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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!