This document discusses the use of computer-aided techniques in the development of microemulsion drug carriers, with a special emphasis on optimization. It describes how artificial neural networks (ANN) can be used as tools to accurately predict the microemulsion area based on formulation composition. Various optimization techniques are explored, including factorial designs, response surface methodology, and the use of ANN for multi-objective optimization problems. Microemulsions offer benefits as drug carriers, and optimization is important to develop formulations that solubilize both water-soluble and oil-soluble compounds for delivery.

1. ESSENTIALS OF COMPUTER IN PHARMACEUTICAL
FORMULATION WITH SPECIAL EMPHASIS ON
MICROEMULASION AS DRUG CARRIERS
Submitted by:-
Surya prabhakar singh
M. Pharma 2nd semester
Roll no. 19125155015
Institute of pharmacy
Bundelkhand university
Jhansi.
Submitted to:-
Dr. Girish Chandra Soni
( Ass. Professor )
Institute of pharmacy
Bundelkhand university
Jhansi.

2. CONTENTS
 Introduction
 computer-aided techniques in development of
microemulsion drug carriers
 Optimization techniques used in micro emulsions
 Optimization modeling
 Optimization parameter
 Microemulsion
 Reference

3. INTRODUCTION
 The concept of formulation development assisted by computer
applications.
 Due to their complex composition, preparation and stability issues of
emulsions were selected to showcase various computer-aided tools in
pharmaceutical formulation development.
 Successful development of an emulsion formulation is dependent on
both formulation ingredients and processing parameters, which is
especially significant for more complex formulation types, such as self-
emulsifying systems or double emulsions.
 The examples provided illustrate techniques used to define a design
space, select the appropriate formulation ingredient, and optimize the
formulation composition as well as process parameters, according to the
quality-by-design (QbD) concept.
 A nonlinear mathematical approach comprising experimental design,
neural networks, GAs, and/or neuro-fuzzy logic represents a promising
tool for in silico modeling of formulation procedures in development of
emulsion and (micro)emulsion drug carriers.

4. TECHNOLOGY
• IF (condition 1) AND (condition 2) OR (condition 3) THEN (action) UNLESS
(exception) BECAUSE (reason). Each rule is easy to understand, implements an
autonomous piece of knowledge, and can be developed and modified
independently of other rules. Unfortunately, a complex domain may require a large
number of rules and other representation methodologies may be necessary.
• These include frames or templates for holding clusters of related knowledge;
semantic networks for representing complex relationships between objects; and
decision trees or tables for organizing knowledge in a tree or tabular format that is
easy to understand and format. Generally, multiple methods are used to express
formulation knowledge
• Expert Systems can be developed with conventional computer languages such as C
or more recently JAVA, with specialized languages such as LISP and PROLOG, or with
the assistance of development shells or toolkits.
• Conventional languages have the advantage of wide applicability and flexibility to
create the strategies required but require considerable time and effort to create the
basic facilities. Specialized languages have been used extensively in the
development of expert systems because they retain the advantages of conventional
languages but are faster to implement

5. computer-aided techniques in development of
microemulsion drug carriers
 Micro emulsions are thermodynamically stable and optically
isotropic transparent colloidal systems consisting of water,
oil, and surfactant. The microstructure of micro emulsions is
determined by physicochemical properties and
concentrations of the constituents.
 ANN models were introduced as useful tools for accurate
differentiation and prediction of the microemulsion area
from the qualitative and quantitative composition of
different microemulsion-forming systems
 The most successful MLP ANN model, with two hidden
layers comprising 14 and 9 neurons, predicted the phase
behavior for a new set of co surfactants with 82.2% accuracy
for the microemulsion region.

6. Optimization techniques used in micro emulsions
 The term Optimize is defined as to make perfect , effective , or
functional as possible.
 I t 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 refers to changing one
variable at a time, so to obtain solution of a problematic formulation.

7. Optimization makes the perfect formulation &
reduce the cost
Primary objective may not be optimize absolutely but to compromise
effectively & thereby produce the best formulation under a given set of
restrictions
The term Optimize is defined as to make perfect , effective , or
functional as possible.
I t 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 refers 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.

10. Example of dependent & independent variables -
Independent variables Dependent variables
X1 Diluent ratio Y1 Disintegration time
X2 compressional force Y2 Hardness
X3 Disintegrant level Y3 Dissolution
X4 Binder level Y4 Friability
X5 Lubricant level Y5 weight uniformity

11. Statistical design :-
Techniques used divided in to two types.
 Experimentation continues as optimization
proceeds
• It is represented by evolutionary
operations(EVOP), simplex methods.
Experimentation is completed before
optimization takes place.
It is represented by classic mathematical &
search methods.
In later one it is necessary that the relation
between any dependent variable and one or
more independent variable is known.

12. 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.
 Optimization may be helpful in shortening the experimenting time.
 T h e design of experiments is determined the relationship between
the factors affecting a process and the output of that process.
 Statistical DOE refers to the process of planning the experiment in
such a way that appropriate data can be collected and analyzed
statistically.

13. These are the designs of choice for simultaneous determination of the
effects of several factors & their interactions-
Symbols to denote levels are:
when both the variables are in low concentration.
a- one low variable and second high variable.
b- one high variable and second low variable
ab- both variables are high.
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.
Factorial Design

14. It identifies the chance variation ( present in the process due to
accident) and the assignable variations ( which are due to specific
cause.)
Factorial design are helpful to deduce IVIVC.
IVIVC are helpful to serve a surrogate measure of rate and extent
of oral absorption.
BCS classification is based on solubility and permeability issue of
drugs, which are predictive of IVIVC.
Sound IVIVC omits the need of bioequivalence study. IVIVC is predicted
at three levels-
Level A- point to point relationship of in vitro dissolution and in
vivo performance.
Level B- mean in vitro and mean in vivo dissolution is compared and
co related.
Level C- correlation between amount of drug dissolved at one time and
one pharmacokinetic parameter is deduced.

15. 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

16. 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 areRBF.
Radial basis function (RBF) is a function whose value depends on
the distance fromtheCentre or origin.

17. Artificial Neural Networks -

18. Optimization modeling
 Optimizations in injection molding have already been very popular with
modern industries showing their substantial power in competitiveness
enhancement.
Computer optimizations can be classified into two categories:
1. Noniteration methods (such as gray relational analysis, es, fuzzy logic, and
CBR).
2. Intelligent optimization algorithms (including GA, simulated annealing
algorithm, and particle swarm algorithm).
Recently, surrogate modeling is often employed in optimization, including
response surface method, ANN, and support vector regression.

19. ANN is one of the widely used methods.
 Generally, ANN approaches were applied far and wide in building a process
model for quality control in injection molding.
 In these approaches, some indices of part quality, such as weight, thickness,
warpage, shrinkage, flash, and/or strength, are established as the output of
neural networks while the inputs are either the process conditions (such as
injection speed, holding pressure, holding time, cooling temperature, and
barrel temperature) or the processing variables (such as nozzle pressure, cavity
pressure, and melt temperature), or a combination of them.
 For example, the ANN method was successfully used in predicting the
shrinkage and warpage of injection-molded thin-wall parts.
 Fuzzy logic was applied in the development of an, which can recommend the
quantitative change of molding parameters.

21.  Only some of these methods are reviewed in this section. It should be
noted that numerical simulation trials are sometimes used as data
sources of optimization algorithms.
 DOE techniques, especially the Taguchi method, were widely used to
generate meaningful experimental data and determine optimal process
parameters for injection molding.
 Taguchi parameter design with a minimum number of test runs.
 DOE to minimize the shrinkage and warpage of a cellular housing part.
 Kuhmann and Ehrenstein combined the Taguchi method and the Shainin
method to improve the robustness of the injection molding process.

22. Microemulsion with optimization technique
 Microemulsions are thermodynamically stable, optically transparent
isotropic mixtures of a biphasic o/w system stabilized with
surfactants.
 The diameter of droplets in microemulsion may be in the range of
100Å to1000Å.
 Microemulsions can have characteristic properties such as ultralow
interfacial tension, large interfacial area and capacity to solubilize both
aqueous and oil-soluble compounds.
 If a co-surfactant is used, it may sometimes be represented at a fixed
ratio to surfactant as a single component, and treated as a single“pseudo-
component”.
This includes normal micelles, revers micelles, cores and droplets of
water or oil and for some system like bi-continuous structure neither oil
nor water surrounds each other.

24. Different catagories of drugs solubilized
in Microemulsion-
Category Drugs
Anti-neoplastics Doxorubicin
Peptide Drug Cyclosporin
Sympatholytic Timolol
Local Anaesthetics Lidocaine, Benzocaine, Tetracaine
Steroids Hydrocortisone, Testosterone
Anxiolytics Diazepam
Vitamins Tocopherol, Ascorbic acid
Anti inflammatory Indomethacin
11/21/2014

25. https://hemonc.mhmedical.com/content.aspx?bookid=1810&sectio
nid=124489864
 https://hemonc.mhmedical.com/content.aspx?bookid=1810&secti
onid=124489864 (9th Mar,2019).
SEAN EKINS, D.SC.,”Book of COMPUTER APPLICATIONS IN
PHARMACEUTICAL RESEARCH AND DEVELOPMENT by A
JOHN WILEY & SONS, INC., PUBLICATION,2006,page
no,495-528 and 679-803 .
REFERNCE