Optimization Techniques

In Pharmaceutical

Formulation &

ProcessingAnirban Saha,

Navneet Kumar Giri

M.Pharm (Pharmaceutics)Year- 2nd , Semester- 3Amity University, Noida

AMITY INSTITUTE OF PHARMACY

Outline Introduction

Why Necessary

Terms Used

Advantages

Optimization parameters

Problem type

Variables

Applied optimisation methods

Other Applications

Factorial Design

Conclusion

9/27/2015

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 variableat a time, so to obtain solution of a problematic formulation.

Modern pharmaceutical optimization involves systematic design ofexperiments (DoE) to improve formulation irregularities.

In the other word we can say that –quantitate a formulation that has beenqualitatively determined . It’s not a screening technique.

9/27/2015

Why is Optimization necessary?

9/27/2015

OPTIMIZATION

Reducing

cost

Safety &

Reducing error

Reproducibility

Save Time

Primary objective may not be optimize absolutely but to compromise effectively & thereby produce the best formulation under a given set of

restrictions .

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

Terms Used

oEFFECT: It is the change in response caused by varying the

levels

It gives the relationship between various factors & levels.

o INTERACTION: 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%

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

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 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

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

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 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

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

Applied Optimization Methods

9/27/2015

Applied optimization

LagrangianMethod

Search Method

Simplex Lattice Method

EVOP METHOD

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

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

• 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 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

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

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

In simplex lattice, the response may be plotted as 2D (contour plotted) or 3D plots (response

surface methodology)

9/27/2015

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

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

• 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

SEARCH MethodoUnlike 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 thecombination of independent variables yielding an optimum.

oIt take five independent variables into account and is computerassisted.

oPersons unfamiliar with mathematics of optimization & with noprevious computer experience could carryout an optimizationstudy.

9/27/2015

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

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

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

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.

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

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

9/27/2015