Optimization techniques are used to find the best formulation and manufacturing process. Traditional optimization varies one variable at a time, while modern techniques use systematic experimental design. Different optimization methods are applied depending on the problem, such as whether the problem has constraints or independent variables. Optimization helps reduce costs and errors while improving safety, reproducibility, and time efficiency in pharmaceutical development and manufacturing.

1. Presented By: Tanmay Upadhyay
M.Pharm. (Pharmaceutics),
Integral University,
Lucknow.

2. 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.

3. OPTIMIZATION
Reducing
cost
Safety &
Reducing
error
Reproducib
ility
Save
Time
WHY IS OPTIMIZATION NECESSARY?

4. o Yield the “Best Solution” within the domain of study.
o Require fewer experiments to achieve an
optimal formulation.
o Can trace and rectify problem in a remarkably
easier manner.

5. PARAMETERS
CONSTRAINED
PROBLEM TYPE
UNCONSTRAINED
VARIABLES DEPENDENT
INDEPENDENT

6. PROBLEM
TYPES
Unconstrained
• In unconstrained optimization problems there are no restrictions.
• For a given pharmaceutical system one might wish to make the
hardesttablet 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.

7. • 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.  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)

10. Applied
optimization
Lagrangian
Method
Search
Method
Simplex
Lattice
Method
EVOP
METHOD

11. (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.

12.  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.

13. • Example: In this example, A formulator can change the concentration
of binder and get the desired hardness.

14.  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

15. • 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.

16. o It represents mathematical techniques.
o It is an extension of classic method.
o applied to a pharmaceutical formulation and processing.
o This technique require that the experimentation be completed
before
optimization so that the mathematical models can be generates

17. oUnlike the Lagrangian method, do not require differentiability
of the objective function.
oIt is defined by appropriate equations.
oUsed 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.

18. 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.

19. 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.

20. 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 !!!