Quality by Design – from
Development to Cost Effective
Commercial Manufacturing
Alex Brindle & Line Lundsberg
NNE Pharmaplan
NNE Pharmaplan at a glance
• Over 80 years of experience in the pharma and biotech industries
• Spanned over 3 continents across Europe, North America and Asia
• Workforce 2009: More than 1600
• Turnover 2008: $309M
• ISO 9001 certified since 1997; certified worldwide in 2008
• ISO 14001 certified since 2003
• OHSAS 18001 certified since 2003
• Winner of the ISPE award ”Company of the Year 2008”
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NNE Pharmaplan Consulting
1. Management and Business Consulting
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Organizational Development
Change Management
Supply chain
Business Transformation
Front end (Feasibility Studies and Conceptual
Design)
Technology strategy*
2. Technology Consulting
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Single Use
Filling and Medical Device
PAT
Containment
Automation and IT
3. Methodology Consulting
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DoE
OpEx and LEAN Six Sigma
QbD
ASTM E2500
GEP
Scale up & Tech Transfer*
Denmark
Sweden
China
Germany
USA
Outline
• QbD during development - key concepts
• Critical Quality Attributes
• Critical Process Parameters
• Material Attributes
• Design Space
• Control Strategy
• Conclusions and optional extras
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R&D Spending vs Approvals
Extract from PWC report – Pharma 2020
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Manufacturing Paradigm Shift?
Old Pharma
New Pharma
Paradigm
Shift
Equipment
Piping
Vessels
Instruments
Valves
Welding
Controls
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Single Use
Flexibility
Bags
Tubes
Fittings
Data
Supervision
Sterilization
Containment
Pharmaceutical R&D Challenges – especially in bio
“THE fundamental
question is whether
it is still worthwhile
to invest in
pharmaceutical
science”
Drug making is “so crude”, he argues, that
half of all known diseases cannot be
treated at all, and the drugs for the other
half work properly only half the time and
with huge side effects.
“Imagine a car that starts only half
the time, and whose brakes often
don’t work,” he says.
He sees this sorry state of affairs as a huge
business opportunity. In particular, he is
convinced that rapid advances in
diagnostics, genomics and biotechnology
will bring “a brand new revolution” in
personalised medicine.
Severin Schwan, CEO of Roche/Genentech
In The Economist, December 2009
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New Mindset: It starts with the patient.....
Linking the patient,
product and process
• Understanding the patient/disease needs
• Designing and developing a product meeting
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these needs
Designing and developing a manufacturing
process capable of delivering the product that
meets these needs
Process
Understanding
Quality by Design, QbD
Definition:
Definition:
AA systematic
systematic approach
approach to
to development
development that
that
begins
begins with
with predefined
predefined objectives
objectives and
and
emphasizes
emphasizes product
product and
and process
process understanding
understanding
and
and process
process control,
control, based
based on
on sound
sound science
science and
and
quality
quality risk
risk management
management
ICH Q8(R2)
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New Mindset: Producing consistent high quality
products…
Current State
Long and varying lead times
Low Utilization
Quality by inspection
10-15 % Scrap
25% Quality Cost
Variable
Process Input
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Fixed
Process
Quality by
Design
Variable
Process Output
Desired State
Process understanding
Improved utilization
Reduction of variability
Predictable quality
Real-time release
Shorter lead time
Robust &
Variable
Consistent
Process Input Adjustable Process Output
Process
QbD in One Page
Quality Target
Product Profile
What is critical
to the Patient?
Critical Quality
Attributes
Identify CQAs from
QTPP
Critical Process
Parameters
Identify CPPs relating
to CQAs
Material
Attributes
Identify MAs relating
to CQAs
Design
Space
Control
Strategy
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SOP
A. Brindle, ISPE Seattle, May 2010
Develop Design Space
and Process Robustness
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PAT
Develop Control Strategy
for future quality
PAT, RTRT
PAT
PAT,
RTRT
SOP
The QbD Framework, the different elements
Science
Pharmaceutical Quality System (Q8)
Quality Risk Management (Q9)
Knowledge Management (Q10)
Quality
Target
Product
Profile
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Product
Prior
& Process Design
Knowledge Dev
Space
(CQA)
(CPP)
Control
Strategy
Continous
Improvement
Quality Target Product Profile (QTPP)
• Summary of the quality characteristics of a drug product to ensure
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patient safety and efficacy
Includes, but not limited to:
• Dosage form
• Route of administration
• Pharmacokinetic characteristics (e.g. dissolution)
• Quality characteristics for intended use (e.g. sterility, purity)
“Begin with the end in mind”
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Example of a QTPP elements for a MAb
Ref: A-Mab, ISPE, PQLI 2009
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CQA – Critical Quality Attribute
• A physical, chemical, biological or microbiological property or
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characteristic that should be within an appropriate limit, range, or
distribution to ensure the desired product quality (Q8)
Relates to the finish product (the patient)
Quantifiable – directly (eg assay) or indirectly (eg dissolution)
Examples of CQAs
• Oral solid dosage: Identity (the right API), dose (right active content –
efficacy/safety), purity (safety), dissolution (efficacy)
• mAb
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Ref: A-Mab, ISPE, PQLI 2009
CPP – Critical Process Parameter
• A process parameter whose
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variability has an impact on a CQA
and therefore should be monitored
or controlled to ensure the process
produces the desired quality (Q8)
CPPs has a direct impact on the
CQAs
A process parameter (PP) can be
measured and controlled
(adjusted)
• Examples of CPPs: temperature,
pressure, compression force,
humidity, rotation speed, addition
rate, flow rate, air shift rate,..
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Material Attribute
• Material Attribute: A physical,
chemical, biological or
microbiological property or
characteristic of a material that
has a direct impact on the CQA
• A Material Attribute (MA) can
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be quantified
Typically fixed per batch, but
can sometimes be changed
during processing (eg PSD –
milling)
• Examples of MAs: PSD,
Impurity profile, porosity, mass,
volume, heat capacity, moisture
level, sterility
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Connection between CQA, MA and CPPs
• The multidimensional combination and interaction of input variables
(e.g., material attributes) and process parameters that have been demonstrated
to provide assurance of quality. (ICH Q8)
• A CQA is dependant on both material attributes and critical process parameters
CQA = f(MA1, MAi, CPP1, CPP2, CPPj)
Design
Space
Ref: A-Mab, ISPE, PQLI 2009
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Control Strategy
• A planned set of controls, derived from current product and process
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understanding, that assures process performance and product quality
The controls can include parameters and attributes related to drug
substance and drug product materials and components, facility and
equipment operating conditions, in-process controls, finished product
specifications, and the associated methods and frequency of monitoring
and control. (ICH Q10)
All products should have a control strategy (in the minimal approach it is
end-product testing)
The control strategy should ensure operation within the design space
The Control Strategy described in Q8 and Q10 is related to CQAs
It can be simple or more advanced
When implemented into manufacturing other controls has to be included
as well, e.g. Control of non-criticals, Operator Heath and Safety
controls, Environmental Controls, Business driver controls, Equipment
controls, Facility controls etc
Manual or automated
Control Strategy potential architecture
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Concentration (%)
NIR Monitor, bioreactor, example
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time
completion
A MAb – A Case Study on QbD for Biotech
• First public full-scale QbD development case for
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a biotech product (MAb)
Cross-industry CMC-Biotech Working Group,
initiated by FDA
Biotechnology case study for teaching and
learning for both Industry and Regulators
QbD: Applying ICH Q8, Q9, Q10
Design Space & Control Strategy
Full example of full manufacturing chain
• Upstream
• Downstream
• Fill-finish
Launched Oct 2009 ISPE & CASSS
Part of ISPE PQLI Initiative
Available at www.ispe.org
ISPE/PQLI 2009
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Product & Process Development
A. Brindle, ISPE Seattle, May 2010
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QbD From R&D Lab to Manufacturing
Quality
Risk
Assessment
Upstream
Design
Space
Control
Strategy
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Downstream
Formulation,
Fill & Finish
QbD in Development – Understand the Process
• Cultural change – new way of work - Cross organizational team
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approach
“New skills” – DoE – Spectroscopy – Risk analysis - Chemometrics
Upfront investment in on-line technology
Using what we are good at – science - Leveraging prior knowledge
More process understanding in less time – systematic approach - DoE
Risk based – helps identify the focus areas
Focus on identification of CQAs and CPPs
Development of Design Space and Control Strategy
Reduction of scale issues
“QbD” submission – shorter, documented understanding and risk
Prediction Profiler
AUPR
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AUPR
1,655289
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Quality Risk
Assessment
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Position
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Design of
Experiments
Design
Space
Control Strategy
Core Model
QbD in Manufacturing – Control the Process
• Empowered staff
• New skills – DoE – Spectroscopy – Risk analysis - Chemometrics
• Upfront investment in on-line technology
• Reduced cycle time
• Focus on the controls that matters
• Manufacturing flexibility
• Real Time Release Testing
• Reduced scrap
• Better planning
• Reduced inspection program
• Alternative approach to qualification and validation
• Cost savings
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Work more systematically
• More Science
• Quality Risk Management
• DoE
• PAT
• Multivariate data analysis
Cause and Effect Diagram
Measurement
Fixture
Man
Calibration
Calibration
Environment
Method Temperature
Maintenance
Humidity
Recipe
Fixture
Upper Part
Moisture Tolerance
Rotary lock
Pressure
Energy
Lower part
Moisture Tolerance
Material
A. Brindle, ISPE Seattle, May 2010
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Holding time
Trigger
Amplitude
Machine
Integrated functions
• QbD & PAT is a team effort
• Can not be done in silos
• Requires a cross functional teams
• R&D
• Manufacturing
• Process engineer
• Process analyser experience
• Chemometrician
• QA
• QC
• IT
• Regulatory Affaires
• Working differently by combining &
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sharing the different capabilities and
skills within a company
Project organisation must support the
teams
Investing in QbD during development, how can
that pay back during commercial manufacturing?
R&D Savings
• New ways of work
• New skills – also in
manufacturing
• PAT – nice to have on-line
analysers, MVA data analysis
software but a lot can be done
without expensive tools
• More involvement with regulators
upfront
• Software
• No extended time to market
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Commercial savings
• Secured manufacturability
because of more robust
process
• Supply security
• Higher yield, less scrap
• Reduced cost of qualification
and validation
• Reduced COGS
Will it be mandatory anyway?
Old
“Process validation is establishing
documented evidence which provides a
high degree of assurance that a specific
process will consistently produce a
product meeting its pre-determined
specifications and quality
characteristics”
Process Validation Guide
May, 1987 FDA CDER, CBER, CDRH
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NEW
“Process validation is the collection
and evaluation of data, from the
process design stage throughout
production, which establishes
scientific evidence that a process is
capable of consistently delivering
quality products.”
Draft Process Validation Guide
Nov, 2008 FDA CDER, CBER
Example from Wyeth- QbD Business Benefits
• Product and Process Robustness
• QbD project required no Variations in 12 months post-launch
• Cycle Time Reduction
• Reduction of 15% for Wyeth product
• ‘Real-Time Release’ possible
• Cost of Goods Reduction
• 5% CoGs reduction for Consumer Healthcare product
• Yield of Wyeth legacy product optimised (several % gained)
• Streamlined Regulatory Assessments
• Worksharing Variations achieved approvals in ca. 6 months vs. up to 2 years and single
outcome
• Other Business Benefits include
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QbD as enabler for e.g. continuous processing technologies
Lower working capital or investment in capital equipment
Common language and QbD tools
Concept also being implemented in Biopharmaceuticals and Vaccine, leveraging
platform technologies
Ref: Graham Cook, Wyeth (now Pfizer) et. al.
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Advanced Monitoring & Control
Use of Multivariate Data Analysis for Process Control
• Traditonal manufacturing
facilities rely on univariate data
visualisation
• Many parameters have to be
observed and interpreted
• Manufacturing is complex and
there is regularly no single
point of failure (2 or more
parameters often compound to
give failure)
• This is reflected in batch
failure rate and variable yields
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Advanced Monitoring & Control
Use of Multivariate Data Analysis for Process Control
• Multivariate data visualisation
based on process models has
significant advantages
• Multivariate models can assign
single AND compounded
causes to manufacuring
deviations
• Corrective actions can be taken
to save batches and make
consistant quality product
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Advanced Monitoring & Control
How it works
• Process signatures or ’Golden’ batches
are built using data from previous batches
• These sigantures are simple 2D
representation of many parameters at the
same time
• The multivariate model enables the correct
weight of the parameter
• A process signature with +/-3 SD action
limits can be used in manufacturing
• When an action event is triggered the
multivariate model can be used to find the
1, 2 or more parameters contrubuting to
the deviation
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Advanced Monitoring & Control
Operability
• 1st Generation
• Used by expert scientists to study batches
• Little or no visibility outside of core
scientific clique
• 2nd Generation
• Plasma screens rolled out to factory floor
• Visibility throughout the facility
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Advanced Monitoring & Control
3rd Generation - hyperconnectivity
• Drill down fault detection
• Model based suggested corrective
• 3rd Generation
• Hyper connectivity to web and
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communication devices
Dashboard early warning system
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actions
Chain of communication established
Reactor 1
EWS
Reactor 1
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Reactor 2
Reactor 3
EWS
EWS
EWS
Reactor 1
Reactor 1
Corrective action –
Reduce stirrer speed
Shift leader –
2:34 am, Nov 3rd 2008
Alex Brindle
919 330 6488