The document discusses the application of Design of Experiments (DOE) in pharmaceutical and analytical Quality by Design (QbD), emphasizing the structured approach to optimizing product quality through systematic experimentation. It covers various experimental designs, including screening and optimization designs, and details the steps necessary to implement QbD, risk assessment, and the statistical methods used to analyze results. Key topics include critical quality attributes, establishing a design space, and the importance of employing DOE to improve product performance and development efficiency.

1. Design of Experiments (DoE)applied to PharmaceuticalandAnalyticalQbD
J O U R N A L C L U B
S E M I N A R O N
MVP Samaj’s College ofPharmacy,Nashik
Guided by
Dr. M.P. Wagh
Dept.of Pharmaceutics
(PG Head)
Co-guidedby
Mrs. M.S. Sonawane
Dept.of QualityAssurance Techniques
Presented by
Ms. Salma R. Shaikh
(M.PharmSem II)
PharmaceuticalQualityAssurance

2. 2
Paper Title: Design Of Experiments(DoE) applied to
Pharmaceutical and Analytical QbD.
Author Name: Isa Martins Fukuda , Camila Francini Fidelis
Pinto, Camila dos Santos Moreira , Alessandro Morais Saviano ,
Felipe Rebello Lourenço.
Journal Name: Brazilian Journal of Pharmaceutical Sciences
DOI:http://dx.doi.org/10.1590/s2175-97902018000001006
Impact Factor: 1.18
Year Of Publication: 2018

3. 3
CONTENT
 Abstract
 Introduction
 Steps of QbD
 Experimental designs
 Conclusion
 References

4. ABSTRACT
 According to ICH Q8 Quality should be built into the product.
 Design of Experiments (DoE) generate knowledge about a product or process and established a
Mathematical relationship of dependent variables and independent variables.
 The most common screening designs, such as two-level full factorial, fractionate factorial, and Plackett-
Burman designs.
 Optimization designs, such as three-level full factorial, central composite designs (CCD), and Box-
Behnken designs.
 Analysis of variance (ANOVA) used in multiple regression analysis to evaluate regression significance,
residual error, and lack-of-fit adjustment.
 Determination coefficients (R2, R2 -adj, and R2 -pred) is also evaluated.
4

5. INTRODUCTION
Quality By Design:
QbD is “a systematic approach to pharmaceutical development that begins with predefined objectives and
emphasizes product and process understanding and process control, based on sound science and quality risk
management”
Goals Of Pharmaceutical QbD:
 To achieve meaningful product quality specifications
 To increase process capability and reduce product variability
 To increase pharmaceutical development and manufacturing efficiencies and
 To enhance cause-effect analysis and regulatory flexibility
5

6. STEPS OF PHARMACEUTICAL QUALITY BY DESIGN AND ANALYTICAL QUALITY BY DESIGN
Pharmaceutical QbD Analytical QbD
Establishing Quality Target Product Profile
Identifying Of CQA
Risk Assessment
DOE (Defining Design Space)
Implementing Control strategy and
continuous Improvement
Establishing Analytical Target profile
Analytical Method Performance Characteristics
Risk Assessment
DoE(Method Operable Design Region)
Implementing Control strategy and
continuous Improvement
6

7. 1. Establishing of Quality Target
Product Profile (QTPP) / Analytical
Target Profile (ATP):
 Quality Target Product Profile (QTPP) is a
summary of quality characteristics of
pharmaceutical product.
 Main goals of a new product or method should
be defined before its development.
Dosage
Strength
Dosage
Form
QTPP
Container
closure
system
stability
Route of
Administ
ration
7

8. 2. Identifying of Critical Quality Attributes (CQA)/Analytical Method Performance
Characteristics (AMPC):
 CQA is chemical, physical, biological or microbiological properties or characteristics of pharmaceutical
product (in-process or finished) that must be within appropriated specifications to ensure quality.
 CQA may include
 Identity
 Assay
 Content uniformity
 Degradation
 Drug release or dissolution
 Moisture content, microbial limits, and physical properties such as color, shape, size, and friability.
 Potential CQA derived from QTPP are used to guide product and process development.
8

9. 3. Risk assessment:
Risk assessment is a systematic process of organizing
knowledge information to support decision.
 There are three essential elements in risk
assessment:
a) Risk identification: systematical use of information
to identify potential sources of hazard.
b) Risk analysis: the estimation of risk associated
with the identified hazards.
c) Risk evaluation: comparison of the estimated risks
using quantitative or qualitative scale to determine
their significance.
9

10. 5. DESIGN OF EXPERIMENTS (DoE):
DoE is a structured and organized method for determining the relationships between input factors
(xi – independent variables) affecting one or more output responses (y – dependent variables),
through the establishment of mathematical models
y = f(xi)
 Terms And Concepts:
 Factors: It is an Independent variable that may affect the response and of which different levels
are used in an experiment. Factors are also known as Explanatory variables, predictor variable, or
input variable.
 Levels: It is the Setting or adjustment of factor at a specific level during an experiment.
High = {+}
Optimum = {0}
Low = {-}
10

11.  Response variable:
It is an output Variable that shows the observed results or values of an experimental treatment. These are one
that we want to optimize. It is also called as Dependant variable or ‘Y’ variable.
 Effect: It is a relationship between a factor and a response variable. It includes-
1. Main Effect
2. Dispersion Effect
3. Interaction effect
 Design Space:
It is multidimensional region of possible treatment combinations formed by the selected factors and their
levels.
11

12. Why use Design of Experiments?
1. To Identify important design variables (screening).
2. Statistical Methodology for systematically investigating a
systems input-output relationship.
3. To Optimize product or process Design.
4. To Achieve robust performance.
5. Key technology in product and process design.
6. Reduce time to develop/design new process and product.
7. To improve performance of product.
12

13. The Nine Step DoE Process:
1. Define problem to be solved/objective using DoE.
2. Determine Responses and their measurements.
3. Determine factors and their Levels
4. Determine Suitable experimental Design
5. Determine experimental Runs based on selected design
6. Conduct experiment and conduct data
7. Analyse data and feed into DoE Software
8. Interpret the results in light of set objectives
9. Verify the results and conduct additional experiments as required.
13

14.  Selection of experimental design:
Selections of best experimental design should consider several aspects, such as
• Defined objectives.
• Number of input factors and interactions to be studied.
• Statistical validity.
• Effectiveness of each design.
 Experimental Design Divided Into Two Types:
1. Screening Design.
2. Optimization Design.
14

15. 1. Screening design:
 Screening designs are often used in the first step to DoE.
 In order to select the most important input factors and discard the insignificant ones.
 Evaluation of large number of input factors with a reduced number of experiments require.
 These experimental designs allow one to study a wide number of input factors with reduced numbers of
experiments.
 Screening designs are useful in the early stages:
A. Two-level full factorial design
B. Fractionate Factorial design
C. Placket-Burman design
15

16. 1. Two-level full factorial designs:
 Allow to estimate main effects of input factors and their interactions on output responses.
 The number of experiments = 2k
Where, k is the number of input factors to be studied.
Run X1 X2
1 -1 -1
2 -1 +1
3 +1 -1
4 +1 +1
16
2 LEVEL 2 FACTORIAL DESIGN

17. RUN X1 X2 X3
1 -1 -1 -1
2 -1 -1 +1
3 -1 +1 -1
4 -1 +1 +1
5 +1 -1 -1
6 +1 -1 +1
7 +1 +1 -1
8 +1 +1 +1
RUN X1 X2 X3 X3
1 -1 -1 -1 -1
2 -1 -1 -1 +1
3 -1 -1 +1 -1
4 -1 -1 +1 +1
5 -1 +1 -1 -1
6 -1 +1 -1 +1
7 -1 +1 +1 -1
8 -1 +1 +1 +1
9 +1 -1 -1 -1
10 +1 -1 -1 +1
11 +1 -1 +1 -1
12 +1 -1 +1 +1
13 +1 +1 -1 -1
14 +1 +1 -1 +1
15 +1 +1 +1 -1
16 +1 +1 +1 +1
2 Level 4 Factorial Design
17
2 Level 3 factorial design

18. 2. Fractional Factorial Designs:
No. Of Runs = 2k-p
 P=1 indicates a half fraction (24-1)
 P=2 indicates a quarter fractional design (24-2)
 Confounding Or Aliased Effect:
 Attention should be paid to the estimation of main effects and interaction effects using fractionate factorial
designs, because some of them are aliased (or confounded).
 Confounded or Aliased means when main effect is mixed with interaction effect.
 “Mixing of Effect”.
18

19.  23-1 III =22 Fractional factorial design:
X1 X2 X3 X1X2 X1X3 X2X3
+ - - - - +
- - + + - -
- + - - + -
+ + + + + +
X3=X1X2 X2=X1X3 X1=X2X3
When Main Effect aliased with two factor interaction –Resolution III
19

20. BCD ACD ABD CD BD AD D
Run A B C AB AC BC ABC
1 -1 -1 -1 +1 +1 +1 -1
2 -1 -1 +1 +1 -1 -1 +1
3 -1 +1 -1 -1 +1 -1 +1
4 -1 +1 +1 -1 -1 +1 -1
5 +1 -1 -1 -1 -1 +1 +1
6 +1 -1 +1 -1 +1 -1 -1
7 +1 +1 -1 +1 -1 -1 -1
8 +1 +1 +1 +1 +1 +1 +1
Main effect aliased with 3-factor interaction-Resolution IV design
When using resolution IV designs, main effects are aliased with 3rd order interactions
2nd order interactions are aliased with 2nd order interactions.
20

21. Design Runs Design generator Resolution
23-1 4 C= AB III
24-1 8 D= ABC IV
25-1 16 E= ABCD V
25-2 8 D= AD, E= AC III
26-1 32 F= ABCDE VI
26-2 16 E= ABC, F= ACD IV
26-3 8 D= AB III
 Design generator:
 For half fraction always alias the highest (additional) factor with the highest order interaction column.
 Resolution III: (1+2) main effect aliased with second order interaction.
 Resolution IV: (1+3 or 2+2) main effect aliased with 3-order interaction and 2 factor interaction aliased with 2nd
order interaction.
 Resolution V: (1+4 or 2+3) main effect are aliased with 4-order interaction and 2 factor interaction are aliased
with 3-factor interaction. 21

22. 3. Plackett-Burman designs:
 Plackett-Burman designs are special types of two-level fractionate factorial designs (resolution III)
 allow one to study up to N-1 input factors with N experiments.
 N should be multiple of 4. 4,8,12,16,20……….so we can study 3,7,11,15,19 factors.
 We can study main effect at 2 levels.
Design Generator for placket-Burman design
N=8 (+ + + - + - - )
N=12 (+ + - + + + - - - + - )
Only main effects are seen.
Dummy variables are there
 Design the experiment in such a way that any combination of levels of any two factors should
repeat for the same no. of time.
22

23. RUN A B C D E F G
1 + - - + - + +
2 + + - - + - +
3 + + + - - + -
4 - + + + - - +
5 + - + + + - -
6 - + - + + + -
7 - - + - + + +
8 - - - - - - -
Plackett - Burman Design Matrix to study 7 input factors (A-G) with 8 experiments
23

24. Pareto Chart:
 To select the most important input factors and discard the insignificant ones.
 Because they allow to put the input factors (and their interactions) in order of
importance.
Pareto chart representation of main effects
(X1,X2 And X3)
Pareto chart representation of main effects
(X1 and X2) and interaction effect
(X1*X2) of input factors.
24

25. Main Effects and Interaction Plots:
Pareto charts does not provide information of how the output responses are affect by varying input
factor level.
Main effects (a) and interaction (b) plots of input factors X1 and X2.
25

26. 2. Optimization designs:
A. Central Composite design matrix:
 For fitting quadratic response surface.
 Incorporates five levels(coded –alpha, -1, 0, +1, +alpha).
 Shares screening runs.
 Alpha = (2k )1/4
Run X1 X2 X3
1 -1 -1 -1
2 -1 -1 +1
3 -1 +1 -1
4 -1 +1 +1
5 +1 -1 -1
6 +1 -1 +1
7 +1 +1 -1
8 +1 +1 +1
9 -1.68 0 0
10 +1.68 0 0
11 0 -1.68 0
12 0 +1.68 0
13 0 0 -1.68
14 0 0 +1.68
15 0 0 0
16 0 0 0
17 0 0 0
18 0 0 0
19 0 0 0
20 0 0 0
26

27. B. Box-Behnken Design:
 A Box-Behnken design is specially designed for response surface methodology.
 In the Box-Behnken design the Levels of the factors are at the midpoints of the edges (Red dots) and in the
center. the Center point (Blue dot).
 For each factor 3 levels required.
27

28. Box-Behnken Matrix
Run X1 X2 X3
1 -1 -1 0
2 -1 +1 0
3 +1 -1 0
4 +1 +1 0
5 0 0 0
6 -1 0 -1
7 -1 0 +1
8 +1 0 -1
9 +1 0 +1
10 0 0 0
11 0 -1 -1
12 0 -1 +1
13 0 +1 -1
14 0 +1 +1
15 0 0 0
 Box-Behnken design are special types of three-
level fractionate factorial designs.
 Which allows modeling 1st and 2nd order
response surfaces.
 These designs are more cost-effective than
three-level full factorial designs, particularly
for large number of input factors.
TRICK:
X1 X2 X3 EXP
± ± 0 4
± 0 ± 4
0 ± ± 4
0 0 0 3
28

29. 29
linear response surface linear + interaction function linear + quadratic function linear + interaction + quadratic

30. Analysis of Variance
(ANOVA)
30

31.  Determination coefficients (R2, R2 -adj, and R2 -pred):
 Determination coefficient (R2) is the proportion of the variance in the output response that is
predicted from the input factors.
 In other words, how much of output response (Y) is explained by the input factors (Xs).
 R2 will always increase by adding new terms to the regression model.
 To overcome this we use adj.R2.
 The R2 -adj increases only if the new term improves the regression model.
 On the other hand, it decreases when the term does not improve the regression model.
31

32.  Predictive R2 (R2 -pred):
 Indicates how well a regression model predicts output responses for new observations.
 R2 -pred is calculated by systematically removing each observation from the data set, estimating the
regression equation.
 And determining how well the model predicts the removed observation.
 R2 -adj and R2-pred are always lower than R2.
32

33. 5. Defining Design Space (DS) / Method Operable Design Region (MODR):
 Design Space (DS) is a multidimensional combination and interaction of input factors (usually CMA
and CPP) that have been proofed to provide assurance of quality, and consequently, safety and
efficacy.
 Ensure regulatory flexibility.
 Design space region may be obtained by graphical optimization from overlaid counter plots of output
responses (Ys) as functions of input factors (Xs).
33

34. The desirability approach is a popular method that assigns a
"score" to a set of responses and chooses factor settings that
maximize that score. optimization of multiple response
processes.
 For each response Yi(x), a desirability function di(Yi) assigns numbers
between 0 and 1 to the possible values of Yi, with di(Yi) = 0 representing
a completely undesirable value of Yi and
 di(Yi) = 1 representing a completely desirable or ideal response value.
 The individual desirabilities are then combined using the geometric
mean, which gives the overall desirability D:
D=(d1(Y1)d2(Y2)⋯dk(Yk))1/k
with k denoting the number of responses.
 Notice that if any response Yi is completely undesirable (di(Yi) = 0), then
the overall desirability is zero.
 Let Li, Ui and Ti be the lower, upper, and target values,
respectively, that are desired for response Yi, with Li ≤ Ti ≤ Ui
...Downloadsdesirability function.pdf
34

35. 6. Implementing control strategy and Continuous improvement:
 Control strategy is required to ensure that critical material attributes (CMA) and critical process parameters
(CPP) are within the expected limits.
 Control space should be within the design space.
 Analytical Process Technology (PAT) is an important tool in control strategy implementation.
 Once it enables real-time release testing and provides an increased level of quality assurance compared to
conventional end product testing.
 Control strategy is derived from the data collected during method development and validation, which
enables to predict the ability of method to meet analytical target profile (ATP).
35

36.  Summary of screening and optimization designs characteristics, number of experiments, levels, and
factors:
Application
Experimental
design
Experiments Levels Factors
Screening
Two level full
factorial
2k 2 2<k<5
Fractional factorial 2k-p
res 2 K>4
Plackett-Burman N 2 <N-1
Optimization
Box-Behnken 2k(k-1)+C 3 3<k<5
Central Composite
design
2k +2k+C 5 2k<5
3 level factorial
3k
3 2<k<3
36

37.  Different Software Used In DOE:
37

38. Conclusion:
 QbD provide knowledge and scientific understanding to support pharmaceutical development.
 QbD Comprises elements like QTPP, Identifying CQA, Risk assessment, DoE, defining design space and
implementing control strategy.
 Experimental design divided into two types-
A) Screening Design
B) Optimization Design
 Experimental design and optimization play an important role in the procedure carried out when a new
analytical method is developed and validated.
 Screening design consist of placket-Burman, FFD, two level full factorial and Optimization design consist of
Box-Behnken, CCD and 3k factorial Design.
 This can be very useful guide to experimenter how to design and conduct experiments, and how to analyse
and interpret data.
 Mathematical model should be selected based on the application of Analysis of Variance (ANOVA). 38

39. References
1. International Conference on Harmonization (ICH), Tripartite guidelines, 2009 ‘ICH Q8 (R2):
Pharmaceutical Development’, London.
2. Fukuda, I. M., Fidelis Pinto, C. F., Santos Moreira, C. dos, Saviano, A. M., & Lourenço, F. R. (n.d.). SciELO
- Brasil - Design of Experiments (DoE) applied to Pharmaceutical and Analytical Quality by Design (QbD) ;
dx.doi.org. Retrieved June 27, 2022, from http://dx.doi.org/10.1590/s2175-97902018000001006
3. Periodicals of Engineering and Natural Sciences (PEN). (n.d.). Periodicals of Engineering and Natural
Sciences (PEN); pen.ius.edu.ba. Retrieved June 27, 2022, from http://pen.ius.edu.ba
4. S.R. Schmidt and R.G. Launsby, Understanding Industrial Designed Experiments (4th ed.), Air Academy
Press, 1997,page no.102-135.
5. Candioti LV, De Zan MM, Cámara MS, Goichoechea HC. Experimental design and multiple response
optimization. Using the desirability function in analytical methods development. Talanta. 2014;124:123-138.
6. Sanford Bolton, pharmaceutical statistics, practical and clinical Applications,Marcel Dekker,INC,New York
and Basel, second edition,volume 44,1990,page no.308-334 ;532-563.
7. Santelli, R., & Bezerra, M. (2021, October 6). Response surface methodology (RSM) as a tool for
optimization in Analytical Chemistry. Talanta. Retrieved June 29, 2022, from
https://www.academia.edu/13910582 39

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