Optimization Technique In Pharmaceutical Formulation(Cocept,Parameters,Techniques & Processing)

Optimization Technique In Pharmaceutical Formulation(Cocept,Parameters,Techniques & Processing)

1. DEPARTMENT OF PHARMACEUTICS TOPIC: Optimization Technique In Pharmaceutical Formulation(Cocept,Parameters,Techniques & Processing) PRESENTED BY: RUSHIKESH SHINDE (M.Pharm,First Year) GUIDED BY: DR.NALANDA BORKAR MADAM (Head Of Department Of Pharmaceutics) Survey No. 50,Marunje,Near Rajiv Gandhi, IT Park, Hinjawadi,Pune,Maharashtra,411028 ALARD COLLEGE OF PHARMACY 1

2. Concept of optimization Parameters of optimization Optimization techniques in pharmaceutical formulation and processing. Contents : 2

3. 1. CONCEPT OF OPTIMIZATION : The term optimize is defined as “ to make perfect ”. In terms of sentence it is defined as choosing the best element from some set of available alternatives. According to Merriam Webster dictionary, optimization means, “ An act, process or methodology of making something (as a design, system or a decision) as a fully perfect, functional or effective as possible; specially the mathematical procedures. Optimization is also defined as “The process of finding the best values for the variables of a particular problem to minimize or maximize an objective function.” 3

4. It is used in pharmacy relative formulation and processing. It is involved in formulating drug products in various forms. Final product not only meets the requirements from the bio- availability but also from the practical mass production criteria. It helps the pharmaceutical scientist to understand theoretical formulation and the target processing parameters which 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 4

5. “It is not a screening technique.” Optimization is necessary because, It reduces the cost. It provides safety and reduces the error. It provides innovation and efficacy. It saves the time. 5

6. 2.PARAMETERS OF OPTIMIZATION : Parameters of optimization are divided into two main types which is shown schematically: optimization parameters problem type variab l es cons t ra i ned unconstrained dependent indep e ndent formulating proc ess ing 6

7. a. Problem type: The r e a r e t w o g en e r al type a r e t h e r e in the p r oble m ty p e of optimization technique: Constrained Un constrained Constrained : T h ese a r e th e r e s trict i o n s place d o n th e s y s t em b y p h y si c al limitations or perhaps by simple practicality. Example : Economical considerations Un constrained: Here there are no restrictions. With the help of flow chart we can predict these two problem type very easily viz., 7

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9. b. Varible s : Mathematically, they can be divided into two groups Independent or primary variables Dependent or secondary variables a. Independent or primary variables: Formulations and process variables directly under control of the formulator. Example: Ingredients Mixing time for given process step. 9

10. B. Dependent or secondary variables: These are the responses or the characteristics of the in-progress material or the resulting drug delivery system. Example: Direct result of any change in the formulation or process. 10

11. If greater the variables in a given system, then greater will be the complicated job of optimization. But regardless of the no.of variables, there will be relationship between a given response and independent variables. Once we know this relationship for a given response, then will able to define a response surface i.e., 11

12. It involves application of calculus to basic problem for maximum/minimum function. Limited applications Problems those are not too complex. They do not involve more than two variables. For more than two variables, graphical representation is impossible, but it is possible mathematically. 12

13. 3.OPTIMIZATION TECHINQUES IN PHARMACEUTICAL FORMULATION AND PROCESSING Deming and king presented a general optimization techniques: 13

14. Considering the chan g es i n inp u t and e f fect on output, t h e optimization techniques are categorized into five types: Evolutionary operations Simplex method Lagrangian method Search method Canonical analysis 14

15. 1.Evolutionary operations: It is the one of the most widely used methods of experimental optimization in fields other than pharmaceutical technology is the evolutionary operation(EVOP), It is well suited to production situation. The basic idea is that the production procedure(formulation and process) is allowed to evolve to the optimum by careful planning and constant repetition. 15

16. Method: This process is run in a such a way that It produces a product that meets all specifications. Simultaneously, it generates information on product improvement. Experimenter makes a very small change in the formulation or process but makes it so many times i.e., repeates the experiment so many times. Then he or she can be able to determine statistically whether the product has improved. And the experimenter makes further any other change in the same direction, many times and notes the results. 16

17. This continues until further changes do not improve the product or perhaps become detrimental. Applications: It was applied to tablets by Rubinstein. It has also been applied to an inspection system for parenteral products. Drawbacks: It is impractical and expensive to use. It is not a substitute for good laboratory scale investigation . It is most widely applied technique. 17

18. 2.Simplex method: It was proposed by Spendley . This technique has even wider appeal in areas other than formulation and processing. A good example to explain its principle is the application to the development of an analytical method i.e., a continuous flow anlayzer, it was predicted by Deming and king. Simplex method is a geometric figure that has one or more point than the number of factors. If two factors or any independent variables are there, then simplex is represented triangle. Once the shape of a simplex has been determined, the method can employ a simplex of fixed size or of variable sizes that are determined by comparing the magnitude of the responses after each successi ve 18 c alculation.

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20. Explaination: The two axes in the figure are nothing but two independent variables show the pump speeds for the two reagents required in the analysis reaction. The initial simplex is represented by the lowest triangle. The vertices represent the spectrophotometric response. The strategy is moves towards a better response. The worst response is 0.25, conditions are selected at the vertex, 0.6 and indeed improvement is obtained. Then the experiment path is followed to obtain optimum, 0.721. 20

21. Applications of method: This method was used by Shek.et.al. to search for an capsule formula. This was applied to study the solubility problem involving butaconazolenitrate in a multicomponent system. Bindschaeder and Gurny published an adaptation of the simplex technique to a TI-59 calculator and applied successfully to a direct compression tablet of acetaminophen. Janeczeck applied the approach to a liquid system i.e., a pharmaceutical solution and was able to optimize physical stability. 21

22. It represents mathematical techniques. It is an extension of classic method. applied to a pharmaceutical formulation and processing. This technique follows the second type of statistical design This technique require that the experimentation be completed before optimization so that the mathematical models can be generates 3) Langrangian Method: 22

23. Where we have to select this technique? This technique can applied to a pharmaceutical formulation and processing. Advantages: lagrangian method was able to handle several responses or dependent variables. Limitation: Although the lagrangian method was able to handle several responses or dependent variables, it was generally limited to two independent variables. 23

24. Unlike the Lagrangian method, do not require differentiability of the objective function. It is defined by appropriate equations. Used for more than two independent variables. The response surface is searched by various methods to find the combination of independent variables yielding an optimum. It take five independent variables into account and is computer assisted. Persons unfamiliar with mathematics of optimization & with no previous computer experience could carryout an optimization study. 4)Search Method: 24

25. Advantages: Takes five independent variables in to account Person unfamiliar with the mathematics of optimization and with no previous computer experience could carry out an optimization study. It do not require continuity and differentiability of function Disadvantage: One possible disadvantage of the procedure as it is set up is that not all pharmaceutical responses will fit a second-order regression model. 25

26. 5 ) Canonical analysis : It is a technique used to reduce a second order regression equation. This allows immediate interpretation of the regression equation by including the linear and interaction terms in constant term. This was firstly adopted by Box and Wilson, It is used to reduce second order regression equation to an equation consisting of a constant and squared terms as follows: Y = Y +λ 1 W 1 2 + λ 2 W 2 2 +… It is described as an efficient method to explore an empherical response. 26

27. References: Modern Pharmaceutics; By Gillbert and S. Banker – edited in 2002. Assesed Date:6 th March 2021 www.slideshare.com/optimization techniques in pharmaceutical formulations.Assesed Date:6 th March 2021 www.google.com/optimization graphs, flow charts, plots.Assesed Date:7 th March 2021 Saypeople.com/Types of problems in optimization.Assesed Date:7 th March 2021 27

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