Optimization techniques

disriptions & introductin of verious optimization techniques

1. H.R.Patel Institute of Pharmaceutical Education & Research, Shirpur. Presented by : Bachchhao Kunal B.. M. Pharm 2 nd Semester Guided by : Mr. G. B. Patil. Department of Quality Assurance 27-Mar-15 1

2. Contents : Introduction Definition Parameter Classic optimization Statistical design Applied optimization metheod Design of experiments Types of experimental design Advantages and applications Conclusion References 27-Mar-15 2 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 27-Mar-15 3 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…. 27-Mar-15 4 OPTIMIZATION TECHNIQUES

5. How we can make Formulation perfect ? What should be characteristics? What should be the conditions? Questions Should be in mind 27-Mar-15 5 OPTIMIZATION TECHNIQUES

6. Optimization parameters Problem types Variable Constrained Unconstrained Independent Dependent Formulating Processing Variables Variables 27-Mar-15 6 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 27-Mar-15 7 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. 27-Mar-15 8 OPTIMIZATION TECHNIQUES

9. General optimization technique 3 INPUTS OPTIMIZATION PROCEDURE RESPONSE MATHEMATICAL MODEL OF SYSTEM INPUT FACTOR LEVEL OUTPUT REAL SYSTEM 27-Mar-15 9 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 30 , 50 Concentration 1%, 2% E.g. 27-Mar-15 10 OPTIMIZATION TECHNIQUES

11. APPLIED OPTIMIZATION METHODS 3 EVALUTIONARY OPERATION SIMPLEX METHOD LAGRANGAIN METHOD SEARCH METHOD CANONICAL ANALYSIS 27-Mar-15 11 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. 27-Mar-15 12 OPTIMIZATION TECHNIQUES

13. CLASSIC OPTIMIZATION 3 Application to unconstrained problem Finding maximum or minimum of a function of independent variable 27-Mar-15 13 OPTIMIZATION TECHNIQUES

14. Design Of Experiment 4 (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 27-Mar-15 14 OPTIMIZATION TECHNIQUES

15. Phases Of DOE 4 Determine the goal Identifying affecting factors Selection of Experimental design Generating a Design Matrix Conducting an Experiment Finding the optimum Results 27-Mar-15 15 OPTIMIZATION TECHNIQUES

16. Types of Experimental Design 1-4 Completely Randomized 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 27-Mar-15 16 OPTIMIZATION TECHNIQUES

17. Factorial Design For evaluation of multiple factors simultaneously. 2 3 means 2 is level while 3 is factor Factorial Design is divided into two types- - Full Factorial Design - Fractional factorial design 27-Mar-15 17 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 = y x Where, y = number of levels, x = number of factors E.g.- 3 factors, 2 levels each, N = 2 3 = 8 runs 27-Mar-15 18 OPTIMIZATION TECHNIQUES

19. 2 X Design 2 = Level X = Input Factors x 2 Number of factors Number of runs 2 4 3 8 4 16 5 32 x 1 x 3 27-Mar-15 19 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 x 1 x 2 x 3 27-Mar-15 20 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 27-Mar-15 21 OPTIMIZATION TECHNIQUES

22. Types of Fractional Factorial Design 4 Homogeneous fractional Mixed level fractional Box-Hunter Plackett-Burman 27-Mar-15 22 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 27-Mar-15 23 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 27-Mar-15 24 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……… 27-Mar-15 25 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……… 27-Mar-15 26 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. 27-Mar-15 27 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 27-Mar-15 28 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. 27-Mar-15 29 OPTIMIZATION TECHNIQUES

30. Application 1,5 Formulation development Dissolution testing Tablet coating Capsule preparation 27-Mar-15 30 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 . 27-Mar-15 31 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 27-Mar-15 32 OPTIMIZATION TECHNIQUES

33. REFERENCES 1) Bolton S, BonC.Pharmacutical statistics practical & clinical application, 5 th 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, 4 th 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. 27-Mar-15 33 OPTIMIZATION TECHNIQUES

34. Thank You 27-Mar-15 34 OPTIMIZATION TECHNIQUES