Advanceformulation Techniques

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Introduction

Traditional Formulation Approaches

The traditional approach of formulation is

based on trial and error, where the focus is

one individual factor at a time.

Advance Formulation Techniques

Advance formulation techniques are

approaches for formulation development

resulting in a dosage form which

demonstrates the optimized properties and

without the drawbacks associated with the

conventional dosage forms.


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OFAT Approach: (One Factor At A Time

Approach)

The desired properties of formulation are obtainedby changing one factor and keeping all others fixed.

Some initial experiments with selected levels of the

ingredients are selected based on the experience

are carried out.

The succeeding experiments are based on the

results obtained after each experimentation in the

direction of increase or decrease of the response(properties).

In this way a maximum or minimum of property is

reached.


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OFAT Approach: (One Factor At A Time

Approach)

Since in this approach, the product

properties are optimized one by one, the

approach is also called as the sequential

approach.

After formulating a product, if it is not the

desired one, then the center of attention is

one specified factor.

One by one, by controlling the other factors

at a constant level, effort is made to get the

desired product/formulation.


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Limitations Of The Traditional Approaches

The traditional OFAT approach has certain

limitation:1. Unplanned and based on trial and error.

2. Less effective.

3. Sequential (optimization one one).4. Unable to obtain the information on factor

interactions.

5. Require more number of experimentation to

obtain information helpful to make formulation

decision.

6. Time consuming, laborious and costly.


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Need for advance formulation approaches

Advance formulation approaches are neededbecause coping with the following is becoming

increasingly difficult for the pharmaceutical

industries by the traditional approaches:

Increasing pressure for developing new productsquickly to cope with market competition.

Products with more stringent quality standards.

Partial or totally unavailability of historical knowledge

for the new formulations.

The task of formulation is complex because there is

often no model or detailed understanding of how

changes in formulation ingredients affect product

properties.


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Need for advance formulation approaches

Data generated during optimization process is

huge and difficult to understand.

Optimization process is multidimensional (some

properties are required to be minimum while

others to be maximum). Existence of opportunity to improve the

formulation operations and resulting profitability

by streamlining the formulation design tasks.

Formulation requires experimentation which is

expensive in terms of laboratory and staff time

and in terms of opportunities missed through

slow response to new customer requirements.


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Need for advance formulation

approaches

New formulation approaches are powerful toolsand are based on the artificial intelligence and

computational approaches.

These are coupled with visualization and statistical

validation and robust optimization methods.

These approaches require desk-top decision

support software. Computational approaches can

reduce the formulators effort by automaticallygenerating knowledge directly form data which is

obtained from the planned experimentation using

different settings of the factors.


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Need for advance formulation

approaches

The newer approaches allow simultaneousoptimization of all properties, thus are also called

as the simultaneous approaches. Such

approaches plan the complete set of experiments,

called as experimental design beforehand. In this,

the experiments are carried out and the results are

fitted to a mathematical model. The response

values can be predicted by using a range for thesettings of variables (formulative, process and

machine). A wide range of possible choices (factor

settings) is available.


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Advantages Of The Advanced

Approaches:

1. Reveals the interaction between different

variables.

2. Enhancement of product quality and

performance at low cost. 3. Shorter time to market.

4. Development of new products.

5. Improved customer response. 6. Improved confidence.

7. Improved competitive edge.


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Applications

1. Formulation design.

2. Optimization of formulation.

3. Optimization of process. 4. Process validation.

5. Scale up.

6. Cost reduction. 7. Prediction.


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Optimization

The word opt imize simply implies to make as

perfect, effective, or functional as possible.

Opt im izat ion of a p roduc t o r process is the

determ inat ion o f exper imen tal condit ionsresu l t ing in op timal perfo rmance.

A product that has desired characteristics and meet

all specifications is anopt imized formulation


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Interaction Of Factors

Cause and Effect Model:

In a cause-and-effect model, the

transformation of a system (ingredients) into

an output (product) depends on the way the

external factors interact with the internal

components of a system.

Four types of interactions between internal

and external factors can be proposed:


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Cause and Effect Model


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Cause and Effect ModelIn case of the active factors, if the system factors are sensitive

to the adjustable external factors, the system can be readilytransformed into a desired output. In case of the partially

desired output, with change of the amount of the factors levels

or modifying the factors themselves may cause favorable

outputs. Cases with no interaction or negative interactions,warrants search for entirely other active factors which can

cause a favorable output.

Thus, a careful selection of a set of controllable external factors

at appropriate amounts (levels) may cause an interaction whichcan manipulate the inaccessible internal factors to yield a

desired output. Advanced formulations pose complex and non-

linear relationships between factors and properties and thus,

require use of computer-aided approaches to understand the

cause and effect relationships.


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Response Surface Methodology(rsm)

This is model independent methodology wherein one or

more selected experimental responses are recorded for

a set of experiments, carried out in a systemic way to

predict the optimum and the interaction effects.

These approaches comprise, the postulation if empiricalmechanical model for each response within zone of

interest.

Rather than estimating the effects of each variable

directly, RSM involves fitting the coefficient into themodel equation of a particular response variable and

mapping the response, i.e., studying the response over

whole of experimental domain in the form of surface.


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Experimental Designs Of RSM:

For implementation of RSM one of following

statistical design is adopted;

Factorial design

Central Composite Design

Uniform shell design

Mixture design Response Surface Analysis:

Usually, the results of RSM are graphically depicted

using one or more of following plots:


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RESPONSE SURFACE PLOT is a 3-D graphical

representation of a response plotted between two

independent variables and one response variable.

The use of 3-D response surface plot allows

understanding of the behaviour of the system by

demonstrating the contribution of independentvariables.

CONTOUR PLOT is the geometric illustration of a

response, obtained by plotting independent variable

versus another while holding magnitude of responselevel and other variables constants. The contour plot

represents 2-D slices of corresponding 3-D

response surface. The resuling curves are called

contour lines.


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Artificial Neural Networks(ANN)

ANN are machine based computational

techniques that attempt to stimulate some ofneurological processing abilities of human brain.

ANN offers unique advantages, as non-linear

processing capacity and the ability to model

poorly understood system. When compared withRSM, the results are comparable with beter

prognostic abilities. However they are difficult to

implement particularly at higher number offactors and/or levels, and no statistical criterion is

revealed to declare degree of aptness of model.


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Artificial Neural Networks(ANN)Architect and work pattern of ANN:

Pattern of connectivity among the ANN units is

equivalent to a mammalian neural architect as

shown below.

A typical ANN forms input and output layers and atleast one or more hidden layers and works by

reducing the error between observed and predicted

outcomes by adjusting the weight.

Mathematically, it detects the underlying patterns in

data that recognizes the functional relationships

between factors and responses and predicts

optimum levels of factors from a limited input data.


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Artificial Neural Networks(ANN)

ANNs thus, are particularly suitable for complex andnon-linear systems for which the conventional

approaches more exhausting.

The neural network makes no assumptions about the

functional form of the relationships; it simply generates

and assesses a range of models to determine one that

best fits the experimental data provided to it.

As such, increasingly, (ANNs) are used to model acomplex behavior in problems like pharmaceuticals

formulation and processing. The models generated by

neural networks allow what if possibilities to be

investigated easily.


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Artificial Neural

Networks(ANN)


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Statistical Optimization Approach

Design of experiment (DoE), a statistics-based

approach carried out systematically, identifies

the critical variables, reveals their interactions

and helps obtain combinations of variables toaccomplish optimum response with lesser

number of experiments.

DoE algorithms are based on the principle

component analysis, polynomial regression,analysis of variance (ANOVA) and mathematical

optimization algorithms.


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References

N K Jain, Progress in controlled and Novel Drug Delievrysystems, CBS Publishers, New Dehli, 2004.

Class lecture by Dr. Nadeem Irfan Bukhari

Colbourn E. Spotlight on Intelligensys. Controlled

release society Newsletter. Vol 21 (3): Ibric, S, Djuric, Z.,Parojcic J., Petrovic, U.

Artificial intelligence in pharmaceutical product

formulation: Neural computing. Chemical Industry &

Chemical Engineering Quarterly 15 (4) 227236 (2009) Rowe, R. C. Roberts, R. J. Intelligent software for

product formulation, Taylor and Francis, London, 1998.


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Advanceformulation Techniques