This presentation provides Basic information of Design of Experiment technology

1. Presented by
Ms. J.Bhargavi,
M.Pharm
Pharmaceutics

2. CONTENTS
 Introduction
 Terminologies used
 DOE
 Advantages
 Execution of an experimental design
 Types of DoE & their comparison
 Types of graphs
 Applications
 Software's used in experimental designs

3. INTRODUCTION :
 At the beginning of the twentieth century, Sir Ronald Fisher introduced the concept of applying
statistical analysis during the planning stages of research rather than at the end of
experimentation.
 The pharmaceutical industry was late in adopting these paradigms, compared to other sectors.
 It heavily focused on blockbuster drugs, while formulation development was mainly performed
by One Factor At a Time (OFAT) studies, rather than implementing Quality by Design (QbD)
and modern engineering-based manufacturing methodologies
 Among various mathematical modeling approaches, Design of Experiments (DoE) is
extensively used for the implementation of QbD in both research and industrial settings.

4.  A drug candidate must be chemically, physically stable and manufacturable throughout the
product life cycle.
 In addition, many quality standards and special requirements must be met to ensure the efficacy
and safety of the product.
 It is always essential to establish the (target product profile) TPP so that the formulation effort
will be effective and focused.
 The TPP guides formulation scientists to establish formulation strategies and keep formulation
effort focused and efficient.
 After the TPP is clearly defined, many studies must be conducted to develop a formulation. DOE
is an effective tool for formulation scientists throughout the many stages of the formulation
process and can help scientists make intelligent decisions.

5. Terminologies used:
INDEPENDENT VARIABLE
DEPENDENT VARIABLE
LEVELS
Quality target product profile (QTPP)
Critical process parameters (CPP)
Critical quality attributes (CQA)

6. DESIGN OF EXPERIMENT
 DOE: It is a systematic approach to determine the relationship between Independent variable and
their effect on response variable.
Design of Experiments (Doe) is the main component of the statistical toolbox to deploy
(Spread/distribute) Quality by Design in both research and industrial settings.
Doe is an approach where the controlled input factors of the process are systematically and
purposefully varied in order to determine their effects on the responses.
The overall scope is the connection of the CPPs with the CQAs through mathematical functions
i.e. polynomial equation.
 Such relationships enable the determination of the most influential factors (CPPs) and
identification of optimum factor settings leading to enhanced product performance and assuring
CQAs.

7. ADVANTAGES:
 Better Innovation due to the ability to Improve processes.
 It allows all potential factors to be evaluated simultaneously, systematically & quickly.
 Less Batch failures
 When the pharmaceutical products are optimized by a systemic approach using DoE, Scale-up &
process validation can be very efficient because of robustness of the formulation & manufacturing.
 Risk based approach and Identification.
 Innovative process validation approaches.
 For the consumer, greater product consistency.

8. Execution of an experimental design..
Setting Solid Objectives
Selection of Process variables & responses
Selection & execution of an experimental design
Analyzing the Result
Use & Interpretation of the result

9. Types of DoE
Types of DoE
Response
Surface
Box
behnken
(BB)
Central
composite
Design(CCD)
Factorial

10. Factorial designs
They refer to parameters that can be adjusted independently
of each other, such as compaction force, temperature, and
spraying rate. In this case, the responses are functions of
factor levels as described in Equation
Responses = f(factor levels)
Factorial designs are mainly used for screening of factors.

11. Response surface designs
 Once screening is completed, the selected significant factors are further studied using
more comprehensive designs aiming at process optimization , which refers to setting
the most influential factors at levels that enhance all product CQAs simultaneously.
 Such designs typically include at least three factor levels and can support quadratic
or higher order effects.
 These designs are most effective when there are less than 5 factors.
 Quadratic models are used for response surface designs and at least three levels of
every factor are needed in the design.

12. CCD
 Four corners of the square represent the
factorial (+/-1)design points.
 Four star points represent the axial (+/-
alpha) design points Replicated center
point(Usually06)

13. Box-Behnken Designs (BBD)
They are very useful in the same setting as the central composite designs (CCD).
Their primary advantage is in addressing the issue of where the experimental
boundaries should be, and in particular to avoid treatment combinations that are
extreme.
One way to think about this is that in the central composite design we have a ball
where all of the corner points lie on the surface of the ball. In the Box-Behnken
design the ball is now located inside the box defined by a 'wire frame' that is
composed of the edges of the box.

15. Types of graphs in DoE :

18.  Compatibility studies between Drug-Drug and Drug-Excipients
 Granulation
 Pre Tablet Granulation
Oral-controlled release formulation
 Modelling of properties of powder
 Dissolution testing
 Tablet formulation
 Coating of tablets
 Inhalation formulation

19. DESIGN EXPERT
FACTOP
OPTIMA
XTAP
OMEGA
ECHIP
MULTI-SIMPLEX
NEMRODW
GRAPHPAD PRISM
DoE PC IV
MINITAB
MODDE

21. EXAMPLE:

22. Experimental Design
To investigate the formulation variables affecting the responses studied, a three-factor, three-level Box–Behnken design was
used, i.e. three formulation variables (amount of oil, surfactant and co-surfactant) were varied at three levels: low (coded as- 1),
middle (coded as 0) and high (coded as +1).
This design requires 15 experimental runs with three replicated centre points for more uniform estimate of the prediction
variance over the entire design space. The independent factors were the amounts of Labrafil M 1944 CS (Oil, X1), Labrasol
(Surfactant, X2), and Capryol PGMC (Co-surfactant, X3).
The responses or dependent variables studied were droplet size (Y1), cumulative percentage of drug released in 30min (Y2)
and equilibrium solubility of fenofibrate in SMEDDS (Y3) from the SMEDDS formulation.
Fifteen experimental runs were generated and evaluated using Design-Expert software (V. 8.0.4, Stat-Ease Inc.,).
To identify the fitting mathematical model by F-test, Design Expert software was used to fit the results from the experimental
runs into three mathematical models: linear, two-factor interaction (2FI) and quadratic model. From these results, we selected the
second-order polynomial model (quadratic model) as fitting model to all of the responses (data not shown). A second-order
polynomial equation can be approximated by the following mathematical model: