Factorial design M Pharm 1st Yr.

Pharmaceutics

1. FACTORIAL DESIGN & ITS APPLICATION IN FORMULATION Presented By Mr. Sanket Chordiya M.Pharm I st Sem. Pharmaceutics Guided By Dr. C. R. Kokare M.Pharm , Ph. D. Pharmaceutics Sinhgad Technical Education Society’s Sinhgad Institute of Pharmacy, Narhe . 1 9/29/2019

2. Overview of Presentation Introduction Various Terminologies Factorial Design Fractional Factorial Design Software Used Application Key References 2 9/29/2019

3. Introduction “Optimization is the act of achieving the best possible result under given circumstances.” The goal is either to minimize effort or to maximize benefit. Various design used in optimization like factorial design, fractional factorial design… etc. 3 9/29/2019 What is Optimization?

4. 4 Why their is need of Optimization? Trial & Error OFAT Approach Knowledge of formulator & Probability Expensive & Time Consuming Unpredictable & Non-Reproducible 9/29/2019 Due to Conventional approach,

5. 5 Based on Statistical method also known as Design of Experiment. Less time consuming. Predictable & Efficient. Require fewer experiment to achieve an optimum formulation. Reduce the error. 9/29/2019 Systematic approach ;

6. Various Terminologies Quality by Design (QbD) 6 Systematic approach to development that begin with predefined objective & focused on product & process understanding based on sound science & Quality risk management. 9/29/2019

7. 7 Quality Target Product Profile (QTPP) It is summary of the quality characteristics of drug product that will be achieved to ensure the desired quality, taking into account safety and efficacy of drug product. To ensure the final product output remain within acceptable quality limits. CQA are used. 9/29/2019 Critical Quality Attributes (CQA)

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9. 9 Factor : It is assigned Variable , i.e. independent variables influencing the response. E.g. Concentration, temperature. Levels : Values assigned to the factor. E.g. Low(-1), high(+1). Response : Is the measured property of the process E.g. dissolution rate, Hardness of tablet. 9/29/2019

10. 10 Effects : Change in response caused by varying levels. Interaction : Overall effect of two or more variables. Runs : Experiment conducted according to the selected design. E.g. 2 2 = 4 Runs 9/29/2019

11. Factorial Design Introduced by “Sir Ronald Fisher” in 1926. It involves studying the effect of each factor at each level. The Number of experiment in factorial design is given as; X n = K Where X represents the number of level. , n is the number of factors. K is the Response. 11 9/29/2019

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13. 13 Full Factorial Design FFD involve studying the effect of all possible factors at various levels, including the interactions, with the total number of runs. Generally Factorial experiment with two level factors are used. 9/29/2019

14. 9/29/2019 14 Merits Of full Factorial Design More efficient than OFAT experiment. Allow additional factors to be examined at no additional cost. Allow to detect interaction which is not possible in OFAT. Less Time Consuming.

15. 9/29/2019 15 Number Factor Main Effects Order of Interactions 2 3 4 5 6 7 8 9 10 2 2 1 3 3 3 1 4 4 6 4 1 5 5 10 10 5 1 6 6 15 20 15 6 1 7 7 21 35 35 21 7 1 8 8 28 56 70 56 28 8 1 9 9 36 84 126 126 85 36 9 1 10 10 45 120 210 252 210 120 45 10 1 Table 1.1 Redundancy in Full Factorial Design. Demerits Of Full Factorial Design

16. 16 (a) (b) Fig. 1.1 Factorial design : (a) 2 2 Factorial design , (b) 2 3 Factorial design 9/29/2019

17. 17 Two Level Factorial Design 2 levels : Low (-1) High (+1) e.g 2 2 Factor + + + - - + - - + - + - 9/29/2019

18. 18 If there are k factors, each at Z levels, a full factorial design has Z k runs. (Levels) factors [ Z k ] 2 factors, 2 levels- 2 2 FD = 4 runs 3 factors, 2 levels- 2 3 FD = 8 runs 2 factors, 3 levels- 3 2 FD = 9 runs 3 factors, 3 levels- 3 3 FD = 27 runs 9/29/2019

19. 19 The simplest form of factorial design is the 2 3 factorial design. e.g. 2 3 Factorial design of Sustained release Metformin tablet Ingredients Category Microcrystalline cellulose Diluent Ethyl cellulose Sustained Release polymer PVP -K30 Binder Magnesium Stearate Lubricant Aerosil Glidant 9/29/2019 Table 1.2 All inactive Ingredients

20. 20 Among all inactive ingredients, microcrystalline cellulose, ethyl cellulose, PVP K30 were taken as the independent factors. Sr. No. Notation Independent factors (mg/tab) Levels -1 +1 1. X1 Microcrystalline cellulose 80 100 2. X2 Ethyl cellulose 5 10 3. X3 PVP K30 3 5 Table 1.3 : Independent factors & their levels 9/29/2019

21. 9/29/2019 21 The experimental plan for a three-factor, two-level 2 3 design is as follows; Experiment Microcrystalline Cellulose (mg/tab) Ethyl- Cellulose (mg/tab) Polyvinyl Pyrrolidone (mg/tab) Drug release (%) 12 hr. 1 80 5 3 80 2 100 5 3 78 3 80 10 3 65 4 100 10 3 64 5 80 5 5 72 6 100 5 5 71 7 80 10 5 62 8 100 10 5 60 Table 1.4 Statistical Data of Experiment

22. 9/29/2019 22 The 2 3 factorial design show seven effect, i.e. three individual factor effects, three two way interaction (X 1 X 2 ,X 1 X 3 & X 2 X 3 ) & one three way interaction (X 1 X 2 X 3 ). The magnitude of the main effect can be calculated by taking mean of all experiment with high level of factor ( X 1, X 2, X 3 ) minus mean of all experiment with low level of same factor. For e.g. Effect of factor X 1 = 1/4{(78+64+71+60)-(85+65+72+62)} = 1/4 {273-279} = -1.5

23. 9/29/2019 23 Experiments Notation X 1 X 2 X 3 X 1 X 2 X 2 X 3 X 1 X 3 X 1 X 2 X 3 Drug release (%) 12 hr. 1 (-1,-1,-1) -1 -1 -1 +1 +1 +1 -1 80 2 (+1,-1,-1) +1 -1 -1 -1 +1 -1 +1 78 3 (-1,+1, -1 ) -1 +1 -1 -1 -1 +1 +1 65 4 (+1,+1,-1) +1 +1 -1 +1 -1 -1 -1 64 5 (-1,-1,+1) -1 -1 +1 +1 -1 -1 +1 72 6 (+1,-1,+1) +1 -1 +1 -1 -1 +1 -1 71 7 (-1,+1,+1) -1 +1 +1 -1 +1 -1 -1 62 8 (+1,+1,+1) +1 +1 +1 +1 +1 +1 +1 60 Table 1.5 Sign to Calculate the main effect & interaction effect of the 2 3 Factorial Design.

24. 9/29/2019 24 Conclusion Table 1.6 Magnitude of main effect & interaction of the factors. Factor and Interaction Results X 1 -1.5 X 2 -15 X 3 -22 X 1 X 2 X 2 X 3 +8.0 X 1 X 3 X 1 X 2 X 3 +5.0

25. 9/29/2019 25 Fractional Factorial Design As the number of variables increases, experimental runs also increases, To overcome these issue in a methodical approach, Fractional Factorial Design is introduced. It expressed as, X n-x , where X = No. of Levels n = No. of Factors x = Degree of Fractionation

26. 9/29/2019 26 Drawbacks Confounding or Aliasing X 1 X 2 X 3 X 1 X 2 X 1 X 3 X 2 X 3 + - - - - + - - + + - - - + - - + - + + + + + + Table 1.7 Concept Of Confounding.

27. 9/29/2019 27 Resolution Resolution III : (1+2) Main effect aliased with 2-order interaction Resolution IV : (1+2 or 2+2) Main effect aliased with 3-order interactions and 2-factor interactions aliased with other 2 factor interactions. Resolution V : (1+4 or 2+3) Main effect aliased with 4-order interactions and 2-factor interactions aliased with 3-factor interactions.

28. 9/29/2019 28 Software Used in FD: Design-Expert Version 12 Minitab Matrex Omega Modde

29. 9/29/2019 29 Design-Expert

30. 9/29/2019 30 Two level factorial design Steps involved Design the experiment Enter the names, levels, unit of measures

31. 9/29/2019 31 Enter an responses Result of calculation Enter the response data

32. 9/29/2019 32 Design an layout (coded ) Pre-analysis of effects via data sorts.

33. 9/29/2019 33 Analyse the result

34. 9/29/2019 34 Application of Factorial Design

35. 9/29/2019 35 Case Study

36. 01-03-2019 36 Case Study

37. Key References Amit G. Mirani and Vandana B. Patravale, 2016. Design of Experiments, Basic Concepts and its application in Pharmaceutical Product Development, University College London. 118-127. Gaurav Gujral, Devesh Kapoor, Manish Jaimini, 2018. An updated Review on Design of Experiment (DOE) in Pharmaceutical, Journal Of Drug Delivery & Therapeutics 147-152. Dnyandev G. Gadhave & Chandrakant R. Kokare, 2019. Nanostructured lipid carriers engineered for intranasal delivery of teriflunomide in multiple sclerosis: Optimization and in vivo studies, Drug Development and Industrial Pharmacy, 1-12. 9/29/2019 37

38. 9/29/2019 38 Key References 44 Rahul Kumar Garg and Indrajeet Singhvi., 2015. Optimization Techniques: An overview for formulation development. Asian Journal of Pharmaceutical Research. 217-221. Singh B., Gupta, R.K. and Ahuja, N., 2006. Computer-assisted optimization of pharmaceutical formulations and processes. Pharmaceutical Product Development (Ed. NK Jain), CBS Publishers, New Delhi. 273-318 . https :// www.statease.com (Accessed 20 th Sept 2019).

39. 9/29/2019 39 THANK YOU…