continuous manufacturing & robust process control for safe ... · 12/16/2018 1 mcmaster...
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12/16/2018
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McMaster University
Continuous manufacturing & robust process control for safe pharmaceutical manufacturing
Qinglin Su, Claire Liu, Marcial Gonzales, Rex Reklaitis, Zoltan K. NAGY
Davidson School of Chemical Engineering
Purdue University, West Lafayette, IN
Deember 12, 2018
Purdue Process Safety and Assurance Center (P2SAC)
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Why do we need to use advanced control techniques in the pharmaceutical industries?
Lack of real-time measurements
Lack of models
Lack of actuators
Quality assurance
Process validation
Continuous verification
Safety
Large variations in product pipeline
Downward pressure by healthcare providers
Growth in emerging markets
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Integrated
Intensified
Monitored
Controlled
Safer
Advanced control enables a new generation of integrated,
intensified & intelligent manufacturing systems with
drastically improved flexibility, predictability, stability, controllability & safety
Advanced control enables a new generation of integrated,
intensified & intelligent manufacturing systems with
drastically improved flexibility, predictability, stability, controllability & safety
Nagy&Braatz, Handbook of Ind. Cryst., 2013; Nagy&Braatz, Annu. Rev. Chem. Biomol. Eng., 2012
Holistic systems view of Integrated & Continuous Manufacturing of Pharmaceuticals
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Develop a methodology for integrated Design-Control-Safety (DCS) platform for pharmaceutical processes
Safety by control: Fault tolerant control (FTC) for safe plant operation
risk-based control system design and validation
Safety by design: Improve inherent process performance and safety via adoption of continuous manufacturing of pharmaceuticals
Overall Project Objectives
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Quality-by-Control (QbC) paradigm
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Past and Present Target Processes for Implementation
Continuous crystallization MSMPR (cascade of MSMPRs)
– Advantages: equipment in place, easier use of PAT
– Disadvantage: similar to batch processes (large residence time distribution, low heat transfer area/volume ratio)
Plug flow crystallizers (PFC) – different technologies– Advantages: faster, smaller volume, higher product consistency
– Disadvantage: need for new equipment, difficulty of using PAT
Continuous tablet manufacturing
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Continuous Crystallization
Two main technologies:
MSMPR (cascade of MSMPRs) – stirred tank or dynamic baffled– Advantages: equipment in place, easier use of PAT
– Disadvantage: similar to batch processes (large residence time distribution, low heat transfer area/volume ratio)
Plug flow crystallizers (PFC) – different technologies– Advantages: faster, smaller volume, higher product consistency
– Disadvantage: need for new equipment, difficulty of using PAT
Typical problems:– Optimal operation
– Control of CQAs
– Startup
– Fouling
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Cascade MSMPR system
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NMPC – attainable regions for control for safety
100 200 300 400 500 600 700 8000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
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Mean size [m]
Yie
ld [-
]
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Nucleation ControlGrowth ControlAntisolvent ControlTemperature ControlGlobal Control 0 100 200 300 400 500
100
300
500
Siz
e [
m]
0 100 200 300 400 5000.5
0.75
1
Yie
ld [-
]
(a) 21 1 3Set-point:
Yield
Size
0 100 200 300 400 500
0
5
10
Sta
ge 1 a
ntis
olven
tadd
ition
rate
(F1) [m
l/min
]
0 100 200 300 400 500
0
5
10
Sta
ge 2 a
ntisolvent
add
ition ra
te (F
2) [m
l/min]
Time [min]
(b)
F1
F2
Point Yield Size
1 Medium Medium
2 High Small
3 Low Large
Attainable region depends on control approach
Depending on safety constraints change of control structure needed
Robust fault-tolerant control
Safer control
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Continuous plug flow crystallization systems
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Encrustation is a process by which solute (API) deposits on to the crystallizer surface.
Encrust reduces heat transfer, and subsequently product quality and yield.
Causes non-steady state process and leads to blockage.
Fouling or encrustation
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On-Off Fault-tolerant Feedback Control
• The Encrust and CSD controller assures state of control.
• The CSD feedback controller detects the upper and lower bounds and trigger the point of collection (blue).
• Example of level-1 control, in which an active process control system is used to monitor CQAs in real-time.
• Process will work indefinitely due to QbC approach despite fault due to encrustation
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Purdue Continuous Tableting Pilot Plant
Hierarchical Control System Development and Analysis
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Pharmaceutical continuous manufacturing pilot plant at Purdue
Feeders: Schenck AccuRate AP‐300, DP‐4Blenders: Gericke GCM 250Roller Compactor: Alexanderwerk WP120Tablet Press: Natoli BLP‐16, Natoli NP‐400
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Layer 2
•Use of dynamic models for prediction•Model Predictive Control, Adaptive Control, Gain Scheduling, etc.•Integrated across multiple unit operations
Layer 1
•Based on PAT to measure CQAs and closed loop control (PFC)•PID based more advanced control schemes•Single or multiple unit operations
Layer 0
•Built‐in control systems by manufacturers at unit operation level•Simple PID control•Single unit operation
Hierarchical control architecture
R1 Medium
R0 Low
R2 High
Risk levels & failure mode
Sensor accuracy, precision, sampling time Sensor location Control architecture reconfiguration Control method Controller tuning⁞
Evaluation & regulatory filling
Good
Excellent
Fail
T2P: Time to productM2P: Magnitude to productMRI: Morari’s resilience index
Continuous improvement
e.g. Good Manufacturing Practices (GMP) guidelines
Manufacturing processes are clearly defined and controlled. All critical processes are validated to ensure consistency and compliance with specifications.
Manufacturing processes are controlled, and any changes to the process are evaluated. Changes that have an impact on the quality of the drug are validated as necessary.
⁞
Formulation optimization Process reconfiguration ⁞
Risk-based systematic design and analysis of feedback control strategies for continuous manufacturing processes
[1] Su Q, Moreno M, Giridhar A, Reklaitis GV, Nagy ZK. Journal of Pharmaceutical Innovation. 2017;12: 327-346.
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Level 1 PID control scheme for Natoli Tablet Press
Cascade control: Tablet weight master loop (by main compression force), dosing slave loop (by dosing level)
Production rate by turret speed
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Level 2 MPC control scheme for Natoli Tablet Press
A linear MPC control of 2 by 2 with an additional constraint variable of main compression force was implemented in DeltaV platform, controlling the tablet weight and production rate by manipulating the fill depth and turret speed.
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Natoli BLP‐16
Mettler Toledo ME4001E 0 500 1000 1500 2000 2500
Time (sec)
0
100
200
300
400
500
Main comp. force
Kawakita model
Dell Laptop
0 100 200 300 400 500
Tablet weight (mg)
0
2
4
6
8
10
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AT4-2%AT4-10%MP
c=0.2499
Formulation characterization: compressibility
Data reconciliation ‐ the power to combine knowledge, speed, accuracy, and precision of sensors
Tablets
0 500 1000 1500 2000 2500
Time (sec)
0
100
200
300
400
500
Sotax Auto Test 4
* Feb 19, 2018, API% = 10.0, MCC% = 89.8%, SiO2% = 0.2
Changes in material properties ‐> compressibility
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Comparison of Level 1 PID and Level 2 MPC control performance (with Level 2 DR)
June 15, 2018, API% =10, MCC% =89.8, SiO2% = 0.2
Data reconciliation reinitialized
Sotax AT4
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Rate
Severity Probability Controllability
1 No effect None < 1 occurrence during the continuous production in 5 years (e.g., control instrument failure)
Very rare All the applicablemeasurements &controls are in place
Alwaysundercontrol
3 No patient effectProcess performance decreasing (e.g., divert of non-conforming product)
Low 1 occurrence during the continuous production in 1to 2 years (e.g., calibration errors)
Rare Most of the applicablemeasurements & controls are in place
High credibility of under control
5 No patient effectProcess performance decreasing(e.g., large amount of non-conforming product, process shut-down)
Medium
< 1 occurrence during a single continuous production campaign (e.g., variance in raw material properties)
Sometimes Some of the applicablemeasurement &controls are in place
Moderate credibility of under control
7 Potential patient effect (e.g., large variance in critical quality attributes)
High > 1 occurrence during a single continuous production campaign (e.g., raw material reloading)
Frequent/No information
Few of the applicable measurements & controls are in place
Remote credibility of under control
9 Significant patient effect (e.g., large variance in target product quality profile, loss of clinicalperformance)
Very high
At any time during the continuous operation (e.g., machinery vibration)
High frequent None of the applicablemeasurements & controls are in place
No control
[1] Potter C. PQLI application of science‐ and risk‐based approaches (ICH Q8, Q9, and Q10) to existing products. Journal of Pharmaceutical Innovation. 2009;4(1):4‐23.
Risk analysis and assessment
A risk mapping, from the perspective of process control, will be designed based on scores of severity and probability of risks during continuous manufacturing, upon which the proposed real‐time release control strategies will be evaluated to give corresponding controllability scores for each risk.
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45 < R227 <R1 <451< R0 < 27
Severity
1None
3Low
5Medium
7High
9Very high
Probability
9High frequent
7Frequent
5Sometimes
3Rare
1Very rare
1 Always under control
3 High credibility of under control
5 Moderate credibility of under control
7 Remote credibility of under control
9 No control
Sensor calibration error
Raw material propertyvariation
System interaction
Sensor noise
Sluggish Level 0 or machine control
Bulk densitychanges by shear
Level 0 control risk analysis in tablet press
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45 < R227 <R1 <451< R0 < 27
Severity
1None
3Low
5Medium
7High
9Very high
Probability
9High frequent
7Frequent
5Sometimes
3Rare
1Very rare
1 Always under control
3 High credibility of under control
5 Moderate credibility of under control
7 Remote credibility of under control
9 No control
SensorCalibrationerror
Raw material propertyvariation
System interaction
Sensor noise
Sluggish Level 0 or machine control
Level 1 control risk analysis in tablet press
Bulk densitychanges by shear
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45 < R227 <R1 <451< R0 < 27
Severity
1None
3Low
5Medium
7High
9Very high
Probability
9High frequent
7Frequent
5Sometimes
3Rare
1Very rare
1 Always under control
3 High credibility of under control
5 Moderate credibility of under control
7 Remote credibility of under control
9 No control
SensorCalibrationerror
Raw material propertyvariation
System interaction
Sensor noise
Sluggish Level 0 or machine control
Level 2 control risk analysis in tablet press
Bulk densitychanges by shear
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Conclusions
Improved safety via better control and use of CM
Continuous pharmaceutical manufacturing enhances safety of manufacturing processes
Developed systematic hierarchical control structure
Quality-by-control (QbC) - new paradigm for risk-based control system development for safer plant operation
Exemplified for continuous crystallization & tablet manufacturing
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