
(21)
P
A
¼ P
V
Vt
work
(22)
Where:
P
acc
¼ amount of product accumulated throughout a run;
t
proc
¼ process time;
t
prep
¼ time required for the preparation of the bioreactor (includes
cleaning and sterilization);
t
growth
¼ time needed for cells to grow up to the concentration desired
for the process. This time is a function of the specific growth rate of the
cell line and of the inoculation density;
t
prod
¼ duration of the production phase. In the case of cell lines that do
not require induction to express the protein product, this time period
also includes the cell growth phase;
P
V
¼ volumetric productivity of the bioreactor;
P
A
¼ annual production obtained in the bioreactor;
t
work
¼ number of working days per year (generally 300 days or 44
weeks per year).
252 Animal Cell Technology
It is important to emphasize the general character of this definition for
the volumetric productivity (P
V
), since it includes all phases of a produc-
tion cycle in a bioreactor, allowing an evaluation of the impact of
bioreactor preparation time and duration of growth and production phases
on productivity. As can be observed from Equations 19 to 22, for an
industrial bioreactor with a given volume and operation mode, the
volumetric productivity depends basically on cell concentration in the
production phase and on the specific product formation rate (q
P
).
The values of q
P
can vary significantly as a function of the expression
system used and of the recombinant protein product. In general, values in
the range of 1–30 pg cell
–1
day
–1
are reported for stably transfected cell
lines. However, Chico and Ja
¨
ger (2000) have reported values as high as
85 pg cell
–1
day
–1
for the expression of a protein using the insect cell-
baculovirus expression vector system. This value approaches the maximum
theoretical value that is expected for animal cells, which is 100 pg cell
–1
day
–1
(Ozturk, 1990).
In this way, the use of bioreactors that allow high cell density cultures is
of great importance as a tool to increase process productivity. Table 9.3
shows volumetric productivity values that can be obtained in the main
types of industrial bioreactors, as well as the typical working volume and
the cell concentration ranges that are usually attained.
As can be observed in Table 9.3, cell concentrations equal to or higher
than 10
7
cells ml
–1
are needed to attain volumetric productivities higher
than 50 mg L
–1
d
–1
. Also, it is important to note that as cell concentration
increases, the complexity of the process increases and, consequently,
bioreactor scale-up becomes limited, for example by physical limitations
of materials used in hollow-fiber cartridges.
Figure 9.19 shows typical cell concentrations reached in the main
industrial bioreactors and a comparison of these values with those found
in microbial fermentations. As can be observed, batch and fed-batch
cultivations attain dry biomass values comparable to those of continuous
cultures of microorganisms, so that mass and heat transfer capacities are
not limited for these operation modes. However, high cell density cultiva-
tion in heterogeneous bioreactors, such as hollow-fiber devices, reaches
dry biomass values similar to the maxima observed in microbial cultures.
Table 9.3 Compa rison of working parameters an d typical productivities found for the main
industrial bioreactors
Parameter Stirred-tank bioreactors
operated in batch and
fed-batch mode
Stirred-tank bioreactors
operated in perfusion
mode
Heterogeneous
bioreactors (packed-bed
or hollow-fiber) operated
in perfusion mode
Cell concentration
(cells mL
–1
)10
6
10
7
10
8
–10
9
Complexity Low Medium High
Bioreactor volume* Up to 20 000 L 100–2000 L 0.2–10 L
Productivity
(mg L
–1
day
–1
)
10–40 50–600 500–1000
*Considering only the volume of the bioreactor compartment where cells grow.
Bioreac tors for animal cells 253
Figure 9.19 also suggests that there is an optimization window available;
developing new cell culture methods that provide cell concentrations in
the range of 10
7
to 10
8
cells mL
1
could represent requirements still lower
than in microbial fermentations, but at the same time a significant increase
in the volumetric productivities of industrial animal cell processes.
As evidenced by Equation 22, the volumetric productivity (P
V
) deter-
mines the cultivation volume (V) needed to reach a desired annual produc-
tion (P
A
). Application of this equation for volumetric productivities up to
500 mg L
–1
d
–1
is illustrated in Figure 9.20 and allows an estimate of the
total culture volume required for obtaining production capacities in the
range of 1–1000 kg per year.
As a result of recent advances in the use of recombinant antibodies for
the treatment of chronic diseases and several types of cancer, a growing
number of new therapeutic antibodies is currently under development (see
Chapters 16 and 17). As a consequence, large investments in the construc-
tion of industrial animal cell facilities are being made, surpassing 1 million
liters in terms of total installed capacity. In the recent years, biotechnology
companies such as Genentech Inc. in the USA and Boehringer Ingelheim
in Germany constructed plants with a total bioreactor volume of more
than 100 000 L and production capacity higher than 1000 kg of antibodies
per year, after investments in the order of hundreds of million US dollars.
However, from the relationship between culture volume and production
capacity reported by many companies, it is possible to observe that
volumetric productivities lower than 50 mg L
–1
d
–1
are still quite usual.
High cell
density
microbial
culture
Continuous
microbial
culture
Cultures in hollow-fiber bioreactors
Perfusion cultures in stirred-tank bioreactors
Batch and fed-batch cultures
Inoculum concentration
(A)
(B)
100
10
1
0.1
10
9
10
8
10
7
10
6
10
5
Figure 9.19
Ranges of cell concentration r eached in: (A) microbial bioprocesses (concentration
expressed as gL
1
of dry biomass); (B) a nimal cell cultures (concentration
expressed as cells mL
1
), considering 10
6
cells ¼ 0.25 mg of dry biomass (adapted
from Frame and Hu, 1990).
254 Animal Cell Technology
Thus, it becomes evident that increases in the volumetric productivity up
to levels in the range of 500 mg L
–1
d
–1
could lead to a decrease of one
order of magnitude in the culture volume needed to obtain a large amount
of therapeutic proteins on an industrial scale. Therefore, the use of high
productivity bioreactors could represent savings in the range of tens of
millions of US dollars in the cost of recombinant protein production
facilities, making this technology feasible for smaller companies and for
developing countries (Lage, 1994).
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258 Animal Cell Technology
10
Monitoring and control of
cell cultures
Aldo Tonso
10.1 Introduction
Animal cell cultures can be considered, in many ways, similar to cultures
of microorganisms, such as bacteria, yeasts, and filamentous molds,
presenting several common characteristics, for instance the major variables
to be monitored and controlled. However, there are some specific culture
variables that may require different strategies. This chapter presents the
basic concepts of bioprocess monitoring and control, with emphasis on
the specific requirements of the cultures of animal cells. This will start
with a description of the general goals of process monitoring and control,
a description of the main variables involved, and finally, the most com-
monly used control strategies.
It should be noted that developments in electronics and computer
science have contributed significantly to the improvement of monitoring
culture systems and process control. However, there are still some areas
for further general improvement of cell culture bioprocess monitoring and
control, such as the estimation of cell concentration in real time. Several
basic texts may supplement this chapter: Kilburn (1991), Schu
¨
gerl (1991,
2001), and Sonnleitner (2000).
10.2 Monitoring and control: basic concepts
Any process, industrial or laboratory-based, presents a series of important
variables that represent its state. In the case of cell culture, there are the
variables related to the environment to which the cells are exposed, such as
temperature, pH, dissolved oxygen, nutrients in the culture medium, and
metabolite concentrations, as well as those related to the cell itself, such as
concentration, average size, or the profile of intracellular enzyme activ-
ities. In order to make meaningful decisions during a process it is desirable
to have at hand the greatest amount of information available about these
variables. At this point, it is important to define some concepts relative to
process monitoring and control.
The first relates to the time the information is available. As an example,
suppose we know that the glutamine concentration at a certain point of
the culture was 0.23 g/L, obtained through a chromatographic run 1 week
after the end of the culture. Such information is considered offline and can
be used if we study the influence of that substrate in cell growth or
product synthesis. However, if such information were available online, it
could be used in an automated system that could replace the nutrient loss
by medium replenishment in a fed-batch process. This would ensure that
glutamine would not be depleted from the medium. In this case, we can
say that the variable glutamine concentration is being monitored in ‘‘real
time’’ (also called ‘‘online’’).
Another important concept is whether the acquired information comes
from the process place itself, or from a sample that is removed and then
analyzed. For instance, in the study of the degree of cell aggregation, if a
sample is removed from the culture, it goes through several manipulations
before its analysis (such as dilution) and it becomes possible that the
observed aggregation does not match that present in the culture. In such
cases, the ideal would be to observe the cells through a microscope
inserted inside the bioreactor. This would be called in situ, or ‘‘in its own
place’’.
There are two basic reasons to have process information by monitoring:
(i) Better knowledge of the process itself, as part of research or in the
search for cause and effect. For instance, when the pH is below a
certain value, the growth of cells decreases. This knowledge is also
fundamental in the development of a model that would represent the
process under study (see Chapter 8).
(ii) Use of certain information, in real time, to take some control action.
For instance, based on the dissolved oxygen concentration at a certain
moment, it is possible to act on the aeration flow, to avoid limitations
to cellular metabolism.
Control refers to the actions taken in the process. A flow chart of a
typical control loop is shown, using the temperature control of a bioreac-
tor as an example.
As can be observed in Figure 10.1, information from the process (value
of the temperature measured by the sensor) arrives at the control loop,
which defines it as a ‘‘closed loop.’’ This is the case for most controllers
installed in bioreactors. If the decisions of a controller do not take into
account any of the monitored information of the process, it is called an
‘‘open loop.’’ An example of an open loop is a fed-batch process, where it
⫺
Controller
Actuator
Sensor
Process
⫹
Thermometer
PID
Bioreactor
Electric
resistance
Measured
value
Error
Set-point
Figure 10.1
Flow chart of a typical control loop showing temperature control elements: a
desired temperature value (set-point) is compared to the measured value by the
thermometer (sensor) and, based on the error measurement, a signal to the
electric resistance (actuator) is generated by the controller, that will heat up the
bioreactor (process).
260 Animal Cell Technology
is desired to keep a nutrient concentration at a certain level, but the flow
rate of the nutrient feed is fixed beforehand (a priori). As there is no
information from the process, that control system is not able to answer to
the behavior fluctuations of the cells during growth.
10.3 Particular characteristics of cell cultures
Animal cell cultures, either from mammalian or insect cells, show specific
characteristics that differ from microbial cultures (bacteria, yeasts, and
filamentous molds). Among the characteristics that most interfere with
monitoring methodologies and process control (and that will be ap-
proached with more detail in the present chapter), are the following.
(i) Slow growth, with specific rates around 10–20 times lower than
microbial cells. This has encouraged the development of bioreactors
with cell retention devices (see Chapter 11), where real-time estima-
tions of cell concentration or nutrients are often necessary. Longer
cultures (of the order of several days) require more robust sensors
(such as pH and dissolved oxygen) or the possibility of replacing
electrodes during the process. Also, any contamination has a greater
effect on the process, demanding more advanced sterility maintenance
systems.
(ii) Requirement of a solid support (substrata) for cells, in the case of
anchorage-dependent cell lines. This causes problems for the estima-
tion of cell concentration.
(iii) Mechanical fragility because of the absence of a cell wall, which is
present in most microbial cells. Animal cells are adapted to grow in
tissue structures, which provide mechanical support in vivo and
which are lost in culture. As a consequence of the fragility, alternative
methods of oxygen transfer have been developed, such as bubble-free
systems (see Chapter 9). Such systems require a suitable strategy for
monitoring and control of the variables involved in oxygen transfer.
(iv) The commonly used method of pH buffering by the manipulation of
the bicarbonate/carbon dioxide concentrations. This alters the overall
gas system control.
10.4 Main bioprocess variables
10.4.1 Temperature
Whatever the scale or method of culture (T-flask, Schott bottle, spinner
flask, or bioreactor), the temperature of the culture medium with which
the cells are in contact is always a fundamental state variable, because it
interferes with growth and the production process. However, it is a
process variable that is easy to monitor and control. On a small scale the
culture flask is usually put in a thermostatically controlled incubator,
where the measured value of a thermometer sends a sign to turn the
heating on or off (‘‘on–off control’’). In bioreactors, there are equivalent
systems, as will be seen later. Usually, however, a resistance thermometer
sensor type is used (resistance temperature detector or RTD), the electric
Monitoring and control of cell cultures 261