This presentation provides an overview of optimization in pharmaceutical processes. It discusses how optimization aims to make formulations as perfect, effective and functional as possible while reducing costs. Modern pharmaceutical optimization uses systematic design of experiments rather than changing one variable at a time. Different types of experimental designs for optimization are described, including factorial designs, response surface designs, and three-level full factorial designs. Key terms used in optimization like factors, levels, responses and effects are also explained.

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…