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NIR - Spectroscopy for Bioprocess Monitoring & Control
IFPAC 2014
Dan Kopec, Sartorius Stedim N.A.
Agenda
- Real-Time Bioprocess Monitoring
- Focus on CPP’s / Decision Making
- Measurement Principle
- CHO Cell Culture Study – TCI Hanover
- Quantitative Parameters- Qualitative Parameters
- BioPAT®Spectro - Suitability for Mfg.- Benefits for Mfg
NUTRIENTS/ Metabolites:
Glucose / other carbon sources
Lactate
Classic Real-Time –REACTOR PARAMETERS:
Oxygen
pH
Temperature
CELL PARAMETERS:
Total Cell Count
Viability
Product:
Antibody
Bioprocess - Cell CultivationCritical Process Parameters (CPP) / Decision Making
Background
Qualitative PARAMETERS:Golden batch comparison
& early fault detectionReal-time visibility to
abnormal process trendsStandard Interface to
BioPAT® Spectro NIR systemBasic Measuring Principle
1 absorption of light (small molecules) Glucose, Lactate,
Glutamine, Product
2 scattering of light (cells)Cell parameter
Cell count, Viability
Measurement Principle
.
Water Band –measurement slit 1,2,5mm - design eliminates any issues with signal saturation in clear liquids.
BioPAT®Spectro NIR systemSpectra
Describes the whole chemical composition
Broad absorption bands
Overlap of information
Powerful software tools needed for
Separation of information
multivariate data analysis / chemometrics
Measurement Principle
Quantitive Analysis
Requires offline reference analysis on multiple batches to build robust
models
Qualitative Analysis
PCA – based on similarities in spectra, no off-line reference required, only multiple high
performance batches
Page 23
Spectro / MVDA - The Art of Compressing and Visualizing Information
Data
MVDA
Multivariate Modeling
Information
BioPAT®Spectro - NIR systemEvaluation in Cooperation with TCI Hannover
Case Study
• Cell CultivationCultivation of suspension cell-line CHO-K11 batch cultivation for process parameter optimization8 batch cultivations in 7.5 L scale
4 high performance cultivationadditional substrate feeds in late deceleration growth phase to minimize analyte correlations (glucose, glutamine)4 cultivation runs with oxygen limitation2 glucose feed control
Offline reference measurementsTarget analytes: Glucose, Total Cell Count, Viabilitysample rate 3 – 6 h
BioPAT®Spectro NIR systemEvaluation in Cooperation with TCI Hannover
Case Study
Scope of CPP’sCell cultivation (CHO)
Case Study
Quantitative
Quantitative AnalysisModel robustness
1) internal validation (Cross Validation / Testset) nice error barsnot suitable for real life
2) unseen batches as prediction set
larger error barsrobust models for prediction possibleamount of calibration batches needed?(which batches cover the whole design space?)
Batch #1Batch #2Batch #3
Batch #1.
Batch #4
Batch #5
.
Batch #7
calibration setprediction set
Quantitative Monitoring
Glucose
ModelGlucose
Glucose (g/l)
Concentration Range 0 – 7
Algorithm PLSPCs 5
Validation procedure
Testset K06
Error of Prediction (SEP) 0.24
reference (g/l)
pred
ictio
n (g
/l)
Quantitative Analysis
ModelTotal Cell Count
Cell countMio/ml
Concentration Range 0 – 16
Algorithm PLSPCs 1
Validation procedure
Testset K06
Error of Prediction (SEP) 0.54
Total Cell Count
reference (g/l)pr
edic
tion
(g/l)
Quantitative Analysis
Viability
viable dead
ModelViability
Viability[%]
Concentration Range 0 – 100
Algorithm PLSPCs 5
Validation procedure
Testset K05
Error of Prediction (SEP) 3
Quantitative Analysis
Glucose Feed
-0,5
0,5
1,5
2,5
3,5
4,5
5,5
6,5
7,5
8,5
9,5
0,00 20,00 40,00 60,00 80,00 100,00 120,00 140,00
time / h
Glu
cose
g/l
ReferenceValidation SetCalibration Set
Estimates Glucose Feed
-0,5
1,5
3,5
5,5
7,5
9,5
0 1 2 3 4 5 6 7 8 9 10
Reference (Glucose g/l)
Pre
dict
ions
(Glu
cose
g/l)
Validation SetCalibration Set
glucose prediction is independent from other analytes
robust & reliable models obtained
Glucose - Feed
Quantitative Monitoring
Scope of CPP’sCell cultivation (CHO)
Applications
Qualitative
Monitor Design Space Online and Save Batches by Early Fault Detection
Chemometrics
Create “road” of normal evolution
Real-time monitoring of new batches
No reference analytics needed
Process Monitoring & ControlThe Batch-Trajectory Score 1
ApplicationsQualitative Analysis
low performer (oxygen limitation)
contamination
Process Monitoring & ControlThe Batch-Trajectory Score 1
ApplicationsQualitative Analysis
Process Monitoring & ControlThe Batch-Trajectory Score 3
Qualitative Analysis
Process Monitoring & ControlThe Batch-Trajectory Score 3
ApplicationsQualitative Analysis
Process Monitoring & ControlThe Batch-Trajectory Score 3
contamination
ApplicationsQualitative Analysis
Alarm signal possible when batch goes out of normal trajectory
Process Monitoring & ControlThe Batch-Trajectory Score 3
contaminationearly glucose limitation
ApplicationsQualitative Analysis
Process Monitoring & ControlPCA – Endpoint determination
Endpoint-spectra from batch 04
ApplicationsQualitative Analysis
Process Monitoring & ControlPCA – Endpoint determination
Endpoint spectra from several batches
building the design space for optimal endpoint
real time monitoring of current batch
optimal endpoint of current batch
Alarm Function
ApplicationsQualitative Analysis
BioPAT®Spectro - NEW Freebeam NIR systemSensor Design / Adaptation – Process Robust
Ingold / Sanitary Port adaptations Adaptable to most SS Bioreactors to 50K+ liters
Fiber free systemNo fragile fiber cablesEliminates issues with fiber variation/ lengthIntegrated Electronics/ SensorSingle Cable for power and communications
Solid State Diode -Array SensorProven Robust in Process Environments1100-1700nm (accessible 950–1750nm) spectrometerNo Moving PartsIP65
CIP/SIP readyNo ConsumablescGMP
Contact materials / Interface
Process Suitability
BioPAT®Spectro - Freebeam NIR systemSensor Design - Process Performance
Fiber free systemNegligible Light LossExcellent Sensor to Sensor correlation
Freebeam – Large Aperture measurement20mm measurement spot Significant improvements (versus standard 5mm probe type)
SNR – for Cell Parametersreduced / eliminated impact from bubbles and foulingStabile signal over time
Multivarite Spectra AnalysisFull spectra MVA analysis 950 – 1750nmAbility to see interactionsQualitative Fingerprinting
Diode Array SpectrometerSolid State – no moving parts
Process Suitability
0,000
2,000
4,000
6,000
8,000
10,000
12,000
14,000
0,00 20,00 40,00 60,00 80,00 100,00 120,00
cultivation time / h
Tota
l Cel
l Cou
nt [E
6]
Glu
cose
(g/l)
BioPAT®Spectro - Freebeam NIR systemPractical Benefits – Quantitative/ Qualitative
Reduce/ eliminate offline analysis for metabolites, Titer and manual cell counting
24/7 process monitoring (10 second intervals )
Reduce Risk of Contamination from frequent Sampling
Real-time glucose concentration control
Real time information for decision making/ Guided Sampling
Real-time visibility to abnormal process trends – guided sampling
Harvest timing prediction – Alarm Function
Practical Benefits
Thank you for your interest
www.sartorius-stedim.com/PAT
Thanks for your attention
Supplemental Information –Bacterial Fermentation
BioPAT®Spectro - NIR system
Application Note – Microbial Fermentation
Qualitatative Quantitative
Batch‐Trajectory
ControlParameters
Sum‐Parameters
Cell Parameters
Analyte‐SUM; Sugar‐SUM; Metabolite‐SUM
OD600
Dry Mass
single Analytes
Sugar 1
PLS
Spectra
Reference Analytics
PCASpectra
ClassificationMedia
Endpoint determination
ProcessUnderstanding
Process profile
Metabolic stages
Acetoin
SummaryBacterial fermentation
Bacillus Spore Production 4 – HLB fermentation runs 45-66 Reference measurements
SEP below 10% for Sum-Parameter 50K L scaleOffline reference measurements
Target – Sugar SUM, Metabolite SUM, Cell Count, Dry Mass, OD600
Full recorded spectra used for qualitiative model
BioPAT®Spectro NIR system
Application Note
Building the model with high performance batches
compare new batcheswith model
significant deviations detected
Process Monitoring & ControlClassification - Media composition
Qualitative analysis
Process Monitoring & ControlThe Batch-Trajectory Score 2
Building golden batch model with high
performance batches
visualize performance of current batch
significant deviations detected
Qualitative analysis
SummaryBacterial fermentation
metabolites exhausted (no more cell growth, sporulation phase)
all sugars exhaustedend of lag phase
sugar 1 exhausted
NIR
Sco
re 2
NIR
Sco
re 1
1 2 3 4 5
Process Monitoring & ControlBatch Trajectory – Process Stages
Qualitative analysis
Analyte Spectra Range Unit PCs SEC SEP SEP[%]
AnalyteSUM 66 3 - 45 g/L 3 1,33 1,65 8
SugarSugarSUM SUM 66 0 - 35 g/L 4 1,14 1,47 8
Metabolite SUM 66 3 - 15 g/L 3 1,00 1,18 15
Dry mass Dry mass (TS)(TS) 45 0 - 1,6 % (w/w) 4 0,06 0,08 10
OD600OD600 45 5 - 50 OD 3 2,1 2,94 13
AcetoinAcetoin 66 0 - 11 g/L 5 0,55 0,83 15
Cells 47 0 - 2,1 1010
cells/mL 4 0,24 0,3 29
Quantitative AnalysisOverview
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