PresentationPDF Available

Automated sampling and on-line analytics to increase process understanding

Authors:

Abstract

Generation of process understanding is the key task to enable quality by design (QbD) and process analytical technology (PAT) as “a system for designing, analyzing and controlling” bioprocesses. Understanding of the underlying physiologic and metabolic mechanisms allows to interpret process variability, control it and guarantee for a safe product. Automation technology for liquid handling, facilitating automated sampling as well as on-line analytics, is a fundamental task enabling deeper process understanding as well as process monitoring and control. The application of an automated sampling and sample processing system (Numera) is demonstrated for raw material characterization and real-time monitoring. Within the raw material characterization, the system enabled the description of enzymatic release kinetics in the complex raw material corn steep liquor. The trigger for this application was the higher frequency, higher reproducibility in comparison to hand-drawn samples and the use in the 4-reactor parallel mode. Real-time monitoring was applied in a mammalian cell culture process for analysis of amino acid patterns allowing conclusions about cell metabolism in real-time. Summarizing, it could be shown, that automated liquid handling is a hard-to-replace PAT tool for current and future challenges in biotechnology.
IFPAC Washington, 5 March, 2019 Slide 1
Automated sampling and
on-line analytics to increase
process understanding
Dr Alexandra Hofer, Dr Paul Kroll and
Prof Christoph Herwig
IFPAC Washington, 5 March, 2019 Slide 2
Short introduction
Securecell AG
Information Technology
Bioprocess automation
Process analytical technology
Process Information
Management Software
IFPAC Washington, 5 March, 2019 Slide 3
Research partners
Technical University Vienna
Division: Biochemical Engineering
Prof. Christoph Herwig
Zurich University of Applied
Sciences
Division: Bioprocess Technology
Dr. Lukas Neutsch
IFPAC Washington, 5 March, 2019 Slide 4
PAT Process Analytical Technology
PAT:
“a system for designing, analyzing, and controlling manufacturing
through timely measurements (i.e., during processing) of critical
quality and performance attributes …” (FDA PAT initiative)
Science- based approach for generation of process understanding
measurements of critical parameters in supernatant or gas phase
systematic observation of the bioprocess monitoring
IFPAC Washington, 5 March, 2019 Slide 5
PAT Process Analytical Technology
State of the art of monitoring
CPP; Critical material attributes (CMA)
Critical quality attributes (CQA)
Key process indicators (KPI)
In-line analysis
On-line analysis
At-line/ Off-line analysis
Biomass (OD, CDW, VCC)
Substrates / Metabolites
Product
Product quality
SIR
M
CW 220V M
ES
;
O2
Air
FIRC
FIRC
QIR O2
QIR R-OH
QIR CO2
Gas
Analyzer
Waste Air
Waste Air
PIRC
HV1
CW
FIRC
M
EA
;
WIR
M
ES
;
OD
QIRC pO2
QIR Biomass
TIRC
M
EA
;
FIRC
Feed 2
Feed 1
Base
WIR
WIR
Steam
Off-line
On-line
In-line
or
Septum
QIRC pH
Sensor
OD
QIR Biomass
QIR Biomass
CDW
Off-line
Manual
Air
HV3
HV2
QIR Biomass
Permit.
IFPAC Washington, 5 March, 2019 Slide 6
Sampling
Basis for monitoring and control task sampling
Workflow today
Manual sampling
tedious
risk of
contamination
Sample processing
centrifugation/
filtration
dilution
extraction
Off-line/at-line analysis
manual transfer Data management
paper-based
merge with
process data
difficult
Bioreactor
IFPAC Washington, 5 March, 2019 Slide 7
Sampling
direct measurement of
specific analytes
usage of highly
automated offline
analyzers
reliability through
application of reference
methods
extensive parallelization
flexibility and storage
of reference samples for
later analysis
risk for contamination of
the process and sample
high sampling volume
needed
operator-to-operator
deviations (error-prone)
tedious; requires a lot of
human resources
low sampling frequency
and commonly no real-
time availability of data
paper-based and error-
prone data management
IFPAC Washington, 5 March, 2019 Slide 8
Solution: Automation and Digitization
Modular system
Automated sampling,
sample preparation (dilution,
extraction, filtration) and
storage of cell suspension
and supernatant
Parallelization
Flexibility: adaptable to
process
First step: automated sampling
IFPAC Washington, 5 March, 2019 Slide 9
Manual versus automated sampling
Numera tested @ ZHAW in Switzerland
Pichia pastoris
Repeated fed-batch
Automated sampling enables
Low volume and high
frequency samples
Highly reliable results
At-line analysis with
reference method
IFPAC Washington, 5 March, 2019 Slide 10
Application example
Complex raw material analysis
IFPAC Washington, 5 March, 2019 Slide 11
Background
Process: Penicillium chrysogenum for production of antibiotics
Goal: Investigation of effects of complex raw material (CSL) on process
performance/ fungal physiology to generate process understanding
Main target: identification of bioavailability of amino acids from CSL
IFPAC Washington, 5 March, 2019 Slide 12
Background
Process: Penicillium chrysogenum for production of antibiotics
Goal: Investigation of effects of complex raw material (CSL) on process
performance/ fungal physiology to generate process understanding
Main target: identification of bioavailability of amino acids from CSL
CS L
AspGlyThr Cys
Cys Gln
Ala
Ala
TyrHis
Glu
His
Glu
Asp
SP
LP
IFPAC Washington, 5 March, 2019 Slide 13
Background
Process: Penicillium chrysogenum for production of antibiotics
Goal: Investigation of effects of complex raw material (CSL) on process
performance/ fungal physiology to generate process understanding
Main target: identification of bioavailability of amino acids from CSL
CS L
Ser
Ala
Phe
Ser
Leu
Asp Gly Thr Cys
Cys Gln
Ala
Ala
TyrHis
Glu
HisGluAsp
IFPAC Washington, 5 March, 2019 Slide 14
Background
Process: Penicillium chrysogenum for production of antibiotics
Goal: Investigation of effects of complex raw material (CSL) on process
performance/ fungal physiology to generate process understanding
Main target: identification of bioavailability of amino acids from CSL
CS L
Ser
Ala
Phe
Ser
Leu
Asp Gly Thr Cys
Cys Gln
Ala
Ala
TyrHis
Glu
HisGluAsp
mSR,i
Ala
Phe
Ser
IFPAC Washington, 5 March, 2019 Slide 15
Background
Process: Penicillium chrysogenum for production of antibiotics
Goal: Investigation of effects of complex raw material (CSL) on process
performance/ fungal physiology to generate process understanding
Main target: identification of bioavailability of amino acids from CSL
CS L
Ser
Ala
Phe
Ser
Leu
Asp Gly Thr Cys
Cys Gln
Ala
Ala
TyrHis
Glu
HisGluAsp
mSR,i
Ala
Phe
Ser
rE
IFPAC Washington, 5 March, 2019 Slide 16
Background
Process: Penicillium chrysogenum for production of antibiotics
Goal: Investigation of effects of complex raw material (CSL) on process
performance/ fungal physiology to generate process understanding
Main target: identification of bioavailability of amino acids from CSL
CS L
Ser
Ala
Phe
Ser
Leu
Asp Gly Thr Cys
Cys Gln
Ala
Ala
TyrHis
Glu
HisGluAsp
mSR,i
Ala
Phe
Ser
rE
rER,i rER,i
Gln
Cys
Thr
Asp
His Ala
Thr Cys
Glu
IFPAC Washington, 5 March, 2019 Slide 17
Background
Process: Penicillium chrysogenum for production of antibiotics
Goal: Investigation of effects of complex raw material (CSL) on process
performance/ fungal physiology to generate process understanding
Main target: identification of bioavailability of amino acids from CSL
CS L
Ser
Ala
Phe
Ser
Leu
Asp Gly Thr Cys
Cys Gln
Ala
Ala
TyrHis
Glu
HisGluAsp
mSR,i
Ala
Phe
Ser
rE
rER,i rER,i
Gln
Cys
Thr
Asp
His Ala
Thr Cys
Glu
IFPAC Washington, 5 March, 2019 Slide 18
How to investigate bioavailability?
Experimental plan
Media experiment (with CSL)
Addition of sterile supernatant of fungi fermentation (enzyme-mix)
Monitor amino acids over time
GOAL: identify enzymatic release rates of amino acids
from complex material (CSL)
IFPAC Washington, 5 March, 2019 Slide 19
How to investigate bioavailability?
Experimental plan
Media experiment (with CSL)
Addition of sterile supernatant of fungi fermentation (enzyme-mix)
Monitor amino acids over time
Set-up
4 parallel reactors (DASbox, Eppendorf)
Numera for automated sampling & sample processing
HPLC for analytics
GOAL: identify enzymatic release rates of amino acids
from complex material (CSL)
IFPAC Washington, 5 March, 2019 Slide 20
Result
Automated sampling with Numera allows high frequency sampling in multibioreactor system
Sample frequency once per
hour; every 6h
Sampling for 72h (over night)
Two phases can be identified
IFPAC Washington, 5 March, 2019 Slide 21
Result
Automated sampling with Numera allows high frequency sampling in multibioreactor system
More data, more information Monod model can be fitted
Enzymatic release rate rER,i for 10 amino acids identified
IFPAC Washington, 5 March, 2019 Slide 22
Result
Automated sampling with Numera allows high frequency sampling in multibioreactor system
More data, more information Monod model can be fitted
Enzymatic release rate rER,i for 10 amino acids identified
Process understanding can be gained
IFPAC Washington, 5 March, 2019 Slide 23
Result
Automated sampling with Numera allows high frequency sampling in multibioreactor system
More data, more information Monod model can be fitted
Enzymatic release rate rER,i for 10 amino acids identified
Process understanding can be gained
Highly automated
Without manual intervention
One experiment more data more information
IFPAC Washington, 5 March, 2019 Slide 24
Automated Sampling
direct measurement of
specific analytes
usage of highly
automated offline
analyzers
reliability through
application of reference
methods
extensive parallelization
flexibility and storage
of reference samples for
later analysis
risk for contamination of
the process and sample
high sampling volume
needed
operator-to-operator
deviations (error-prone)
tedious; requires a lot of
human resources
low sampling frequency
and commonly no real-
time availability of data
paper-based and error-
prone data management
IFPAC Washington, 5 March, 2019 Slide 25
Application example
On-line monitoringof CHO process
IFPAC Washington, 5 March, 2019 Slide 26
Automated sampling & on-line analytics
Challenge From measurement to monitoring!
(1) Automated sampling
(2) Transfer to analyzer
(3) Data management
(4) Process control
(1) (2)
(3)(4) ?
?
?
IFPAC Washington, 5 March, 2019 Slide 27
Automated sampling & on-line analytics
Online analytics available through automated sampling (Numera)
Feedback/ control enabled via process management system (Lucullus PIMS)
IFPAC Washington, 5 March, 2019 Slide 28
Set-up @ TU Vienna
Fully automated analysis of product, vitamins and amino acids by HPLC
and of substrates, metabolites and product by Cedex BioHT enabled by
Numera and Lucullus
IFPAC Washington, 5 March, 2019 Slide 29
On-line monitoring of CHO cultivation
Substrate & metabolites
Glucose, Lactate and Ammonia
Monitoring with Cedex Bio HT
Data point every 2 h (every 30 min
possible)
Analytical data available in Lucullus
PIMS
IFPAC Washington, 5 March, 2019 Slide 30
On-line monitoring of CHO cultivation
Product
IgG
Monitoring with HPLC
Data point every 1 h (every 15 min
possible)
High-frequency
24/7 no attendance needed
IFPAC Washington, 5 March, 2019 Slide 31
On-line monitoring of CHO cultivation
Amino acids
Monitoring with HPLC
21 amino acids can be analyzed
IFPAC Washington, 5 March, 2019 Slide 32
On-line monitoring of CHO cultivation
Amino acids
Monitoring with HPLC
21 amino acids can be analyzed
Benefits
Process transparency
Early decisions in process development
Possibilities for control actions
IFPAC Washington, 5 March, 2019 Slide 33
Automated sampling, on-line analytics and Lucullus PIMS
direct measurement of
specific analytes
usage of highly
automated offline
analyzers
reliability through
application of reference
methods
extensive parallelization
flexibility and storage
of reference samples for
later analysis
risk for contamination of
the process and sample
high sampling volume
needed
operator-to-operator
deviations (error-prone)
tedious; requires a lot of
human resources
low sampling frequency
and commonly no real-
time availability of data
paper-based and error-
prone data management
IFPAC Washington, 5 March, 2019 Slide 34
Automated sampling, on-line analytics and Lucullus PIMS
direct measurement of
specific analytes
usage of highly
automated offline
analyzers
reliability through
application of reference
methods
extensive parallelization
flexibility and storage
of reference samples for
later analysis
no risk of contamination
of the process and sample
low sampling volume
possible
High reliability
manual intervention
reduced to minimum
high sampling frequency
and real-time availability of
data
Automated data
management
IFPAC Washington, 5 March, 2019 Slide 35
Summary
PAT tools are essential in pharmaceutical development, manufacturing, and
quality assurance
Numera and Lucullus represent an integrated PAT solution
High frequent, on-line measurements facilitate
process transparency and knowledge generation (standardized, reliable, accurate, automated
procedures)
early decisions in process development (real time, high frequent, 24/7 measurement)
control strategies (feed-back control loop)
IFPAC Washington, 5 March, 2019 Slide 36
Thank you for
your attention!
Contact
alexandra.hofer@securecell.ch
Securecell AG
www.securecell.ch
Follow us on
IFPAC Washington, 5 March, 2019 Slide 38
Sampling frequency
Sample processing
Duration
min
Frequency
h
-1
day
-1
Direct transfer
6’
10 h
-1
240 day
-1
Sampling + Filtration
10’
6 h
-1
144 day
-1
Sampling + Dilution
12’
5 h
-1
120 day
-1
Sampling + Dilution+ Filtration
15’
4 h
-1
96 day
-1
IFPAC Washington, 5 March, 2019 Slide 39
Application I: raw material analysis
Approach Experimental assessment of hypothesis
Result Pulse of limiting amino acid could increase titer by three-fold
Chapter
In recent years process modelling has become an established method which generates digital twins of manufacturing plant operation with the aid of numerically solved process models. This article discusses the benefits of establishing process modelling, in-house or by cooperation, in order to support the workflow from process development, piloting and engineering up to manufacturing. The examples are chosen from the variety of botanicals and biologics manufacturing thus proving the broad applicability from variable feedstock of natural plant extracts of secondary metabolites to fermentation of complex molecules like mAbs, fragments, proteins and peptides.Consistent models and methods to simulate whole processes are available. To determine the physical properties used as model parameters, efficient laboratory-scale experiments are implemented. These parameters are case specific since there is no database for complex molecules of biologics and botanicals in pharmaceutical industry, yet.Moreover, Quality-by-Design approaches, demanded by regulatory authorities, are integrated within those predictive modelling procedures. The models could be proven to be valid and predictive under regulatory aspects. Process modelling does earn its money from the first day of application. Process modelling is a key-enabling tool towards cost-efficient digitalization in chemical-pharmaceutical industries.
ResearchGate has not been able to resolve any references for this publication.