For Formulation or Process Enter around 1970’s Search for the best result

1. OPTIMIZATION TECHNIQUES
Apr 21, 2016 sagar kishor savale 1
Mr. Sagar Kishor savale
Department of Pharmaceutics
avengersagar16@gmail.com
2015-2016
Mr. Sagar Kishor savale
Department of Pharmaceutics
avengersagar16@gmail.com
2015-2016
Department of Pharmacy (Pharmaceutics) | Sagar savale

2. Introduction
 Definition
 Parameter
Classic optimization
Statistical design
Applied optimization metheod
Design of experiments
Types of experimental design
 Advantages and applications
 Conclusion
 References
CONTENT
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3. For Formulation or Process
Enter around 1970’s
Search for the best result
Introduction1-2
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4. Definition1-2
It is defined as “To make as perfect, effective or
functional as possible”.
How we can make Formulation perfect ?
What should be characteristics?
What should be the conditions?
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5. Optimization Parameters1,3
A) Problem Type-
-Constrained
-Unconstrained
B) Variables
-Independent
-Dependent
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6. CLASSIC OPTIMIZATION3
• Application to unconstrained problem
• Finding maximum or minimum of a function of independent variable
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Y= f(X) , where
Y- Response
X- Single independent variable
Y= f(X1, X2)
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7. General optimization technique3
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INPUTS
OPTIMIZATION
PROCEDURE
RESPONSE
MATHEMATICAL
MODEL OF
SYSTEM
INPUT FACTOR
LEVEL
OUTPUTREAL
SYSTEM
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8. APPLIED OPTIMIZATION METHODS3
A) EVALUTIONARY OPERATION
B) SIMPLEX METHOD
C) LAGRANGAIN METHOD
D) SEARCH METHOD
E) CANONICAL ANALYSIS
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9. 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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10. Phases Of DOE4
 Determine the goal
 Identifying affecting factors
 Selection of Experimental design
 Generating a Design Matrix
 Conducting an Experiment
 Finding the optimum
 Results 10Apr 21, 2016 sagar kishor savale

11. Parsimony Principle4
 This principle states that, some of the factors are
important while others are not.
 80-20 rule- few variables more effects
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12. Types of Experimental Design1-4
 Completely Randomised 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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13. • Completely Randomized Design
 Comparison of response values
 Levels of factor are randomly assigned
• Randomized Block Design
 To control nuisance factors
 Block of non-significant factors
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14. 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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15. 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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16. Runs Sr . No. A B C
8 1 - - -
4 2 - - +
5 3 - + -
1 4 - + +
3 5 + - -
7 6 + - +
2 7 + + -
6 8 + + +
Factorial Design 23
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17. 2X
Design
2 = Level
X = Input Factors
Number of
factors
Number of runs
2 4
3 8
4 16
5 32
x1
x2
x3
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18. 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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19. Types of Fractional Factorial Design4
• Homogeneous fractional
• Mixed level fractional
• Box-Hunter
• Plackett-Burman
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20. Response Surface Design
 It is a model that describes a continuous curve or surface that
connects the measured data taken at strategically important
places in the experimental window.
 Studying the response in the form of small surface.
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21. • It can be performed by two types
– Central Composite Design
– Box Behnken Design
Response Surface Design
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22. Central Composite Design
• Also called as Box Wilson Design
• It contains fractional factorial design with center points
surrounded by star points.
• Study of system by varying the parameters around a central
point.
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23. Central Composite Graph
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24. Box- Behnken Design
• For three levels only.
• All points appears at a distance√2
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25. Box- Behnken Graph
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26. Three Level Full Factorial Design
 Three levels
- Low
-Intermediate
- High
 Prohibitive because high number of runs, cost and efforts.
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=27 runs
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27. Data Summarisation1,3
Standard Deviation
Graphs
ANOVA
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28. 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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29. Software's for Optimization
• Design Expert 7.1.3
• SYSTAT SigmaStat 3.11
• CYTEL East 3.1
• Minitab
• Matrex
• Omega
• Compact
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30. 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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31. Application1,5
 Formulation development
 Dissolution testing
 Tablet coating
 Capsule preparation
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32. 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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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, 4th
ed. 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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