Concept of optimization, optimization parameters and factorial design

optimization, optimization parameters and factorial design

1. To: Dr. Gururaj S. Kulkarni D epartment of Pharmaceutics MALLIGE COLLEGE OF PHARMACY By : Manikant Prasad Shah Mpharm II Sem. Concept of Optimization, Optimization Parameters and Factorial Design 18-03-2018 1

2. Optimization Concept: 2 The term Optimize is defined as to make perfect , effective , or as functional as possible. 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 Traditionally, optimization in pharmaceuticals refer to changing one variable at a time, so to obtain solution of a problematic formulation. Modern pharmaceutical optimization involves systematic design of experiments (DoE) to improve formulation irregularities . 18-03-2018

3. Optimization is used in pharmacy relative to formulation and processing . 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. Final product not only meets the requirements from the bioavailability but also from the practical mass production criteria. . 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 18-03-2018 3

4. Target processing parameters – ranges for each excipients & processing factors . Questions optimization requires : How we can make Formulation perfect ? What should be characteristics? What should be the conditions? 18-03-2018 4

5. Why is Optimization necessary? 5 Primary objective may not be to optimize absolutely but to compromise effectively & thereby produce the best formulation under a given set of restrictions . 18-03-2018

6. 6 APPLICATIONS: 18-03-2018

7. Terms Used 7 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. RESPONSE: It is an outcome of the experiment. It is the effect to evaluate. Ex- Disintegration time. 18-03-2018

8. Terms Used 8 EFFECT: It is the change in response caused by varying the levels It gives the relationship between various factors & levels. INTERACTION: It gives the overall effect of two or more variables Ex- Combined effect of lubricant and glidant on hardness of the tablet FACTOR LEVELS Temperature 30 , 50 Concentration 1%, 2% 18-03-2018

9. Advantages Yield the “Best Solution” within the domain of study. Require fewer experiments to achieve an optimum formulation. Can trace and rectify problem in a remarkably easier manner. 9 18-03-2018

10. 18-03-2018 10

11. Softwares for Optimization Design Expert 7.1.3 SYSTAT Sigma Stat 3.11 CYTEL East 3.1 Minitab Matrex Omega Compact 21-Apr-15 O 18-03-2018 11

12. 18-03-2018 INDEPENDENT Optimization Parameters 12

13. Problem Types 18-03-2018 Unconstrained In unconstrained optimization problems there are no restrictions . For a given pharmaceutical system one might wish to make the hardest tablet possible. The making of the hardest tablet is the unconstrained optimization problem. Constrained The constrained problem involved in it, is to make the hardest tablet possible, but it must disintegrate in less than 15 minutes. 13

14. Variables 18-03-2018 Independent variables : The independent variables are under the control of the formulator . These might include the compression force or the die cavity filling or the mixing time. Dependent variables : The dependent variables are the responses or the characteristics that are developed due to the independent variables . The more the variables that are present in the system the more the complications that are involved in the optimization. 14

15. Example of Variables 18-03-2018 15

16. Once the relationship between the variable and the response is known, it gives the response surface as represented in the Fig. 1. Surface is to be evaluated to get the independent variables, X1 and X2, which gave the response, Y. Any number of variables can be considered, it is impossible to represent graphically, but mathematically it can be evaluated. 18-03-2018 16

17. 18-03-2018 17

18. Factorial Design (FD) Factorial experiment is an experiment whose design consist of two or more factor each with different possible values or “levels”. FD technique introduced by “Fisher” in 1926. Factorial design applied in optimization techniques. Factors : Factors can be “Quantitative” (numerical number) or they are qualitative. They may be names rather than numbers like Method 1, site B, or present or absent . 18-03-2018 18

19. Factorial design depends on independent variables for development of new formulation . Factorial design also depends on Levels as well as Coding There are three types of levels : 1) LOW 2)INTERMEDIATE 3) HIGH Simultaneously CODING takes place for Levels : 1 ) for LOW = (-1) 2)For intermediate = (0) 3 ) for HIGH =([phone]

20. FD is for the evaluation of multiple factors simultaneously. 2 3 means 2 is level while 3 is factor . Factorial Design is divided into two types 1.Full Factorial Design 2.Fractional factorial design 18-03-2018 20

21. 1.Full Factorial Design A design in which every setting of every factor appears with setting of every other factor is full factorial design. Simplest design to create, but extremely inefficient. If there is k factor , each at Z level , a Full FD has Z K Number of runs (N ) N = y x Where, y = number of levels, x = number of factors E.g.- 3 factors, 2 levels each, N = 2 3 = 8 runs 18-03-2018 21

22. Factorial Design : 2 2 , 2 3 , 3 2 , 3 3 2 2 FD = 2 Factors , 2 Levels = 4 runs 2 3 FD = 3 Factors , 2 Levels = 8 runs 3 2 FD = 2 Factors , 3 Levels = 9 runs 3 3 FD = 3 factors , 3 Levels = 27 runs 18-03-2018 22

23. TWO Levels Full FD : 2 factors : X 1 and X 2 (Independent variables ) 2 levels : Low and High Coding : (-1) , (+1 ) Three level Full FD : In three level factorial design , 3 factors: X 1 , X 2 and X 3 3 levels are use , 1 ) low (-1 ) 2 ) intermediate (0) 3 ) high ([phone]

24. FRACTIONAL FACTORIAL DESIGN In Full FD , as a number of factor or level increases , the number of experiment required exceeds to unmanageable levels . In such cases , the number of experiments can be reduced systemically and resulting design is called as Fractional factorial design (FFD). Applied if no. of factor are more than 5 . Means “less than full” Levels combinations are chosen to provide sufficient information to determine the factor effect More efficient 18-03-2018 24

25. Types of Fractional Factorial Design Homogeneous fractional Mixed level fractional Plackett-Burman 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. 18-03-2018 25

26. 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 18.03.2018 26

27. summary Factorial design depends on Factors and Levels. Factorial design also depends on Variables. Factorial design divide in either in Full FD and Fractional FD (FFD) Full FD have two types : Two levels and Three level Full FD Full FD not applicable to factors more than 5. Fractional FD overcome on the limitation of Full FD (applicable to factors more than 5) 18-03-2018 27

28. Thank you.. 18-03-2018 28