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1. 13.02.2013 Ch. Herwig BioProcess Technology 1
PAT beyond an analytic tool –
reduce data to knowledge in real-time
BILS
February 2013
Christoph Herwig
PAT: A system for designing, analyzing and controlling manufacturing
through timely controlled measurements of critical quality and
performance attributes with the goal of ensuring final product quality –
ICHQ8 R2
The Product Life Cycle
example: A-mAb Case Study Mapping
DRUG
DISCOVERY
Process
Development
Manufacturing

2. The Product Life Cycle
Knowledge Management Tasks
Knowledge Management Gap Analysis
example: A-mAb Case Study Mapping
http://www.ispe.org/
patcop/resources
WHAT HOW WHY

3. Show Tools to Extend the PAT Definition to
Knowledge Management
I)
Data from
Analytical
Devices
II)
Combination
of Data for
new Data
III)
Extract
Information
from Data and
Control
IV)
Extract
Knowledge
from
Information
Manage
Knowledge:
Q10!
13.02.2013 Ch. Herwig BioProcess Technology 5
PAT: A system for designing, analyzing and controlling
manufacturing through timely controlled measurements
of critical quality and performance attributes with the
goal of ensuring final product quality – ICHQ8 R2
13.02.2013 Ch. Herwig BioProcess Technology 6
I
Data From Analytical
Devices

4. Temporal resolution? What for?
#1, #2 or #3? When was
the final biomass reached?
Low temporal resolution High temporal resolution
Choose adequate temporal
resolution!
What happened in
between?
13.02.2013 Ch. Herwig BioProcess Technology 7
13.02.2013 Ch. Herwig BioProcess Technology 8
Bioreactor process flow diagram for
bioprocess development
QIR N.N
QIR N.N.
QIR Gluc
QIR EtOH
QIR MeOH
QIR Gly
SIR
M
CW 220V
MES;
FIRCFIRC
QIR O2
QIR R-OH
QIR CO2
Gas
Analyzer
Figaro
Waste Air
PIRC
Air
HV1
HV3
HV2
CW
MES;
To Inactivation
Collection
FIRC
MES;
MEA;
WIR
MES;
QIR MeOH
QIR EtOH
QIR
HAc
QIR FAc
QIR
FAl
QIR AcAl
Gas Chromatograph
QIR MeOH
QIR EtOH
QIR Gly
QIR Gluc
QIR Sorb
QIR N.N
Enyzmatic Phomotric Robot
FTIR
Turbidity
QIRC pH
QIRC pO2
QIR
Biomass
QIR Biomass
Capcit.
QIR NADH
Fluoresc.
MEA;
FIRC
In-line
On-line
Off-line
Feed 2
Feed 1
Base
WIR
WIR
Steam
Off-line
WIRC
orSeptum
Indication Recording
FIRC
FI
FIRC
Control
PIMS
PIMS
Local
PIMS
PIMS
NONE
PLS
PIMS
NONE
LEGEND
On-line
On-line
In-line

5. Online HPLC
Metabolites, substrates, but also Product Quantitiy
and Quality
• 0.2 µm Filtration Probe
• HPLC Setup
RP-HPLC Column for Online Protein and Peptide
Analytics
Ion Exchange Collumn for Online small Metabolite
and Substrate Quantification
• 3D Autosampler
• Flow Cell
13.02.2013 Ch. Herwig BioProcess Technology 9
Di-Electric Spectroscopy
Capacitance
• Capacitance as an indicator for morphological variations
• Differentiate biomass aspects!
A: Start exponential fed-batch phase
B: Start linear induction fed-batch phase
C: Effects of Induction
Green: Ratio biomass / capacitance
Purple: Feed profile
Blue: Biomass
Red: Capacitance
13.02.2013 Ch. Herwig BioProcess Technology 10

6. Continuous, inline morphological measurements
for real-time morphological process monitoring
Log Frequency [Hz]
Capacitance [pF]
Mean cell size
Volume/Surface ratio
Morphology
Biomass concentration
+++
- - -
+++
- - -
Real-time morphological analysis
13.02.2013 Ch. Herwig BioProcess Technology 11
on-line multiple component analysis for efficient bioprocess development
raw data consistent for rate and yield calculation?
quantitative bioprocess development
Online Enzymatic Photometric Robot
Dietzsch et al. Journal of Biotechnology, doi: 10.1016/j.jbiotec.2012.03.010
13.02.2013 Ch. Herwig BioProcess Technology 12

7. But Data are noisy and information
is difficult to be extracted
• Effect of noise on data, but even worse on information
P. Wechselberger. C. Herwig, Biotechn Progr., 2011
13.02.2013 Ch. Herwig BioProcess Technology 13
Why Control Glucose
Concentration
in MAb Processes?
• Glucose concentration impacts on specific antibody
production rate
• Glucose concentration impacts on metabolite
formation rate
• Glucose concentration impacts on MAb glycosylation
pattern
 Glucose concentration is critical in
respect to productivity and product quality!
13.02.2013 Ch. Herwig BioProcess Technology 14

8. Goal: Control of
Glucose Concentration
Incomplete Glycosylation, Quality Issues!
X
Low Productivity, High Lactate Production
Process Performs Well
X
OK!
 Control of glucose concentration in a narrow range
favourable!
13.02.2013 Ch. Herwig BioProcess Technology 15
Error Propagation on
Control Strategies
 Fatal Decision Making on High Error Signals
Error12%Error0%
Optimal Parameters for 0% Error Optimal Parameters for 12% Error
13.02.2013 Ch. Herwig BioProcess Technology 16

9. 13.02.2013 Ch. Herwig BioProcess Technology 17
II
Combining Data for
New Data
17
Scope & Goal for Softsensor Development
• Software solution for
dynamically changing
– Process conditions
– Growth stoichiometry
• Softsensor should be
– Based on minimum prior knowledge
– No need for training data sets
– Generic; Valid for different hosts and process conditions
– Easily adaptable from fed-batch to induced states
Generic
Prior
Knowledge
Accurate
13.02.2013 Ch. Herwig BioProcess Technology 18

10. Real-time Hybrid Exploitation
based on
first principle relationships
13.02.2013 Ch. Herwig BioProcess Technology 19
Off-line Process Monitoring
Outputs:
Concentrations: ci, x, p
Substrates, Metabolites
Products, Biomass
Nucleotides, Proteome
Internal components
Tools:
13C labelled metabolite profile
2D- / µ-Array proteomics
Stoichiometry
Outputs:
ri, Yj/i, µ, rp
ci, x, p,
Tools:
Black Box, MFA
Mass & Elemental Bal.
Kinetic models
Outputs:
ri, Yj/i, µ, rp
ci, x, p,
Tools:
Unstructured, Structured
Segregated, etc.
Observer Algorithms
Outputs:
ri, Yj/i, µ, rp
ci, x, p,
Tools:
State Observer
Ext. Kalman Filter
Experiment Validation
Outputs:
Failure Detection
Tools:
Mass & Elemental Bal.
Statistics PLSR
Chemometrics DPCA
Data Analysis
Outputs:
ci, x, p
Tools:
Statistics PLSR
Chemometrics DPCA
On-line Process Monitoring
Outputs:
Process controls
Reaction parameters
Rates: Offgas OUR, CER
Concentrations: ci, x
Substrates, Metabolites
Products, Biomass
Nucleotides
Internal components
Tools:
On-line Sensors
At-line Sensors
Real-Time
Off-line
Off-line to
running process
Data
Information
Estimation of biomass concentration in
induced bioprocesses
• Rate based soft-sensors
– No strain specific information
necessary
– Simple input variables (Off-
gas analysis, feed flow rates)
• Robust estimation of
biomass concentration
– even under induced
conditions
– yield coefficients are
changing over time
Sagmeister et al. 2013 Chemical Engineering Science
submitted manuscript

11. 13.02.2013 Ch. Herwig BioProcess Technology 21
III
Extract Information
from Data
and Control
21
Why is the conversion of
Data to Information important?
13.02.2013 Ch. Herwig BioProcess Technology 22
0
0.02
0.04
0.06
0.08
0.1
0.12
0
0.5
1
1.5
2
2.5
3
3.5
4
0 2 4 6 8 10 12 14 16
HAc,AcetAl[g/l]
EtOH[g/l]
time [h]
Dcrit SHIFTSHIFT--DOWNDOWN
REDRED--OXOX  OXOX
DcritDcritDcrit SHIFTSHIFT--DOWNDOWN
REDRED--OXOX  OXOX
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0 2 4 6 8 10 12 14 16
Yields[C-mol/C-mol]
time [h]
Raw Data can be misleading!
Own method to quantify consistent rates in dynamic process conditions in real time
Herwig, C, et.al.. 2001. On-line stoichiometry and identification of metabolic state in dynamic process conditions. Biotechnol. Bioeng. 75:345-354.
DATA INFORMATION

12. Concept of
physiological and mechanistic understanding
Process
Process
Parameters Product
Physiological
Assessment
Rates/Yields
Sagmeister et.at. PDA Journal, 2012
13.02.2013 Ch. Herwig BioProcess Technology 23
Multivariate understanding of recombinant
protein production using specific rates
• Combine raw data to
scalable entities
• Use of specific entities
for knowledge extraction
24
Feed-Profiles Biomass [g/L]
/
=
Specific rate of substrate
uptake qs [g/g/h]

13. Kalman Filter Closed Loop
Control Strategy
PIMS
Simatic PCS 7 Box
13.02.2013 Ch. Herwig BioProcess Technology 25
Induced P. pastoris fed-batch with methanol as C-source
On-line Performance of
Closed Loop Controller
 Demonstration of Kalman closed-loop control of the specific substrate uptake rate qs in
induced P. Pastoris fed-batch process
qs from Kalman
Filter
qs setpoint
qs calculated from off-line
data
13.02.2013 Ch. Herwig BioProcess Technology 26

14. Comparison of developed modules
Highest variance in off-line estimation, lowest variance in tools that use reconciliation
Comparison based on three individual
fermentation runs for all three devices
13.02.2013 Ch. Herwig BioProcess Technology 28
IV
Extract Knowledge
from Information
28

15. Current Solution:
„How“ but not „Why“
CPPs
k=-0.007 k=0 k=+0.007
T=20°CT=27.5°CT=35°C
Specific Activity
[kU/gbiomass]
Induction Phase
Temperature
[°C]
Induction Phase
Feeding
Exponent k
13.02.2013 Ch. Herwig BioProcess Technology 29
Knowledge
Data
CPPs
CQA
Multivariate understanding of recombinant
protein production using specific rates
• Combine raw data to
scalable entities
• Use of specific entities
for knowledge extraction
30
Feed-Profiles Biomass [g/L]
/
=
Specific rate of substrate
uptake qs [g/g/h]

16. Reduction of development time:
Integration of physiological factors in DoE
Usual feed profile design Physiological feed profile design
 Careful selection of physiological factors for the design
of the DoE significantly reduces number of experiments
 Physiological factors allow mechanistic understanding of expression system
and build up platform knowledge
Reduced factors as based on
specific substrate uptake rate
Wechselberger, P., Sagmeister, P., Engelking, H., Schmidt, T., Wenger, J., Herwig, C., (2012) Bioprocess and Biosystems Engineering
13.02.2013 Ch. Herwig BioProcess Technology 31
13.02.2013 Ch. Herwig BioProcess Technology 32
Stress analysis ensures robustness for scale up:
On-line analysis of key regulations using
specific parameters and metabolic modeling
qox.phos
O2, NADH
qEtOH qferm
qHAc
qcat, ox
qcat
qana
CO2
CO2, NADH
Pyr
HAc
EtOH
AcAl
CO2, NADH
Glc
X
NADH
qglc
NADH
NADH
qox.phos
O2, NADH
qEtOH qferm
qHAc
qcat, ox
qcat
qana
CO2
CO2, NADH
Pyr
HAc
EtOH
AcAl
CO2, NADH
Glc
X
NADH
qglc
NADH
NADH
qEtOH qferm
qHAc
qcat, ox
qcat
qana
CO2
CO2, NADH
Pyr
HAc
EtOH
AcAl
CO2, NADH
Glc
X
NADH
qglc
NADH
NADH
20 30 40 50 60 70 80 90 100
5
5.5
6
6.5
7
7.5
q
cat
, q
glc
, q
ana
[mmol
glc
/mol
X
/h]
q
O2
[mmol/g
X
/h]
q glucoseq catq ana
q ox phos
0
0.1
0.2
0.3
0.4
0.5
2
4
6
8
10
12
0 4 8 12 16 20
Glc[g/l]
Biomass[g/l],q
o2
,q
co2
[mmol/g/h]
time [h]
phase
i)
phase
ii)
phase
iii)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0
0.5
1
1.5
2
2.5
3
0 5 10 15 20
AcetAl[g/l]
EtOH,HAc[g/l]
time [h]
Herwig, C, von Stockar, U. 2002. A
small metabolic flux model to
identify transient metabolic
regulations in S. cerevisiae.
Bioprocess Biosyst. Eng. 24:395-
403

17.  Well Controlled Cultivations
Execution of the knowledge
generation cycle
G6P
S7P
E4P
R5P
F6P
G3P
GLC
his
RNA
phen
trp
tyr
3PG
cys
glc
ser
RNA
PEP
PYR
AcCoAmit
ala
ileu
leu
val
phen
trp
tyr
leu
AKG
OAAasn
asp
ileu
met
thr
RNA
arg
glum
glut
lys
pro
POLY
ACET
HAC
ETOH
1-C
NADH NADPH
GOH AA
LIPID
RNA
BIOMASS
POLY
O
AcCoAcyt
SUC
NADH cyt
NADHmit
NADPHcyt
NADPHmit
LIPID
lys
PYR
OAA
AcCoAmit
AcCoAcyt
NADPH
-2
-1
0
1
2
3
4
5
-5 0 5 1 0 1 5 2 0 2 5
-1
0
1
2
3
-5 0 5 1 0 1 5 2 0 2 5
-1
0
1
2
3
4
-5 0 5 1 0 1 5 2 0 2 5
-0.5
0
0.5
1
1 .5
-5 0 5 1 0 1 5 2 0 2 5
-0.1
0
0.1
0.2
0.3
-5 0 5 1 0 1 5 2 0 2 5
-0.04
-0.02
0
0.02
0.04
0.06
0.08
-5 0 5 1 0 1 5 2 0 2 5
-2
0
2
4
6
8
-5 0 5 1 0 1 5 2 0 2 5
-2
0
2
4
6
8
-5 0 5 1 0 1 5 2 0 2 5
-1
0
1
2
3
4
5
-5 0 5 1 0 1 5 2 0 2 5
-2
0
2
4
6
8
-5 0 5 1 0 15 2 0 25
-0.2
0
0.2
0.4
0.6
0.8
-5 0 5 1 0 1 5 2 0 2 5
-2
-1
0
1
2
3
-5 0 5 1 0 15 2 0 25
-1
0
1
2
3
4
-5 0 5 10 1 5 20 2 5
-0.5
0
0.5
1
1.5
2
-5 0 5 10 1 5 20 2 5
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
-5 0 5 10 1 5 20 2 5
-2
-1
0
1
2
3
-5 0 5 1 0 15 2 0 25
-1 .5
-1
-0.5
0
0.5
1
1 .5
2
2 .5
-5 0 5 1 0 15 2 0 25
-1.5
-1
-0.5
0
0.5
1
1.5
2
-5 0 5 1 0 1 5 2 0 2 5
-0.2
0
0.2
0.4
0.6
-5 0 5 10 1 5 20 2 5
-10
-5
0
5
10
1 5
20
-5 0 5 10 15 20 25
-4
-2
0
2
4
6
8
-5 0 5 1 0 15 2 0 25
-2
-1
0
1
2
3
-5 0 5 1 0 15 2 0 25
-0.5
0
0.5
1
1 .5
-5 0 5 1 0 15 2 0 25
-0.04
-0.02
0
0.02
0.04
0.06
0.08
-5 0 5 10 15 20 25
-5
0
5
1 0
1 5
-5 0 5 1 0 15 2 0 25
-2
-1
0
1
2
3
4
5
6
-5 0 5 1 0 15 2 0 25
-0.02
0
0.02
0.04
0.06
-5 0 5 1 0 15 2 0 25
-1 .5
-1
-0.5
0
0.5
1
1 .5
2
2 .5
-5 0 5 1 0 15 2 0 25
0.00
20.00
40.00
60.00
80.00
1 00.00
-5 0 5 10 1 5 20 2 5
QIR N.N
QIR N.N.
QIR Gluc
QIR EtOH
QIR MeOH
QIR Gly
SIR
M
CW 220V
MES;
FIRCFIRC
QIR O2
QIR R-OH
QIR CO2
Gas
Analyzer
Figaro
Waste Air
PIRC
Air
HV1
HV3
HV2
CW
MES;
To Inactivation
Collection
FIRC
MES;
MEA;
WIR
MES;
QIR MeOH
QIR EtOH
QIR
HAc
QIR FAc
QIR
FAl
QIR AcAl
Gas Chromatograph
QIR MeOH
QIR EtOH
QIR Gly
QIR Gluc
QIR Sorb
QIR N.N
Enyzmatic Phomotric Robot
FTIR
Turbidity
QIRC pH
QIRC pO2
QIR
Biomass
QIR Biomass
Capcit.
QIR NADH
Fluoresc.
MEA;
FIRC
In-line
On-line
Off-line
Feed 2
Feed 1
Base
WIR
WIR
Steam
Off-line
WIRC
orSe
ptu
m
Indication Recording
FIRC
FI
FIRC
Control
PIMS
PIMS
Local
PIMS
PIMS
NONE
PLS
PIMS
NONE
LEGEND
On-line
On-line
In-line
 Knowledge Generation Tools
• Multivariate Regression
• Metabolic Flux Modelling
 Dynamic Pertubations
• Scale Down Models
• Advanced Feeding Regimes
Efficient &
Scalable
Time (h)Time (h)
D (hD (h--11))
0 Time (h)Time (h)0
Dcrit SHIFTSHIFT--DOWNDOWN
REDRED--OXOX  OXOX
Dcrit SHIFTSHIFT--UPUP
OXOX REDRED--OXOX
Dcrit
SHIFTSHIFT--UPUP
OXOX  OXOX
PULSEPULSE
glucoseglucose
< 0.004 h-2
Dcrit
AA--statstat--
OXOX REDRED--OXOX
< 0.004 h-2
Dcrit
AA--statstat--
REDRED--OXOX OXOX
Time (h)Time (h)
D (hD (h--11))
0 Time (h)Time (h)0
Dcrit SHIFTSHIFT--DOWNDOWN
REDRED--OXOX  OXOX
DcritDcritDcrit SHIFTSHIFT--DOWNDOWN
REDRED--OXOX  OXOX
Dcrit SHIFTSHIFT--UPUP
OXOX REDRED--OXOX
DcritDcritDcrit SHIFTSHIFT--UPUP
OXOX REDRED--OXOX
Dcrit
SHIFTSHIFT--UPUP
OXOX  OXOX
DcritDcritDcrit
SHIFTSHIFT--UPUP
OXOX  OXOX
PULSEPULSE
glucoseglucose
PULSEPULSE
glucoseglucose
< 0.004 h-2
Dcrit
AA--statstat--
OXOX REDRED--OXOX
< 0.004 h-2
Dcrit
< 0.004 h-2
Dcrit
< 0.004 h-2
Dcrit
AA--statstat--
OXOX REDRED--OXOX
< 0.004 h-2
Dcrit
AA--statstat--
REDRED--OXOX OXOX
< 0.004 h-2
Dcrit
AA--statstat--
REDRED--OXOX OXOX
13.02.2013 Ch. Herwig BioProcess Technology 33
13.02.2013 Ch. Herwig BioProcess Technology 34
Conclusions
34

18. Current Coverage
Information-Management Knowledge-Management
Collectdata
Storedata
Organizedata
Structuredata
Convertdata
toinformation
Investigate
Modeling/
(Re-)Design
Monitoringfor
continous
improvement
Product-and
process-
design
Siemens / SIPAT
AEGIS / Discoverant
S-Matrix
Umetrics
Modde/Simca
4Tune
Exputec
Very little functionality  complete functionality
Strategy to transfer data from
process development to manufacturing
13.02.2013 Ch. Herwig BioProcess Technology 36
Process
Development
Manufacturing
Scaleup
Accuracy Frequency
raw data
rates
vol. rates
specific
rates
g/g/h
Independent from
scale
&
initial conditions
Process Control
CPP
Process
Understanding

19. Tools to Extend the PAT Definition to
Knowledge Management
I)
Data from
Analytical
Devices
II)
Combination
of Data for
new Data
III)
Extract
Information
from Data and
Control
IV)
Extract
Knowledge
from
Information
Manage
Knowledge:
Q10!
13.02.2013 Ch. Herwig BioProcess Technology 37
Bioprocess
Development:
Data Quality!
Softsensors:
Inexpensive,
High quality,
Increases
process
transparency!
Data Reduction,
Platform knowledge
Transferable
Less Experiments
Mechanistic knowledge
Scalable!
Standards, across
phases and products!
Establish business
process
Thank you for your attention!
Univ.Prof. Dr. Christoph Herwig
Vienna University of Technology
Institute of Chemical Engineering
Research Division Biochemical Engineering
Gumpendorferstrasse 1a/ 166 - 4
A-1060 Wien
Austria
emailto: christoph.herwig@tuwien.ac.at
Tel (Office): +43 1 58801 166400
Tel (Mobile): +43 676 47 37 217
Fax: +43 1 58801 166980
URL : http://institute.tuwien.ac.at/chemical_engineering/bioprocess_engineering/EN/
13.02.2013 Ch. Herwig BioProcess Technology 38
https://www.facebook.com/BioVTatTUWien