Microfluidics Technology Fair, October 3, 2006 Parallel Integrated Bioreactor Arrays for Bioprocess Development Harry Lee, Paolo Boccazzi, Rajeev Ram, Anthony Sinskey

Microfluidics Technology Fair, October 3, 2006 Outline Bioprocesses and bioprocess development Alternative approaches and advantages of microfluidics Parallel Integrated Bioreactor Arrays (PIBA) Preliminary biological validation Applications Next steps

Microfluidics Technology Fair, October 3, 2006 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

Microfluidics Technology Fair, October 3, 2006 Bioprocess development Optimal microbial strains or cell lines must be screened Growth conditions must be empirically optimized pH, temperature, nutrients, O 2, 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 Experimental Throughput

Microfluidics Technology Fair, October 3, 2006 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

Microfluidics Technology Fair, October 3, 2006 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

Microfluidics Technology Fair, October 3, 2006 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

Microfluidics Technology Fair, October 3, 2006 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 port Injector 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

Microfluidics Technology Fair, October 3, 2006 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 DO (% Air Sat) pH Cell density (g-dcw/L) Time (h) 3X No pH OD 650nm (1cm) Similar to Flasks 6X 2.4M x 2 Similar to Stirred Tank

Microfluidics Technology Fair, October 3, 2006 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 Doubling Time (h) OD 650nm, 1cm Time (h) Nutrient Limitation Lag phase E. coli growth on LB medium

Microfluidics Technology Fair, October 3, 2006 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

Microfluidics Technology Fair, October 3, 2006 Value Proposition Improved process screening Screen under production scale conditions  Early determination of production process yield  Impacts 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

Microfluidics Technology Fair, October 3, 2006 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

Microfluidics Technology Fair, October 3, 2006 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