3rd International Symposium on Continuous Manufacturing of Pharmaceuticals
Addressing Control Challenges in Flexible, Continuous Bio Manufacturing Thomas Steiner Emerson Automation Solutions Global Strategic Account Director – Life Science
Delivering Quality in Continuous Manufacturing Requires Advanced Automation Satisfy Critical Quality Attributes Quality by design for new unit operations equipment Inline, at-line, and real time monitoring necessary
Controlling Disturbances, Dynamics, Constraints Direct measurement Simulation Predictive Modeling
Effective Start-up / Shutdown Procedures
Material Traceability
Effective, repeatable start-up to minimize ramp up time to an “in control” state
Detailed reporting of material genealogy
Effective, repeatable shutdown procedures to minimize waste
Time and process based tracking of materials through the process
Feedback vs Feedforward Process parameters manipulated in response to disturbances to maintain quality
“In Control” vs Steady State
New Strategies for Unit Operations Simple, low level controls Integrated control across production
Maximizing Uptime
Adjusting for variability in materials
Reduced time for diagnostic or maintenance work
Adjusting for variability in the process and disturbances
High equipment utilization necessary
Downstream and upstream interactions International Symposium on Continuous Manufacturing of Pharmaceuticals, MIT May 20-21, 2014 White Paper 6: Control Systems Engineering in Continuous Pharmaceutical Manufacturing Allen S. Myerson, Markus Krumme, Moheb Nasr, Hayden Thomas, Richard D. Braatz
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Process Analytical Technology Required for Continuous Manufacturing FDA Definition: PAT is a system for designing, analyzing and controlling manufacturing through… critical quality attributes … with the goal to ensure final product quality. Quality Prediction (Critical Quality Attributes (CQA))
Fault Detection
Process Data CQA
CQA
CQA
Spectral Analyzer and Chemometric Models
In-line Property Analyzers (multiple)
Process Data CQA
Predicted Quality (soft sensors)
Models
Abnormal Fault Detection (PCA)
Models
Lab Data
Chromatography • • • •
NIR / RAMAN sensors
Conductivity, Dissolved O2
Biomass concentration pH Growth rate Distillation point
Control & Optimization Process Data, CQA
Manipulated Variables (MVs)
MPC Optimization Models
• • • • • •
Analyzer Validation Sensor Failure PV Drift Equipment Malfunction Abnormal yields Etc.
• Dissolved Oxygen • pH • Crystallization particle size • Concentration • Etc.
Typical “Layered� PAT Applications Challenges (PAIN)
HMI Models
Spectral History Data and Chemometric Models
L3
L2
Predicted Quality (soft sensors)
Abnormal Fault Detection (PCA)
MPC Optimize
OPC Client In DCS / PLC
- Fragile architecture - Difficult to implement - Difficult to maintain - Perceived as Less Secure - Multiple operator interfaces - Difficult to validate process Example Large Molecule Continuous Manufacturing: Merck Vision for Biologics Agile Supply
Controller
DCS
L1 In-line Property Analyzers (multiple)
NIR / RAMAN sensors Emerson Confidential
Dissolved O2
Chromatography
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Future Solution Architecture for PAT In Flight‌ -
Models
Off-line Chemometric Models
L3 L2
OPC-UA
On-line Spectral Data & Models
L1
Spectral Data History
Predicted Quality (soft sensors)
Abnormal Fault Detection (PCA)
OPC-UA Client
Robust architecture Easy to implement Easy to maintain More Secure Single operator interface Traditional DCS validation effectiveness
MVDA Optimize
Controller
DCS
OPC-UA
In-line Property Analyzers (multiple)
NIR / RAMAN sensors Emerson Confidential
Dissolved O2
Chromatography
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Continuous Biologics Processing Challenges / Solutions: Integrate Islands of Automation • Unit operations are not really islands but interdependent • Bags for process and variability in process – pH change requirements between steps – Step 3 slows -> Need to slow Step 2
• Coordination of unit operations • Comprehensive batch report • Material consumption including single use components and material generation tracking • • • • • • Emerson Confidential
Operators have normalized interface Small OEM to DeltaV interface Small effort compared to coordinating many skid vendors Skid suppliers focus on their strengths Suppliers are not charging for control outside their unit op DeltaV produces a coordinated batch report at the end 6
Continuous Biologics Processing Challenges / Solutions: Track Materials via Residence Time Distribution Across the Process • • • •
Models relationship between input and output concentrations Input pulse dissipates through unit operation as function of time Output concentration is characterized by Mean Residence Time (MRT) and distribution Maximum output concentration amplitude represents maximum possible response to input disturbance
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Continuous Biologics Processing Challenges / Solutions: Requires a Two Stage Qualification / Validation Strategy
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Single Use Enabled Automated Continuous mAb Production Nuno Pinto Ph.D., Life Sciences Symposium Mar. 28-30, 2017 8
Unlocking the Value of Flexible and Continuous Manufacturing Requires Planning Flexible and Continuous Manufacturing in Life Sciences are progressing Assess people, processes and technologies within your organization to determine how to get the benefits Emerson Confidential
Approach requires re-thinking your manufacturing processes and business Technology barriers are being addressed Secondary manufacturing is moving the fastest but primary is also progressing Requires people with a combination of process and automation / data technology skills Requires flexible and adaptable automation and data management platforms 9