Seth Rodgers - High Throughput Cell Culture Platform for Bioprocess Optimization.ppt

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A high throughput cell culture platform for
bioprocess optimization
Seth Rodgers, CTO Bioprocessors
CPAC Rome
March 20, 2007

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© 2005 BioProcessors • http://www.bioprocessors.com • info@bioprocessors.com • (781) 935-1400
Outline
• The role of model systems in process understanding
• A scale-down model bioreactor and the data sets we get now
• Challenges remaining – especially the data sets we’d like to get

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After discovery comes development, lots and lots of it!
Expression
System
Development
Flasks
Clone
Evaluation
Media
Development*
Process
Optimization**
Note : ** Represent iterative processes
Source : Nature Biotechnology Vol. 22 (11) 2004
• Screen and
select the
highest
producing and
most stable
clone
• Develop optimal
growth and
production
media for each
cell line
• Optimize
conditions for
biomanufacturing
process in a
“scale-down”
version
Scale Up
• Scale up
process for use
in large
bioreactors for
production of
therapeutic
• Identify target,
isolate gene,
and develop
expression
system
• Knowing gene for the protein you want is great, but what cell line to use? What
clone form that cell line is best. 100s of possibilities!
• 60 or more nutritional components in culture media, how many combinations?
When to feed them? Inducers, promoters?
• What temperature? What oxygen level? CO2? pH any shifts? When to harvest?
• A strategy of multi-factorial design is the natural way to attack this type of
problem, but is difficult to execute in cell culture because the parameters interact
strongly-requiring a lot of experiments. This means models!

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The role of model systems
• Here, the data is the product, faithful representation of process equipment is the
goal
• Experiments with the systems that provide the best data, and the most
understanding, i.e. production bioreactors themselves, are very time consuming
and expensive.
• Model systems are universally used, but represent a compromise: reduced time
and expense in exchange for imperfect data, which leads to imperfect
understanding
• The same cost vs. data quality trade off that restricts experimentation in plant
scale equipment often dictates the choice of model system
Process
understanding
Model systems: Data is the product
Real system:
The API is the product

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Can current models provide deeper process understanding?
• I can compromise on quality
to a point, but only so far. The
data still has to tell us what
we want to know
– Are we at optimum?
– Where to go next?
• But we need to know NOW!
• And we are going to need to
know more soon!
– QbD ideas of variance
identification and
reduction.
– Statistical process control
– Follow-on biologics
• Particular challenge with animal
cells. (long experiment times,
sensitive to culture conditions)
• A new model system could be
very helpful
Throughput
Experimental
capacity per
researcher
Quality
Ability to predict manufacturing bioreactor
performance
Well
plate
1’s
10’s
100’s
1000’s
Flask
Low High
High Quality, High
Quantity
Bioreactor

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Some High-Throughput Cell Culture System Requirements
• Deliver meaningful scalable data
• Sustain cells, control temperature, O2, CO2, pH, agitation
• Maintain sterility
• Monitor cell density, pH, DO, metabolites, product titer
• Operate with accuracy and precision and provide control of process
parameters comparable to bench top bioreactor systems
• Automatic operation with minimal operator supervision
• Integration with tools for designing experiments and handling data

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SimCell MicroBioreactor Array
• 6 micro-bioreactors per array.
• Working volume: ~700 µL.
• Fluidic ports and channels for
inoculation, feeds, pH adjustment and
sampling.
• Culture monitoring of biomass (OD),
pH (immobilized sensors) and DO
(immobilized sensors) by optical
interrogation of micro-bioreactors.
• Proprietary gas permeable materials
result in kLa ~ 10 hr-1 for oxygen and ~
25 hr-1 for CO2.
• Experimental factors such as media
composition, inoculation density, pH
and feeds can be adjusted at the
micro-bioreactor level.
• But sensing through thin plastic
windows can be a challenge!

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SimCell Automated Management System
• Incubators
– One to five per system.
– T, CO2, O2 and agitation control.
• Sensing module
– Total biomass by OD.
– pH by immobilized sensors.
– DO by immobilized sensors.
• Sampling module
– Sample removal to well plate.
– Capable of dilution with single diluent
(PBS).
– Capable of volumetric dilution or
dilution to specific cell density in well
plate or MBR.
• Dispensing module
– One to eight pumps.
– Real-time mixing at point of delivery.
– Fluid sources may be swapped in
between cycles for increased
capacity.

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SimCell Automated Management System (SAMS)

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0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
0
1x10
6
2x10
6
3x10
6
4x10
6
5x10
6
6x10
6
7x10
6
8x10
6
9x10
6
1x10
7
biomass
OD633nm
OD calibration curve
SimCell™ On-Line Measurements: Cell Density
• Measured using Optical Density at 633 nm on Sensor Station
– OD is linear to 2.2
– Working OD range is extended by dynamic neutral density filtering
– accurate measurements up to 50M cells/ml have been demonstrated
• OD vs. cells/ml curve is specific to cell type
• OD yields total intact cells: live + dead
• Error bars are +/- 1 std. dev. (+/- 16% variance)
• Inteferences matter how to
compensate for cell size?
aggregation?
• A better measurement might also tell
us how many live and how many
dead – dielectric spectroscopy?

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Measurements: Immobilized pH Sensors
0 2 4 6 8 10
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
CD-CHO
media 1
media 2
media 3
media 4, w/phenol red
curve fit: CD-CHO data
pH
fluorescent ratio
• Response is independent of media
• Precision is < 0.06 (±3 std. dev.) over
the pH range 6.0 – 8.0
manual pH I2/I1 (R) precision
+/- 3 std. dev.
5.98 0.76615 0.02
6.40 1.58483 0.01
6.76 2.76599 0.02
6.91 3.44210 0.02
7.26 5.41638 0.03
7.91 8.96870 0.06
media #1
• pH Sensor Composition
– Hydrogel (Water-swellable polymer)
– Covalently bound dye fluorescent pyrene derivative.
• Sensor manufactured by screen-printing, followed by UV polymerization

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SimCell On-Line Measurements: pH Measurement Technology
Immobilized pH Sensor
• Covalently bond fluorescent pH
dye to hydrogel
• Hydrogel polymerized to
bottom surface of MBA
• Retains ratiometric pH
response
Four sensors/chamber

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Automated pH Control
 
)
(
)
(
3
10
10
]
[
2 pKa
pH
pKa
pH
add
H
CO
total
add
i
f
HCO
k
P
V
V 







• Vadd: volume of solution of base to add
• Vtotal: total volume of the sample in the microbioreactor before addition
• PCO2: pressure of CO2
• kH: Henry’s Law constant for CO2
• [HCO3
-]add: concentration of bicarbonate in the adjustment solution
• pHinitial and pHfinal: starting pH value and pH setpoint, respectively
• Similar equations are derived for use of sodium carbonate, sodium hydroxide, and
monoprotic acids for pH adjustment.

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Measurement and Control: Maintaining pH
Setpoints
•3 pH setpoints
•18 subprotocols
•9 μBR/subprotocol
•pH adjusted 2x/day
•Chart shows average pH for
each subprotocol over the course
of the experiment.

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SimCell™ On-Line Measurements: Dissolved Oxygen
(DO) Measurement
• Oxygen-sensitive dye (platinum porphyrin
derivative)
– Excitation of dye yields emissive triplet state.
– As [O2] increases, dye emission is quenched
and τF decreases
– τF is correlated to phase shift (φ) between
modulated excitation and emission signals
0 5 10 15
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Excitation
Emission
Signal
(arbitrary
units)
time
φ
10 15 20 25 30 35 40 45
-10
0
10
20
30
40
50
60
70
80
90
100
110
120
pO
2
(%
air
saturation)
phase
Correlation of φ to DO: error bars are +/- 1SD
(+/- 10% variance).
Phase shift between excitation (blue)
and emission (red) signals.

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Novo’s Comparison with Current Technologies
Significant improvement n process yield at lower cost and shorter time
6.6
6.8
7
7.2
7.4
28
29
30
31
32
33
34
35
36
37
0
5000
10000
15000
20000
25000
Yield
pH
Temperature
Peak Production
Summary
• 84% increase in yield
• Scalable to 1,000 liter production vessels

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Application across platforms and processes
• The central question is “To what extent
is the MBA performance a predictor of
the bioreactor result?”
• The R^2 statistic is a well-established
way to quantify the answer to this
question, computed by constructing
ordered pairs of MBA and reference
model system results
• Advantages of R^2 are that is can be
constructed independent of platform,
process cell line, etc.
– Compare relative predictive power
across model systems: flask, MBA,
bioreactor, well plate, etc.
– Metric of continuous improvement as
technology evolves
• This graphic shows results over many
cell lines, processes and vessels,
predictive power might be even better
for data with these factors kept
constant
R2=0.96
R2 for 2006 client evaluation projects
y = 1.0926x + 36.042
R2
= 0.9628
0
1000
2000
3000
4000
0 1000 2000 3000 4000
MBA Titer
Reference
Vessel
Titer

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What’s missing
• Protein titer of course!
– Enzymatic like ELISA is the most common, but it takes work, even with automation
– Something else?
• Viability
• Some understanding of the protein quality (glycosylation, aggregation)
• All those media components in the culture broth
– Nutrients: glucose, glutamine, amino acids, vitamins
– Metabolic products: lactate, etc.
• Can spectroscopy (NIR, MIR, Raman work here?)
• Anything else useful in characterizing and ‘fingerprinting’ the process, that is, a
useful predictor of process outcomes.
• Ideal measurement (for us at least) is
– Non invasive – it it needs a sample, best case is
• Small sample
• Works with crude broth, no pre- treatment
– Matched throughput
– Calibrated less frequently than once per MBA
– Compatible with flexible! plastic cell culture device (challenge for some spectroscopy)
– Cost competitive pulling samples and using well plates

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Conclusions
• Model systems are indispensable tools, and increasing
demands for data will be difficult to meet with current platforms.
• A high-throughput cell culture system presents a possible
solution if the data is of sufficient quality to predict process
outcomes.
• BioProcessors SimCell system represents one possible solution
that combines high throughput with highly representative data.

Seth Rodgers - High Throughput Cell Culture Platform for Bioprocess Optimization.ppt

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