nir - spectroscopy for bioprocess monitoring & control · nir - spectroscopy for bioprocess...

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

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


Algorithm PLSPCs 1


Testset K06

Error of Prediction (SEP) 0.54

Total Cell Count

reference (g/l)pr

edic

tion

(g/l)


Viability

viable dead

ModelViability

Viability[%]


Algorithm PLSPCs 5


Testset K05

Error of Prediction (SEP) 3


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




Qualitative Analysis


contamination


Alarm signal possible when batch goes out of normal trajectory


contaminationearly glucose limitation


Process Monitoring & ControlPCA – Endpoint determination

Endpoint-spectra from batch 04


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


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


Building golden batch model with high

performance batches

visualize performance of current batch

significant deviations detected


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


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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