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- 1. Optimization Techniques In Pharmaceutical Formulation & Processing Anirban Saha, Navneet Kumar Giri M.Pharm (Pharmaceutics) Year- 2nd , Semester- 3 Amity University, Noida AMITY INSTITUTE OF PHARMACY
- 2. Outline Introduction Why Necessary Terms Used Advantages Optimization parameters Problem type Variables Applied optimisation methods Other Applications Factorial Design Conclusion 9/27/2015
- 3. Introduction 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 in to 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. In the other word we can say that –quantitate a formulation that has been qualitatively determined . It’s not a screening technique. 9/27/2015
- 4. Why is Optimization necessary? 9/27/2015 OPTIMIZATION Reducing cost Safety & Reducing error Reproducib ility Save Time Primary objective may not be optimize absolutely but to compromise effectively & thereby produce the best formulation under a given set of restrictions .
- 5. Terms Used o 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. o LEVELS: Levels of a factor are the values or designations assigned to the factor. o RESPONSE: It is an outcome of the experiment. • It is the effect to evaluate. Ex- Disintegration time. 9/27/2015
- 6. Terms Used oEFFECT: It is the change in response caused by varying the levels It gives the relationship between various factors & levels. oINTERACTION: It gives the overall effect of two or more variables Ex- Combined effect of lubricant and glidant on hardness of the tablet 9/27/2015 FACTOR LEVELS Temperature 300 , 500 Concentration 1%, 2%
- 7. Advantages o Yield the “Best Solution” within the domain of study. o Require fewer experiments to achieve an optimum formulation. o Can trace and rectify problem in a remarkably easier manner. 9/27/2015
- 8. 9/27/2015 PARAMETERS PROBLEM TYPE CONSTRAINED UNCONSTRAINED VARIABLES DEPENDENT INDEPENDENT Optimization Parameters
- 9. 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 • The constrained problem involved in it, is to make the hardest tablet possible, but it must disintegrate in less than 15 minutes. 9/27/2015
- 10. Variables • Independent variables : The independent variables are 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. 9/27/2015
- 11. Example of Variables 9/27/2015
- 12. Classical Optimization Classical optimization is done by using the calculus to basic problem to find the maximum and the minimum of a function. The curve in the fig represents the relationship between the response Y and the single independent variable X and we can obtain the maximum and the minimum. By using the calculus the graphical represented can be avoided. If the relationship, the equation for Y as a function of X, is available. Y = f(X) 9/27/2015
- 13. Drawbacks Limited applications • Problems that are too complex. • They do not involve more than two variables. For more than two variables graphical representation is impossible. 9/27/2015
- 14. Applied Optimization Methods 9/27/2015 Applied optimization Lagrangian Method Search Method Simplex Lattice Method EVOP METHOD
- 15. EVOP Method(Evolutionary Operation) • Make very small changes in formulation repeatedly. • The result of changes are statistically analyzed. • If there is improvement, the same step is repeated until further change doesn’t improve the product. Where we have to select this technique? This technique is especially well suited to a production situation. The process is run in a way that is both produce a product that meets all specifications and (at the same time) generates information on product improvement. 9/27/2015
- 16. Advantages: • generates information on product development. • predict the direction of improvement. • Help formulator to decide optimum conditions for the formulation and process. Limitations: • More repetition is required • Time consuming • Not efficient to finding true optimum • Expensive to use. 9/27/2015
- 17. • Example: In this example, A formulator can change the concentration of binder and get the desired hardness. 9/27/2015
- 18. SIMPLEX Method A simplex is a geometric figure, defined by no. of points or vertices equal to one more than no. of factors examined. 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 magnitudes of the responses after each successive calculation It is of two types: A. Basic Simplex Method B. Modified Simplex Method. 9/27/2015
- 19. SIMPLEX LATTICE • It is an experimental techniques & mostly used in analytical rather than formulation & processing. Simplex is a geometric figure that has one more point than the number of factors. • e.g-If 2 independent variables then simplex is represented as triangle. • The strategy is to move towards a better response by moving away from worst response. • Applied to optimize CAPSULES, DIRECT COMPRESSION TABLET), liquid systems (physical stability). • It is also called as Downhill Simplex / Nelder-Mead Method. 9/27/2015
- 20. The simplex method is especially appropriate when: • Process performance is changing over time. • More than three control variables are to be changed. • The process requires a fresh optimization with each new lot of material. The simplex method is based on an initial design of k+1, where k is the number of variables. A k+1 geometric figure in a k-dimensional space is called a simplex. The corners of this figure are called vertices. 9/27/2015
- 21. In simplex lattice, the response may be plotted as 2D (contour plotted) or 3D plots (response surface methodology) 9/27/2015
- 22. Advantage • This method will find the true optimum of a response with fewer trials than the non-systematic approaches or the one- variable-at-a-time method. Limitations : • There are sets of rules for the selection of the sequential vertices in the procedure. • Requires mathematical knowledge. 9/27/2015
- 23. LAGRANGIAN Method o It represents mathematical techniques. o It is an extension of classic method. o applied to a pharmaceutical formulation and processing. o This technique follows the second type of statistical design o This technique require that the experimentation be completed before optimization so that the mathematical models can be generates 9/27/2015
- 24. • Determine constraints. Determine objective formulation. • Change inequality constraints to equality constraints. • Form the Lagrange function F. • Partially differentiate the lagrange function for each variable & set derivatives equal to zero. • Solve the set of simultaneous equations. • Substitute the resulting values in objective functions. 9/27/2015 Steps involved
- 25. SEARCH Method oUnlike the Lagrangian method, do not require differentiability of the objective function. oIt is defined by appropriate equations. o Used for more than two independent variables. oThe response surface is searched by various methods to find the combination of independent variables yielding an optimum. oIt take five independent variables into account and is computer assisted. oPersons unfamiliar with mathematics of optimization & with no previous computer experience could carryout an optimization study. 9/27/2015
- 26. Advantages: o Takes five independent variables in to account o Person unfamiliar with the mathematics of optimization and with no previous computer experience could carry out an optimization study. o It do not require continuity and differentiability of function Disadvantage: o One possible disadvantage of the procedure as it is set up is that not all pharmaceutical responses will fit a second-order regression model. 9/27/2015
- 27. New Network for Optimization Artificial Neural Network & optimization of pharmaceutical formulation- • ANN has been entered in pharmaceutical studies to forecast the relationship b/w the response variables &casual factors . This is relationship is nonlinear relationship. • ANN is most successfully used in multi objective simultaneous optimization problem. • Radial basis functional network (RBFN) is proposed simultaneous optimization problems. • RBFN is an ANN which activate functions are RBF. • RBF is a function whose value depends on the distance from the Centre or origin. 9/27/2015
- 28. Formulation and Processing Clinical Chemistry Medicinal Chemistry High Performance Liquid Chromatographic Analysis Formulation of Culture Medium in Virological Studies. Study of Pharmacokinetic Parameters. APPLICATIONS 9/27/2015
- 29. Uses 9/27/2015 Provide solution to large scale manufacturing problems. Provides string assurances to regulatory agencies superior drug product quality. In microencapsulation process. Improvement of physical & biological properties by modification.
- 30. Conclusion o Optimization techniques are a part of development process. o The levels of variables for getting optimum response is evaluated. o Different optimization methods are used for different optimization problems. o Optimization helps in getting optimum product with desired bioavailability criteria as well as mass production. o More optimum the product = More the company earns in profits !!! 9/27/2015
- 31. References • Jain N. K., Pharmaceutical product development, CBS publishers and distributors, 1st edition,297-302, 2006. • Cooper L. and Steinberg D., Introduction to methods of optimization, W.B.Saunders, Philadelphia, 1970, 1st Edition, 301-305. • Bolton. S., Stastical applications in the pharmaceutical science, Varghese publishing house,3rd edition, 223. • Deming S.N. and King P. G., Computers and experimental optimization, Research/Development, vol-25 (5),22-26, may 1974. • Rubinstein M. H., Manuf. Chem. Aerosol News,30, Aug 1974. • Digaetano T.N., Bull.Parenter.Drug Assoc., vol-29,183, 1975. • Spendley, W., Sequential application of simplex designs in optimization and evolutionary operation, Technometrics, Vol- 4 441–461, 1962. • Forner D.E., Mathematical optimization techniques in drug product design and process analysis, Journal of pharmaceutical sciences. , vol-59 (11),1587-1195, November 1970. 9/27/2015
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