This document summarizes optimization techniques used in pharmaceutical formulation and processing. It discusses key concepts like the definition of optimization, parameters that can be optimized including problem type and variables, and techniques like evolutionary operations, simplex method, Lagrangian method, search method, and canonical analysis. The techniques are applied to problems with different numbers of variables and formulation or processing steps to find the combination of factors that optimize a desired response.
Optimization Technique In Pharmaceutical Formulation(Cocept,Parameters,Techniques & Processing)
1. DEPARTMENT OF PHARMACEUTICS
TOPIC: Optimization Technique In Pharmaceutical
Formulation(Cocept,Parameters,Techniques & Processing)
PRESENTED BY: RUSHIKESH SHINDE
(M.Pharm,First Year)
GUIDED BY: DR.NALANDA BORKAR MADAM
(Head Of Department Of Pharmaceutics)
Survey No. 50,Marunje,Near Rajiv Gandhi,
IT Park, Hinjawadi,Pune,Maharashtra,411028
ALARD COLLEGE OF PHARMACY
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2. Concept of optimization
Parameters of optimization
Optimization techniques in pharmaceutical
formulation and processing.
CONTENTS:
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3. 1. CONCEPT OFOPTIMIZATION:
The term optimize is defined as “ to make perfect”.
In terms of sentence it is defined as choosing the best element
from some set of available alternatives.
According to Merriam Webster dictionary, optimization
means, “ An act, process or methodology of making something
(as a design, system or a decision) as a fully perfect, functional
or effective as possible; specially the mathematical procedures.
Optimization is also defined as “The process of finding the
best values for the variables of a particular problem to
minimize or maximize an objective function.”
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4. It is used in pharmacy relative formulation and processing.
It is involved in formulating drug products in various forms.
Final product not only meets the requirements from the bio-
availability but also from the practical mass production
criteria.
It helps the pharmaceutical scientist to understand theoretical
formulation and the target processing parameters which ranges
for each excipients & processing factors.
In development projects, one generally experiments by a series
of logical steps, carefully controlling the variables & changing
one at a time, until a satisfactory system is obtained
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5. “It is not a screening technique.”
Optimization is necessary because,
1. It reduces the cost.
2. It provides safety and reduces the error.
3. It provides innovation and efficacy.
4. It saves the time.
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6. 2.PARAMETERS OF OPTIMIZATION:
Parameters of optimization are divided into
two main types which is shown schematically:
optimization parameters
problem type variables
constrained unconstrained dependent independent
formulating processing
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7. A. PROBLEM TYPE:
There are two generaltype are there in the problem type of
optimization technique:
1. Constrained
2. Un constrained
3. Constrained :
Theseare the restrictions placed on the system by
physical
limitations or perhaps by simple practicality.
Example : Economical considerations
2.Un constrained:
Here there are no restrictions.
With the help of flow chart we can predict these
two problem type very easily viz.,
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9. B.VARIBLES:
Mathematically, they can be divided into two groups
a. Independent or primary variables
b. Dependent or secondary variables
a. Independent or primary variables:
Formulations and process variables directly under control of the
formulator.
Example: Ingredients
Mixing time for given process step.
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10. B. Dependent or secondary variables:
These are the responses or the characteristics of
the in-progress material or the resulting drug delivery system.
Example: Direct result of any change in the formulation or
process.
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11. If greater the variables in a given system, then greater will be
the complicated job of optimization.
But regardless of the no.of variables, there will be relationship
between a given response and independent variables.
Once we know this relationship for a given response, then will
able to define a response surface i.e.,
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12. It involves application of calculus to basic problem for
maximum/minimum function.
Limited applications
i. Problems those are not too complex.
ii. They do not involve more than two variables.
For more than two variables, graphical representation is
impossible, but it is possible mathematically.
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14. Considering the changes in input and effect on output,
the optimization techniques are categorized into
five types:
1. Evolutionary operations
2. Simplex method
3. Lagrangian method
4. Search method
5. Canonical analysis
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15. 1.EVOLUTIONARY OPERATIONS:
It is the one of the most widely used methods of experimental
optimization in fields other than pharmaceutical technology is
the evolutionary operation(EVOP),
It is well suited to production situation.
The basic idea is that the production procedure(formulation
and process) is allowed to evolve to the optimum by careful
planning and constant repetition.
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16. Method:
This process is run in a such a way that
A. It produces a product that meets all specifications.
B. Simultaneously, it generates information on product
improvement.
Experimenter makes a very small change in the formulation
or process but makes it so many times i.e., repeates the
experiment so many times.
Then he or she can be able to determine statistically whether
the product has improved.
And the experimenter makes further any other change in the
same direction, many times and notes the results.
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17. This continues until further changes do not improve the
product or perhaps become detrimental.
Applications:
1. It was applied to tablets by Rubinstein.
2. It has also been applied to an inspection system for parenteral
products.
Drawbacks:
1. It is impractical and expensive to use.
2. It is not a substitute for good laboratory scale investigation.
3. It is most widely applied technique.
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18. 2.SIMPLEX METHOD:
It was proposed by Spendley.
This technique has even wider appeal in areas other than
formulation and processing.
A good example to explain its principle is the application to
the development of an analytical method i.e., a continuous
flow anlayzer, it was predicted by Deming and king.
Simplex method is a geometric figure that has one or more
point than the number of factors.
If two factors or any independent variables are there, then
simplex is represented triangle.
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 magnitude of the responses after
each successive
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calculation.
20. Explaination:
The two axes in the figure are nothing but two independent
variables show the pump speeds for the two reagents required
in the analysis reaction.
The initial simplex is represented by the lowest triangle.
The vertices represent the spectrophotometric response.
The strategy is moves towards a better response.
The worst response is 0.25, conditions are selected at the
vertex, 0.6 and indeed improvement is obtained.
Then the experiment path is followed to obtain optimum,
0.721.
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21. APPLICATIONS OF METHOD:
1. This method was used by Shek.et.al. to search for an capsule
formula.
2. This was applied to study the solubility problem involving
butaconazolenitrate in a multicomponent system.
3. Bindschaeder and Gurny published an adaptation of the
simplex technique to a TI-59 calculator and applied
successfully to a direct compression tablet of acetaminophen.
4. Janeczeck applied the approach to a liquid system i.e., a
pharmaceutical solution and was able to optimize physical
stability.
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22. It represents mathematical techniques.
It is an extension of classic method.
applied to a pharmaceutical formulation and processing.
This technique follows the second type of statistical design
This technique require that the experimentation be completed before
optimization so that the mathematical models can be generates
3)Langrangian Method:
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23. Where we have to select this technique?
This technique can applied to a
pharmaceutical formulation and
processing.
Advantages:
lagrangian method was able to handle several
responses or dependent variables.
Limitation:
Although the lagrangian method was able to
handle several responses or dependent
variables, it was generally limited to two
independent variables.
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24. o Unlike the Lagrangian method, do not require
differentiability of the objective function.
o It is defined by appropriate equations.
o Used for more than two independent variables.
o The response surface is searched by various methods to find
the combination of independent variables yielding an
optimum.
o It take five independent variables into account and is
computer assisted.
o Persons unfamiliar with mathematics of optimization &
with no previous computer experience could carryout an
optimization study.
4)Search Method:
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25. Advantages:
Takes five independent variables in to account
Person unfamiliar with the mathematics of optimization and with no
previous computer experience could carry out an optimization study.
It do not require continuity and differentiability of function
Disadvantage:
One possible disadvantage of the procedure as it is set up is that not
all pharmaceutical responses will fit a second-order regression
model.
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26. 5)CANONICAL ANALYSIS:
It is a technique used to reduce a second order regression
equation. This allows immediate interpretation of the
regression equation by including the linear and interaction
terms in constant term.
This was firstly adopted by Box and Wilson,
It is used to reduce second order regression equation to an
equation consisting of a constant and squared terms as follows:
Y = Y0 +λ1W1
2 + λ2W2
2 +…
It is described as an efficient method to explore an empherical
response.
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27. References:
1. Modern Pharmaceutics; By Gillbert and S.
Banker – edited in 2002.Assesed Date:6th
March 2021
2. www.slideshare.com/optimization techniques in
pharmaceutical formulations.Assesed Date:6th
March 2021
3. www.google.com/optimization graphs, flow
charts, plots.Assesed Date:7th March 2021
4. Saypeople.com/Types of problems in
optimization.Assesed Date:7th March 2021
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