Concept of optimization Optimization parameters.pptx

Optimization technique is a rational approach for selecting the excipients, their concentrations and process conditions for obtaining the best possible product satisfying the quality characteristics.
Optimization is an act, process or methodology of making design, system or decisions as fully perfect, functional or as effective as possible.

1. Concept of optimization AND Optimization parameters Presented by-Himadri Priya Gogoi ACP22PHCE004 ACHARYA & BM REDDY COLLEGE OF PHARMACY M .Pharm 1 year(2 nd semester) 10/5/2023 1

2. Content Concept of optimization Parameter of optimization Factorial design 10/5/2023 2

3. OPTIMIZATION Optimization technique is a rational approach for selecting the excipients, their concentrations and process conditions for obtaining the best possible product satisfying the quality characteristics. Optimization is an act, process or methodology of making design, system or decisions as fully perfect, functional or as effective as possible. 10/5/2023 3

4. FLOWCHART FOR OPTIMIZATION 10/5/2023 4

5. CONCEPT OF OPTIMIZATION: 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 into 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. 10/5/2023 5

6. 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. It is used in pharmacy relative to formulation and processing. Involved in formulating drug products in various forms. It is not a screening technique. 10/5/2023 6

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8. 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% 10/5/2023 8

9. RESPONSE: It is an outcome of the experiment. It is the effect to evaluate. Ex: Disintegration time etc. 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 10/5/2023 9

10. OPTIMIZATION PARAMETER 10/5/2023 10

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13. Problem Types: 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: Constrained are restrictions placed on the system. For a given Pharmaceutical system make the hardest tablet possible, but it must disintegrate in less than 15 minutes 10/5/2023 13

14. Variables : Independent variables: The independent variables are the formulation and process variables directly 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 10/5/2023 14

15. Relationship between independent variables and response defines response surface. Representing becomes graphically impossible. Higher the variables , higher are the complications hence it is to optimize each & everyone. 10/5/2023 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 . 10/5/2023 16

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18. 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 10/5/2023 18

19. EXPERIMENTAL DESIGN Modern pharmaceutical optimization involves systematic design of experiments (DoE) to improve formulation irregularities. Design of Experiment (DoE) may be defined as the strategy for setting up experiments in such a manner that the required information is obtained as efficiently & precisely as possible 10/5/2023 19

20. Types of Experimental Designs Completely randomized designs Randomized block designs Factorial designs a) Full factorial b) Fractional factorial 4. Response surface designs Central composite designs b) Box behnken designs 5. Adding centre points 6. Three level full factorial design 10/5/2023 20

21. FACTORIAL DESIGN • A factorial design is type of design of experiment that lets us study the effects that several factors can have on a response. • When conducting an experiment, varying the levels of all factors at the same time instead of one at a time lets you study the interactions between factors 10/5/2023 21

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23. a)Full Factorial Design A full factorial design is a design in which researchers measure responses at all combinations of the factor levels. The number of runs necessary for a full factorial design is LF L = number of levels F= number of factors 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. 10/5/2023 23

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25. b)Fractional Factorial Design • A fractional factorial design is a design in which experiments conduct only a selected subsets or fraction of the runs in the full factorial design. • Fractional factorial designs are a good choice when resources are limited or the number of factors in the design is large because they use fewer runs then the full factorial designs. • A fractional factorial design uses a subset of a full factorial design • The fractional factorial design that includes half of the runs that a full factorial design has 10/5/2023 25

26. The number of runs necessary for a fractional factorial design is L F-1 Half factorial with 2 levels & 4 factors 2 4-1 = 8 runs 10/5/2023 26

27. The full factorial design contains twice as many design points as the ½ fraction design. The response is only measured at four of the possible eight factorial portion of the design. 10/5/2023 27

28. Types of fractional factorial designs Homogenous fractional Mixed level fractional Box-Hunter Plackett -Burman Taguchi Latin square 10/5/2023 28

29. 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. 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. 10/5/2023 29

30. 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. Taguchi It allows estimation of main effects while minimizing variance. Latin square They are special case of fractional factorial design where there is one treatment factor of interest and two or more blocking factors 10/5/2023 30

31. Advantages: • Best solution in the presence of competing objectives. • Fewer experiments needed to achieve an optimum formulation. • Significant saving of time, effort, materials and cost. • Easier problem tracing and rectification. • Possibility of estimating interactions. • Simulation of the product or process performance using model equation(s). • Comprehension of process to assist in formulation development and subsequent scaleup. 10/5/2023 31

32. REFERENCE Gilbert S. Banker., Fourth edition “Modern pharmaceutics” Page no-608-625 www.slideshare.com 10/5/2023 32

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