microfluidics technology fair, october 3, 2006 parallel integrated bioreactor arrays for bioprocess...

Microfluidics Technology Fair, October 3, 2006

Parallel Integrated Bioreactor Arrays for Bioprocess Development

Harry Lee, Paolo Boccazzi, Rajeev Ram, Anthony Sinskey


Outline

• Bioprocesses and bioprocess development

• Alternative approaches and advantages of microfluidics

• Parallel Integrated Bioreactor Arrays (PIBA)

• Preliminary biological validation

• Applications

• Next steps


Bioprocesses

• Microbial fermentation is used to produce• Human insulin, human growth hormone• Plasmid DNA vaccine, protein subunit vaccine

Human insulin

Monoclonal antibody

• Mammalian cell culture is used to produce• Monoclonal antibodies, Protein therapeutics (ie. erythropoietin)• Viruses for vaccines

E. coli bacteria

Mammalian cell lines 1000L Bioreactor


Bioprocess development

• Optimal microbial strains or cell lines must be screened• Growth conditions must be empirically optimized

• pH, temperature, nutrients, O2, induction, etc.

Conventional technology

• Uncontrolled culture conditions• Oxygen starvation during

sampling• Low cell density culture

Uncertain transfer of results to larger scale

• Labor intensive operation• Low experimental throughput

Process Knowledge

Exp

erim

enta

l Thr

ough

put


Properties of ideal system

• Controlled growth conditions (pH, DO)• High oxygen transfer rate• Online optical density and growth rate• Parallelism of shake flasks• Automation• Improved data quality• Ease of use

Potential to predict performance

in large scale bioreactor


Conventional approaches

• Miniature stirred tanks, enhanced well plates Online cell density measurements not reliable

(bubble interference)

Measurements require sampling• Mechanical multiplexing

minimal labor savings• Robotic multiplexing

Expensive


Microfluidic advantage

• Microfluidics enables high oxygen transfer rate without bubbles• Online optical density measurements• Online growth rate estimation

• Integrated sensors and fluidics• Measurements do not perturb the fermentation• Minimal mechanical parts• Compact, bench scale instrument


PIBA device module (patent pending)

Integrated optical oxygen and pH sensors. (Fluorescence lifetime)

•

1.5cm

pH sensor

oxygen sensor Base

reservoir

Acid reservoir

Molded interface gaskets for ease of use

Metering valves to control injected volume

•

•

Filling portInjector channel

Metering valves

Molded interface gaskets

Filling port

Pressure chamber generates positive pressure to drive fluid into channels.

Membrane acts as sterile barrier

•

•

PDMS membrane

Pressure chamber

Fluid reservoir

Growth well

Peristaltic Mixing Tubes

Growth well

optical density


E. Coli fermentation in PIBA

• Highest oxygen transfer rate in bioreactor array

• First pH and DO controlled bioreactor array

• Growth to cell densities (13g-dcw/L) 4X higher than previous bioreactors

• Online optical density enabled by bubble free oxygenation

6

6.5

7

7.5

0 1 2 3 4 5 6 7 8 90

20

40

60

80

100

120

DO

(%

Air

Sat

)pH

0

5

10

15

Cel

l den

sity

(g-

dcw

/L)

15.2

30.5

45.7

0

Time (h)

3XNo pH

OD

650

nm (

1cm

)

Similar to Flasks

6X2.4M x 2

Similar to Stirred Tank


Unique capability: Real time OD monitoring

• Detailed growth kinetics are observable quantitative study of lag phase

• Identify nutrient limitations by change in growth rate• Screening to high cell density is important to see nutrient limitations• Important to isolate cell density dependent phenomena

0 1 2 3 4 5 6 7 80

5

10

15

20

25

30

0

0.5

1

1.5

2

2.5

3

Dou

blin

g T

ime

(h)

OD

650

nm,

1cm

Time (h)

Nutrient Limitation

Lag phase

E. coli growth on LB medium


Applications

• Standard platform for fermentation and cell culture• Standardization allows sharing data, improved data

interpretation • Standardization was the driver for microfluidics in

analytics

• Bioprocess development• Improved process optimization• Screening based on higher quality data

• Production scale conditions, growth rate changes

• Production bioreactor modeling• Inhomogeneities, dynamically changing conditions


Value Proposition

• Improved process screening• Screen under production scale conditions

Early determination of production process yieldImpacts investment decision on $500M - $1B

production facility

• Production reactor modeling• Time varying environment• High cell density growth

Faster manufacturing scale-up• One year shorter time to market for a $500M product ~ $30M


Next Steps

• Improved understanding of economic model• Case-studies

• Beta prototype development• Improved user friendliness fluidic interfaces• Improved manufacturing process Injection

molded layers

• Deploy Beta to collaborators/customers• Rigorous biological validation

• Rank order of process screen the same in PIBA and bench scale reactor

• Production reactor modeling


Team

• Dr. Paolo Boccazzi• Microbial Physiology, Molecular Biology, Bioprocess Development

• Dr. Harry Lee• Electrical Engineering, Microfabrication, System Integration

• MIT $50K Entrepreneurship Competition Winning team member, 2005

• Prof. Rajeev J. Ram• Electrical Engineering, Optoelectronic devices, Optical

Spectroscopy

• Director, MIT Center for Integrated Photonic Systems

• Associate Director, Research Laboratory of Electronics

• Prof. Anthony J. Sinskey• Biology, Health Sciences and Technology, Metabolic Engineering

• Co-Founder: Genzyme, Merrimack Pharmaceuticals, Metabolix

microfluidics technology fair, october 3, 2006 parallel integrated bioreactor arrays for bioprocess...

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