integrated design of experiments (doe) for benchtop bioreactors€¦ · · 2012-05-10integrated...

Integrated Design of Experiments (DoE) for Benchtop Bioreactors

Dr. Karl Rix Chief Executive Officer DASGIP BioTools, LLC

May 1-3, 2012 Javits Center New York, NY

Where are we now?

Where is the bioprocessing industry now? • There is a disconnect between methods used to plan

and analyze experiments and the tools to execute those experiments

• The process of moving from the plan to the execution and from the results to the analysis is time consuming and prone to (human) errors.

Where does the industry need to be? • Seamless integration between design, execution and

analysis. Integrated DoE for Benchtop Bioreactors – May 2, 2012 – Javits Center – New York, NY

Agenda

Integrated Design of Experiments (DoE) for Benchtop Bioreactors • Role of DoE and Benchtop Bioreactor Systems • Integrated DoE Workflow • Case Study: Three Factor Full Factorial DoE • Minimizing Risk by Automation • Summary

Integrated DoE for Benchtop Bioreactors – May 2, 2012 – Javits Center – New York, NY

Concept of DoE

What is Design of Experiment (DoE) • Design of Experiment (DoE) is a statistical approach to

experimental design and analysis • DoE will reveal or model relationships between an input

or factor and an output or response • DoE methodology was already proposed in 1930s


Process

Factor X1

Factor X2

Factor Xn

Inputs (being varied)

Response Y1, Y2, …

Outputs (being observed)

Benefits of DoE

• Reduces the number of experiments required compared to a “One-factor-at-a-time” (OFAT) approach at a similar statistical significance

• Uncovers how multiple factors jointly affect a response • Systematic approach eases documentation and analysis • Allows for estimation of costs and timeline prior to

performing the experiments (cost-benefit analysis) • Design of Experiments (DoE) is considered an advanced

method in the Six Sigma programs

Analysis of process data is key to better process understanding.


DoE in Bioprocessing

• Design of Experiment (DoE) – Is widely used and a critical element in bioprocessing – Plays a prominent role in Process Development and

Screening – Is implemented using statistical software packages

• Industry leading DoE software providers include – JMP – Umetrics – Design Expert – Minitab


Today’s Role of Benchtop Bioreactors

• Benchtop bioreactors are the workhorses for screening and process development in the biotech, pharmaceutical and chemical industry.

• Products of the processes developed include – Active Ingredients of Drugs, Vaccine, … – Biofuels, Biopolymers, Fine chemicals, Enzymes, … – Food additives, Starter Cultures (yogurt), Amino Acids, …

• The 3L volume is/was regarded as “The Gold Standard” – Standard benchtop bioreactor volumes range from 1L to 20L

• Trend of recent years is to using smaller volumes – Mini bioreactors with volumes of approx. 100mL to 1L – Micro bioreactors with volumes of approx. 1mL to 10mL


Requirements by DoE

General Requirements for Benchtop Bioreactors • Scalability

– Results need to be significant for next scale(s), up to production

• Reproducibility – Results need to be reproducible from one set of runs to the next

and from one instance i.e. reactor to the next position

• Reliability and Robustness – Results need to be reliable – Equipment needs to be robust

• Bioreactors need to be able to support target process


Considerations for Bioreactor Selection

Considerations for Benchtop Bioreactor (Systems) to support DoE effectively • Number of required and

available reactors • Ease of use • Turn around time

– Benefits of single use

• Automation features – Control and data acquisition – DoE Integration and Execution


Necessities for Integrated DoE

Requirements on Bioreactor Control Software (Integrated or SCADA) • Batch functionality • Recipe management • Data acquisition and data aggregation • High level of automation suitable for DoE • Seamless Handshake (Import and export)

– Recipes including automation instructions – Historical data (run time) – Data aggregates: end point data, sample

data, inoculation density etc.


Parallel Bioreactor Systems


Key aspects of Parallel Bioreactor Systems compared to Multiple Bioreactor Set-ups • Several bioreactors can be manipulated simultaneously

– in parallel (hence the name) as well as individually – through an integrated user interface – by reactors being in close

proximity (on one bench top)

• Integrated batch functionality, recipe management, data acquisition and aggregation

• High level of automation

Agenda



Integrated DoE Workflow

• Objective – Create a Seamless DoE Workflow

• Tools Selected – DASGIP Parallel Bioreactor System (DGC) and JMP

• Challenges Addressed – Creation of DoE constructs in JMP – Seamless import into DGC software – Generation of recipes based on DoE and a template – Execution of DoE based recipes using DGC – Export of results back to JMP for statistical analysis


General Workflow

Supervisor Level

Operator Level

Supervisor Level

Recipe Template

JMP/ DGC DoE Builder

Individual Recipes

SOP

JMP

Information Management

Process Information

Parallel Bioreactor System

Plan

Execute

Analyze

Automation

Automation

Resource Mapping


Planning Phase using JMP

• Create DoE Construct e.g. – Choose Design – Define Responses – Define Factors – Define Center

Points

• Generate DoE Table • Save Table w/ DoE Constructs


Import using DGC Software

• Import DoE Constructs into DGC Software – Tables are

populated automatically

– No additional user input required


Create Individual Recipes

• Merge with Template – Define name

prefix

– Select Template

– Press “Create Workflow” (= Recipes)

– Individual recipes are generated automatically Integrated DoE for Benchtop Bioreactors – May 2, 2012 – Javits Center – New York, NY

Execute Individual Recipes

• Map Resources – Assign recipes to

an actual bioreactor • Start Execution

Point Click Grow Integrated DoE for Benchtop Bioreactors – May 2, 2012 – Javits Center – New York, NY

Collect and Export Results

• After End of Experiments – Select Runs

– Add Response Data

• Export to JMP

• DASGIP Information Manager facilitates auto-population of DoE export table


Insert: Quality Assessment

• Check and compare runtime data – Between comparable DoE constructs

(e.g. center points) – With historical runs

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

0:00:00 2:24:00 4:48:00 7:12:00 9:36:00 12:00:00

DO.P

V [%

DO]

Sync. Inoculation Time

0,

1000,

2000,

3000,

0:00:00 2:24:00 4:48:00 7:12:00 9:36:00 12:00:00

N.P

V [r

pm]

Sync. Inoculation Time

Block 3_Center Point




Statistical Analysis

Use statistical methods provided by JMP

Integrated DoE – Made Easy

Workflow steps: 1. Plan DoE constructs in JMP

2. Import DoE constructs using DGC software

3. Create individual recipes

4. Use Point, Click Grow to assign and execute recipes

5. Collect and export results back into JMP

6. Analyze process data using JMP statistical methods


Agenda



DoE Case Study

• Initial considerations – Select factors that may affect the response

variable to form the Design Space – Range of the factors must be

biologically reasonable, i.e. a temperature of 90°C or a pH-value of pH 2 would not be suitable for most biological processes

– Discrete factor values will be determined in preliminary screening experiments and/or are based on prior knowledge

Design Space


Selection of DoE Design

• Full Factorial Design of Experiments – In statistics, a full factorial experiment

is an experiment whose design consists of two or more factors, each with discrete values or levels.

– Such an experiment allows studying the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable.

• Other designs used include Response Surface

Design Space


Creation of DoE Constructs

• Full Factorial 3 Factor DoE for E. coli Batch Run – Factors

• pH, • Temperature, • Feed Stock Concentration

– Response • Biomass (OD600)

– Center Points – Randomization – Resource Mapping

• Limited to only 4 reactors in this example for better illustration

Design Space

T

Gluc.-conc.

pH

Center Point


Allocation of Resouces

Task: Perform the DoE on Limited Resources • Needs 23=8 runs • Choose 3 center points • Needs 3 blocks (4+4+3)

for 11 runs in total

Factor A Factor B Factor CT

[°C]pH Gluc.-conc.

[g/L]Level + 40 7.2 60Level - 34 6.4 20Center 37 6.8 40

Factor A Factor B Factor CSyst. no. T

[°C]pH Gluc.-conc.

[g/L]1 + + +2 - + +3 + - +4 - - +5 + + -6 - + -7 + - -8 - - -9 center center center

System DoE-run no.

Syst. no. Unit No. Block T [°C]

pH Gluc.-conc. [g/L]

1 1 9 1 1 37 6.8 401 2 1 2 1 40 7.2 601 3 2 3 1 34 7.2 601 4 5 4 1 40 7.2 201 5 3 1 2 40 6.4 601 6 9 2 2 37 6.8 401 7 6 3 2 34 7.2 201 8 7 4 2 40 6.4 201 9 4 1 3 34 6.4 601 10 8 2 3 34 6.4 201 11 9 3 3 37 6.8 40

Randomization

Full factorial design Ressource Mapping


Experimental Details

• Bioreactor design: Stirred Tank Reactor (glass) • Cultivation system: 4 position DASbox® System • Working volume: 200mL • Medium: PAN-Medium • Temp set points: 34°C/ 37°C/ 40°C • pH set points: 6.4/ 6.8/ 7.2 • Glucose conc.: 20/ 40/ 60g/L • Fermentation mode: Batch • Corrective agent (pH): 8% Ammonia w/ 10% Struktol

(Antifoam)


Execution of DoE Designs

• Process on its way


Compare Runtime Data


0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH2.

PV [p

H],

DO

2.PV

[%D

O],

T2.P

V [°

C],

XO2

2.PV

[%],

VA2.

PV [m

L], V

B2.

PV [m

L],

Offl

ine2

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N2.

PV [r

pm]

DO2.PVOffline2.BpH2.PVT2.PVVA2.PVVB2.PVXO2 2.PVN2.PV

Unit 2, DoE-run No.: 2, syst. No.: 1

40°C_pH 7.2_60g/L Glucose

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH3.

PV [p

H],

DO

3.PV

[%D

O],

T3.P

V [°

C],

XO2

3.PV

[%],

VA3.

PV [m

L], V

B3.

PV [m

L],

Offl

ine3

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N3.

PV [r

pm]




0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH4.

PV [p

H],

DO

4.PV

[%D

O],

T4.P

V [°

C],

XO2

4.PV

[%],

VA4.

PV [m

L], V

B4.

PV [m

L],

Offl

ine4

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N4.

PV [r

pm]




0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH1.

PV [p

H],

DO

1.PV

[%D

O],

T1.P

V [°

C],

XO2

1.PV

[%],

VA1.

PV [m

L], V

B1.

PV [m

L],

Offl

ine1

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N1.

PV [r

pm]




0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH3.

PV [p

H],

DO

3.PV

[%D

O],

T3.P

V [°

C],

XO2

3.PV

[%],

VA3.

PV [m

L], V

B3.

PV [m

L],

Offl

ine3

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N3.

PV [r

pm]




0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH4.

PV [p

H],

DO

4.PV

[%D

O],

T4.P

V [°

C],

XO2

4.PV

[%],

VA4.

PV [m

L], V

B4.

PV [m

L],

Offl

ine4

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N4.

PV [r

pm]




0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH2.

PV [p

H],

DO

2.PV

[%D

O],

T2.P

V [°

C],

XO2

2.PV

[%],

VA2.

PV [m

L], V

B2.

PV [m

L],

Offl

ine2

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N2.

PV [r

pm]




0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH1.

PV [p

H],

DO

1.PV

[%D

O],

T1.P

V [°

C],

XO2

1.PV

[%],

VA1.

PV [m

L], V

B1.

PV [m

L],

Offl

ine1

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N1.

PV [r

pm]




0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH3.

PV [p

H],

DO

3.PV

[%D

O],

T3.P

V [°

C],

XO2

3.PV

[%],

VA3.

PV [m

L], V

B3.

PV [m

L],

Offl

ine3

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N3.

PV [r

pm]




0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH1.

PV [p

H],

DO

1.PV

[%D

O],

T1.P

V [°

C],

XO2

1.PV

[%],

VA1.

PV [m

L], V

B1.

PV [m

L],

Offl

ine1

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N1.

PV [r

pm]




0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

0:00:00 2:00:00 4:00:00 6:00:00 8:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00

Inoculation Time

pH2.

PV [p

H],

DO

2.PV

[%D

O],

T2.P

V [°

C],

XO2

2.PV

[%],

VA2.

PV [m

L], V

B2.

PV [m

L],

Offl

ine2

.B []

0,

500,

1000,

1500,

2000,

2500,

3000,

N2.

PV [r

pm]




Center point runs

Statistical Analysis


Result 1: Identification of best factor combination

The process parameters 40°C, pH 6.4 and 60g/L glucose resulted in the highest biomass concentration (OD600)

Statistical Analysis (2)


Result 2: Identification of main effects

1-1

20,0

30,0

40,0

50,0

60,0

OD6

00

Level

T center point

TempA

pHB

Gluc.-conc.C OD600 (t1)

- - - 33,6+ - - 26,1- + - 25,6+ + - 23,6- - + 56,8+ - + 67,4- + + 65,8+ + + 46,7

center center center 48,0

TempA

pHB

Gluc.-conc.C

41,0 40,4 59,2 Average Level "-"48,0 48,0 48,0 Average Center Points45,5 46,0 27,2 Average Level "+"-4,5 -5,6 32,0 Effect

1-1

20

30

40

50

60

OD6

00

Level

pH center point

1

-120

30

40

50

60

OD6

00

Level

Gluc.Conc center point

Factor Glucose concentration has most significant impact on response variable biomass.

Statistical Analysis (3)


Result 3: Factor interactions

010203040506070

-2 -1 0 1 2

OD

600

(t1)

Gluc.-conc.

Temp 1

Temp -1

010203040506070

-2 -1 0 1 2

OD

600

(t1)

pH

Temp 1

Temp -1

010203040506070

-2 -1 0 1 2

OD

600

(t1)

Gluc.-conc.

pH 1

pH -1

Possible interaction between parameters pH set point and temperature set point This indicates that the parameters pH and temperature have to be considered together for further process optimization.

Benefit of Center Points

• Using Center Points – Get idea of data precision – Analyze deviation from linearity – Identify influence of different inoculation

cultures (differences between different block runs)

1-1

20,0

30,0

40,0

50,0

60,0

OD6

00

Level

T center point

1-1

20

30

40

50

60

OD6

00

Level

pH center point

1

-120

30

40

50

60

OD6

00

Level

Gluc.Conc center pointCenter Point

For center point analysis, at minimum, only one additional run is necessary


Benefits of Randomization

• Using Center Points and Randomization • Run one center point in each block • Change the reactor position in each block

Block 1 Unit 1

System DoE-run no.

Syst. no. Unit No. Block T [°C]

pH Gluc.-conc. [g/L]

1 1 9 1 1 37 6.8 401 2 1 2 1 40 7.2 601 3 2 3 1 34 7.2 601 4 5 4 1 40 7.2 201 5 3 1 2 40 6.4 601 6 9 2 2 37 6.8 401 7 6 3 2 34 7.2 201 8 7 4 2 40 6.4 201 9 4 1 3 34 6.4 601 10 8 2 3 34 6.4 201 11 9 3 3 37 6.8 40

Center Point

Block 2 Unit 2

Block 3 Unit 3


Case Study Results

Analysis Results: • Glucose concentration is the dominant factor

• A low variance of Center Point results shows

• good reproducibility between sequential blocks

• no dominant effects of single reactors


Agenda



Manual DoE - Risk Analysis

DoE with Stand-alone Bioreactors Risk A: Preparation of the two different glucose stocks by hand

Risk C: Manual setting of factors (pH set points) on each individual controller

Risk B: Addition of the correct feed stock to the correct (random) vessel

pH pH pH pH pH pH pH pH

Gluc - Gluc +


Automated DoE – Risk Analysis

DoE with Parallel Bioreactors Risk A: Reduced Preparation of one glucose stock by hand

Risk C: Eliminated Automatic creation of individual recipes and assignment to individual controllers

Risk B: Nearly eliminated Connection of all feed lines to one feed stock and use feed profile to dispense

pH pH pH pH pH pH pH pH

Gluc -

T1

Feed rate [mL/h]

Gluc +

T2

Volume [mL]

h h

T1 T2 h h


Benefits of DoE and Automation

DoE works well for • Process Development • Clone and Cell Line Screening • Media Optimization Automation simplifies DoE • Variations of set-points (pH, DO, Temperature)

• Variations of feed profiles (flow-rate, shape, delay)

• Variations of induction time

• Mixing of media ingredients using multiple feeds Integrated DoE for Benchtop Bioreactors – May 2, 2012 – Javits Center – New York, NY

Agenda



DoE and Parallel Bioreactor Systems

A Perfect Fit • Every manual operation is a risk and hard to track.

Automated DoE workflows, as supported by Parallel Bioreactor Systems, reduce or eliminate those risks.

• DoE lends itself to parallel execution/operation and therefore saves time.

• Parallel operation improves reproducibility • Inoculum • Feed Stock • Ambient Conditions • Raw Material


Where are we now?

With Integrated DoE for Benchtop Bioreactors • There is a now a connection between methods used to

plan and analyze experiments and the tools to execute those experiments.

• The process of moving from the plan to the execution and from the results to the analysis is now only a few clicks.

Seamless integration between design, execution and analysis.


Thanks to Sebastian Kleebank, Falk Schneider and Carol Stanton For a Demonstration of Integrated DoE : Visit our Exhibit in Booth 3478 For more information contact: Karl Rix, [email protected] For a copy of this presentation please contact: Carol Stanton, [email protected]

Thank you!


integrated design of experiments (doe) for benchtop bioreactors€¦ · · 2012-05-10integrated...

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