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1 How to create a PAT data management platform to support Continuous Production 8th Annual PAT and QbD Conference, London, February 16 th, 2011 Jan Verelst

2 Back in time Page 2

3 What caused this disaster? Information : Hidden Critical Knowledge Knowledge Data Information Page 3

4 Oral Solid Dosage today Delay Delay Delay Delay Raw Material 1 to 2 months to release Granulator Blender Dryer Blender Tablet press Coating Page 5

5 PAT: Key Enabler for Continuous Manufacturing and Real Time Product Release Raw Material SIPAT Quality check Right First Time Real time release Blender Dryer Coating Granulator Tablet press In/At line check Page 6

6 PAT Basics Holistic Approach PAT Advanced Control Quality build in by design Right first time monitoring product quality Real-time release Closed loop control Classic control Temp., Speed, Liquid addition, Compression Force, mathematical translation monitoring process data Lab LIMS Analyzer Hold / release Sample feed output Page 7

7 SIPAT functions Page 8

8 SIPAT Collectors Collector = Interface to read data from external systems as input to SIPAT Method Standard available collectors within SIPAT Instrument collectors (dedicated interfaces to range of analytical instruments) collector (OPC DA Client) File collectors (XML, SPC, JCAMP) PAT Collector (results from other SIPAT Method) Databrowse collectors (ODBC, SQL, OLEDB) Page 9

9 Analyzers Standard analyzer connection available with actual productivity pack: Kaiser Raman (Raman RXN1, RXN3, RXN3L, RXN4R and PhAT); Kaizer s Analyser Control V4.2 Thermo Fischer Antaris and Antaris II family (via Result software) PAA Ltd with GranuMet XP (acoustic emission measurement), Version 4 Bruker NIR via OPUS V6.1 software Bruker NIR via OPUS 6.5 embedded DLL Malvern Insitec Laser Diffraction PSD (RT Sizer software) Malvern Morphology G3 Mettler Toledo collector for ReactIR 4000 (FT-IR) via IC4 software Mettler Toledo MonARC (FT-IR) via IC4 software Mettler Toledo Lasentec FBRM (PSD) via IC4 software Zeiss collector MCS 500 serie (photodiode array UV/visible spectrophotometer) Zeiss collector MCS 600 serie (NIR) Brimrose Luminar 5030 and 4030 AOTF-NIR Expo epat 601 (including an Axsun NIR) Dr. Schleuniger tablet tester (Tandem using Dr. Schleuniger Pharmatron - Bruker) ABB FTPA2000 Bruker LancIR via embedded DLL New productivity packs will be released on a frequent basis Page 10

10 SIPAT: data collection in real time Alignment of Collectors Startup off set Measurement duration Measurement rate REFERENCE COLLECTOR NIR Negative Validity Range Positive Validity Range value (e.g. Temp) Start batch Page 12 Aggregation function : Average Last Value Maximum Middle Minimum First Value Nearest to reference

11 SIPAT: data collection in real time Principle LIMS Raw Material Characteristics SCADA + Context data Analytical Data NIR, PSD Data alignment Contextualization Rules separating non-representative data SIPAT Analyzer Page 13

12 SIPAT functions Page 14

13 SIPAT: data monitoring and control in real time Application data (ph, Temp, Pressure, ) Instrument data (NIR, Raman, PSD, ) Meta & context data Raw Material Data / IPC data (Moisture, ) Alignment Real Time Prediction Quantitative prediction Qualitative prediction Other Application data SIPAT brings all data (in-line, at-line, on-line and off-line) in relation for prediction Page 15 Quelle: Siemens IT Solutions and Services

14 SIPAT Data Model Static data Semi-static data Meta-data Manual/automatic entry Info Info Data Data Perform Calculations Make Predictions using Models Basic Calculations Advanced Calculations Collector Collector Data Data SIPAT SIPAT Method Method Calculations Calculations Input from Analytical Instruments /Automation Level Raw Materials Other Systems Previous process step using Collectors Visualizations Visualizations Configure operator screens Using Charting types Page 17

15 SIPAT: data monitoring and control in real time Principle LIMS RTR Raw Material SCADA Characteristics + Context data Analytical Data Data alignment Contextualization Rules separating non-representative data Statistical RT Models e.g. PLS, PCA Chemometrics (SIMCA, Matlab, Unscrambler, PharmaMV, ) engine embedded in SIPAT CQA Critical to Qualty Attribute Aggregation of CQA over process context - Average, - Standard deviation On line Monitoring Out of control detection Deviation tracking NIR, PSD SIPAT APC Analyzer Page 18

16 SIPAT functions Page 19

17 SIPAT: data mining Principle SIPAT External application Meta & context data Analyser Data ( NIR, Raman, PSD, ) Data Predicted data Off- line lab data (RM,process samples, ) Other data - Batch number - Trial number - step - Product - Campaign. optimization Page 20

18 SIPAT: data mining Model creation & process optimization Turning data into knowledge Improved process understanding Base of QbD Model Builder Integrate all relevant PAT data Send to Chemometrics application Umetrics - Simca Camo - Unscrambler Matlab Perceptive Engineering - PharmaMV optimization Page 21

19 SIPAT functions Page 22

20 Reporting Automated or ad hoc Page 23

21 Reporting Table output Page 24

22 Reporting Graphical output Page 25

23 Reporting Graphical output - spectrum Page 26

24 Reporting Graphical output batch comparison Page 27

25 Why SIPAT? Increase process understanding / manage variability Bring all data (in-line, at-line, on-line and off-line) in relation Provide on-line predictions on product/process quality Shift from lab testing towards in process testing SIPAT 1 common interface for all PAT tools Close the gap between R&D and Manufacturing Page 28

26 Project context End-User Product Know-How Specify required machinery to fulfill a specific production operation Equipment must comply with the own specific norms and standards Focus on the optimized functioning of the complete production line Easy integration of the control level into Plant-IT Siemens Industry know How Standard platform Scalable solutions Easy integration GMP supporting functions Cost effective solutions Long term Support (investment protection) Worldwide Product Support End-user Supplier OEM GEA Pharma Systems Know-How Supply specific equipment + instrumentation to handle a specific production operation Focus on equipment specific optimization Equipment automation specification (PLC, HMI, drive, instrumentation,...) Equipment validation Equipment specific 21 CFR Part 11 functionality Worldwide Packaged Unit Support Page 29

27 PAT Instruments NDC - Moisture NIR (J&M LHP) Moisture+ C. uniformity Malvern Particle Size Page 30 At line analyzer Weight, Hardness, Thickness, Assay

28 High Level System Architecture Real Time Release reporting SITE MES LIMS Supervisory & Control Alarm handling (6) Line Control Analyzers Multivariate Data handling Line Recipe handling Trending Univariate Data Deviation Tracking Critical Parameters SCADA (2) (5) SIPAT (4) PLC / Motion Control ANALYZERS (1) (3) On-line monitoring of the Critical to Quality Attributes (CQA) Deviation Tracking CQA Out of Control Detection & input to APC PROCESS Page 31

29 Traditional PAT in solid dosage Content Uniformity Bulk Physical control parameters Dispense & Blend API Excipients control parameters Wet Granulation Liquid addition control parameters Drying Moisture control parameters Milling Blending Lubrication Lubricant excipient Particle size control parameters Compression Defects Sampling & Off-line analysis control parameters Coating Bulk Physical Defects Test against specifications control parameters Packaging Coating solution Page 32

30 End product quality predictions Input material characteristics Content Assay Uniformity Dissolution control Moisture Disintegration parameters Dispense & Blend Api Excipients control parameters Wet Granulation Liquid addition control parameters Drying (NIR) control parameters Blending / Lubrication Lubricant excipient Particle size (Malvern) control parameters Compression Weight Hardness Thickness control parameters Coating Visual Inspection Coating thickness control parameters Packaging Coating solution Page 33

31 Material Residence Time distribution Product Plug tracking Ingredients API Lot 1 Lot 2 Excipient 1 Lot 1 Lot 2 Excipient 2 Lot 1 Lot 2 Granulation Drying F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 F4C4 F4C5 Millling/Blending F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 F4C4 Compression F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 Coating F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 Time Output Drum 1 Drum 2 Drum 3 Page 34

32 Material Residence Time distribution Real Time Release floating window Ingredients API Lot 1 Lot 2 Excipient 1 Lot 1 Lot 2 Excipient 2 Lot 1 Lot 2 Granulation Drying Release window F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 F4C4 F4C5 Millling/Blending F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 F4C4 Compression F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 Coating F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 Time Output Drum 1 Drum 2 Drum 3 Page 35

33 SIPAT: data monitoring and control in real time Principle LIMS RTR SCADA Raw Material Characteristics + Context data Analytical Data Data alignment Contextualization Rules separating non-representative data Statistical Real Time Models e.g. PLS, PCA Chemometrics (SIMCA, Matlab, Unscrambler, PharmaMV, ) engine embedded in SIPAT CQA Critical to Qualty Attribute Aggregation of CQA over process context - Average, - Standard deviation On line Monitoring Out of control detection Deviation tracking NIR, PSD SIPAT APC Analyzer Page 36

34 Data Driven Manufacturing SiPAT allows to measure inline the CQA s, real time Increased number of measurements Test case of 50hrs material - Moisture measured 960 times - Particles size distribution measurements: 960 times - Content uniformity: 100 times - Tablet weight: each tablet (1,8 Mio) Enhanced product security Data correlation across units Page 37 37

35 Results of the trials Main findings of the feasibility study: Reduced process development time Page 38 Unlimited number of reference batches as a basis for model creation Real Time Release enabling Quality information available on tablet level rather than on batch level Increased process understanding Through Real Time Quality Assurance Time-based process: scalability improvement Savings in construction, space, energy, maintenance Increase of yield and equipment efficiency Reduction of scrap, waste & rework Reduction of raw material Reduction of human interference (cost, safety)

36 Article available on request Page 39

37 Better process understanding leads to a more efficient result! Page 40

38 Thank you for your attention! Jan Verelst Siemens Industrial IT