1. Restricted © Siemens AG 2013 All rights reserved.siemens.com/answers Data Management and Control Strategies for Continuous BioProduction Kjell Francois, Ivo Backx, Barbara Kavsek.

2. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 2Dr. Kjell François/ I IA AS PA VSS Pharm What to expect? 1 PAT data management? 2 Examples from secondary manufacturing - OSD 3 PAT data mgt for continuous BioProduction

3. Restricted © Siemens AG 2013 All rights reserved.siemens.com/answers PAT Data management SIPAT core business

4. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 4Dr. Kjell François/ I IA AS PA VSS Pharm “In God we trust. All others must bring data” (W. Edwards Deming)

5. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 5Dr. Kjell François/ I IA AS PA VSS Pharm Process feed Hold / release Lab Process data (Temp, Pressure, Oxygen, …) Closed loop control Process output LIMS Sample Classic control Principle of PAT & QbD Process Analyzer monitoring analyzer data monitoring product quality PAT Real-time release Advanced Control Quality buillt in by design Right first time mathematical translation

6. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 6Dr. Kjell François/ I IA AS PA VSS Pharm SIPAT Runtime Data Raw material data Off line results LIMS dataReal Time Calculations Context & Info Data PAT data management Process data Analyzer data

7. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 7Dr. Kjell François/ I IA AS PA VSS Pharm PAT data management SIPAT Runtime data Raw material data Off line results LIMS dataReal time calculations Context & Info data Process data Analyzer data Quantitative result Qualitative result

8. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 8Dr. Kjell François/ I IA AS PA VSS Pharm PAT data management SIPAT Meta & Context Data Analyzer Data (NIR, Raman, PSD, …) Process Data Predicted Data Off- line lab Data (RM, process samples,…) External application Other data  Batch number  Trial number  Process step  Product  Campaign  …. Create Model

9. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 9Dr. Kjell François/ I IA AS PA VSS Pharm PAT Toolbox PAT requires multidisciplinary skills & Tools  Complex data management Process Analytics Data Analysis & mining DoE Information management tools Data Collection storage & retrieval Product & process design PAT (Advanced) Process Controls

10. Restricted © Siemens AG 2013 All rights reserved.siemens.com/answers Examples from secondary manufacturing & OSD

11. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 11Dr. Kjell François/ I IA AS PA VSS Pharm Going continous – the consequences Continuous manufacturing needs a mindshift on different levels:  Need for a tight integration between process, PAT analyzers and control mechanisms  Continuous quality verification must be established  Translation of Batch oriented release guidelines to a continuous production  How to define a batch?  Ensure the traceability of products trough the line  product genealogy  Alignment of information  Completely different production equipments are needed

12. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 12Dr. Kjell François/ I IA AS PA VSS Pharm Why moving to continuous manufacturing? Why move towards CM operation? Many advantages perceived in CM  Smaller equipment  Smaller facility  Easier scale-up  Better control  Improved yield  Reduced waste  Improved Safety  Flexible Manufacturing (For personalized medicine/Targeted therapies)  Reduced cost  Improved quality

13. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 13Dr. Kjell François/ I IA AS PA VSS Pharm SIPAT as a key Enabler for Continuous Manufacturing and Real Time Release Raw Material Dryer Granulator Tablet press Blender Mill Coating SIPAT Right First Time Real time release

14. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 14Dr. Kjell François/ I IA AS PA VSS Pharm Example 1 = Continuous Tabletting trough wet granulation Focus Process StepsPAT technology SolutionsBusiness Cases - References Spectroscopy (NIR) Particle Size Measurements Integrated PAT data management Traceability of microbatches Real Time Release APC (Feedforward & Feedback)

15. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 15Dr. Kjell François/ I IA AS PA VSS Pharm Traditional PAT in solid dosage Loss On Drying Bulk Physical Defects Test against specifications Bulk Physical Defects  Api  Excipients Liquid addition Lubricant excipient Coating solution control Process parameters Dispense & Blend control Process parameters Blending / Lubrication control Process parameters Compression control Process parameters Drying Particle size Sampling & Off-line analysis Wet Granulation control Process parameters Content Uniformity Packaging control Process parameters control Process parameters Coating

16. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 16Dr. Kjell François/ I IA AS PA VSS Pharm End product quality predictions Lubricant excipient Drying control Process parameters Blending / Lubrication control Process parameters Compression control Process parameters Coating control Process parameters  Api  Excipients Dispense & Blend control Process parameters Liquid addition Wet Granulation control Process parameters Coating solution Loss On Drying (NIR) Assay Dissolution/disintegration (NIR) Packaging control Process parameters Particle size (Malvern) Weight Hardness Thickness Input material characteristics Visual Inspection Coating thickness Content Uniformity

17. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 17Dr. Kjell François/ I IA AS PA VSS Pharm Example 2 = Hot Melt Extrusion Focus Process StepsPAT technology SolutionsBusiness Cases - References Spectroscopy (NIR, Raman) + MVDA Advanced data time alignment API Content analysis Polymer structure Real Time Release Closed loop control

18. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 18Dr. Kjell François/ I IA AS PA VSS Pharm Time alignment of data

19. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 19Dr. Kjell François/ I IA AS PA VSS Pharm Data management  Calcs  Evaluation  Control Process data of extruder & cooling line In-process material attributes NIR blend NIR strand PCA PLS PCA PLS Western electric rules (WER) for SME, t- scores ---------- Outlier detection for spectra (DModX, Hotelling T2) Out of WER SME Control heat zone setpoints based on SME & diameter of strand Outlier / Out of WER Open Diverter

20. Restricted © Siemens AG 2013 All rights reserved.siemens.com/answers PAT data management & control in Continuous Bio

21. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 21Dr. Kjell François/ I IA AS PA VSS Pharm Continuous BioProduction setups Warikoo V, et al. Integrated Continuous Production of Recombinant Therapeutic Proteins. Biotechnol. Bioeng. 109(12) 2012: 3018–3029.

22. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 22Dr. Kjell François/ I IA AS PA VSS Pharm Bring this is in a continuous production train PreculturesFermentationHarvestingPurification Solutions Smaller reactor sizes vs “Stainless Steel Cathedrals” Perfusion reactors Focus more on disposables Changes upstreamChanges downstream Smaller chromatography Columns Integrated operations (adjusted flow rates, pH, osmolality etc) The challenges on data mgt & control Need for online monitoring  PAT! Need for integrated data management Alignement of data & information Batch definition Traceability & genealogy

23. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 23Dr. Kjell François/ I IA AS PA VSS Pharm Bring this is in a continuous production train PreculturesFermentationHarvestingPurification Solutions Integrated PAT data management Harvest Point - optimums Golden batch comparison Real Time Release Advanced Process Control SolutionsPAT technology Spectroscopy (NIR, MIR, UV, Raman, LIF) Off-Gas analysis Online GC/HPLC Online rapid analysis equipment + MVDA

24. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 24Dr. Kjell François/ I IA AS PA VSS Pharm Data flows… PreculturesFermentationHarvestingPurification Closed loop control inside reactor (Feedback control) Feeding control in reactor (Feedback control) FeedForward control from precultures to next steps (Precultural conditions!) Upstream Control & Information flows Read data from chromatography or other purification steps Integrate data from fermentation step to predict impurities etc. in purification steps Downstream Control & Information flows

25. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 25Dr. Kjell François/ I IA AS PA VSS Pharm Where are the challenges? Batch definition Traceability & genealogy Flexible & Modular dynamic production environments required Must be reflected in data management landscape! Flexible production skids Disposable systems Flexible analysers Robust models Dynamic logics & control models Must be based on standardisation (OPC DA & UA) Requires high flexibility and interaction between different components like SCADA DCS PAT analysers Control strategies

26. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 26Dr. Kjell François/ I IA AS PA VSS Pharm Siemens as solution provider in this area M3C Group Siemens works together with different reasearch organisations and industrial partners in this area

27. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 27Dr. Kjell François/ I IA AS PA VSS Pharm Dr. Kjell Francois Siemens AG Industry Automation Vertical Pharma Mobile: +32 496 816577 Kjell.Francois@siemens.com www.siemens.com/SIPAT Thank you for your attention!

28. Restricted © Siemens AG 2013 All rights reserved.siemens.com/answers SIPAT Project at NVI, The Netherlands A project example

29. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 29Dr. Kjell François/ I IA AS PA VSS Pharm Bioreactor monitoring for process development for the Bordetella pertussis process at NVI We aim at fully understanding and controlling the most critical steps in the B. Pertussis vaccine manufacturing process. (Whooping cough) Production process of the bulk product consists of three steps Batch cultivation Concentration Inactivation Cultivation is the most critical and complex step Full understanding of the critical process parameters allows in line Quality Assurance and ultimately real time product release

30. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 30Dr. Kjell François/ I IA AS PA VSS Pharm Bioreactor monitoring for process development 4 litres working volume pH, DO, T, stirrer control pH, DO, T, nIR, MS (gas) on line measurements Almost all chemical reactions are controlled using basic physical constraints (Temperature, pH, pressure, Dissolved Oxygen / RedOx potential) Except that a single bacterial cell contains up to 6000 different chemical compounds, most of which are synthesised by the organism itself

31. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 31Dr. Kjell François/ I IA AS PA VSS Pharm SIPAT data collection NIR Spectrum from Bruker Matrix  Minutes level Via OPUS driver Process data from Win CC system  Seconds level Inlet gas Outlet gas Temperature pH control Stirring speed Umetrics SIMCA QP+ used as chemometrical tool PCA PLS

32. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 32Dr. Kjell François/ I IA AS PA VSS Pharm Which CQA’s? Main CQA  Vaccine activity (Mice tests  Micro Array analysis at # timestamps during fermentation) Main derived CPP’s  Cell density (OD measured at # timestamps during fermentation)  Nutrient concentration (Lac & Glu measured at # timestamps during fermentation)

33. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 33Dr. Kjell François/ I IA AS PA VSS Pharm References Streefland et.al. (2007) Vaccine 25(16) Van de Waterbeemd et.al. (2009) Biotechnol Bioeng. 103(5) Francois et al. (2009) Pharmaceutical Technology 33(7)

34. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 34Dr. Kjell François/ I IA AS PA VSS Pharm M. Streefland Presented at PAT/QbD Conference, Amsterdam, 2008

35. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 35Dr. Kjell François/ I IA AS PA VSS Pharm The qualitative fingerprint Process data NIR spectral data End-product Quality data Temp., pH, pO2, pressure, … LIMS Qualitative Fingerprint MVDA (PCA) MVDA (PLS) CQA’s

36. Restricted © Siemens AG 2013 All rights reserved. 2013-10-23Page 36Dr. Kjell François/ I IA AS PA VSS Pharm Results Quantitative model for Substrates  Calibration models Qualitative model for Batch progress  Golden Batch tunnel End point detection with link to CQA  Ensure Vaccine activity Potential out these key learnings Use a continuous process  No nutrient depletion, ensure vaccine activity