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Artificial neural networks in the development
of pharmaceutical formulations
Aleksander Mendyk*, Przemyslaw Dorozynski,
Renata Jachowicz
Department of Pharmaceutical Technology
and Biopharmaceutics, Faculty of Pharmacy,
Jagiellonian University, Collegium Medicum, Cracow, Poland,
Medyczna 9 St., 30-688 Cracow, Poland
* e-mail: mfmendyk@cyf-kr.edu.pl
Introduction to artificial neural networks
The artificial neural networks (ANNs)
are non-linear, information-processing systems designed in
a manner similar to the biological neural structures, which
is expressed in the structural and the functional composition
of ANNs. The latter is based on so-called connectionist model
of neural systems. It assumes that topology and electrophysiology
of synapses (connections) in the brain or other biological
neural systems are the key factors of neural systems’ ability
to process information [1].
One of the several definitions of ANNs
is that they are dispersed knowledge processing systems built
from so-called “nodes” hierarchically organized
into the layers. This definition does not implement the most
important feature of ANNs which is their ability to learn
on the available data. Thus, ANNs are representatives of
Computational Intelligence in contrast to classical Artificial
Intelligence systems, where all the knowledge of the system
must be implemented from the scratch by the programmer.
Typical ANN of the most common Multi
Layer Perceptron type (MLP) is built on four main elements
(Fig. 1):
1. input layer
2. hidden layer(s)
4. output layer
5. connections (weights)
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| Fig.1. Typical structure
of MLP ANN. |
Each layer consists of few “nodes” which
in fact are artificial neurons connected between layers via “weights” – artificial
synapses. The information flow is unidirectional from the
input to the output.
MLP ANN works in two phases:
1. training
2. testing
The training phase is based on the
iterative presentations of available data patterns in order
to teach ANN to perform designated task. Since MLP ANNs are
supervised training systems, they have to be presented with
data on the input and output as well. This allows to adjust
weights values in such a manner that ANN becomes competent
in the designated task. Adjusting of the weights is performed
automatically with use of special algorithm designed for
this purpose. One of the most common training algorithms
for ANNs is back propagation (BP), where the teaching signal
is the difference between current output and the desired
one and propagated backwards from the output layer to the
input layer in order to modify weights values (Fig. 2). The
whole procedure is automatic and once started does not require
any intervention from the user.
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| Fig. 2. Scheme of
the back propagation algorithm. |
According to the connectionist model
of neural systems, ANNs topology is the most important factor
influencing their modeling abilities. The topology of ANNs,
called also architecture, is expressed in terms of number
of layers and nodes in each layer. However, it is not the
nodes themselves but number, signs and values of connections
between the particular node, which encode the knowledge of
the system. Since all the BP procedure is automatic, user
does not have to put any assumptions about model shape a
priori to the system, thus ANNs represent empirical modeling
approach. Automatic training procedure and model identification
by ANNs are the most commonly known advantages of these systems.
Another advantage is their superior ability to identify non-linear
systems. It is because ANNs are usually built on non-linear
activation functions, therefore being non-linear systems
themselves. Next distinguishing feature of ANNs is their
relative ease of dealing with large number of data cases
and features. However, so-called curse of dimensionality
is also applicable to the ANNs, nevertheless it is less pronounced
than for classical statistical systems. Moreover, ANNs are
able to decide on inputs importance, thus providing sensitivity
analysis feature, which is a way to reduce unnecessary inputs.
It improves system performance but also provides knowledge
about analyzed problem derived from ANNs behavior. Therefore,
ANNs are also used as data-mining tools allowing for automated
knowledge extraction.
All the features of ANNs described
above, allow to use them as generic, empirical modeling tools
in vast areas of science and technology:
• economy
• engineering
• chemistry
• neurobiology
• medicine and pharmacy
Although, it is impossible to present
all applications of neural networks, there might be named
major areas of their usage:
• signal processing (noise reduction, compression)
• pattern recognition and features extraction (handwriting, facial recognition,
medical imaging, fraud detection)
• forecasting (financial, medical, weather).
• data-mining
Pharmaceutical applications of ANNs
are still far from being routine, however ANNs are gradually
coming into the focus in different pharmacy areas: pharmacokinetics
[2-7], drug discovery and structure-activity relationships
[8-10], pharmacoeconomics and epidemiology [11-13], in vitro
in vivo correlation [14] and pharmaceutical technology.
Hussain et al. [15] as one of the first
pointed that ANNs could be beneficial in the development
of dosage forms. In the pilot study the proof was provided
that ANNs allowed for accurate prediction of kinetics of
chlorpheniramine maleate release from hydrophilic matrix-loaded
capsules. Neural network inputs consisted of qualitative
and quantitative composition of matrix. Higher accuracy of
ANNs models was demonstrated in comparison to the RSM method.
Today, there are numerous applications
of ANNs in the pharmaceutical technology providing confirmation
of ANNs suitability to this field [16-31]. In general, they
could be divided into the two main classes:
• predictive models
• data-mining systems
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