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Mammalian Cell Culture Sensors and Models. Trish Benton Michael Boudreau. Presenters. Trish Benton Michael Boudreau. 483? That means big trouble. Landscape. New at-line and inline sensors. Concentration Control. Modeling. Data Analytics. Sensors. At –line Nova, HPLC In-line - PowerPoint PPT Presentation
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Mammalian Cell CultureSensors and Models
Mammalian Cell CultureSensors and Models
Trish Benton Michael Boudreau
PresentersPresenters
Trish Benton
Michael Boudreau
483? That means big
trouble
483? That means big
trouble
LandscapeLandscape
New at-line and inline sensors
Concentration Control
ModelingData Analytics
SensorsSensors
At –line Nova, HPLC
In-line Fogale, Aspectrics, Optek, CO2, differential pressure
On-line viable cell densityOn-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.
Beta Dispersion SpectraBeta Dispersion Spectra
Fogale uses Entire Dielectric SpectrumFogale 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 cells0 - 200 g/l dry weight for yeast and bacteria
Resolution: 0.01 - 106 cell/ml for animal cells0.02 g/l dry weight for yeast
T3V1 Cell Density
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Viable Density Capacitance
T1V1 Cell Density
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Viable Density Capacitance
Automated Multifunction AnalyzersAutomated 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.
AutosamplersAutosamplers
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.
Encoded Photometric Infrared SpectroscopyEncoded 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.
EP IR Measurement in Cell CultureEP IR Measurement in Cell Culture
A single analysis function can measure:– Glucose– Glutamine– Glutamate– Proline– Lactic Acid– Ammonia– Dissolved Carbon Dioxide
Concentration ControlConcentration 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.
Manual Glucose AdditionManual 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.
Glucose Addition under Feedback ControlGlucose 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.
Benchtop Bioreactor with SensorsBenchtop Bioreactor with Sensors
Place picture of bioreactor with sensors here
Tuning of Concentration LoopsTuning of Concentration Loops
New concentration
Loops are usually
Integrators.
InSight Learning and
Adaptive Tuning
can identify these
Integrators on in-line
analyzers.
Bio-Process Modeling in Process Development
Bio-Process Modeling in Process Development
High fidelity modeling can help determine the impact of operating conditions on yield and product quality.
Bioprocess Modeling and ControlBioprocess 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.
Sequential Modular SimulationSequential Modular Simulation
PumpSimulation
ValveSimulation
ReactorSimulation
Flow measurement
Simulated Properties Flow Temperature Pressure Etc.
Pressuremeasurement Temperature Measurement
Process simulation blocks
Sequential Modular Simulation on DeltaV
Sequential Modular Simulation on DeltaV
jii
i
jii
ji
KX
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KX
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jir 21
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Michaelis-Menten
Rate of synthesis of i by j
Bioreactor Simulation on BioNet Control System
Bioreactor Simulation on BioNet Control System
Add picture of simulation here
Bioreactor Control System with Concentration Loops
Bioreactor Control System with Concentration Loops
Place bionet main view here
On-line Adaptation of SimulationOn-line Adaptation of Simulation
Actual Plant
Virtual Plant
Online KPI:Yield and Capacity
Inferential Measurements:
Biomass Growth and Production Rates
Adaptation
Key Actual Process VariablesKey Virtual
Process Variables
Model Parameters
Error between virtual and actual process variables
are minimized by correction of model parameters
Actual BatchProfiles
Process Analytical Technology in Process DevelopmentProcess 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.
Batches Not AlignedBatches Not Aligned
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1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115
Batches Aligned with DTWBatches Aligned with DTW
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PAT Online in Process DevelopmentPAT Online in Process Development Media comparisons Tech Transfers
ReferencesReferences 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.
About the PresentersAbout the Presenters
Michael Boudreau is a Principal Consultant at Emerson Process Management.
Trish Benton is a Life Sciences Consultant at Broadley-James Corporation.