Optimization process
- 1. PRESENTATION ON OPTIMIZATION PROCESS Presented by- SURAJ KHALI M.PHARM(PHARMACEUTICS)
- 2. BASIC AIM OF OPTIMIZATIOMN :- * Optimization makes the perfect formulation & reduce the cost * The term Optimize is defined as to make perfect , effective , or functional as possible. * It is the process of finding the best way of using the existing resources. * Traditionally, optimization in pharmaceuticals refers to changing one variable at a time.
- 3. Cond… • Modern pharmaceutical optimization involves systematic design of experiments (DoE). • In the other word we can say that –quantitate a formulation that has been qualitatively determined. • It’s not a screening techniques.
- 4. Why Optimization is necessary? optimization Reduce the cost Safety & reduce the error reproducibi lity Save the time
- 5. 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 300 , 500 Concentration 1%, 2%
- 6. Cond… 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 Guidant on hardness of the tablet
- 7. Optimization Parameter:- optimization parameter Problem type Variables constaintunconstaint constaint constaint
- 8. Optimization parameters VARIABLES Independent Dependent Formulating Processing Variables Variables
- 9. Cond… • Optimization Parameters 1.Problem types: • Constraints • Example-Making hardest tablet but should disintegrate within 20 mins ( Constraint) • Unconstraint • Example: Making hardest tablet ( Unconstraint) • 2. Variables: • Independent variable- E.g. - mixing time for a given process step. granulating time.
- 10. • Dependent variables, which are the responses or the characteristics of the in process material Eg. Particle size of vesicles, hardness of the tablet. Higher the number of variables, more complicated will be the optimization process.
- 11. Example of dependent & independent variables:- Independent variables Dependent variables Diluent ratio Disintegration time compressional force Hardness Lubricant level weight uniformity
- 12. Classic optimization • It involves application of calculus to basic problem for maximum/minimum function. • Limited applications. • For more than two variables graphical representation is impossible.
- 13. Statistical design • Techniques used divided in to two types. There are two possible approaches for this- • Theoretical approach- If theoretical equation is known , no experimentation is necessary. • Empirical or experimental approach – With single independent variable formulator experiments at several levels.
- 14. FLOW CHART FOR OPTIMIZATION:-
- 15. TYPES OF EXPERIMENTAL DESIGN Completely randomized designs Randomized block designs Factorial designs Full Fractional Response surface designs Central composite designs Box-Behnken designs Three level full factorial designs
- 16. Cond… Completely randomized Designs These designs compares the values of a response variable based on different levels of that primary factor. Randomized block designs For this there is one factor or variable that is of primary interest. To control non-significant factors, an important technique called blocking.
- 17. Factorial Design:- • These are the designs of choice for simultaneous determination of the effects of several factors & their interactions. • Factorial designs are optimal to determined the effect of pressure & lubricant on the hardness of a tablet • Effect of disintegrant & lubricant conc . on tablet dissolution . • It is based on theory of probability and test of significance.
- 18. Cond… Factorial design Full • Used for small set of factors Fractional • It is used to examine multiple factors efficiently with fewer runs than corresponding full factorial design. Types of fractional factorial designs Homogenous fractional Mixed level fractional Box-Hunter Plackett - Burman Taguchi Latin square
- 19. Response surface designs:- This model has quadratic form γ =β0 + β1X1 + β2X2 +….β11X1 2 + β22X2 2 Designs for fitting these types of models are known as response surface designs. If defects and yield are the outputs and the goal is to minimize defects and maximize yield
- 20. Three-level full factorial designs It is written as 3k factorial design. It means that k factors are considered each at 3 levels. These are usually referred to as low, intermediate & high values. These values are usually expressed as 0, 1 & 2
- 21. Applied optimization method • There are several methods use for optimization and they are:- • Evolutionary operations • Simplex Method(Simplex Lattice) • Lagrangian method • Search methods (RSM) • Canonical analysis
- 22. Thank You…