A high throughput cell culture platform for bioprocess optimization

1
A high throughput cell culture platform for
bioprocess optimization

• Seth Rodgers, CTO Bioprocessors
• CPAC Rome
• March 20, 2007

2
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
  wed like to get

3
After discovery comes development, lots and lots
of it!
Expression SystemDevelopment
Flasks

• 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 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!

Note Represent iterative processes Source
Nature Biotechnology Vol. 22 (11) 2004
4
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
Real system The API is the product
Model systems Data is the product
5
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

1000s
High Quality, High Quantity
Well plate
100s
Throughput Experimental capacity per researcher
10s
Flask
Bioreactor
1s
Low
High
Quality Ability to predict manufacturing
bioreactor performance
6
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

7
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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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

• 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?

• Error bars are /- 1 std. dev. (/- 16 variance)

11
Measurements Immobilized pH Sensors

• pH Sensor Composition
• Hydrogel (Water-swellable polymer)
• Covalently bound dye fluorescent pyrene
  derivative.
• Sensor manufactured by screen-printing, followed
  by UV polymerization

• Response is independent of media
• Precision is lt 0.06 (3 std. dev.) over
• the pH range 6.0 8.0

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SimCell On-Line Measurements pH Measurement
TechnologyImmobilized 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

• Vadd volume of solution of base to add
• Vtotal total volume of the sample in the
  microbioreactor before addition
• PCO2 pressure of CO2
• kH Henrys 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.

14
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
  tF decreases
• tF is correlated to phase shift (f) between
  modulated excitation and emission signals

Phase shift between excitation (blue) and
emission (red) signals.
Correlation of f to DO error bars are /- 1SD
(/- 10 variance).
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Novos Comparison with Current Technologies

• Summary
• 84 increase in yield
• Scalable to 1,000 liter production vessels

Significant improvement n process yield at lower
cost and shorter time
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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 R2 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 R2 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 for 2006 client evaluation projects
R20.96
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Whats 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.