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Data Management and Control Strategies for Continuous BioProduction

Data Management and Control Strategies for Continuous BioProduction. Kjell Francois, Ivo Backx, Barbara Kavsek . What to expect ?. SIPAT core business. PAT Data management. “In God we trust. All others must bring data” ( W. Edwards Deming ). Principle of PAT & QbD. Real-time release.

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Data Management and Control Strategies for Continuous BioProduction

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  1. Data Management and Control Strategies for Continuous BioProduction Kjell Francois, Ivo Backx, Barbara Kavsek.

  2. What to expect?

  3. SIPAT core business PAT Data management

  4. “In God we trust. All others must bring data” (W. Edwards Deming)

  5. Principle of PAT & QbD Real-timerelease Advanced Control Quality buillt in by design Right first time PAT monitoring product quality mathematical translation Classic control Lab Process data (Temp, Pressure, Oxygen, …) monitoring analyzer data LIMS Closed loop control Hold / release Process feed Process Analyzer Sample Process output

  6. PAT data management Analyzer data Context & Info Data Raw material data SIPAT Runtime Data Off line results Real Time Calculations LIMS data Process data

  7. PAT data management Quantitative result Analyzer data Context & Info data Raw material data SIPAT Runtime data Off line results Real time calculations LIMS data Process data Qualitative result

  8. PAT data management SIPAT External application Meta & Context Data Analyzer Data (NIR, Raman, PSD, …) Process Data Predicted Data Off- line lab Data (RM, process samples,…) Other data • Batch number • Trial number • Process step • Product • Campaign • …. Create Model

  9. PAT Toolbox Product & process design PAT (Advanced) ProcessControls Information managementtools Data Collection storage & retrieval DoE Data Analysis & mining ProcessAnalytics PAT requires multidisciplinary skills & Tools  Complex data management

  10. Examplesfromsecondarymanufacturing& OSD

  11. 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. 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. SIPAT as a key Enabler for Continuous Manufacturingand Real Time Release SIPAT Raw Material Mill Coating Right First Time Real time release Blender Dryer Tablet press Granulator

  14. Example 1 = Continuous Tabletting trough wet granulation Focus Process Steps PAT technology • Spectroscopy (NIR) • Particle Size Measurements Solutions Business Cases - References • Integrated PAT data management • Traceability of microbatches • Real Time Release • APC (Feedforward & Feedback)

  15. Traditional PAT in solid dosage control control control control control control control Processparameters Processparameters Processparameters Processparameters Processparameters Processparameters Processparameters Dispense & Blend WetGranulation Drying Blending /Lubrication • Api • Excipients Compression Liquid addition Coating Packaging Lubricant excipient • Coating solution Content Uniformity Bulk PhysicalDefects Loss On Drying Test against specifications Particle size Sampling &Off-line analysis Bulk PhysicalDefects

  16. End product quality predictions control control control control control control control Process parameters Process parameters Process parameters Process parameters Process parameters Process parameters Process parameters Dispense & Blend Wet Granulation Drying Blending / Lubrication • Api • Excipients Compression Liquid addition Coating Packaging Input material characteristics Content Uniformity Assay Dissolution/disintegration (NIR) Loss On Drying (NIR) Visual Inspection Particle size (Malvern) Weight Hardness Thickness Coating thickness Lubricant excipient • Coating solution

  17. Example 2 = Hot MeltExtrusion Focus Process Steps PAT technology • Spectroscopy (NIR, Raman) • + MVDA Solutions Business Cases - References • Advanced data time alignment • API Content analysis • Polymer structure • Real Time Release • Closed loop control

  18. Time alignment of data

  19. Data management  Calcs  Evaluation  Control Process data of extruder & coolingline SME Western electric rules (WER) for SME, t-scores ---------- Outlier detection for spectra (DModX, Hotelling T2) PCA Control heat zone setpoints based on SME & diameter of strand Out of WER In-processmaterial attributes PCA NIR blend PLS PCA Outlier / Out of WER NIR strand PLS Open Diverter PCA

  20. PAT data management & control in Continuous Bio

  21. ContinuousBioProductionsetups Warikoo V, et al. Integrated Continuous Production of Recombinant Therapeutic Proteins. Biotechnol. Bioeng. 109(12) 2012: 3018–3029.

  22. Bring this is in a continuous production train Solutions Changes upstream Changes downstream • Smaller reactor sizes vs “Stainless Steel Cathedrals” • Perfusion reactors • Focus more on disposables • Smaller chromatography Columns • Integrated operations • (adjusted flow rates, pH, osmolality etc) The challenges on data mgt & control • Batch definition • Traceability & genealogy • Need for online monitoring  PAT! • Need for integrated data management • Alignement of data & information

  23. Bring this is in a continuous production train Solutions Solutions PAT technology • Integrated PAT data management • Harvest Point - optimums • Golden batch comparison • Real Time Release • Advanced Process Control • Spectroscopy (NIR, MIR, UV, Raman, LIF) • Off-Gas analysis • Online GC/HPLC • Online rapid analysis equipment + MVDA

  24. Data flows… Upstream Control & Information flows Downstream Control & Information flows • Read data from chromatography or other purification steps • Integrate data from fermentation step to predict impurities etc. in purification steps • Closed loop control inside reactor (Feedback control) • Feeding control in reactor (Feedback control) • FeedForward control from precultures to next steps (Precultural conditions!)

  25. Where are the challenges? Batch definition Traceability & genealogy Flexible & Modulardynamicproduction environments required • Must bereflected in data management landscape! • Flexibleproductionskids • Disposablesystems • Flexibleanalysers • Robust models • Dynamiclogics & control models • Must bebasedonstandardisation (OPC DA & UA) • Requires high flexibility and interactionbetween different componentslike • SCADA • DCS • PAT analysers • Controlstrategies

  26. Siemens as solution provider in thisarea Siemens workstogetherwith different reasearchorganisations and industrial partners in thisarea M3C Group

  27. Thank you for your attention! Dr. Kjell Francois Siemens AG Industry Automation Vertical Pharma Mobile: +32 496 816577 Kjell.Francois@siemens.com www.siemens.com/SIPAT

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