Requirements for Process Control of Continuous Processes: Sensorics and
Automation
Marek Hoehse
1/18/2014
CONFIDENTIAL - PROPERTY OF SARTORIUS STEDIM BIOTECH GmbH
Potential advantages of continuous processing in cell cultivation
Improved product quality
 Constant maintenance of nutrients
 Less cell stress (e.g. lag phase or drop off viability at the end of the
culture)
 Reduction of product degradation by minimized processing delays
Improved scalability
 Same equipment for process development and production (or
marginal scale-up factor)
 “Number-up” instead of scale-up feasible
Production of labile products
22. Octobre 2013
Increased profitability
 More efficient / economical facility utilization
 Smaller footprint  smaller size
 Lower energy consumption
 Higher portability
 Higher flexibility (“Number-up”)
 Higher cell densities / titer
 Increase in automation
 Reduction in service requirements
 Reduction in operator labor
 Lower chance of product loss or reprocessing
 Shorter processing times
 Better material utilization
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Page 2
Challenges of continuous cultivation
Challenges (technical)
Challenges (process)
New forms of in-process testing
More complex process
Robust sensors
Robust control systems
Higher risk of failure
Increased training requirements
Demanding process requirements
Minimizing blackout of operation units
High oxygen consumption
High CO2 formation
Pot. challenging clarification
Higher need of process understanding
Full knowledge of CPP´s and CQA´s required
Sophisticated process control strategies
More complex DSP approach
Online control of DSP process
Complex batch definition
Limited number of reliable & scaleable cell retention
devices
Raw material concerns
New forms of release approaches
Higher material control or higher process robustness required
Sophisticated feeding technologies
Regulatory aspects
CONFIDENTIAL - PROPERTY OF SARTORIUS STEDIM BIOTECH GmbH
Challenges of continuous cultivation
Challenges (technical)
Challenges (process)
New forms of in-process testing
More complex process
Robust sensors
Robust control systems
Higher risk for failure
Increased training requirements
Demanding process requirements
Minimizing blackout of operation units
High oxygen consumption
High CO2 formation
Pot. challenging clarification
Higher need of process understanding
Full knowledge of CPP´s and CQA´s required
Sophisticated process control strategies
More complex DSP approach
Online control of DSP process
Complex batch definition
Limited number of reliable & scaleable cell retention
devices
Raw material concerns
New forms of release approaches
Higher material control or higher process robustness required
Sophisticated feeding technologies
Regulatory aspects
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But can single-use equipment cope with demanding process requirements?
Connectivity to cell retention system?
High oxygen demand?
Media logistics?
Sufficient CO2 removal?
Long operation times (> 14d)?
Robust Sensor Technology?
High performance clarification?
Proteinaceous aerosol formation?
Predictive scale-down model?
22. Octobre 2013
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Page 5
BIOSTAT® STR single-use bioreactor designed for demanding processes
Conventional stirred tank design
Optical and redundant conventional pH and
DO probes
3 blade segment &
rushton impeller options
Working volume:
50 L, 200 L, 500 L, 1000 L, 2000L
Inhouse bag production
Optimized for high cell density cultures
22. Octobre 2013
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Page 6
Continuous Biomanufacturing
Critical Process Parameters (CPP)
Cell Cultivation / MO Fermentation
METABOLITES:
Lactate
Acetate
Glutamate
Acetoin
Ammonia
NUTRIENTS:
Glucose
Sucrose
Glutamin
Fructose
Cell parameters:
Biomass
dry mass
Total Cell Count
OD600
Viability
microbial contamination
REACTOR PARAMETERS:
Oxygen, CO2
Product (CQA):
pH
Antibody
Spores
Temperature
Vaccines
Enzymes
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Analytical Systems
NIR/ UV-VIS Spectroscopy
Online Glucose | Lactate Analysis
Viable Biomass Analysis
Exhaust Gas Analysis
22. Octobre 2013
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Page 8
Automated monitoring and control of continuous processes – a reality?
Implementation of glucose | lactate
measurement and control strategy
Single-use bioenzymatic sensors and fluidics
Integrated autocalibration & autosampling
BioPAT®Trace
Global Trends
DCU – Local Control Unit
BioPAT®MFCS/win + Recipe Module
 Dialysis or filtration mode
 Compact design
 Reducing off-line sampling
 Direct display of
glucose | lactate conc.
 Quick and simple
process monitoring at a
glance
 Fully automated glucose feed &
perfusion rate control
 Recipes for automatic event-based
control of cultures
 Improved batch-to batch consistency
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Automation
BioPAT®Xgas – Integrated Off-Gas Analysis and Process Control
BioPAT®Xgas
Global Trends




Parallel measurement of O2 | CO2
Moisture and pressure independent
Directly integrated into exhaust gas line
Compact design for flexible integration
DCU – Local Control Unit
BioPAT®MFCS/win + Recipe Module
 Direct Display of
%O2 | %CO2
 Quick and simple process
monitoring
 Functional monitoring of DO
probe
 Automatic OUR | CER | RQ calculation
 Automatic event based control of microbial and animal
cell cultivations:
 Adding feeds | induction media
 Harvest initiation
 Gas mixing strategies
Process and yield optimization
22. Octobre 2013
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Page 10
Automated monitoring and control of continuous processes – a reality?
In-line, real-time monitoring of viable biomass
 Automated process monitoring & control
 Automated control of cell bleed via BioPAT®MFCS
 Event based control (induction, temp change, point
harvest, etc.)
Dielectric Spectroscopy
Dead cells “invisible” to radio frequency field
Only viable cells polarized
RF capacitance proportional to viable biomass
Benefits
 Consistent cell count (operator and sampling
independent)
 Consistent automated manufacturing
Sensor integration for BIOSTAT®STR and BIOSTAT®RM
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Bioprocess control with NIR spectroscopy and Multivariate Data Analysis
NIR-System (Ingold-Adaption)
- New system design – patent granted
- Fiber free system
- usable for 25mm standard Ingolt port
- length 60mm (others on request)
- sapphire window
- Optical pathlength 1, 2 or 5mm
- Sensor
- 1100-1700 / 2500 nm spectrometer
- Detector: diode array
- IP65
Automatic detection of window fouling
CIP/SIP ready
Bigger measurement spot compared to fiber solutions
SU adaption in progress
NIR - Qualitative
Multivariate Data Analysis
PCA – The Score Plot (Spectra only)
PC2 = Sum signal metabolites
glutamine exhausted
PC1 = cell count
Start
End
NIR - Qualitative
Qualitative Process Monitoring
The Batch-Trajectory Score 1
contamination
low performer (oxygen limitation)
Comparison to “golden batch”
Alteration within batches can be observed in process trajectories
Ability to detect deviations and similarities
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Qualitative Process Monitoring
The Batch-Trajectory Score 3
contamination
early glucose limitation
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Qualitative Process Monitoring
NIR Score 3 & Viability
viability
(offline analysis)
!strong correlation!
PCA-Score3
(spectral variations
of NIR spectra)
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NIR - Qualitative
NIR Media Classification
Media composition in bacillus spore production
NH-Vibration
Batch 2: Lower concentration of N-source
22. Octobre 2013
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Page 20
NIR - Quantitative
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NIR - Quantitative
prediction (Mio cells / ml)
Total Cell Count
reference (Mio cells / ml)
Model
Total Cell Count
Concentration Range
Algorithm
PCs
Cell count
Mio/ml
0 – 16
PLS
1
Validation procedure
Testset K06
Error of Prediction (SEP)
0.54
NIR - Quantitative
Viability
viable
dead
Model
Viability
Concentration Range
Algorithm
PCs
Viability
[%]
0 – 100
PLS
5
Validation procedure
Testset K05
Error of Prediction (SEP)
3
direct scatter pattern or correlation to cell lyses?
NIR - Quantitative
prediction (g/l)
Glucose
reference (g/l)
Model
Glucose
Glucose (g/l)
Concentration Range
0–7
Algorithm
PCs
PLS
5
Validation procedure
Testset K06
Error of Prediction (SEP)
0.24
Summary
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Acknowledgements
Co-workers
Christian Grimm
José Fernandes
Roland Bienert
Thomas Scheper
Dörte Solle
Marko Sandor
Thank you !
22. Octobre 2013
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Page 26
Agenda
1.
Continuous Manufacturing in Cell Cultivations
2.
Single Use in continuous Cell Cultivation
3.
Sensors in Cell Cultivation
4. Multivariate Data Analysis for enhanced process control
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High cell density inoculum production using Biostat® RM perfusion
MCB/WCB
vial
Perfusion expansion
Controlled freeze of HCD
seed stocks
References:
Direct seed of production
bioreactor
N. C. Bögli et al.: Cell Banking, One-Step Inoculation, and Repeated Fed-Batch Expansion in a Single-Use Bioreactor. BioProcess International 10(5)s May 2012
G. Seth et al.: Development of a new bioprocess scheme using frozen seed train intermediates to inoculate CHO cell culture manufacturing campaigns. Biotech &
Bioeng., Volume 110, Issue 5, May 2013, Pages: 1376–1385
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Multivariate Statistical Process Control (MSPC)
Classification - Media composition in bacillus spore production
2-m01
1-m01
1-m02
4-m01
3-m01
3-m02
4-m03
4-m02
3-m03
2-m02
2-m03
1-m03
Building the model with high performance
batches
compare new batches
with model
 significant deviations
detected
THANK YOU VERY MUCH FOR YOUR ATTENTION!
QUESTIONS?
22. Octobre 2013
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Page 30
Example - Cultivation of CHO cells in a Biostat® RM
Comparison of in-line (capacitance measurement) vs offline data (Cedex)
22. Octobre 2013
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Page 31
G. Seth et al.: Development of a new bioprocess scheme using frozen seed train intermediates to inoculate CHO cell culture manufacturing campaigns. Biotech &
Bioeng., Volume 110, Issue 5, May 2013, Pages: 1376–1385
CONFIDENTIAL - PROPERTY OF SARTORIUS STEDIM BIOTECH GmbH
Rec. blood factors, enzymes & mAbs commercially produced in
continuous culture
Protein
Trade name/Company
Driver
rFVII
NovoSeven (NovoNordisk)
product quality
(γ-carboxylated, labile)
rFVIII
Kogenate (Bayer)
Refacto (Pfizer)
Product quality (labile)
rProtein C
Xigris (Eli Lilly)
Product quality
(γ-carboxylated, labile)
mAbs
Reopro (Janzen)
Remicade (Janzen)
Simulect (Novartis)
Rebif (Merck-Serono)
Space-time yield
Enzymes
Genzyme
Shire
Biomarin
Product quality (labile)
22. Octobre 2013
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Page 33
Continuous culture process modes
Perfusion
Concentrated fed-batch
Y
nutrients
cells
nutrients
product
cells
product
5-20 days
10-80 days
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Can perfusion help to address industry challenges of today?
Industry drivers
Opportunities - Perfusion
reduce development costs
reduce time to market
reduce COGs
reduce complexity
regional production
Only way forward for labile or
difficult to express products
Biopharma trends
Better controlled product quality
due to stable environment
follow-on biologics
biosimilars
new mab formats
smaller indications
Faster material supply for medium –
low titre products
Reduced process development time
line for non-platform processes
Smaller production bioreactors
Fewer seed steps
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Where are we moving to in the age of single-use?
Single-Use
Multi-Use
Low fixed cost
High fixed cost
No steam & CIP, limited WFI
WFI, Steam, CIP system
Decision late (<1y)
Decision early (> 3 y)
Equipment IQ/OQ
Equipment IQ/OQ (significant!)
Sterilisation via vendor
Sterilisation
No Cleaning
Cleaning
E&L PV risk based;
generic E&L dossiers?
Appropriate quality of materials!
Carefully evaluate vendors!
Carefully evaluate vendors!
Does it work for my
process?
Stirred tank design dominating
Refurbishment
Very easy
Challenging
Footprint
Reduced
Investment/
Costs
Qualification
Design
22. Octobre 2013
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BioPAT®Trace – Glucose | Lactate Analyzer
Features
Bioreactor
Sampling
Seed Bioreactor
Bioreactor
Celsius FFT
•
•
•
•
•
•
•
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Fully automated, self calibrating
Modes: Filtration, Dialysis, Sampling
Plug & Play
Reusable system
Fully disposable sensor & fluidics set
Ethernet, Modbus, (OPC)
Compact device
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One key requirement of continuous processing is good process control to keep operations at steady
state.
Together with disposable technology this provides some powerful opportunities (K. Konstantinov,
Genzyme)
Industry leaders see the design of completely closed, disposable and continuous biomanufacturing
systems for biopharma on the horizon (W.G. Whitford, Thermo Scientific cell culture and
bioprocessing)
22. Octobre 2013
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Principle of measurement
Patch Composition DO-Sensor
e offluorophore
measurement
Medical grade silicone
with sinosoidal modulated light
Determination of Fluorescence Lifetime•for Excitation
DO-measurement
Ind
Ind
Ind
Ind
Ind
Ind
Ind
•
•
Ind
Ind
Ind
Ind
•
Ind
Timeshift of fluorescence due to fluorescence lifetime
Measurement of phase-shift between Excitation-signal and
fluorescence-signal
Conversion of phase-shift to lifetime by:
Ind
Ind
Ind
N2
O2
22. Octobre 2013
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Sensors & Automation: Implementation Along Production Chain (mAb)
SCADA
MFCS
Automation
Sensors
pH
cond.
SCADA
pH
Biomass.
flow.
DO
pH
Glc
pH
pressure.
flow.
pressure.
DCU
DCU
pH
cond.
pH
cond.
pressure.
DCU
Automation
22. Octobre 2013
flow.
pH
flow.
pressure.
pH
flow.
pressure.
MFCS
SCADA
Sensors
pressure.
DCU
DCU
MFCS
Automation
Sensors
DCU
DCU
pH
pressure.
DCU
DCU
flow.
pH
cond.
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pressure.
pH
cond.
flow.
pressure.
Page 41
Biostat STR set-up for continuous perfusion or concentrated fed-batch
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ϕ [°]
Principle of measurement
Calibration for pH-Sensor
A1
56
54
52
50
48
46
44
42
40
38
36
34
32
30
28
26
24
22
•
Sigmoidal calibration function
•
Fit by Boltzmann-Equation:
x0
dx
A2
4
5
6
7
8
9
pH
22. Octobre 2013
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SU sensor development – development process workflow
Sensor
Evaluation
• Requirements
• Specification
• Performance tests
Sensor
Qualification
• Qualification tests
• Robustness
• Production transfer
Sensor
Integration
• Bag integration
• DCU integration
Application
Qualification
22. Octobre 2013
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• Application test
• Documentation
Page 44
SU sensor development – general design rules / requirements
Requirement
Specification
Shelf life
< 3 years after Irradiation  Sterility has to be confirmed
Lipids, Oil, aqueous solutions, saline solutions, alcoholes,
acids, alcalic solutions, WFI
Media consistency
Material properties
FDA admission, USP Class VI certification, Whole
production process TSE/BSE-free, Free of animal origin
Irradiation consistency
50 kGray gamma irradiation
Certificates / Qualification tests
Performance Tests, Handling Tests, Partikel free (USP 788),
Cleaning procedures, USP Class VI, Cytotoxicity, Biological
assays, Extractable / Leachables, Bioburden
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Application of Single Use pH and DO sensors
Sensors integrated in the cultivation bags and Univessel-SU Example: pH, DO
22. Octobre 2013
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Measurement principle: Dielectrically essential part of cell structure
Cytoplasmic Lipid
membrane
 impermeable to the free flow
of ions
22. Octobre 2013
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SU Biomass Sensor
Status
• Beta side tests in stirred bioreactors systems showing good results
• Special algorithms for rocking systems is developed –> RM 50 plus
•Software implementation in our DCU software is ongoing
N
Cultivation of CHO cells
22. Octobre 2013
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Page 48
Introduction Glucose / Lactate Measurement
Connected to Univessel SU
Online measurement for all fermentation system
Easy to handle and easy connection
Different measurement strategies (continuous or time dependent)
Linear Measurement Range from 0 – 40 g/l Glucose
and 0 – 5 g/l Lactate
Connection to SU Bioreactors
BioPAT®Trace
22. Octobre 2013
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Application: BioPAT® Trace - System
The BioPAT® Trace can be connected with every bioreactor | fermenter
– coming from Sartorius or competitors
22. Octobre 2013
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Page 50
Online biomass detection (cell culture)
Bioreactor
Sampling
Seed Bioreactor
Bioreactor
Celsius FFT
•Real-time monitoring of viable biomass for entire batch
allows automated process control and process modeling
automated substrate feed strategies with MFCS
harvest point determination
time of induction / infection
Targets:
•Reduced risk of contamination due to limited sampling need
•Reduced risk of unrepresentative samples (manual handling)
•Decreased labor cost for offline analysis
•Single use sensor integration for various SU bioreactors
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Measurement principle:
In situ four pin electrode probe used for radio-frequency impedance measurement
Cytoplasmic Lipid membrane
impermeable to the free flow of ions
Dead cells “invisible” to RF Field
Only live cells polarized
field area approx 25mm
22. Octobre 2013
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Page 52
METABOLITES:
Bioprocess –
Cell Cultivation / MO Fermentation
Critical Process Parameters (CPP)
Lactate
Acetate
Glutamate
Acetoin
Ammonia
Cell parameters:
NUTRIENTS:
Glucose
Sucrose
Glutamin
Fructose
Total Cell Count
dry mass
Viability
OD600
microbial contamination
REACTOR PARAMETERS:
Product (CQA):
pH
Antibody
Spores
Temperature
Vaccines
Enzymes
7
6
800
5
600
100
1.6x107
90
1.4x107
80
1.2x107
70
7
60
1.0x10
50
200
1
0
0
12
24
36
48
60
72
time [h]
84
96
108
120
132
0
144
Cell Count Total
400
2
glutamate [mg/L]
glucose [g/]
8.0x106
3
40
6.0x106
30
4.0x106
20
2.0x106
10
0.0
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0
12
24
36
48
60
72
time [h]
84
96
108
120
132
0
144
Viability [%]
4
glutamine [mg/L]
lactate [g/L]
1000
Cell Count Vital [N/mL]
Oxygen, CO2