Integrated Design of Experiments (DoE) for Benchtop Bioreactors

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1 May 1-3, 2012 Javits Center New York, NY Integrated Design of Experiments (DoE) for Benchtop Bioreactors Dr. Karl Rix Chief Executive Officer DASGIP BioTools, LLC

2 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.

3 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

4 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 Inputs (being varied) Outputs (being observed) Factor X1 Factor X2 Factor Xn Process Response Y1, Y2,

5 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.

6 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

7 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

8 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

9 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

10 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.

11 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

12 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

13 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

14 General Workflow Plan Supervisor Level Recipe Template JMP/ DGC DoE Builder Individual Recipes Resource Mapping Automation Execute Operator Level Parallel Bioreactor System Process Information SOP Analyze Supervisor Level JMP Information Management Automation

15 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

16 Import using DGC Software Import DoE Constructs into DGC Software Tables are populated automatically No additional user input required

17 Create Individual Recipes Merge with Template Define name prefix Select Template Press Create Workflow (= Recipes) Individual recipes are generated automatically

18 Execute Individual Recipes Map Resources Assign recipes to an actual bioreactor Start Execution Point Click Grow

19 Collect and Export Results After End of Experiments Select Runs Add Response Data Export to JMP DASGIP Information Manager facilitates autopopulation of DoE export table

20 Insert: Quality Assessment Check and compare runtime data Between comparable DoE constructs (e.g. center points) With historical runs DO.PV [%DO] 140,0 120,0 100,0 Block 3_Center Point Block 2_Center Point Block 1_Center Point 80,0 60,0 40,0 20,0 N.PV [rpm] 3000, 2000, 0,0 0:00:00 2:24:00 4:48:00 7:12: , 9:36:00 12:00:00 Sync. Inoculation Time 0, 0:00:00 2:24:00 4:48:00 7:12:00 9:36:00 12:00:00 Sync. Inoculation Time

21 Statistical Analysis Use statistical methods provided by JMP

22 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

23 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

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

25 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. Design Space Other designs used include Response Surface

26 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 Design Space T Gluc.-conc. ph Center Point Limited to only 4 reactors in this example for better illustration

27 Allocation of Resouces Task: Perform the DoE on Limited Resources Factor A Factor B Factor C T [ C] ph Gluc.-conc. [g/l] Level Level Center Full factorial design Factor A Factor B Factor C Syst. no. T [ C] ph Gluc.-conc. [g/l] center center center Randomization Needs 2 3 =8 runs Choose 3 center points Needs 3 blocks (4+4+3) for 11 runs in total Ressource Mapping System DoE-run no. Syst. no. Unit No. Block T [ C] ph Gluc.-conc. [g/l]

28 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)

29 Execution of DoE Designs Process on its way

30 Compare Runtime Data Unit 2, DoE-run No.: 2, syst. No.: 1 Unit 3, DoE-run No.: 3, syst. No.: 2 Unit 1, DoE-run No.: 5, syst. No.: 3 Unit 1, DoE-run No.: 9, syst. No.: 4 40 C_pH 7.2_60g/L Glucose 34 C_pH 7.2_60g/L Glucose 40 C_pH 6.4_60g/L Glucose 34 C_pH 6.4_60g/L Glucose 100,0 3000, 100,0 3000, 100,0 3000, 100,0 3000, 90,0 90,0 90,0 90,0 ph2.pv [ph], DO2.PV [%DO], T2.PV [ C], XO2 2.PV [%], VA2.PV [ml], VB2.PV [ml], Offline2.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N2.PV [rpm] DO2.PV Offline2.B ph2.pv T2.PV VA2.PV VB2.PV XO2 2.PV N2.PV ph3.pv [ph], DO3.PV [%DO], T3.PV [ C], XO2 3.PV [%], VA3.PV [ml], VB3.PV [ml], Offline3.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N3.PV [rpm] DO3.PV Offline3.B ph3.pv T3.PV VA3.PV VB3.PV XO2 3.PV N3.PV ph1.pv [ph], DO1.PV [%DO], T1.PV [ C], XO2 1.PV [%], VA1.PV [ml], VB1.PV [ml], Offline1.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N1.PV [rpm] DO1.PV Offline1.B ph1.pv T1.PV VA1.PV VB1.PV XO2 1.PV N1.PV ph1.pv [ph], DO1.PV [%DO], T1.PV [ C], XO2 1.PV [%], VA1.PV [ml], VB1.PV [ml], Offline1.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N1.PV [rpm] DO1.PV Offline1.B ph1.pv T1.PV VA1.PV VB1.PV XO2 1.PV N1.PV 10,0 10,0 10,0 10,0 0,0 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 0,0 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 0,0 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 0,0 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 Inoculation Time Inoculation Time Inoculation Time Unit 4, DoE-run No.: 4, syst. No.: 5 Unit 3, DoE-run No.: 7, syst. No.: 6 Unit 4, DoE-run No.: 8, syst. No.: 7 Unit 2, DoE-run No.: 10, syst. No.: 8 40 C_pH 7.2_20g/L Glucose 34 C_pH 7.2_20g/L Glucose 40 C_pH 6.4_20g/L Glucose 34 C_pH 6.4_20g/L Glucose 100,0 3000, 100,0 3000, 100,0 3000, 100,0 3000, 90,0 90,0 90,0 90,0 ph4.pv [ph], DO4.PV [%DO], T4.PV [ C], XO2 4.PV [%], VA4.PV [ml], VB4.PV [ml], Offline4.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N4.PV [rpm] DO4.PV Offline4.B ph4.pv T4.PV VA4.PV VB4.PV XO2 4.PV N4.PV ph3.pv [ph], DO3.PV [%DO], T3.PV [ C], XO2 3.PV [%], VA3.PV [ml], VB3.PV [ml], Offline3.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N3.PV [rpm] DO3.PV Offline3.B ph3.pv T3.PV VA3.PV VB3.PV XO2 3.PV N3.PV ph4.pv [ph], DO4.PV [%DO], T4.PV [ C], XO2 4.PV [%], VA4.PV [ml], VB4.PV [ml], Offline4.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N4.PV [rpm] DO4.PV Offline4.B ph4.pv T4.PV VA4.PV VB4.PV XO2 4.PV N4.PV ph2.pv [ph], DO2.PV [%DO], T2.PV [ C], XO2 2.PV [%], VA2.PV [ml], VB2.PV [ml], Offline2.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N2.PV [rpm] DO2.PV Offline2.B ph2.pv T2.PV VA2.PV VB2.PV XO2 2.PV N2.PV 10,0 10,0 10,0 10,0 0,0 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 0,0 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 0,0 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 0,0 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 Inoculation Time Inoculation Time Inoculation Time Unit 1, DoE-run No.: 1, syst. No.: 9 Unit 3, DoE-run No.: 11, syst. No.: 9 Unit 2, DoE-run No.: 6, syst. No.: 9 37 C_pH 6.8_40g/L Glucose 37 C_pH 6.8_40g/L Glucose 37 C_pH 6.8_40g/L Glucose 100,0 3000, 100,0 3000, 100,0 3000, 90,0 90,0 90,0 ph1.pv [ph], DO1.PV [%DO], T1.PV [ C], XO2 1.PV [%], VA1.PV [ml], VB1.PV [ml], Offline1.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N1.PV [rpm] DO1.PV Offline1.B ph1.pv T1.PV VA1.PV VB1.PV XO2 1.PV N1.PV ph3.pv [ph], DO3.PV [%DO], T3.PV [ C], XO2 3.PV [%], VA3.PV [ml], VB3.PV [ml], Offline3.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N3.PV [rpm] DO3.PV Offline3.B ph3.pv T3.PV VA3.PV VB3.PV XO2 3.PV N3.PV ph2.pv [ph], DO2.PV [%DO], T2.PV [ C], XO2 2.PV [%], VA2.PV [ml], VB2.PV [ml], Offline2.B [] 80,0 70,0 60,0 50,0 40,0 30,0 20,0 2500, 2000, 1500, 1000, 500, N2.PV [rpm] DO2.PV Offline2.B ph2.pv T2.PV VA2.PV VB2.PV XO2 2.PV N2.PV 10,0 0,0 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 10,0 0,0 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 10,0 0,0 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 Center point runs

31 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 (OD 600 )

32 Statistical Analysis (2) Result 2: Identification of main effects Temp A ph B Gluc.-conc. C OD600 (t1) , , , , , , , ,7 center center center 48,0 Temp A ph B Gluc.-conc. C 41,0 40,4 59,2 Average Level "-" 48,0 48,0 48,0 Average Center Points 45,5 46,0 27,2 Average Level "+" -4,5-5,6 32,0 Effect Factor Glucose concentration has most significant impact on response variable biomass. T center point ph center point Gluc.Conc center point 60,0 50, OD600 40,0 30,0-1 1 OD OD ,0 Level 20 Level 20-1 Level

33 Statistical Analysis (3) Result 3: Factor interactions OD600 (t1) Gluc.-conc Temp 1 Temp -1 OD600 (t1) ph Temp 1 Temp -1 OD600 (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.

34 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) OD600 OD600 60,0 50,0 40,0 30,0 20, T -1 ph -1 center point Level center point Level Center Point Gluc.Conc center point For center point analysis, at minimum, only one additional run is necessary OD Level

35 Benefits of Randomization Using Center Points and Randomization Run one center point in each block Change the reactor position in each block Center Point System DoE-run no. Syst. no. Unit No. Block T [ C] ph Gluc.-conc. [g/l] Unit 1 Block 1 Unit 2 Block 2 Unit 3 Block 3

36 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

37 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

38 Manual DoE - Risk Analysis DoE with Stand-alone Bioreactors Risk A: Preparation of the two different glucose stocks by hand Risk B: Addition of the correct feed stock to the correct (random) vessel Gluc - Gluc + Risk C: Manual setting of factors (ph set points) on each individual controller ph ph ph ph ph ph ph ph

39 Automated DoE Risk Analysis DoE with Parallel Bioreactors Risk A: Reduced Preparation of one glucose stock by hand Risk B: Nearly eliminated Connection of all feed lines to one feed stock and use feed profile to dispense Volume [ml] Feed rate [ml/h] T 1 Gluc - T 1 h T 2 h T 2 Gluc + h h Risk C: Eliminated Automatic creation of individual recipes and assignment to individual controllers ph ph ph ph ph ph ph ph

40 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

41 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

42 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

43 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.

44 Thank you! 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, For a copy of this presentation please contact: Carol Stanton,

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