1. Mammalian Cell Culture Sensors and Models Trish Benton Michael Boudreau

2. Presenters Trish Benton Michael Boudreau

3. 483? That means big trouble 483? That means big trouble

4. Landscape New at-line and inline sensors Concentration Control Modeling Data Analytics

5. Sensors At –line Nova, HPLC In-line Fogale, Aspectrics, Optek, CO2, differential pressure

7. On-line viable cell density In and induced electrical field, an intact cell membranes is a physical barrier to ion migration. Capacitance measured in picoFarads plotted against the frequency of change of an electrical field, measured in MHz, gives a beta-dispersion spectrum.

8. Beta Dispersion Spectra

9. Fogale uses Entire Dielectric Spectrum Older analyzers measured capacitance at only one frequency. Newer analyzers use a non-linear least squares fit of random measurements to generate the whole spectrum. Concentration range: 0 -109 cell/ml for animal cells 0 - 200 g/l dry weight for yeast and bacteria Resolution: 0.01 - 106 cell/ml for animal cells 0.02 g/l dry weight for yeast

11. Automated Multifunction Analyzers A robotic combination of enzymatic, amperometric, potentiometric and Coulter counter or CCD camera analyzers. They can measure: –Sugar and amino acid substrates –Metabolic byproducts –Dissolved Oxygen and Dissolved Carbon Dioxide –pH –Cell Density and viability –Sodium, potassium, calcium, phosphate –“Gold Standard” freezing point test for osmolality.

12. Autosamplers Autoclavable, multipoint auto-samplers enable multifunction analyzers to make at-line measurements. Small sample size allow more frequent analysis. A 5L cell culture bioreactor can be sampled once every 4 hours.

15. Encoded Photometric Infrared Spectroscopy Encoded Photometric infrared analyzers can detect the constituents of multiple frequencies simultaneously EP IR analyzer is a non-dispersive measurement where the radiation beam is dispersed according to wavelength after it has passed through a sample Chemometric analysis is off-line.

16. EP IR Measurement in Cell Culture A single analysis function can measure: –Glucose –Glutamine –Glutamate –Proline –Lactic Acid –Ammonia –Dissolved Carbon Dioxide

17. Concentration Control Glucose in high concentration attaches non-specifically to amino acids. The quality and possibly the quantity of protein product can be increased by maintaining glucose concentration in a bioreactor at physiological levels of about 1 g/L.

18. Manual Glucose Addition Typically glucose is added once a day throughout a cell culture run. The result is a saw-tooth glucose concentration profile that ranges from 3 g/L to near 0 g/L.

19. Glucose Addition under Feedback Control Multifunction analyzers can be used in a feed back loop if the sample time is 25% of the dominant system response time. In line analyzers, like Fogale viability and EP IR perform analysis on each sample within minutes. Their results can be used in most liquid concentration loops.

20. Benchtop Bioreactor with Sensors Place picture of bioreactor with sensors here

23. Tuning of Concentration Loops New concentration Loops are usually Integrators. InSight Learning and Adaptive Tuning can identify these Integrators on in-line analyzers.

24. Bio-Process Modeling in Process Development High fidelity modeling can help determine the impact of operating conditions on yield and product quality.

25. Bioprocess Modeling and Control Chapter 6 of the book “New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits” describes in detail how to build a model in DeltaV.

26. Sequential Modular Simulation Pump Simulation Valve Simulation Reactor Simulation Flow measurement Simulated Properties  Flow  Temperature  Pressure  Etc. Pressure measurement Temperature Measurement Process simulation blocks

27. Sequential Modular Simulation on DeltaV Michaelis-Menten Rate of synthesis of i by j

28. Bioreactor Simulation on BioNet Control System Add picture of simulation here

29. Bioreactor Control System with Concentration Loops Place bionet main view here

30. On-line Adaptation of Simulation Actual Plant Virtual Plant Online KPI: Yield and Capacity Inferential Measurements: Biomass Growth and Production Rates Adaptation Key Actual Process Variables Key Virtual Process Variables Model Parameters Error between virtual and actual process variables are minimized by correction of model parameters Actual Batch Profiles

31. Process Analytical Technology in Process Development Dynamic Time Warping allows comparison of matched bioreactors when they progress at different rates. PCA can weed out unimportant process parameters quickly.

32. Batches Not Aligned

33. Batches Aligned with DTW

34. PAT Online in Process Development Media comparisons Tech Transfers

35. References 1. Kleman G.L.,Chalmers J. J., Luli G W, Strohl W R, A Predictive and Feedback Control Algorithm Maintains a Constant Glucose Concentration in Fed-Batch Fermentations, APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Apr. 1991, p. 910-917 2. Luan Y T, Mutharasan R, Magee W E, Effect of various Glucose/Glutamine Ratios on Hybridoma Growth, Viability and Monoclonal Antibody Formation, Biotechnology Letters Vol 9 No 8 535-538 (1987) 3. McMillan G, Benton T, Zhang Y, Boudreau M, PAT Tools for Accelerated Process Development and Improvement, BioProcess International Supplement MARCH 2008. 4. Boudreau M A, McMillan G K, New Directions in bioprocess Modeling and Control. ISA. Research Triangle Park, NC 2006. 5. Lee J M, Yoo C K, Lee I B, Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis. Journal of Biotechnology, 110 (2004) 119-136. 6. Cinar A, Parulekar S J, Ündey C, Birol G, Batch Fermentation Modeling, Monitoring, and Control. Marcel Dekker, Inc. New York, NY 2003.

36. About the Presenters Michael Boudreau is a Principal Consultant at Emerson Process Management. Trish Benton is a Life Sciences Consultant at Broadley-James Corporation.