Transcript PAT Data management

Data Management and Control Strategies for
Continuous BioProduction
Kjell Francois, Ivo Backx, Barbara Kavsek.
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What to expect?
1
• PAT data management?
2
• Examples from secondary
manufacturing - OSD
3
• PAT data mgt for continuous
BioProduction
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Dr. Kjell François/ I IA AS PA VSS Pharm
SIPAT core business
PAT Data management
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“In God we trust.
All others must bring data”
(W. Edwards Deming)
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Principle of PAT & QbD
PAT
Quality buillt in by design
Right first time
Classic control
Closed loop
control
Process data (Temp,
Pressure, Oxygen, …)
monitoring
product quality
Real-time
release
mathematical
translation
Lab
monitoring
analyzer data
LIMS
Hold /
release
Advanced
Control
Process feed
Process
Analyzer
Sample
Process output
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PAT data management
Analyzer data
Context & Info Data
SIPAT Runtime Data
Real Time Calculations
Characterisation Lab
Raw material data
Off line results
LIMS data
Process data
Regular Lab data
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PAT data management
Quantitative
result
Analyzer data
Characterisation Lab
Context & Info data
SIPAT
Runtime data
Raw material data
Real time calculations
LIMS data
Off line results
Process data
Regular Lab data
Qualitative
result
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PAT data management
SIPAT
Meta &
Context Data
Analyzer Data
(NIR, Raman,
PSD, …)
Process
Data
External application
Predicted
Data
 Batch number
 Trial number
 Process step
Off- line lab Data
(RM, process
samples,…)
Other data
 Product
 Campaign
 ….
Create Model
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PAT Toolbox
PAT requires multidisciplinary
Product &
process
design
skills & Tools
 Complex data management
(Advanced)
Process
Controls
PAT
Data
Collection
storage &
retrieval
Data
Analysis
& mining
Information
management
tools
DoE
Process
Analytics
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Examples from secondary manufacturing &
OSD
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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
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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
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SIPAT as a key Enabler for Continuous Manufacturing
and Real Time Release
SIPAT
Raw Material
Mill
Coating
Blender
Dryer
Tablet press
Granulator
Right
First Time
Real time
release
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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)
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Traditional PAT in solid dosage
Content
Uniformity
Bulk Physical
Defects
control
Loss On Drying
Process
parameters
control
Particle size
Dispense
& Blend
Process
parameters
control
Process
parameters
Wet
Granulation
Bulk Physical
Defects
control
Process
parameters
Drying
 Api
 Excipients
Sampling &
Off-line
analysis
control
Process
parameters
Test against
specifications
control
Process
parameters
Blending /
Lubrication
Process
parameters
Compression
Liquid
addition
control
Coating
Packaging
Lubricant
excipient
•
Coating solution
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End product quality predictions
Input material
characteristics
Content
Uniformity
control
Process
parameters
Loss On Drying
(NIR)
control
Particle size
(Malvern)
Process
parameters
control
Process
parameters
Wet
Granulation
Blending /
Lubrication
Liquid addition
Coating
thickness
control
Process
parameters
Drying
 Api
 Excipients
Visual
Inspection
Weight
Hardness
Thickness
control
Process
parameters
Dispense
& Blend
Assay
Dissolution/disintegration
(NIR)
control
Process
parameters
Compression
control
Process
parameters
Coating
Packaging
Lubricant excipient
•
Coating solution
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Example 2 = Hot Melt Extrusion
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
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Time alignment of data
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Data management  Calcs  Evaluation  Control
Process data of
extruder &
cooling line
SME
PCA
In-process
material
attributes
PCA
Western
electric
rules
(WER) for
SME, tscores
Out of
WER
Control heat zone
setpoints based on SME
& diameter of strand
---------PLS
NIR blend
PCA
NIR strand
PLS
PCA
Outlier
detection
for
spectra
(DModX,
Hotelling
T2)
Outlier / Out
of WER
Open Diverter
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PAT data management & control in
Continuous Bio
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Continuous BioProduction setups
Warikoo V, et al. Integrated Continuous Production of Recombinant
Therapeutic Proteins. Biotechnol. Bioeng. 109(12) 2012: 3018–3029.
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Bring this is in a continuous production train
Precultures
Fermentation
Harvesting
Changes
Solutions
upstream
Purification
Changes downstream
• Smaller reactor sizes vs “Stainless Steel Cathedrals”
• Smaller chromatography Columns
• Perfusion reactors
• Integrated operations
• Focus more on disposables
(adjusted flow rates, pH, osmolality etc)
The challenges on data mgt & control
• Need for online monitoring  PAT!
• Batch definition
• Need for integrated data management
• Traceability & genealogy
• Alignement of data & information
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Bring this is in a continuous production train
Precultures
Fermentation
Harvesting
Solutions
Purification
PAT technology
• Integrated PAT data management
• Spectroscopy (NIR, MIR, UV, Raman, LIF)
• Harvest Point - optimums
• Off-Gas analysis
• Golden batch comparison
• Online GC/HPLC
• Real Time Release
• Online rapid analysis equipment
+ MVDA
• Advanced Process Control
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Data flows…
Precultures
Fermentation
Upstream Control & Information flows
Harvesting
Purification
Downstream Control & Information flows
• Closed loop control inside reactor (Feedback control)
• Read data from chromatography or other purification steps
• Feeding control in reactor (Feedback control)
• Integrate data from fermentation step to predict impurities
etc. in purification steps
• FeedForward control from precultures to next steps
(Precultural conditions!)
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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
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Siemens as solution provider in this area
Siemens works together with different reasearch organisations
and industrial partners in this area
M3C
Group
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Thank you for your attention!
Dr. Kjell Francois
Siemens AG
Industry Automation Vertical Pharma
Mobile: +32 496 816577
[email protected]
www.siemens.com/SIPAT
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