Parallel Integrated Bioreactor Arrays for Bioprocess Development

1. Parallel Integrated Bioreactor Arrays for Bioprocess Development Harry Lee, Paolo Boccazzi, Rajeev Ram, Anthony Sinskey

2. Outline • Bioprocesses and bioprocess development • Alternative approaches and advantages of microfluidics • Parallel Integrated Bioreactor Arrays (PIBA) • Preliminary biological validation • Applications • Next steps

3. Bioprocesses • Microbial fermentation is used to produce • Human insulin, human growth hormone • Plasmid DNA vaccine, protein subunit vaccine Human insulin E. coli bacteria Monoclonal antibody Mammalian cell lines 1000L Bioreactor • Mammalian cell culture is used to produce • Monoclonal antibodies, Protein therapeutics (ie. erythropoietin) • Viruses for vaccines

4. Uncontrolled culture conditions • Oxygen starvation during sampling • Low cell density culture  Uncertain transfer of results to larger scale • Labor intensive operation • Low experimental throughput 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 Experimental Throughput Process Knowledge

5. 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

6. 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

7. 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

8. Pressure chamber generates positive pressure to drive fluid into channels. Pressure chamber Peristaltic Mixing Tubes Growth well Growth well 1.5cm Fluid reservoir PDMS membrane pH sensor Membrane acts as sterile barrier Acid reservoir oxygen sensor Base reservoir Metering valves to control injected volume Molded interface gaskets Metering valves Molded interface gaskets for ease of use Injector channel Filling port Filling port PIBA device module (patent pending) optical density Integrated optical oxygen and pH sensors. (Fluorescence lifetime)

9. Similar to Stirred Tank 6X 2.4M x 2 3X No pH E. Coli fermentation in PIBA 45.7 • Highest oxygen transfer rate in mbioreactor array • First pH and DO controlled mbioreactor array • Growth to cell densities (13g-dcw/L) 4X higher than previous mbioreactors • Online optical density enabled by bubble free oxygenation 15 30.5 OD 650nm (1cm) 10 Cell density (g-dcw/L) 15.2 5 Similar to Flasks 0 0 7.5 7 pH 6.5 6 120 100 80 DO (% Air Sat) 60 40 20 0 0 1 2 3 4 5 6 7 8 9 Time (h)

10. 3 2.5 Nutrient Limitation 2 1.5 Doubling Time (h) 1 0.5 Lag phase 0 Unique capability: Real time OD monitoring E. coli growth on LB medium 30 25 20 OD 650nm, 1cm 15 10 5 0 0 1 2 3 4 5 6 7 8 Time (h) • 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

11. 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

12. 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

13. 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

14. 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