This document discusses optimization techniques used in pharmaceutical development. It defines optimization as making a formulation or process perfect by finding the best use of resources while considering all influencing factors. It describes independent and dependent variables, different optimization methods like evolutionary operation, simplex method, and statistical experimental designs including factorial, response surface, and Plackett-Burman designs. The advantages of optimization include determining important variables, measuring interactions, and allowing extrapolation to find the best product. Optimization has applications in formulation development, dissolution testing, tablet coating, and capsule preparation.

1. Presented by :
Bachchhao Kunal B..
M. Pharm 2nd
Semester
Guided by :
Mr. G. B. Patil.
Department of Quality
Assurance
21-Apr-15 1

2. Contents :
 Introduction
 Definition
 Parameter
 Classic optimization
 Statistical design
 Applied optimization metheod
 Design of experiments
 Types of experimental design
 Advantages and applications
 Conclusion
 References21-Apr-15 2OPTIMIZATION TECHNIQUES

3. INTRODUCTION
The term Optimize is defined as “to make perfect”.
It is used in pharmacy relative to formulation and processing
Involved in formulating drug products in various forms
It is the process of finding the best way of using the existing
resources while taking in to the account of all the factors that
influences decisions in any experiment
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4. Final product not only meets the requirements from the bio-
availability but also from the practical mass production criteria
Pharmaceutical scientist- to understand theoretical
formulation.
Target processing parameters – ranges for each excipients &
processing factors
In development projects , one generally experiments by a
series of logical steps, carefully controlling the variables &
changing one at a time, until a satisfactory system is obtained
It is not a screening technique.
Continue….
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5.  How we can make Formulation perfect ?
 What should be characteristics?
 What should be the conditions?
Questions Should be in mind
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6. Optimization parameters
Problem types Variable
Constrained Unconstrained Independent Dependent
Formulating Processing
Variables Variables
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7.  Independent variables or primary variables :
Formulations and process variables directly under control of the
formulator.
These includes ingredients
 Dependent or secondary variables :
These are the responses of the inprogress material or the
resulting drug delivery system.
It is the result of independent variables .
Optimization Parameters
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8. General optimization
By MRA the relationships are generated from experimental
data , resulting equations are on the basis of optimization.
These equation defines response surface for the system under
investigation
After collection of all the runs and calculated responses
,calculation of regression coefficient is initiated.
Analysis of variance (ANOVA) presents the sum of the squares
used to estimate the factor main effects.
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9. General optimization
technique3
INPUTS
OPTIMIZATION
PROCEDURE
RESPONSEMATHEMATICAL
MODEL OF SYSTEM
INPUT FACTOR LEVEL
OUTPUTREAL SYSTEM
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10. TERMS USED
 FACTOR: It is an assigned variable such as concentration ,
Temperature etc..,
 Quantitative: Numerical factor assigned to it
Ex; Concentration- 1%, 2%,3% etc..
 Qualitative: Which are not numerical
Ex; Polymer grade, humidity condition etc
 LEVELS: Levels of a factor are the values or designations
assigned to the factor
FACTOR LEVELS
Temperature 300 , 500
Concentration 1%, 2%
E.g.
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11. APPLIED OPTIMIZATION
METHODS3
EVALUTIONARY OPERATION
SIMPLEX METHOD
LAGRANGAIN METHOD
SEARCH METHOD
CANONICAL ANALYSIS
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12. Continued………
Evolutionary Method:
Constant , Repetitive and Care full planning of
production process to move towards better process.
Simplex Method:
It is simplex algorithm i.e. mathematical process which
is adopted for simplex process & generally represented in
geometrical figers.
LAGRANGAIN METHOD:
It is extension of classical method for simplifying the
formulae & equations .the disadvantage of this method is
that it is applicable to for only two variable problems.
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13. CLASSIC OPTIMIZATION3
• Application to unconstrained problem
• Finding maximum or minimum of a function of independent
variable
Y= f(X) , where
Y- Response
X- Single independent variable
Y= f(X1, X2)
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14. Design Of Experiment4
(DOE)
It is a structured, organized method used to determine
relationship between the factor affecting a process and
output of that process.
 Reduce experiment time
 Reduce experimental cost
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15. Phases Of DOE4
Determine the goal
Identifying affecting factors
Selection of Experimental design
Generating a Design Matrix
Conducting an Experiment
Finding the optimum
Results
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16. Types of Experimental Design1-4
 Completely Randomized Design
 Randomised block Design
 Factorial Design
 Response surface design
 Three level full factorial design
Full Factorial Design
Fractional Factorial Design
Central Composite Design
Box- Behnken Design
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17. Factorial Design
 For evaluation of multiple factors simultaneously.
 23 means 2 is level while 3 is factor
 Factorial Design is divided into two types-
- Full Factorial Design
- Fractional factorial design
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18. Full Factorial Design
– Simplest design to create, but extremely inefficient
– Each factor tested at each condition of the factor
– Number of runs (N)
N = yx
Where, y = number of levels,
x = number of factors
E.g.- 3 factors, 2 levels each,
N = 23 = 8 runs
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19. 2X Design
2 = Level
X = Input Factors
x2
Number of
factors
Number of
runs
2 4
3 8
4 16
5 32
x1
x3
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20. Fractional factorial design
– Means “less than full”
– Levels combinations are chosen to provide
sufficient information to determine the
factor effect
– More efficient
– Used for more than 5-factors
x1
x2
x3
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21. Summary
Design Merits Limitations
Full Factorial Screening of factors Limited runs
Fractional Factorial
Design
For maximum
number of factors
Effects are not
uniquely estimated
Response surface
design
Curves of response
graphically
Become complex if
maximum number
of factors
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22. Types of Fractional Factorial
Design4
• Homogeneous fractional
• Mixed level fractional
• Box-Hunter
• Plackett-Burman
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23. Homogenous fractional
Useful when large number of factors must be screened
Mixed level fractional
Useful when variety of factors need to be evaluated for main
effects and higher level interactions can be assumed to be
negligible.
Box-hunter
Fractional designs with factors of more than two
levels can be specified as homogenous fractional or
mixed level fractional
TYPES OF EXPERIMENTAL DESIGN
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24. Plackett-Burman
 It is a popular class of screening design.
 These designs are very efficient screening designs when only
the main effects are of interest.
 These are useful for detecting large main effects economically
,assuming all interactions are negligible when compared with
important main effects
 Used to investigate n-1 variables in n experiments proposing
experimental designs for more than seven factors and
especially for n*4 experiments.
TYPES OF EXPERIMENTAL DESIGN
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25.  Two most common designs generally used in this response
surface modelling are
 Central composite designs
 Box-Behnken designs
 Box-Wilson central composite Design
 This type contains an embedded factorial or fractional
factorial design with centre points that is augemented with the
group of ‘star points’.
 These always contains twice as many star points as there are
factors in the design
Continued………
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26.  The star points represent new extreme value (low & high) for
each factor in the design
 To picture central composite design, it must imagined that
there are several factors that can vary between low and high
values.
 Central composite designs are of three types
 Circumscribed(CCC) designs-Cube points at the corners of
the unit cube ,star points along the axes at or outside the cube
and centre point at origin
 Inscribed (CCI) designs-Star points take the value of +1 & -1
and cube points lie in the interior of the cube
 Faced(CCI) –star points on the faces of the cube.
Continued………
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27. Box-Behnken design
They do not contain embedded factorial or fractional
factorial design.
Box-Behnken designs use just three levels of each
factor.
These designs for three factors with circled point
appearing at the origin and possibly repeated for
several runs.
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28. Software's for Optimization
• Design Expert 7.1.3
• SYSTAT Sigma Stat 3.11
• CYTEL East 3.1
• Minitab
• Matrex
• Omega
• Compact
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29. Advantages
 Helps to determine important variables
 Helps to measures interactions.
 Allows extrapolations of the data and search for the
best possible product .
 Allows plotting of graphs to depict how variables
are related.
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30. Application1,5
 Formulation development
 Dissolution testing
 Tablet coating
 Capsule preparation
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31. Conclusion
Immense potential in development of pharmaceutical product and
processes
Less involvement of men, material, machine and money.
Improvement in formulation characteristics.
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32. REFERENCE
 Modern pharmaceutics- vol 121
 Textbook of industrial pharmacy by sobha rani
R.Hiremath.
 Pharmaceutical statistics
 Pharmaceutical characteristics – Practical and
clinical applications
 www.google.com
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33. REFERENCES
1) Bolton S, BonC.Pharmacutical statistics practical & clinical
application, 5th ed. New York London ;informa healthcare
publishing ; 2010.p. (223- 39,424-51).
2) Jain NK,Pharmaceutical Product Development, New Delhi ;
CBS Publisher ; 2010. p. 295-340.
3) Schwartz JB,Connere RE,Schnaar RL,In: Banker GS & Rodes
CJ , editor . Modern Pharmaceutics, 4thed. informa healthcare
publishing ; 2010.p. 727-728.
4)Hirmanth RR,Vanjaka KI , Textbook of Industry Pharmacy
;Drug Delivery System and Cosmetics and Herbal Drug
Technology ; 2009.p.148-68.
5) Lewis GH, Mathieu DG, Pharmaceutical experimental design;
Dekker series publishing;Vol-92; 2008. p. 237-240.
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