Apr 15, 2023 1
H.R.Patel Institute of Pharmaceutical Education & Research, Shirpur.
Presented by :Bachchhao Kunal B..M. Pharm 2nd Semester
Guided by :Mr. G. B. Patil.Department of Quality Assurance
Apr 15, 2023 OPTIMIZATION TECHNIQUES 2
Contents :Introduction Definition Parameter Classic optimization Statistical designApplied optimization metheodDesign of experimentsTypes of experimental design Advantages and applications Conclusion References
Apr 15, 2023 OPTIMIZATION 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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….
Apr 15, 2023 OPTIMIZATION TECHNIQUES 5
How we can make Formulation perfect ?
What should be characteristics?
What should be the conditions?
Questions Should be in mind
Apr 15, 2023 OPTIMIZATION TECHNIQUES 6
Optimization parameters
Problem types Variable
Constrained Unconstrained Independent Dependent
Formulating Processing Variables Variables
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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.
Apr 15, 2023 OPTIMIZATION TECHNIQUES 9
General optimization technique3
INPUTS
OPTIMIZATION PROCEDURE
RESPONSEMATHEMATICAL MODEL OF SYSTEMINPUT FACTOR LEVEL
OUTPUTREAL SYSTEM
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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.
Apr 15, 2023 OPTIMIZATION TECHNIQUES 11
APPLIED OPTIMIZATION METHODS3
EVALUTIONARY OPERATION
SIMPLEX METHOD
LAGRANGAIN METHOD
SEARCH METHOD
CANONICAL ANALYSIS
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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.
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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 variableY= f(X1, X2)
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 16
Types of Experimental Design1-4
Completely Randomized Design Randomised block Design
Factorial Design
Response surface design
Three level full factorial design
Full Factorial DesignFractional Factorial Design
Central Composite Design
Box- Behnken Design
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 19
2X Design
2 = LevelX = Input Factors
x2
Number of factors
Number of runs
2 43 84 165 32 x1
x3
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
x2x3
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 22
Types of Fractional Factorial Design4
• Homogeneous fractional
• Mixed level fractional
• Box-Hunter
• Plackett-Burman
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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………
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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………
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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.
Apr 15, 2023 OPTIMIZATION TECHNIQUES 28
Software's for Optimization
• Design Expert 7.1.3
• SYSTAT Sigma Stat 3.11
• CYTEL East 3.1
• Minitab
• Matrex
• Omega
• Compact
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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.
Apr 15, 2023 OPTIMIZATION TECHNIQUES 30
Application1,5
Formulation development
Dissolution testing
Tablet coating
Capsule preparation
Apr 15, 2023 OPTIMIZATION TECHNIQUES 31
Conclusion
Immense potential in development of pharmaceutical product and processes
Less involvement of men, material, machine and
money.
Improvement in formulation characteristics.
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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
Apr 15, 2023 OPTIMIZATION TECHNIQUES 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.
Apr 15, 2023 OPTIMIZATION TECHNIQUES 34
Thank You