1Biogen | Confidential and Proprietary
Continuous Process Verification of Next-
Generation Processes Utilizing Advanced
Process Control (APC)
CMC Forum Europe, Paris, 09May2016
Rohin Mhatre, Ph.D.
Vice-President, Regulatory Affairs
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• Multiple tools for analyzing raw materials
• Good understanding of critical components in the raw materials
affecting product quality
• Understanding of process levers that control product quality
• Linkage of the unit operations
• Feed back/forward adaptive control
• Extensive real time monitoring systems
• Sophisticated models for predicting product quality
• Significant process history for commercial products
State of our Processes…
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15kL3750L950L
Seed
train
Production
ReactorHarvest
Raw
MaterialsPurification Filling
DS
Rele
ase
Bioreactor: in-line
Raman, capacitance
In-line: protein concentration
At line: purity, aggregation,
charge variants
DS
• Endotoxin
• Bioburden
• Potency
UF/DF: in-line Protein
concentration
Biotech Drug Substance Advanced Process Controls
feedback feedback
Feed
forward
Raw Materials
• Geneology
• Spectroscopic tools
• Rapid ID testing
Multivariate discrete analysis for process monitoring and
disposition decisions, Predictive model for RTRT or FF control
APC
CQA Predictive
models for
feedforward control
Real-time multivariate analysis for trending, actions and disposition decisions
Bioreactor and/or harvest:
At line multi-attribute testing and/or CQA
Predictive models for feedforward control
Media/buffer
Prep control
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Advanced Process Control
Product Quality &
Yield
Process Input
Pro
cess
Process Control • Robust process design • Advanced process control
Raw Material
RM Control• Screening and/or • Control by Vendor• Learn from MFG experience
PAT PAT
4 4
4 Real time quality
PATActual or virtual
sensor
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• Develop a clear connection of process parameters that affect
product quality attribute/s
• Predictive models that can predict product quality
• Process signatures that are indicative of product quality (growth
curves, peak shapes, etc.)
• On-line measurement and control
Use these tools to assure process and
product quality consistency
General Approach -APC
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Test Goal Analytics
Raw Material
(individual and
media)
Media feed control (for
consistency in Yield,
Product Quality)
Raman spectroscopy
Fluorescence
UV
NMR
XRF
Product Quality in
Process
Intermediates
Process understanding
Process monitoring
Feed forward control
Feedback control
Real time release
LC-MS or 2D-LC
CE Western
With rapid purification:
• Peptide mapping LC/MS
DNA/HCP in Process
Intermediates
Column performance
monitoring
qPCR with automated
extraction (DNA)
High throughput
immunoassays (HCP)
APC – In-Process Analytics
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• Test for: %aglycosylated protein and specific glycoforms
• Collect time-course data during cell culture process characterization (PC)
• Simple, high throughput method can support the large number of PC samples
• Data supports predictive model building (and possible control levers, if any)
• Based on outcome of PC studies, determine if there are feed forward or feedback control opportunities
• Define testing point(s) and target range of the product quality attributes (including method/process variability)
Production Bioreactor Testing using
Reduced Intact Mass Spectrometry
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Reduced Intact Mass for mAb
DTT
HC-Agly LCMan5
G0F G1F
G2F
G0F-GlcNAc
LC-MS
%Agly
%Man 5
%Biantennary
Galactosylation
Agly
G0FG1F
G2FMan5
G0F-GlcNAc
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APC – Upstream Control
Feedback control - Glucose / Lactate control
Feed Automation using Raman Spectroscopy
Maintain glucose at low levels to minimize glycation and restrict lactate formation
Low glucose
feedback control
High glucose
feedback control
Consistent Cell Growth = Consistent Titer and PQ
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DS
Cap
ture
Inte
rme
dia
te
Po
lis
h
Bioreactor
HarvestPurification
APC – Downstream Control
Goals:
Real time release
Real time adaptive
downstream control
Fast response to issues
Multi-attribute method
for product quality
On
-lin
e s
am
plin
g
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Viral Inactivation
Capture Column
Polish Column #1
Polish Column #2
≤ “x” g/L Loading
Viral Filter
UF/DF
Harvest At Line
Rapidly
Measure %
Impurity in
Column Load
No FF
Load ≤ [x]
(n cycles)
≤2.5%
FF
Load ≤ [x – y]
(n + 1 cycles)
FF
Load ≤ [x – 2y]
(n + 2 cycles)
≥3.5%2.6-3.4%
APC Feed Forward (FF) Control Opportunity
Impurity controlled to ≤ 2.5%
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CQA Predictive Modeling for FF Control and RTRT
Purpose Traditional Future State
Product quality prediction in near real time
Releasebased on CQA test
Release based on Predictive Model
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Predictive Model Development Strategy
Data Acquisition Data Population
Data Pre-treatment
Exploratory Analysis Model Optimization Model Validation
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Identifying Product Attributes Possible for Predictive Model Development
Modelability? (Y)Release or FF control? (Y)
mAb CQA’s
CQA 1
CQA 2
CQA 3
CQA 4
CQA 5
CQA 9
CQA 10
PROCESS PERFORMANC
PARAMETER 1
CQA 6
CQA 7
CQA 8
CQA 15, CQA 16, CQA 17
CQA 11
CQA 12
CQA 13
CQA 14
Model
Considerations
• Modelability is assessed based on:
• CQA variability in development/mfg experience
• Assay variability
• Simple or complex source of variability
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APC – Informatics
Real Time Univariate and Multivariate
Statistical Process Monitoring
Advanced Analytics for Predictive
Modeling of Product Quality
Real Time Quality
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Process Design
• FDA
• Design
• EMA
• Evaluation
‘Qualification’ (PPQ)
• FDA
• Qualification
• EMA
• Verification
Verification (CPV)
• FDA
• Continued Process Verification
• EMA
• Ongoing Process Verification
PV for Traditional Process Control
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Process Design
• FDA
• Design
• EMA
• Evaluation
Continuous Verification
• FDA
• Combine Stages 2/3
• ICH/EMA
• Continuous Process Verification
PV for Advanced Process Control
• Performance qualification applied to all batches
• Employs continuous measurement on every batch produced
• Data trending is an integral part of the approach
• Validation based on development knowledge and on-going manufacturing
performance confirmed on every batch
• Combining stages 2/3 builds upon established examples (e.g. reprocessing)
• Scientific knowledge and risk assessment reduce the need for discrete re-
validation exercises
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APC process characteristics:
Raw materials control minimizes process input variability
Quick and decisive product quality analytical methods for intermediates
real time product quality at process intermediates for every batch
Feedback control loops in the bioreactor for nutrients (based on cell density)
and glucose (to maintain low glucose levels) consistent cell growth
Consistent cell growth = consistent titer and product quality
Real time adaptive downstream product quality control
Advanced informatics tools provide real time process data and product quality
predictions
Process Validation for APC Process
Continuous Process Verification (ICH Q8)/
Continuous Quality Verification (ASTM)
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What is Continuous Process Verification?
EU Annex 15, Mar 2015
Continuous process verification
5.23. For products developed by a quality by design approach, where it has been scientifically established
during development that the established control strategy provides a high degree of assurance of product quality,
then continuous process verification can be used as an alternative to traditional process validation.
5.24. The method by which the process will be verified should be defined. There should be a science based
control strategy for the required attributes for incoming materials, critical quality attributes and critical process
parameters to confirm product realisation. This should also include regular evaluation of the control
strategy. Process Analytical Technology and multivariate statistical process control may be used as tools.
Each manufacturer must determine and justify the number of batches necessary to demonstrate a high level
of assurance that the process is capable of consistently delivering quality product.
5.25. The general principles laid down in 5.1 – 5.14 above still apply.
Hybrid approach
5.26. A hybrid of the traditional approach and continuous process verification could be used where there is a
substantial amount of product and process knowledge and understanding which has been gained from
manufacturing experience and historical batch data.
5.27. This approach may also be used for any validation activities after changes or during ongoing process
verification even though the product was initially validated using a traditional approach.
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What is the future…
Much greater level of process understanding and control
Process signatures will help predict product quality
Adaptive control will be a critical element of process consistency
RTRT will be the new paradigm
Process validation through continuous process verification
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Acknowledgements
Eliana Clark – Manufacturing Sciences
• Sarah Yuan, Rob Guenard, Gregory Stromberg,
• Steve Doares, Canping Jiang
Valerie Tsang – Technical Development
Rashmi Kshirsagar - Cell Culture Development
• John Paul Smelko
• Alan Gilbert
John Pieracci – Process Biochemistry
• Brad Stanley
Li Zang – Analytical Development
Patrick Swann – Regulatory Affairs
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