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Design of Experiments for Bioprocess ApplicationsAndree Ellert
Agenda
- A short profile of Sartorius
- DoE for Bioprocess Applications
- Detailed Case Study
- Integration of DoE into SCADA software
16 June 2011 Page 3
Group Structure*
Approx. 75%
100%Approx. 25%
Sartorius AG
* as of February, 2011
Sartorius AGOther
Shareholders
Sartorius Stedim Biotech S.A. Sartorius Mechatronics
Revenue 2010 €226.7 mnEmployees 1,934
Revenue 2010 €432.6 mnEmployees 2,581
16 June 2011 Page 4
Two Group Divisions
Sartorius Stedim Biotech (SSB)Sartorius Stedim Biotech (SSB)Sartorius Stedim Biotech (SSB)Sartorius Stedim Biotech (SSB)
Sartorius Mechatronics (SMT)Sartorius Mechatronics (SMT)Sartorius Mechatronics (SMT)Sartorius Mechatronics (SMT)
� Strategically realigned as of 2009:
Evolving from a weighing technology
specialist into an applications expert
with a focus on the food and
pharmaceutical industries
� Listed on the Eurolist of the EuroNext Paris stock exchange
� Market leader in filtration, fermentation and in fluid management
� The No. 2 worldwide
� Total solution provider of laboratory andprocess weighing equipment
� Total solution provider for the biopharmaceutical industry
� Focus on single-use products
16 June 2011 Page 5
Global Presence
Global manufacturingWorldwide sales subsidiaries
16 June 2011 Page 6
Total Solution Provider: Single-use Biomanufacturing
Buffer
Pre
para
tion
Buffer
Pre
para
tion
Buffer
Pre
para
tion
Buffer
Pre
para
tion
Preparation Storage
Cell H
arve
stin
gCell H
arve
stin
gCell H
arve
stin
gCell H
arve
stin
g
Cell Removal Clarification Recirculation
Sterile Filtration Storage
CrossflowVolume Reduction Monitoring & Control
Purifica
tion
Purifica
tion
Purifica
tion
Purifica
tion
Affinity Chromat.Capturing Step
Polishing 2 Membrane Chromat.
SterileFiltration
CrossflowBuffer Exchange
CrossflowConcentration|Diafiltration
Low pH VirusInactivation
Polishing 1
Form and Fill
Controlled Freeze-thaw System
Preparation Storage
Med
ia P
repa
ration
Med
ia P
repa
ration
Med
ia P
repa
ration
Med
ia P
repa
ration
Cell L
ine
Cell L
ine
Cell L
ine
Cell L
ine
Ferm
enta
tion
Ferm
enta
tion
Ferm
enta
tion
Ferm
enta
tion
Seed Bioreactor Bioreactor
BioreactorSampling
Sterile Filtration
VirusInactivation Freeze-thaw Bags
Agenda
- A short profile of Sartorius
- DoE for Bioprocess ApplicationsDoE for Bioprocess ApplicationsDoE for Bioprocess ApplicationsDoE for Bioprocess Applications
- Detailed Case Study
- Integration of DoE into SCADA software
16 June 2011 Page 8
Media & buffer preparation
• drying time• water content• temperature• excipients• API content• ...
• flow rate• resin loading• bed height• resin type• pH value• ...
• filtration flux• TMP• retentate flow• membrane type• pore size• ...
• DO value• pH value• growth rate• temperature• inducer conc.• ...
• C-sources• amino acids• trace salts• pH value• mixing time• ...
factors
responses
• optimization of fill & finish steps
• improvement of flowability & compressibility
• ...
• optimization of clearance / polishing steps
• testing for process robustness
• ...
Fermentation
• optimization of clarification & concentration
• enhanced throughput capacity
• ...
Initial recovery
• optimization of fermentation conditions
• improvement of space-time yield / productivity
• ...
Polishing / virus clearance
Fill & finish
1111 2 3 4
• optimization of media & buffer composition
• reducing labor time and expense
• ...
5
Design of Experiments can be applied along the entire bioprocess chain
16 June 2011 Page 9
Bioprocess Application: Media Preparation
Media & buffer preparation
Fermentation Initial recoveryPolishing /
virus clearanceFill & finish
1111 2 3 4 5
16 June 2011 Page 10
Bioprocess Application: Media Preparation
- Shandong Lukang Pharmaceutical Group Co. (Jining, China) � 1,000 t/year
- same concentration of glutamine
- reduction of glutamate concentration by 53.6 %
- reduction of production costs by 7.1 % � 481,400 $/year
Media & buffer preparation
Fermentation Initial recoveryPolishing /
virus clearanceFill & finish
1111 2 3 4 5
16 June 2011 Page 11
Bioprocess Application: Polishing | Virus Clearance
Media & buffer preparation
Fermentation Initial recoveryPolishing /
virus clearanceFill & finish
1111 2 3 4 5
16 June 2011 Page 12
Bioprocess Application: Polishing | Virus Clearance
Media & buffer preparation
Fermentation Initial recoveryPolishing /
virus clearanceFill & finish
1111 2 3 4 5
- parameter variation over wide range does not affect AEX process
- design space was found, where removal of viral impurities can be assured
- AEX process is highly robust over the design space
16 June 2011 Page 13
Bioprocess Application: Cell Culture | Fermentation
Media & buffer preparation
Fermentation Initial recoveryPolishing /
virus clearanceFill & finish
1111 2 3 4 5
Agenda
- A short profile of Sartorius
- DoE for Bioprocess Applications
- Detailed Case StudyDetailed Case StudyDetailed Case StudyDetailed Case Study
- Integration of DoE into SCADA software
16 June 2011 Page 1516 June 2011
E. coli MSD 5247
wild-type hEphB2
(D604-S898)
E. coli MSD 5248
mutated hEphB2
[L767P](D604-S898)
Model protein, expression vector and E. coli BL21 (DE3)
16 June 2011 Page 1616 June 2011
Fluorescence measurement of protein expression reporter ZsGreen
- isolated from non-bioluminescent anemone Zoanthus sp. [Matz et al. 1999]- excitation/emission maximum: λex 496 nm / λem 506 nm
- Tecan GENiosTM fluorescence reader with 96 well microplates- excitation/emission wavelength: λex = 480 nm + 20 nm / λem= 530 nm + 20 nm
16 June 2011 Page 1716 June 2011
inclusionbodies
solubleprotein
Typical course of a protein production process with E. coli MSD 5248
0
20
40
60
80
100
0
30
60
90
120
150
0 4 8 12 16 200
7
14
21
28
35
S48/53Zsol
S48/53Z_IB
t1
t2
ϑL
ϑL
[°C][%] [gl-1][103 RFU]
induction
pO2
S48/53Zk
production fed batchbatch
cXL_OD
cXL
pO2
t [h]
(0.8·10-3 moll-1 IPTG)
0.0
0.2
0.4
30
35
40
FR1
µ̂O2
[mlh-1]
FR1
µ̂O2
[h-1]
µ̂O2
16 June 2011 Page 1816 June 2011
screening optimisation robustness
1. Screening
- Which factors significantly influence the response?
2. Optimisation
- Which factor settings result in optimal operation conditions?
3. Robustness testing
- How sensitive is the response to small changes in the optimal factors settings?
Strategy of experimentation – three primary DoE objectives
16 June 2011 Page 1916 June 2011
15 20 25 30 35 400.0
0.1
0.2
0.3
0.4
0.5
µwhigh
ϑLmax
µmax
µmin
ϑLmin
µwhigh
[h-1]
ϑLlow
[°C]
screening
3 3
k 0 i i ij i ji 1 1 i j
linear terms interaction terms
y x x x k = 1 (sol), 2 (IB)= ≤ <
= β + β ⋅ + β ⋅ ⋅ + ε∑ ∑14243 1442443
Screening for significant expression factors in a large search domain
16 June 2011 Page 2016 June 2011
Exemplary setup with multi-bioreactor system BIOSTAT® Qplus
MFCS/win
16 June 2011 Page 2116 June 2011
- positive effect of point mutation on soluble space-time yield
soluble space-time yield
-6400
-4800
-3200
-1600
0
1600
3200
4800
6400
x2 · x
3x
1 · x
3x
3 (C
IPTG)
βIB
E. coli MSD 5247 (wt-hEphB2) E. coli MSD 5248 (mut-hEphB2)
x1 · x
2x
2 (ϑ
L)x
1 (µ
w)
-1200
-900
-600
-300
0
300
600
900
1200β
sol
E. coli MSD 5247 (wt-hEphB2) E. coli MSD 5248 (mut-hEphB2)
x2 · x
3x
1 · x
3x
3 (C
IPTG)
x
1 · x
2x
2 (ϑ
L)x
1 (µ
w)
insoluble space-time yield
Identification of significant factors after model fitting
16 June 2011 Page 2216 June 2011
- positive effect of point mutation on soluble space-time yield- significance: confidence interval does not include zero & p-value < 0.05- all terms related to x3 (CIPTG) are not significant and hence deleted from the models
soluble space-time yield
-1200
-900
-600
-300
0
300
600
900
1200β
sol
E. coli MSD 5247 (wt-hEphB2) E. coli MSD 5248 (mut-hEphB2)
x2 · x
3x
1 · x
3x
3 (C
IPTG)
x
1 · x
2x
2 (ϑ
L)x
1 (µ
w)
Identification of significant factors after model fitting
X XXTerm Coefficient Conf.int. ± p-value
β1sol_47 163 73 0.003
β2sol_47 -207 73 0.001
β3sol_47 -9 73 0.756
β12sol_47 -117 73 0.011
β13sol_47 -22 73 0.458
β23sol_47 58 73 0.095
16 June 2011 Page 2316 June 2011
model
ysol_5248
ysol_5247
0.576 > -0.0460.921 > 0.913
0.797 > 0.7240.894 < 0.922
model
yIB_5248
yIB_5247 0.929 > 0.8540.968 < 0.983
0.852 > 0.2650.965 > 0.939
2adjR 2Q
2adjR 2Q
-1200
-900
-600
-300
0
300
600
900
1200β
sol
E. coli MSD 5247 (wt-hEphB2) E. coli MSD 5248 (mut-hEphB2)
x1 · x
2x
2 (ϑ
L)x
1 (µ
w)
-6400
-4800
-3200
-1600
0
1600
3200
4800
6400β
IB
E. coli MSD 5247 (wt-hEphB2) E. coli MSD 5248 (mut-hEphB2)
x1 · x
2x
2 (ϑ
L)x
1 (µ
w)
soluble space-time yield insoluble space-time yield
Model pruning results in higher model quality
16 June 2011 Page 2416 June 2011
MSD 5247
(wt-hEphB2)
MSD 5248
(mut-hEphB2)
soluble space-time yield insoluble space-time yield
Use of regression models for definition of an optimisation region
16 June 2011 Page 2516 June 2011
15 20 25 30 35 400.0
0.1
0.2
0.3
0.4
0.5
µwhigh
ϑLmax
µmax
µmin
ϑLmin
µwhigh
[h-1]
ϑLlow
[°C]
screeningoptimisation
2 2 22
k 0 i i ij i j ii ii 1 1 i j i 1
linear terms interaction terms quadratic terms
y x x x x k = 1 (sol), 2 (IB)= ≤ < =
= β + β ⋅ + β ⋅ ⋅ + β ⋅ + ε∑ ∑ ∑14243 1442443 14243
The central composite circumscribed (CCC) optimisation design
16 June 2011 Page 2616 June 2011
- optimal point at µw = 0.25 h-1 / ϑL = 26.6 °C
- STYsol 28-times higher compared to STYsol
in screening with µw = 0.08 h-1 / ϑL = 37 °C
- optimal point at µw = 0.25 h-1 / ϑL = 30.5 °C
- STYIB 30-times higher compared to STYIB
in screening with µw = 0.08 h-1 / ϑL = 37 °C
soluble space-time yield
2 2adjR 0.950 / Q 0.942= =
3130
2928
2726
3500
7000
10500
14000
17500
0.160.18
0.200.22
0.240.26
ST
Yso
l [RF
Uh-1
]
µw [h-1]ϑ
L [°C]
3130
2928
2726
12000
24000
36000
48000
60000
0.160.18
0.200.22
0.240.26
ST
YIB
[RF
Uh-1
]
µw [h-1]ϑ
L [°C]
insoluble space-time yield
2 2adjR 0.966 / Q 0.918= =
Use of regression models for response surface modelling
16 June 2011 Page 2716 June 2011
2adjR 0.999 =
soluble space-time yield
0 3 6 9 12 15 180
3
6
9
12
15
18
^
[103 RFUh-1]
ysol
[103 RFUh-1]ysol
0 10 20 30 40 50 600
10
20
30
40
50
60
^
[103 RFUh-1]
yIB
[103 RFUh-1]yIB
2adjR 0.994=
9
2
2
4
5
DF
167.0
3.563
0.172
3.735
163.3
SS x 106
Total
Pure Error
Lack of Fit
Residual
Model
source
34.97 > 6.2632.65
0.048 < 19.00.086
0.934
F-valueMS x 106
18.56
1.781
9
2
2
4
5
DF
867.7
7.610
5.430
13.04
854.7
SS x 106
Total
Pure Error
Lack of Fit
Residual
Model
source
52.43 > 6.26170.9
0.714 < 19.02.715
3.260
F-valueMS x 106
96.41
3.805
insoluble space-time yield
ANalysis Of VAriance (ANOVA) for the regression models
16 June 2011 Page 2816 June 2011
- space-time yield must show robustness compared to small deviations in factor levels
- response is robust, if Q² is low and values vary around ± σ of CP mean
The 2² full factorial robustness design
15 20 25 30 35 400.0
0.1
0.2
0.3
0.4
0.5
µwhigh
ϑLmax
µmax
µmin
ϑLmin
µwhigh
[h-1]
ϑLlow
[°C]
screeningoptimisationrobustness
16 June 2011 Page 2916 June 2011
insoluble space-time yield
0
10000
20000
30000
40000
50000
- σ
+ σ
- σ
+ σ
soluble space-time yield insoluble space-time yield
6
6
5
5
4
4
3
3
2
2
1
1
replicate index5432
1
spac
e-tim
e yi
eld
[RF
Uh-1
]
2sol2IB
Q < 0.001
Q < 0.001
soluble space-time yield
Response robustness compared to small factor changes
16 June 2011 Page 3016 June 2011
Observed problems during experimentation
- recipes for each bioreactor had to be changed according to strategy
- individual start of multiple experiments
- lot of manual data typing
→ time-consuming work, inefficient workflow, error-prone procedure!
MODDE 9.0 by UMETRICSBioPAT® MFCS/win 3.0
X
Agenda
- A short profile of Sartorius
- DoE for Bioprocess Applications
- Detailed Case Study
- Integration of DoE into SCADA software Integration of DoE into SCADA software Integration of DoE into SCADA software Integration of DoE into SCADA software
16 June 2011 Page 3216 June 2011
The BioPAT® MFCS/win DoE module
16 June 2011 Page 3316 June 2011
The BioPAT® MFCS/win DoE module
MODDE 9.0 by UMETRICSBioPAT® MFCS/win 3.0
�
Thank you!