This document discusses how design of experiments (DOE) and multivariate data analysis (MVDA) using Umetrics software can help optimize biomanufacturing processes and ensure quality. DOE is used to build process understanding, identify critical parameters, and define a design space. MVDA condenses large process data sets into informative plots to improve process monitoring, control, and verification. Case studies from companies like Novartis, Biogen, and Lonza demonstrate benefits like increased yield, reduced risk, and improved root cause analysis through the use of DOE and MVDA tools.

1. Efficient process development and
robust manufacturing
Stefan Rännar, PhD
Senior Application Specialist, Umetrics AB

2. Quality by Design (QbD) leads to
Cost Savings, Risk Mitigation, and more…
Process Optimization &
Process Understanding
Critical Process Parameters
Design Space
à Ease of process changes
à Robust Manufacturing
Process
Analytics
Sensors
SCADA
DoE
MVDA
Yields & Titers
à CoGs | R&D costs
à Tech Transfer
à Time to Market

3. • DoE (Design of Experiments)
– Knowledge building tool for process development
– Product optimisation
– Critical Process Parameters
– Design Space
– Umetrics Software
• MODDE
• MVDA (Multivariate Data Analysis)
– Tool for process understanding and process monitoring
– Robust manufactoring
– Umetrics Software
• SIMCA (offline modelling)
• SIMCA-online (online monitoring)
DoE and MVDA

4. Shake flasks
2 l fermentors
10 l fermentors
Pilot
Production
Use of DOE and MVDA, Example from Fermentation
“Design
space”
On-line
applications:
Monitor Design
space
DOE
MVDA
4

5. Umetrics, The Company
• The market leader in software for
multivariate analysis (MVDA) & Design
of Experiments (DOE)
• 25+ years in the market
• Off line analysis tools
• On-Line process monitoring and fault
detection
• 700+ companies, 7,000+ users
• Pharmaceutical, Biotech, Chemical,
Food, P&P, M&M, Semiconductors and
more
• Worldwide Presence with MKS (2006)
• Offices: USA: Boston MA, San Jose CA
Sweden: Umeå, Malmo
UK: London, England
Singapore
China, Japan, Israel & More
• Close collaboration with universities in
USA, Sweden and Canada

6. • All software are fully validated
– Software development and QA audited and approved by big pharmaceutical
companies on regular basis
• World leading user friendly solutions for PAT and QbD
– More than 700 leading companies & organizations
• World leading graphically driven software solutions
– More than 7000 users
• World leading consulting, support and training services
– More than 15000 engineers, scientists and managers educated
• Strong research cooperation with leading Chemometric research groups
Why Umetrics?
In-house training
Open courses
Explore, analyze and
interpret
For ensuring process
quality
For easier DOE and
QbD For embedded OEM
solutions

7. Design of Experiments (DoE)
MODDE Software

8. Motivation for Design of Experiments (MODDE software)
§ Key technology in Quality by Design (QbD) discussions REGULATORY
§ Improve process understanding with a clear project scope KNOWLEDGE
§ Increase manufacturing efficiency and lower production costs MONEY
§ Speed up development of new therapeutics with less experiments TIME
§ Statistically verified statements with graphical representation COMMUNICATION
§ ... and many more
￭ “A structured, organized method for determining the relationship between factors
affecting a process and the output of that process.” [FDA, Q8(R2)]

9. DoE – The Efficient Way of Experimentation
Random Approach Efficient Approach (DoE) Intuitive Approach (COST)
§ Changing all factors
arbitrarily
§ High number of
experiments
§ Pure luck to find optimum
§ Changing One Single
factor at a Time
§ High number of
experiments
§ Only “quasi”-optimum can
be found
§ Changing all factors at
the same time according
to a well designed plan
§ Perform least number of
experiments
§ Optimum can be found

10. Design Space Estimation (and definition)
§ Probability plot illustrates risk of not meeting the specifications
Classical Contour Plot Design Space = Probability Plot

11. Three Typical DoE-Applications in Biotechnology Processing
Medium Optimization Parameter Optimization Parameter Screening
¡ Optimization of the composition of
growth and production culture
media
¡ Screening of control parameters
and basic process state variables
¡ Critical Process Parameters (CPP)
¡ Optimization of control parameters
including feeding strategy

12. Page 28
DoE integrated with equipment – MODDE-Q

13. Multivariate Data Analysis (MVDA)
SIMCA software
13

14. INFORMATION
Multivariate data analysis (MVDA)
Converts data into informative plots
DATA

15. MVDA
Objectives for the pharmaceutical & biopharmaceutical industry
• Increase of process understanding
– Identification of influential process parameters
– Identification of process signatures
– Relationship between process parameters and quality attributes
• Increase of process control
– Efficient on-line tool for
• Multivariate statistical control (MSPC)
• Analysis of process variability
• Real time quality assurance
– Online early fault detection
– Excellent tool for root cause, trending analysis and visualization
– Fundament for Continued Process Verification (CPV)
DevelopmentProduction

16. Why Multivariate Analysis and Monitoring?
The information is found in the
correlation pattern - not in the
individual variables!

17. Multivariate Monitoring
Multivariate Control Limits

18. MVDA - The Art of Compressing and Visualizing Information
Data
§ Post-batch MVDA enables easy interpretation and analysis of large process data sets
§ Find key trends, correlations, patterns and relationships from historical data
§ Improved process understanding & performance resulting in e.g. yield increase or impurity reduction
§ MVDA used for Continous Process Verification (CPV)
Multivariate
Modeling
Information

19. File
ModelDB
DBHistorical
data
MVDA offline
Model Generation
MVDA online
Data Interpretation
DB
DBProcess
data
offlineonline
historical
real-time
Multivariate Data Analysis for Continuous Process Verification
§ Build models offline to find key trends, patterns and relationships from historical data
§ Real-time process monitoring for quality control and early fault detection

20. What makes SIMCA-online so powerful?
• Data from all relevant process
parameters are condensed
into a few highly informative
variables
– Simplifies overview, analysis
and interpretation
– Enable use of data by
increasing ease of use
• Early fault detection with
simple drill-down functionality
for analysis of root cause
• Works for both batch and
continuous processes

21. Early fault detection
§ SIMCA-online technology is
acknowledged for its ability to detect
process issues before they become
critical
§ Full drill-down to raw data for cause
analysis
§ Instant analysis of process changes
improves understanding
§ Easy-to-grasp graphics makes the
process status accessible to colleagues
at all levels
Early Detection of Process Deviations with
Guiding to Potential Root Cause

22. • Biogen – fermentation monitoring applications.
• Novartis – tableting & bioprocess monitoring
• AstraZeneca – drug screening & production monitoring (packaging)
applications
• Tembec – production monitoring and product change-out optimization
– Published savings of $1M per year
• Merck– SIMCA-online and SIMCA-Q for production monitoring and
PAT
– Published savings of $2M per year
• PepsiCo – prediction of product quality
• GSK – real-time batch production monitoring
• DuPont – production monitoring
• Sunoco – Oil production monitoring
• IBM – Real-time monitoring of silicon chip wafer production
– Published savings of $10M+ per year
Umetrics online references

23. Novartis: Root Cause Analysis and Parametric
Release
• Novartis is one of the largest Pharma companies
in the world with drugs in a large amount of
disease areas.
• Presented by Marianna Machin and Lorenz Liesum at UUM in
Frankfurt 2011
Customer Results / Benefits
• Improved process
consistency
• Enabled Root Cause
Analysis
• Established key parameters
for cell cultivation
Solution
• Statistical process control
i.e. SBOL/ SIMCA-online
• Validation of model
• Simulate an incident by
for example changing the
flow rate of a pump.
Customer Business Challenge
• Established process, most of
process understanding based
on experience
• Root Cause Analysis and
Statistical Process Control
for Proof of specificity

24. Lonza: Multivariate Online Batch modeling
• Lonza is a global company serving the needs
of the pharmaceutical and specialty
ingredients markets.
• Presented by Christine Bernegger /
Head Program Management,
Visp Seminar 2013-02-28.
February - Workshop der ISPE Affiliate D/A/CH
Solution
Six sigma approach variability
analysis for Yield optimization
And Time Based MVA of On-
line Process Parameters
.
Customer Results / BenefitsCustomer Business Challenge
• Average Yield was lower
than expected
• Variation in Yield gave a
more difficult situation to
plan work and delivery to
end customer

25. Biogen: Development to Manufacturing
“SBOL has increased our real-time fault detection
capabilities. We have improved process and
equipment knowledge and understanding. It has
expanded our capability to prevent deviations.”
– Jeff Simeone, Process Engineer
Customer Results / Benefits
• Proven as extremely useful
• SIMCA-online has improved
real time fault detection
to prevent deviation
• SIMCA-online has improved
process and equipment
knowledge
• Deployed to 3 sites
• Site-wide acceptance with
large LCD monitors
displaying SIMCA-online
real time trends
Solution
• Use SIMCA for developing
MVA model using batches
with appropriate control
• Use SIMCA-online to
provide real time
indication of batch
Customer Business Challenge
• Evaluate batch consistency
• Analyze relationship
between all measured
process variables
• Compare current batch in
real time against the
golden batch

26. • Improved understanding
• Enhanced productivity and efficiency
• Reduced risk
• Continuously meet product specifications
Benefits of DOE & MVDA

27. Come and find out more!
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