Optimization Techniques
In Pharmaceutical
Formulation &
Processing
Anirban Saha,
Navneet Kumar Giri
M.Pharm (Pharmaceutics...
Outline
 Introduction
 Why Necessary
 Terms Used
 Advantages
 Optimization parameters
 Problem type
 Variables
 Ap...
Introduction
The term Optimize is defined as to make perfect , effective , or as functional
as possible.
It is the proce...
Why is Optimization necessary?
9/27/2015
OPTIMIZATION
Reducing
cost
Safety &
Reducing
error
Reproducib
ility
Save
Time
Pri...
Terms Used
o FACTOR: It is an assigned variable such as concentration , Temperature
etc..,
• Quantitative: Numerical facto...
Terms Used
oEFFECT: It is the change in response caused by varying the
levels
 It gives the relationship between various ...
Advantages
o Yield the “Best Solution” within the domain of study.
o Require fewer experiments to achieve an optimum
formu...
9/27/2015
PARAMETERS
PROBLEM TYPE
CONSTRAINED
UNCONSTRAINED
VARIABLES DEPENDENT
INDEPENDENT
Optimization Parameters
Problem Types
Unconstrained
• In unconstrained optimization problems there are no restrictions.
• For a given pharmaceutic...
Variables
• Independent variables : The independent variables are under the control of
the formulator. These might include...
Example of Variables
9/27/2015
Classical Optimization
 Classical optimization is done by using the calculus to basic problem to find
the maximum and the...
Drawbacks
 Limited applications
• Problems that are too complex.
• They do not involve more than two variables.
 For mor...
Applied Optimization Methods
9/27/2015
Applied
optimization
Lagrangian
Method
Search
Method
Simplex
Lattice
Method
EVOP
ME...
EVOP Method(Evolutionary Operation)
• Make very small changes in formulation repeatedly.
• The result of changes are stati...
 Advantages:
• generates information on product development.
• predict the direction of improvement.
• Help formulator to...
• Example: In this example, A formulator can change the concentration
of binder and get the desired hardness.
9/27/2015
SIMPLEX Method
 A simplex is a geometric figure, defined by no. of points or
vertices equal to one more than no. of facto...
SIMPLEX LATTICE
• It is an experimental techniques & mostly used in analytical rather than
formulation & processing.
Simp...
 The simplex method is especially appropriate when:
• Process performance is changing over time.
• More than three contro...
In simplex lattice, the response may be plotted as 2D (contour plotted) or 3D plots (response
surface methodology)
9/27/20...
Advantage
• This method will find the true optimum of a response with
fewer trials than the non-systematic approaches or t...
LAGRANGIAN Method
o It represents mathematical techniques.
o It is an extension of classic method.
o applied to a pharmace...
• Determine constraints.
 Determine objective formulation.
• Change inequality constraints to equality constraints.
• For...
SEARCH Method
oUnlike the Lagrangian method, do not require differentiability of
the objective function.
oIt is defined by...
Advantages:
o Takes five independent variables in to account
o Person unfamiliar with the mathematics of optimization and ...
New Network for Optimization
Artificial Neural Network & optimization of pharmaceutical
formulation-
• ANN has been entere...
Formulation and Processing
Clinical Chemistry
Medicinal Chemistry
High Performance Liquid Chromatographic
Analysis
Formula...
Uses
9/27/2015
Provide solution to large scale manufacturing
problems.
Provides string assurances to regulatory agencies
s...
Conclusion
o Optimization techniques are a part of development process.
o The levels of variables for getting optimum resp...
References
• Jain N. K., Pharmaceutical product development, CBS publishers and distributors, 1st
edition,297-302, 2006.
•...
9/27/2015
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Optimization Techniques In Pharmaceutical Formulation & Processing

  1. 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. 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. 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. 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. 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. 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. 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. 8. 9/27/2015 PARAMETERS PROBLEM TYPE CONSTRAINED UNCONSTRAINED VARIABLES DEPENDENT INDEPENDENT Optimization Parameters
  9. 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. 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. 11. Example of Variables 9/27/2015
  12. 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. 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. 14. Applied Optimization Methods 9/27/2015 Applied optimization Lagrangian Method Search Method Simplex Lattice Method EVOP METHOD
  15. 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. 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. 17. • Example: In this example, A formulator can change the concentration of binder and get the desired hardness. 9/27/2015
  18. 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. 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. 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. 21. In simplex lattice, the response may be plotted as 2D (contour plotted) or 3D plots (response surface methodology) 9/27/2015
  22. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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