Quality by Design (QbD)
Myth : An expensive development tool !
Fact : A tool that makes product development and
commercial scale manufacturing simple !
Actually saves money !
How ?
Outline
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FDA initiatives for quality
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Application of statistical tools in QbD
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The desired state
Quality by design (QbD) and design space (ICH Q8)
Design of experiments
Model building & evaluation
Statistical process control
How can Innoworks assist you?
FDA’s Initiative on Quality by Design
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In a Quality-by-Design system:
 The product is designed to meet patient requirements
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The process is designed to consistently meet product critical
quality attributes
The impact of formulation components and process parameters
on product quality is understood
Critical sources of process variability are identified and
controlled
The process is continually monitored and updated to assure
consistent quality over time
Quality
by
Design
Design Space (ICH Q8)
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Definition: The multidimensional combination and interaction
of input variables (e.g., material attributes) and process
parameters that have been demonstrated to provide
assurance of quality
Working within the design space is not considered as a
change. Movement out of the design space is considered to
be a change and would normally initiate a regulatory postapproval change process.
Design space is proposed by the applicant and is subject to
regulatory assessment and approval
Current vs. QbD Approach to
Pharmaceutical Development
Current Approach
Quality assured by testing and
inspection
QbD Approach
Quality built into product & process
by design, based on scientific
understanding
Data intensive submission – disjointed Knowledge rich submission – showing
information without “big picture”
product knowledge & process
understanding
Specifications based on batch history
Specifications based on product
performance requirements
“Frozen process,” discouraging
changes
Flexible process within design space,
allowing continuous improvement
Focus on reproducibility – often
avoiding or ignoring variation
Focus on robustness – understanding
and controlling variation
Pharmaceutical Development &
Product Lifecycle
Product Design & Development
Process Design & Development
Manufacturing Development
Continuous Improvement
Candidate
Selection
Product
Approval
Pharmaceutical Development
& Product Lifecycle
Statistical Tool
Product Design & Development:
Initial Scoping
Product Characterization
Product Optimization
Design of
Experiments
(DOE)
Process Design & Development:
Initial Scoping
Process Characterization
Process Optimization
Process Robustness
Model Building
And Evaluation
Manufacturing Development
and Continuous Improvement:
Develop Control Systems
Scale-up Prediction
Tracking and trending
Statistical
Process Control
Design of Experiments (DOE)
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Structured, organized method for determining the relationship
between factors affecting a process and the response of that
process
Application of DOEs:
 Scope out initial formulation or process design
 Optimize product or process
 Determine design space, including multivariate relationships
DOE Methodology
(1) Choose experimental design
(e.g., full factorial, d-optimal)
A
(2) Conduct randomized
experiments
Experiment
Factor A
Factor B
Factor C
1
+
+
+
+
+
-
+
+
2
3
B
4
C
(4) Create multidimensional
surface model
(for optimization or control)
(3) Analyze data
www.minitab.com
Model Building & Evaluation Examples
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Models for process development
 Kinetic models – rates of reaction or degradation
 Transport models – movement and mixing of mass or heat
Models for manufacturing development
 Computational fluid dynamics
 Scale-up correlations
Models for process monitoring or control
 Chemometric models
 Control models
All models require verification through statistical analysis
Model Building & Evaluation Chemometrics
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Chemometrics is the science of relating measurements
made on a chemical system or process to the state of the
system via application of mathematical or statistical
methods (ICS definition)
Aspects of chemometric analysis:
 Empirical method
 Relates multivariate data to single or multiple responses
 Utilizes multiple linear regressions
Applicable to any multivariate data:
 Spectroscopic data
 Manufacturing data
Quality by Design & Statistics
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Statistical analysis has multiple roles in the Quality by Design
approach
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Statistically designed experiments (DOEs)
Model building & evaluation
Statistical process control
Sampling plans
How can Innoworks assist you?
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We can help you every step of the way in implementing
Quality- by- Design in your work processes.
Contact us : [email protected]