Factorial design \Optimization Techniques

Factorial Design
Basics Of Factorial Design

1. Factorial Design Presented by: Priyanka Dinkar Tambe. F. Y. M. Pharm (Pharmaceutics). Roll No: PH113. P.E.S Modern College Of Pharmacy Guided By : Assist. Professor A. G.Purohit Mam. Department Of Pharmaceutics. Date:23/10/2018 P. E. S. Modern College Of Pharmacy Nigdi, Pune. Semester -1 Seminar.

2. CONTENTS: 1. Optimization Techniques. 2. Factorial Design definition. 3. Types Of Factorial Design. 4. Basics Of Factorial Design . 5. Characteristics Of Factorial Design. 6. Factorial Design Testing . 7. Factorial Design Advantages . 8. Factorial Design Disadvantages. 9. Factorial Design Applications. 10. Reference

3. Optimization Techniques: Optimization is choosing the best element from some set of available alternative. According to the Merriam Webster Dictionary Optimization means "an act, process or methodology of making something as fully perfect, functional or effective as possible. In the mathematical procedures"It means to optimize to make as much perfect as possible.It is the process of obtaining optimum formulation.It means to optimize something, or use something at it best , finding a perfect effective or functional answer.

4. Formulation development is process of selection of components and processing. Aim of optimization is to understand formulation and target processing parameters and formulation ingredients.It is used for quality selection. Many a times finding the correct answer is not a simple and straight forward in such cases using an Optimization procedure for best compromise is the smarter way to solve the problem.

5. In Pharmacy word " Optimization"is found in the literature referring to any study of formula.In development of projects , pharmacist generally experiments by a series of logical steps, carefully controlling the variables and changing one at a time until satisfactory results are obtained.This is now optimization done in pharmaceutical industry . Optimization is defined as follow It is the process of finding the best way of using the existing resources while taking in to account all the factors that influence decision in any experiment ..

6. Factorial Design Definition: Factorial experiment is an experiment whose design consist of two or more factor each with different possible values or levels. Factorial Design technique introduced by fisher in 1926. Factorial design applied in optimization techniques.

7. Types Of Factorial Design: There are two types of factorial designs. 1. Full Factorial Design . 2. Fractional Factorial Design. 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 2 3 = 8

8. Factors: Factors can be quantitative (numerical number) or they are qualitative . Factorial design depends on independent variables for development new formulation . Factorial design also depends on levels as well as coding.

9. 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

10. Two Levels Full FD: 2 Factors: X 1 and X 1 (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 (+1)

11. Fraction 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 experiment can be reduced systematically and resulting design is called as Fractional Factorial Design (FFD). Applied if no of factor are more than 5. Levels combinations are chosen to provide sufficient information to determine the factor effect.

12. Types of Fractional Factorial Design: 1. Homogeneous Fractional 2. Mixed level fractional 3. 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.

13. 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.

14. Basics Of Factorial Design: Factorial designs are most efficient for the experiments involve the study of the effects of two or more factors. By a factorial design , we mean that in each complete trial or replication of the experiment all possible combination of the levels of the factors are investigated. When Factors are arranged in a factorial design, they are often said to be crossed.

15. Characteristics Of Factorial Design: The treatment must be amenable to being administered in combination without changing dosage in the presence of each other treatment. It must be acceptable not administer the individual treatment (I.e. placebo is ethical ) or administer them at lower doses if that will be required for the combination. It must be genuinely interested in learning about treatment combination require for the factorial design . The therapeutic question must be chosen appropriately e.g. treatment that use different mechanisms of action are more suitable candidates for a factorial clinical trial.

16. Factorial Design Testing: In chromatographic condition responses can be 1. Efficiency. 2. Retention Factor. 3. Asymmetry. 4. Retention Time. 5. Resolution.

17. Example: In this example resolution is considered as response. No Factors Low level High level 1 Temp(x 1 ) 30 50 2 % Ethanol (X 2 ) 55 60 3 Flow Rate Of Mobile Phase 0.1 0.2

18. Experiments for a 2 3 Factorial Design: No X 1 X 2 X 3 1 -1 -1 -1 2 -1 1 -1 3 1 -1 -1 4 1 1 -1 5 -1 -1 1 6 -1 1 1 7 1 -1 1 8 1 1 1

19. Data analysis for 2 3 : Temp % Ethanol Flow rate Resolution 30 55 0.1 2.4 50 55 0.1 1.8 30 60 0.1 1.9 50 60 0.1 1.4 30 55 0.2 2.4 50 55 0.2 1.8 30 60 0.2 1.6 50 60 0.2 1.1

20. Advantages: Its easier to study the combined effect of two or more factors simultaneously and analyze their interrelationships. It has a wide range of factor combination are used. It saves time. It permits the evaluation of interaction effects.

21. Disadvantages: Its complex when several factors are involved simultaneously. Wasting of time and experimental material. Increase in factor size leads to increase in block size which increase the chance of error.

22. Applications of Factorial Design:

23. Software Used: Design Expert 7.1.3 SYSTAT sigma Stat 3.11 CYTEL East 3.1 Minitab Matrex Omega Compact 21-Apr-15 O

24. Reference: Textbook Of Industrial Pharmacy by Shobha Rani Hiremath page no 158-168. Fractional factorial designs that maximize the probability of identifying the important factors. Article in International Journal of Industrial and Systems Engineering · January 2009.

25. Reference: full factorial design for optimization, development and validation of HPLC method to determine valsartan in nanoparticles by Lalit Kumar , M sreenivasa Reddy. Department of pharmaceutics Manipal college of pharmacy. Factorial Design considerations by Stephanie Green,ping_ Tuesday Liu from journal of clinical oncology .An American society.

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