Requirements for process control of continuous processes

CONFIDENTIAL - PROPERTY OF SARTORIUS STEDIM BIOTECH GmbH

Requirements for Process Control of Continuous Processes: Sensorics and

Automation Marek Hoehse

1/18/2014


22. Octobre 2013 Page 2

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

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


Demanding process requirements

High oxygen consumption

High CO2 formation

Pot. challenging clarification

More complex DSP approach

Online control of DSP process

Complex batch definition

Challenges (technical)

Challenges of continuous cultivation

More complex process

Higher risk of failure

Increased training requirements

Limited number of reliable & scaleable cell retention

devices

Challenges (process) New forms of in-process testing

Robust sensors

Robust control systems

New forms of release approaches

Regulatory aspects

Minimizing blackout of operation units

Higher need of process understanding

Full knowledge of CPP´s and CQA´s required

Sophisticated process control strategies

Raw material concerns

Higher material control or higher process robustness required

Sophisticated feeding technologies

Presenter

Presentation Notes

key point of the challenges is sensor technology Sensor technology <-> automation PAT not limited to sensors – adding new sensors does not convert your process into a QbD process. Risk analysis control strategies data analysis tools to gain process knowledge


Demanding process requirements

High oxygen consumption

High CO2 formation

Pot. challenging clarification

More complex DSP approach

Online control of DSP process

Complex batch definition

Challenges (technical)

Challenges of continuous cultivation

More complex process

Higher risk for failure

Increased training requirements

Limited number of reliable & scaleable cell retention

devices

Challenges (process) New forms of in-process testing

Robust sensors

Robust control systems

New forms of release approaches

Regulatory aspects

Minimizing blackout of operation units

Higher need of process understanding

Full knowledge of CPP´s and CQA´s required

Sophisticated process control strategies

Raw material concerns

Higher material control or higher process robustness required

Sophisticated feeding technologies

Presenter

Presentation Notes

Sensor adaption to CM processes is not that critical for SS bioreactors (multi use sensoric) It becomes very critical for SU

CONFIDENTIAL - PROPERTY OF SARTORIUS STEDIM BIOTECH GmbH 22. Octobre 2013 Page 5

But can single-use equipment cope with demanding process requirements?

High oxygen demand?

Sufficient CO2 removal?

Robust Sensor Technology?

Proteinaceous aerosol formation?

Long operation times (> 14d)?

Predictive scale-down model?

Media logistics?

High performance clarification?

Connectivity to cell retention system?

Presenter

Presentation Notes

Connectivity to cell retention is not really an issue here But Robustness ! Sensor lifetime – Some SU sensors are limited to a certain number of measurement cycles (~10000) Online control over 6 months 50 times a day


BIOSTAT® STR single-use bioreactor designed for demanding processes


Optimized for high cell density cultures

Conventional stirred tank design Optical and redundant conventional pH and DO probes

Working volume: 50 L, 200 L, 500 L, 1000 L, 2000L

3 blade segment & rushton impeller options

Inhouse bag production

Presenter

Presentation Notes

pH dissolved oxygen redundant probes


NUTRIENTS:

Glucose

Glutamin

REACTOR PARAMETERS:

Oxygen, CO2

pH

Temperature

Cell parameters:

Biomass

Total Cell Count

Viability

microbial contamination

Product (CQA):

Antibody

Vaccines

Continuous Biomanufacturing Critical Process Parameters (CPP)

Cell Cultivation / MO Fermentation METABOLITES:

Lactate

Glutamate

Ammonia

Acetate

Acetoin

dry mass

OD600 Sucrose

Fructose

Spores

Enzymes


Analytical Systems

NIR/ UV-VIS Spectroscopy

Viable Biomass Analysis Exhaust Gas Analysis


Online Glucose | Lactate Analysis


Automated monitoring and control of continuous processes – a reality?

Implementation of glucose | lactate measurement and control strategy

Global Trends BioPAT®Trace

Dialysis or filtration mode Compact design Reducing off-line sampling

DCU – Local Control Unit

Direct display of glucose | lactate conc.

Quick and simple process monitoring at a glance

BioPAT®MFCS/win + Recipe Module

Fully automated glucose feed & perfusion rate control

Recipes for automatic event-based control of cultures

Improved batch-to batch consistency

Single-use bioenzymatic sensors and fluidics Integrated autocalibration & autosampling


CONFIDENTIAL - PROPERTY OF SARTORIUS STEDIM BIOTECH GmbH 22. Octobre 2013

BioPAT®Xgas – Integrated Off-Gas Analysis and Process Control

Global Trends BioPAT®Xgas

Parallel measurement of O2 | CO2

Moisture and pressure independent

Directly integrated into exhaust gas line

Compact design for flexible integration

DCU – Local Control Unit

Direct Display of

%O2 | %CO2

Quick and simple process

monitoring

Functional monitoring of DO

probe

BioPAT®MFCS/win + Recipe Module

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

Automation

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

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)

Start End

PC2

glutamine exhausted

PC1 = cell count

= Sum signal metabolites

NIR - Qualitative


low performer (oxygen limitation)

contamination

Comparison to “golden batch”

Alteration within batches can be observed in process trajectories

Ability to detect deviations and similarities

Qualitative Process Monitoring

The Batch-Trajectory Score 1



The Batch-Trajectory Score 3

contamination early glucose limitation



NIR Score 3 & Viability

viability

(offline analysis)

PCA-Score3

(spectral variations

of NIR spectra)

!strong correlation!

NIR - Qualitative


NIR Media Classification

Media composition in bacillus spore production

NH-Vibration

Batch 2: Lower concentration of N-source


NIR - Quantitative

Model

Total Cell Count

Cell count

Mio/ml

Concentration Range 0 – 16

Algorithm PLS

PCs 1

Validation procedure Testset K06

Error of Prediction (SEP) 0.54

NIR - Quantitative

Total Cell Count

reference (Mio cells / ml)

pred

ictio

n (M

io c

ells

/ m

l)

NIR - Quantitative

Viability

viable dead

direct scatter pattern or correlation to cell lyses?

Model

Viability

Viability

[%]


Algorithm PLS

PCs 5


Error of Prediction (SEP) 3

NIR - Quantitative

Glucose

Model

Glucose Glucose (g/l)


Algorithm PLS

PCs 5


Error of Prediction (SEP) 0.24

reference (g/l)

pred

ictio

n (g

/l)


Summary


Acknowledgements

Co-workers

Christian Grimm

José Fernandes

Roland Bienert

Thomas Scheper

Dörte Solle

Marko Sandor

Thank you !

http://www.uni-hannover.de/


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


High cell density inoculum production using Biostat® RM perfusion

MCB/WCB

vial

Perfusion expansion Controlled freeze of HCD

seed stocks

Direct seed of production

bioreactor

References: 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

Presenter

Presentation Notes

For perfusion processes the hardware is not an issue – it already exists. But what about sensoric?

1-m011-m02

1-m03

3-m013-m02

3-m03

4-m01

4-m024-m03

2-m01

2-m02

2-m03

Building the model with high performance

batches compare new batches

with model

significant deviations detected

Multivariate Statistical Process Control (MSPC)

Classification - Media composition in bacillus spore production


THANK YOU VERY MUCH FOR YOUR ATTENTION! QUESTIONS?


Example - Cultivation of CHO cells in a Biostat® RM

Comparison of in-line (capacitance measurement) vs offline data (Cedex)


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


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)


Continuous culture process modes

Y

Perfusion Concentrated fed-batch

nutrients

product

cells

10-80 days

nutrients

product

cells

5-20 days


reduce development costs reduce time to market reduce COGs reduce complexity regional production

Opportunities - Perfusion

Only way forward for labile or difficult to express products

Faster material supply for medium – low titre products

Reduced process development time line for non-platform processes

Better controlled product quality due to stable environment

Smaller production bioreactors

Fewer seed steps

Industry drivers

Can perfusion help to address industry challenges of today?

follow-on biologics biosimilars new mab formats smaller indications

Biopharma trends


Where are we moving to in the age of single-use? Single-Use Multi-Use

Investment/ Costs

Low fixed cost

No steam & CIP, limited WFI

Decision late (<1y)

High fixed cost

WFI, Steam, CIP system

Decision early (> 3 y)

Qualification Equipment IQ/OQ

Sterilisation via vendor

No Cleaning

E&L PV risk based; generic E&L dossiers?

Equipment IQ/OQ (significant!)

Sterilisation

Cleaning

Appropriate quality of materials!

Design Carefully evaluate vendors!

Does it work for my process?

Carefully evaluate vendors!

Stirred tank design dominating

Refurbishment Very easy Challenging

Footprint Reduced


BioPAT®Trace – Glucose | Lactate Analyzer

Seed Bioreactor Bioreactor Celsius FFT

Bioreactor

Sampling

Features

• Fully automated, self calibrating

• Modes: Filtration, Dialysis, Sampling

• Plug & Play

• Reusable system

• Fully disposable sensor & fluidics set

• Ethernet, Modbus, (OPC)

• Compact device



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)


Principle of measurement Patch Composition DO-Sensor e of measurement Determination of Fluorescence Lifetime for DO-measurement

N2

O2

• Excitation with sinosoidal modulated light

• 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

Ind

Ind

Ind

Ind

Ind

Ind

Ind Ind

Ind

Ind

Ind

Ind

fluorophore Medical grade silicone


Sensors & Automation: Implementation Along Production Chain (mAb)

Sensors

Automation

SCADA

pH pH

pH pH pH pH

pH pH pH

cond.

cond.

cond. cond.

pressure. pressure. pressure.

pressure. pressure. pressure.

pressure. pressure.

pH pH DO flow. flow.

flow. cond. flow. flow.

flow. flow. pressure.

Sensors

Sensors

Automation DCU DCU DCU

DCU DCU DCU DCU

SCADA MFCS

MFCS

DCU DCU

MFCS

Automation

SCADA

Biomass. Glc

Presenter

Presentation Notes

Sensors are used all along the process chain


Biostat STR set-up for continuous perfusion or concentrated fed-batch

CONFIDENTIAL - PROPERTY OF SARTORIUS STEDIM BIOTECH GmbH 22. Octobre 2013

Principle of measurement Calibration for pH-Sensor

• Sigmoidal calibration function

• Fit by Boltzmann-Equation:

222426283032343638404244464850525456

4 5 6 7 8 9pH

ϕ [°]

A1

A2

dx

x0

Page 43


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 • Application test

• Documentation


SU sensor development – general design rules / requirements

Requirement Specification

Shelf life < 3 years after Irradiation Sterility has to be confirmed

Media consistency Lipids, Oil, aqueous solutions, saline solutions, alcoholes,

acids, alcalic solutions, WFI

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


Application of Single Use pH and DO sensors

Sensors integrated in the cultivation bags and Univessel-SU Example: pH, DO


Measurement principle: Dielectrically essential part of cell structure

Cytoplasmic Lipid

membrane

impermeable to the free flow

of ions

CONFIDENTIAL - PROPERTY OF SARTORIUS STEDIM BIOTECH GmbH Page 48

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

22. Octobre 2013

Cultivation of CHO cells


Introduction Glucose / Lactate Measurement

BioPAT®Trace

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

22. Octobre 2013

Connection to SU Bioreactors


Application: BioPAT® Trace - System

The BioPAT® Trace can be connected with every bioreactor | fermenter – coming from Sartorius or competitors

22. Octobre 2013


Online biomass detection (cell culture)

Seed Bioreactor Bioreactor Celsius FFT

Bioreactor

Sampling


•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


Measurement principle: In situ four pin electrode probe used for radio-frequency impedance measurement

Dead cells “invisible” to RF Field

Only live cells polarized

field area approx 25mm

Cytoplasmic Lipid membrane

impermeable to the free flow of ions


NUTRIENTS:

Glucose

Glutamin

REACTOR PARAMETERS:

Oxygen, CO2

pH

Temperature

Cell parameters:

Total Cell Count

Viability

microbial contamination

Product (CQA):

Antibody

Vaccines

Bioprocess – Cell Cultivation / MO Fermentation

Critical Process Parameters (CPP)

METABOLITES:

Lactate

Glutamate

Ammonia

0 12 24 36 48 60 72 84 96 108 120 132 1440

1

2

3

4

5

6

7

glu

cose

[g/]

la

ctat

e [g

/L]

time [h]

0

200

400

600

800

1000 glutamine [m

g/L] glutam

ate [mg/L]

0 12 24 36 48 60 72 84 96 108 120 132 1440.0

2.0x106

4.0x106

6.0x106

8.0x106

1.0x107

1.2x107

1.4x107

1.6x107

time [h]

Cel

l Cou

nt T

otal

C

ell C

ount

Vita

l [N

/mL]

0

10

20

30

40

50

60

70

80

90

100

Viability [%]

Acetate

Acetoin

dry mass

OD600 Sucrose

Fructose

Spores

Enzymes

Documents

Requirements for process control of continuous processes