Deploying SIMCA-Batch On-Line in a new facility · Batch Record Bioreactor Purification. Drug Raw...
Transcript of Deploying SIMCA-Batch On-Line in a new facility · Batch Record Bioreactor Purification. Drug Raw...
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Optimization of processes by multivariate datanalysis
DAu – Dansk Automationsselskab
October 2nd 2014
Henrik Toft, Process Analytics
Hillerød, Denmark
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Overview of presentation
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Biogen Idec, Hillerød, Denmark
► Introduction
► Traditional biologics manufacturing
► Future biologics manufacturing
► Advanced Process Control at
Biogen Idec
► The multivariate advantage and
how we use it
► Chromatography review example
► Automatic process control
► How to get to the next level
► Q & A
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Traditional Biologics Manufactring
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►Lab and pilot scale experiments to identify process
parameters setpoint
►Large scale process verification runs to verify process
parameters and product quality
►Filing of production process (parameters)
►Regular process trend reporting to verify that process is
in a state of control
Product release based on end-point testing
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Future Biologics Manufacturing
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►Lab and pilot scale experiments conducted using Design
of Experiments to establish Design Space†
►Large scale process verification runs to verify design
space and product quality
►Filing of production process (design space)
►Continous process verification to verify that process is in
a state of control
Product released real time
† Applying uncertainty estimation e.g. through Monte-Carlo simulation
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Advanced Process Control at Biogen Idec
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Data-information pyramid
SBOL, Discoverant,
Delta V,
Trending Program Management, Availability, Analysis, Interpretation
SPC, trends,
Shewhart, Offline MVA
Feedback, Alarms,
Trending controls
SBOL models, MVA,
Transition, Cpk, Golden Batch
MPC, SBOL back to Delta V,
Virtual Sensors, PAT tools
BASIC Monitoring
BASIC Control
ADVANCED Monitoring
ADVANCED Control
Real time
Quality
DATA
INFORMATION
Pro
ce
ss
Un
de
rsta
nd
ing
Highest level of
Understanding/
knowledge –
LEANEST State
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Centralized Data Management System
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Batch Record
Bioreactor Purification. Drug
Substance Inoculation Raw Material
Data/Information
Flow
Material Flow
OPM LIMS
Interface
System allows user to
access data from any
system
Built-in analytics provide
for control charts, CpK
analysis, etc.
PI
Data Management System (Discoverant)
User
Drug
Product
Delta V
http://www.nevalab.ru/news.detailed.php?s=64
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Advanced Process Control at Biogen Idec
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►Establishment of infrastructure for data centralization and real time process monitoring:
►2003: 1st Discoverant Hierarchy & 1st Cell Culture SBOL †
►2006: SBOL on Manufacturing Floor
►2007: 1st Purification SBOL model
►2013: Approved patent on “Systems and Methods for Evaluating Chromatography Column Performance”, US008410928B2, 02Apr2013
►Built a culture of advanced process monitoring
† SIMCA-Batch On-Line
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The Multivariate Advantage
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The two variables are correlated The information is found NOT in the individual signals, but in the correlation pattern through MVA A lot of outliers are not detected unless all the variables are analysed together
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Advanced Monitoring through SBOL
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Multivariate Plot
Contribution Plot
Univariate Plot
Multivariate Plot
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SBOL – easy monitoring everywhere
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► In the facility large monitors are present at
several locations:
► In the inocolutionation room
► In the seed train area
► In the bioreactor hall
► In the purificaiton area
► In office areas
► SBOL status included in shift-handover
reports
► Simple physical setup with local PC for
each extended large monitors
► Not all process steps are monitored by
SBOL models
Easy monitoring with SBOL large wall screen
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SBOL catches – selected examples
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Multivariate Chromatogram Analysis
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►Motivation
► Chromatograms contain a lot of information that we are not utilizing
► Exploit already available continuous data
► UV, conductivity, pH, pressure, volumetric flow
►Goals
► Non-subjective, quantitative method for evaluating chromatograms
►Example: Analysis of elution peak UV tracing
► MVA model using discrete parameters that describe the
characteristics of the elution UV peak
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Elution UV Chromatogram Analysis
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►Residual host cell protein was showing a gradual upward
trend in the drug substance
►Purification step was the suspected root cause
►Investigation was launched:
►Started by building a multivariate process model
(approximately 60 variables covering cell culture and
purification)
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Disection of UV elution peak chromatograms
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UV
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0.3
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0.5
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0 200 400 600 800 1000 1200 1400 1600 1800 2000
UV
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1 2 3 4 5
YV
arP
S(H
CP
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YPredPS[2](HCP)
HCP Model2.M10 (PLS), HCP Model, PS-HCP Model2
YPredPS[Last comp.](HCP)/YVarPS(HCP)
RMSEP = ---
y=0.9141*x+0.1952R2=0.8139
SIMCA-P+ 11.5 - 5/5/2008 3:48:16 PM
Multivariate model to predict HCP*
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* Host Cell Protein
Q2=0.73
Further correlation analysis indicated: Chromatogram shapes changed as resin aged
• Eight chromatogram parameters adequately predicts HCP level in the Drug Substance
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Chrotogram MVA Summary
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►Chromatogram MVA enabled successful root cause
investigation of the HCP upward shift:
►Multivariate analysis of cell culture and chromatogram
parameters indicated that chromatogram parameters
were strongly correlated with HCP
►Further correlation analysis indicated that resin age
contributed to the chromatogram shape shift
►Lab studies confirmed that resin age contributed to the
column performance shift and the gradual upward trend
of HCP
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Automatic Process Control
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►Controlling nutrient feeding:
► Using multivariate sensors it is possible to monitor ”chemistry”
inside the bioreacotr e.g. glucose, lactose, ammonia and more.
►Realtime monitoring of (viable) cell density:
► Feeding accoring to the number of live cells to enable extented
bioreactor growth phase and hence increase titer per batch.
► Transfer between seed train bioreactors.
►Set up automated systems base on advanced sensors to
automatically run the batch based on recipie and sensor feedback.
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How to get to the next level
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►Upgrade from SIMCA-Batch On-Line (SBOL)
to SIMCA-online
►Implement use of spectroscopic sensors for
real-time process control and monitoring
►Implementation of real-time prediction models
for product quality forecasting
►Work towards full implementation of
continous process verification
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Acknowledgement
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Manufacturing
• Joydeep Ganguly
• Robert Genduso
• Ben Gilbert
• Ed Goodreau
• Andre Walker
• Sarah Yuan
• Lilong Huang
Engineering
• Jeff Simeone
• Jorg Thommes
• Jennifer Mitchell
Technical Development
• Doug Cecchini
• John Pieracci
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Thank you for listening – Questions?
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