This document provides an overview of quality by design (QbD) principles including ICH guidelines Q8, Q9, and Q10. It discusses key QbD elements such as quality target product profiles, critical quality attributes, risk assessment, design space, design of experiments, and continual improvement. Examples are given of various statistical experimental designs that can be used including factorial, response surface, and mixture designs. The document aims to facilitate understanding of a QbD approach to pharmaceutical development and manufacturing.

1. QUALITY- BY-
DESIGN
Presented by,
Pratiksha C Chandragirivar
M pharma 2nd sem
Dept. of pharmaceutics
HSK COP Bagalkot
Facilitated to,
Dr. Laxman Vijapur
Assistant professor
Dept. of pharmaceutics
HSK COP Bagalkot
caad
1

2. CONTENTS:
1. ICH GUIDELINE – 8
2. ICH GUIDELINE – 9
3. ICH GUIDELINE– 10
4. METHODS OF DEVELOPMENT OF DOE
5. REFERENCES
caad
2

3. ICH Q8(R)
 Annex to ICH Q8
 Describes principles of QbD vs. minimal approach
 Provides further clarification of key concepts of Q8
caad
3

4. ICH Q8(R) UPDATE
 Reached Step 4 in Brussels, November 11, 2008
 Only a few minor step 4 revisions:
Quality Target product Profile
QTPP forms the basis of design for development
Design space versus proven acceptable ranges
Combination of PARs doesn’t constitute design space
Real Time Release Testing (RTRT)
To distinguish between RTRT and batch release
caad
4

5. ICH Q9: QUALITY RISK MANAGEMENT
 A systematic process for the assessment, control, communication
and review of risks to the quality of the drug product.
 Guidance includes principles and examples of tools for quality risk
management
 Evaluation of risk to quality should:
 be based on scientific knowledge
 link to the protection of the patient
 Applies over product lifecycle: development, manufacturing and
distribution
caad
5

6. QUALITY RISK MANAGEMENT
caad
6

7. ROLE OF QUALITY RISK MANAGEMENT IN DEVELOPMENT &
MANUFACTURING
caad
7

8. ICH Q 10: WHY FOCUS ON PQS?
 The regulatory flexibility provided with a design space approach
requires effective change management at the manufacturing site.
 Track and trend product quality.
 Respond to process trends before they become problems.
 Maintain and update models as needed.
 Internally verify that process changes are successful.
caad
8

9. EXAMPLE QBD APPROACH (ICH Q8R)
 Target the product profile
 Determine critical quality attributes (CQAs)
 Link raw material attributes and process
parameters to CQAs and perform risk
assessment
 Develop a design space
 Design and implement a control strategy
 Manage product lifecycle, including
continual improvement
caad
9

10. QUALITY TARGET PRODUCT PROFILE
“Begin with the end in mind”
 Summary of the quality characteristics of a
drug product to ensure safety and efficacy.
 Includes, but not limited to:
1.Dosage form
2.Route of administration
3.Pharmacokinetic characteristics
e.g., dissolution, aerodynamic performance
4.Quality characteristics for intended use
e.g., sterility, purity
caad
10

11. CRITICAL QUALITYATTRIBUTES (CQAS)
 Physical, chemical, biological or microbiological
property or characteristic.
 Drug product, drug substance, intermediates, and
excipients can possess CQAs
1. Directly affect product quality
2. Affect downstream processability
 Drug product CQAs affect product quality,
safety, and/or efficacy
1. Attributes describing product purity,
potency, stability and release
2.Additional product specific aspects
(e.g., adhesive force for transdermal patches)
caad
11

12. RISK ASSESSMENT (ICH Q9)
 Tools for parameter screening
Examples: Ishikawa diagrams, What-if
analysis, HAZOP analysis
 Tools for risk ranking
Examples: FMEA/FMECA, Pareto
analysis, Relative ranking
 Experimental tools for process
understanding
Examples: Statistically designed
experiments (DOE), mechanistic models
caad
12

13. DESIGN SPACE (ICH Q8)
 Definition: The multidimensional combination
and interaction off input variables (e.g., material
attributes) and process parameters that have
been demonstrated to provide assurance of
quality
 Regulatory flexibility
1. Working within the design space is not
considered a change
2. Important to note
caad
13

14. DESIGN SPACE DETERMINATION
 First-principles approach
combination of experimental data and mechanistic
knowledge of chemistry, physics, and engineering to model
and predict performance
 Non-mechanistic/empirical approach
statistically designed experiments (DOEs)
linear and multiple-linear regression
 Scale-up correlations
translate operating conditions between different scales or
pieces of equipment
 Risk Analysis
determine significance of effects
 Any combination of the above
caad
14

15. Describing Design Spaces
 Linear Ranges of Parameters
 Mathematical Relationships
 Time-dependent functions
 Combinations of variables
e.g., Principle components of multivariate model
 Scaling Factors
 Single or multiple unit operations
“The applicant decides how to describe
and present the design space”
caad
15

16. DESIGN SPACES EXAMPLE #1
caad
16

17. •Design space can be described as a mathematical function or simple
parameter range
•Operation within design space will result in a product meeting the
defined quality attributes
caad
17

18. DESIGN SPACES EXAMPLE #2
caad
18

19. caad
19

20. CONTINUAL IMPROVEMENT
 Flexibility for movement within design space
1. Wider range of material attributes or process
parameters.
2. No reporting if moving operating range within
design space.
3. Potential scale or equipment change without
supplement (subject to regional regulatory
requirements).
 Post-Approval Management Plan (CMC-PMP)
1. A mechanism for applicant to propose a
regulatory strategy specific to a product and/or
process.
2. Currently under development by FDA.
caad
20

21. ELEMENTS OF QBD
caad
21
QTPP
CMA
CQA CPP
Quality Target
Product Profile
Quantitative surrogate for aspects
of clinical safety and efficacy that
can be used to design and
optimize a formulation and
manufacturing process
Ex: Route of administration,
strength, identity, Target patient
population…
Critical Quality
Attributes
Critical Material
Attributes
Physical, chemical,
biological or
microbiological properties
of an input Material
Ex: particle size, solubility,
polymorphic form,
stability….
Critical
Process
Parameters
Parameters which influence the
CQA of the drug product are
called as CPPs.
Ex: Homogenization speed,
duration of agitation, machine
parameters……
Physical, chemical, biological
or microbiological properties
of an output Material
Ex: Appearance, particle size,
zeta potential, encapsulation
efficiency….

22. caad
22

23. DESIGN OF EXPERIMENTS (DOE)
 A structured, organized, method for determining the relationship
between factors affecting a process and the output of that process
is known as DoE.
 It helps in identification of optimal conditions, CMA’s, CPP’s and
design space.
 It is widely employed for formula optimization and process
optimization technique for designing of dosage form and unit
operations.
caad
23

24. Statistical Tools for Design of Experiments
caad
24

25. Factorial
Response
Surface
Mixture
Simplex Lattice
Simplex Lattice Centroid
Augmented Simplex
centroid
Mixture
Box Behnken
Central composite
Face centered CCD
Response
Surface
Plackett Burman
2 Level Factorial
3 Level Factorial
Full and Fractional
factorial
Factorial
ROADMAP FOR DESIGN OF EXPERIMENTS
caad
25

26. Goal of Design of Experiments
How to select a design ?
 To screen out insignificant factors and
identify significant factors and to get some
idea about the existence of interaction
effects. Use a Factorial Design.
 To characterize how the significant factors
affect your responses. Use a Response
surface Design. Use Central composite
or Box-Behnken to study factors as
various levels.
 If your product is actually a mixture, or a
formulation, then you should use a
Mixture Design. These designs allow you
to set a total amount for the mixture.
caad
26

27. Goal of Design of Experiments
• Inlet Temp
• Outlet Temp
• Product Temp
• Spray rate
• Atomization air
• Pattern air
• Gun to bed distance
• Pan speed
• Spray viscosity
• Differential pressure
• % weight gain
Screening
• Inlet Temp
• Pan Speed
• Spray rate
Design Space
Control Strategy
Optimization
Design-Expert® Software
Factor Coding: Actual
Overlay Plot
Drug release at 30 min
RSD for UOD
Flow Function
Hardness at 10KN
Design Points
X1 = A: Particle size D90
X2 = B: Disintegrant concentration
Actual Factor
C: Ratio of MCC & Lactose = 50.00
10.00 15.00 20.00 25.00 30.00
1.00
2.00
3.00
4.00
5.00
Overlay Plot
A: Particle size D90
B:Disintegrantconcentration
3
• Inlet Temp (40 to 60 degrees C)
• Pan Speed (2 to 10 RPM)
• Spray rate ( 2 – 6 gm/min/Kg)
Factorial
Designs
Factorial Designs
Response Surface
caad
27

28. MODELS USED IN DOE:
caad
28

29. 1. SCREENING DESIGN
 Screening designs are used to identify the most influential factors
from those that potentially have an effect on studied responses.
 Huge number of factors (f) can be screened by varying them on 2
levels in a relatively small number of experiments N ≥ f + 1.
 If number of factors are less, then full factorial designs can also be
used for screening purposes.
caad
29

30.  The number of experiments, N, in this design is Lf = 2f. They
are usually denoted as ef, meaning that in a 23 design, 3 factors
(f) are varied on 2 levels (e).
 Coded values - lower factor level is denoted as −1, and 1
stands for the upper factor level.
 Factorial design types.
- 2-level factorial designs (2-21 factors)
- Min Run Res V designs (6-50 factors)
- Min Run Res IV designs (5-50 factors)
- General Factorial designs (1-12 factors)
caad
30

31. SCREENING DESIGN
caad
31
2 Level
Fractional
Factorial design
4 Runs
2 Level Full
Factorial design
8 Runs
2 Level Full
Factorial design
with Center
Point
9 Runs
3 Level Full
Factorial design
27 Runs

32. SCREENING DESIGN
caad
32
23 Full Factorial Design
1
6
2
5
8
4
7
3
8 Factorial Points + 1 Center Point
= 9 Points / Runs

33. SCREENING DESIGN
caad
33
 After experimentation, responses are
added and factor effects are used to build
the regression model:
Y = b + b1X1 + b2X2 + b3X3
Y is the measured response, b is the intercept
and b1 to b3 are regression coefficients
computed from observed experimental values of
Y
 In addition to main effects, full factorial
design also allows identification of factor
interactions.
Interaction factors

34. SCREENING DESIGN
 Plackett - Burman designs (up to 31 factors) – These are
highly confounded designs that are useful if you can safely
assume that interactions are not significant. Another useful
application is ruggedness testing where you are testing factor
levels that you hope will NOT effect the response.
 Factor effects f = N – 1 factors
 Experiments are usually in multiples of 4.
 Useful for preliminary investigations.
caad
34
Plackett-Burman design for 7 factors

35.  Taguchi Designs (up to 63 factors) – A set of classic designs from
Taguchi teachings. These may be used as a base to build a
particular design. Note that all analyses will be completed using
standardized ANOVA reports and interaction graphs.
 Optimal designs (2-30 factors) – This is offered as an
alternative to the General Factorial designs, which may
produce a design with too many runs.
caad
35

36. 2. Response Surface Methodology
 These are quadratic or cubic model designs and yield a
curvature effects.
 Quadratic models are commonly used in Pharmaceuticals.
 Classic quadratic designs include:
 Box-Wilson central composite design (CCD’s)
 Box Behnken Design
 CCD has three groups of design points:
a) Two-level factorial or fractional factorial design points
b) Axial points / Star points
c) Center point
caad
36

37. Response Surface
Methodology
Inscribed CCD Circumscribed CCD
No. of Factor: 3
No. of Levels: 3
Center Points: 5
Total Runs: 8FP +
6SP + 5CP = 19 Runs
No. of Factor: 3
No. of Levels: 5
Center Points: 6
Total Runs: 8FP +
6SP + 6CP = 20
Runs
Face centered CCD
No. of Factor: 2
No. of Levels: 3
Center Points: 5
Total Runs: 4FP +
4SP + 5CP = 13
Runs
caad
37

38. Response Surface Methodology
Inscribed CCD
No. of Factor: 3
No. of Levels: 3
Center Points: 5
Total Runs: 8IFP + 6ISP
+ 5ICP = 19 Runs
In Response surface designs, the
regression model are defined as:
Y = b + b1X1 + b2X2 + b3X3 + b12X1X2 +
b13X1X3+ b23X2X3
Y is the measured response, b is the
intercept and b1 to b3 are regression
coefficients computed from observed
experimental values of Y.
X1X2, X2X3 and X1X3 are interaction
terms
caad
38

39. Response Surface Methodology
Box Behnken Design
No. of Factor: 3
No. of Levels: 3
Center Points: 3
Total Runs: 12 MP+ 3 CP =
15 Runs
• BBD is an independent quadratic design and
does not contain an embedded factorial or
fractional factorial points.
• In BBD, design points are mid points of the
edges of the process space and at the
center.
caad
39

40. 3.Mixture Designs
 Mixture designs are used to study mixture variables such as
excipients in a formulation.
 The characteristic feature of a mixture is that the sum of all its
components adds up to 100%, meaning that the mixture factors
(components) cannot be manipulated completely independently of
one another.
caad
40

41.  In comparison to other experimental designs, mixture designs
cannot be viewed as squares, cubes, instead viewed as a triangle.
 Mixture designs can be Simplex lattice, or Simplex lattice-
centroid design or augmented Simplex lattice – centroid
mixture design
caad
41

42. Mixture Designs
Simplex lattice mixture designs
can be defined with 3 (experiments
1 – 3) or six experiments (1 – 6). If
experiment 7 is included, then it is
a simplex lattice-centroid design
and if all 10 experiments are
considered, then it is an
augmented simplex lattice –
centroid mixture design.
caad
42

43. Simplex Lattice
Design
Simplex Lattice
Centroid Design
Augmented
Simplex Lattice
Centroid Design
caad
43

44. ISHIKAWA DIAGRAM: FISHBONE DIAGRAM
caad
44

45. REFERENCES:
 QBD FOR ANDA IR FINAL SUBMISSION
APRIL- 12-2012,
 SLIDES FROM CONFERENCES ON
QUALITY BY DESIGN, CHINA, BEINJING.
caad
45