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Computer- aided formulation

Ljiljana Djekic, Dragana Vasiljevic, and
Marija Primorac, Department of
Pharmaceutical Technology and

Cosmetology, Faculty of Pharmacy,
University of Belgrade

Abstract: T his chapter introduces the concept of formulation
development assisted by computer applications. Development and
optimization of various types of pharmaceutical emulsions
microemulsions, self-m icroemulsifying systems, and double
emulsions are presented. Illustrative examples are presented to
demonstrate the ability of computer- aided tools to facilitate
formulation development. Various techniques, such as design of
experiments and artifi cial neural networks, are implemented for
optimization of the formulation and/or processing parameters.
Furthermore, some of the critical quality attributes and processing
parameters are optimized simultaneously. The examples presented
should serve as the foundation for the future quality- by-design
development of pharmaceutical emulsion and (self) microemulsion

Key words: p harmaceutical emulsions, microemulsions, self-
microemulsifying drug delivery systems (SMEDDS), double
emulsions, formulation, optimization.

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Computer-aided applications in pharmaceutical technology

2.1 Introduction
This chapter introduces 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
signifi cant for more complex formulation types, such as self- emulsifying
systems or double emulsions. The examples provided illustrate techniques
used to defi ne 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.
Importantly, methods that allow simultaneous optimization of multiple
factors are also presented. The following chapters will provide a deeper
insight into selected in silico methods.

2.2 Application of computer- aided
techniques in development of
pharmaceutical emulsions
Emulsions are disperse systems made of two immiscible liquids. One
liquid is dispersed into the other, in the presence of surface active agents,
such as emulsifi er(s). The two immiscible liquids are usually oil and
water, and the main types of simple emulsions are oil- in-water (o/w) or
water- in-oil (w/o). In the pharmacy, emulsions have a great potential as
vehicles for active ingredients for different routes of administration
(topical, parenteral, oral). However, emulsions are thermodynamically
unstable systems, and different phenomena during storage could occur,
including gravitational separation (creaming/sedimentation), fl occulation,
coalescence, Ostwald ripening, and phase inversion.

Stability and properties of emulsions are infl uenced by different factors.
Formulation and process optimization techniques would be useful for
fi nding the ideal emulsion formulation. The main parameters relating to
the stability, effectiveness, and safety of the pharmaceutical emulsion
should be optimized simultaneously.

More intense application of different i n silico techniques in process
and formulation optimization started at the end of the last century
(Gašperlin et al., 1998, 2000; Prinderre et al., 1998; Simovic et al., 1999).

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Computer-aided formulation development

Prinderre et al. (1998) applied factorial design methods to optimize the
stability and suggested the required hydrophilic lipophilic balance (HLB)
of o/w emulsions prepared with sodium lauryl sulfate as the surfactant.
The independent variables and their levels (low/high) were mixing rate
(rpm) (500/900), homogenization (no/yes), and mixing time (min)
(10/20). Dependent variables were the average size of the droplets, the
emulsion viscosity, and the conductivity. Experimental design determined
the required HLB with good approximation in fi ve runs for the average
diameter and viscosity studies, while the conductivity study needed at
least eight runs.

Similarly, Simovic et al. (1999) investigated the infl uence of the
processing variables on performance of o/w emulsion gels stabilized by a
polymeric emulsifi er (Pemulen® TR-2NF). A two-f actor three- level
experimental design at two sets was applied: using a laboratory mixer
and a disperser. Independent variables were mixing speed and time,
whereas dependent variables were millimeters of oil phase separated after
centrifugation at 3500 rpm in a laboratory centrifuge, and viscosity at
shear rate of 180 l/s. The responses were fi tted into a second- order model
by means of multiple regression analysis. The authors (Simovic et al.,
1999) could defi ne the most favorable conditions for preparing stable
o/w emulsions, using the laboratory mixer, with a mixing speed at
1500 rpm and mixing time of 20 minutes.

Rahali et al. (2009) found the optimal preservative combination and
concentration for preparing topical emulsions by using a D-optimal
experimental design (mixture design). In this study, three preservatives
were tested, benzoic acid, sorbic acid, and benzyl alcohol. The preservative
effects were evaluated using the antimicrobial preservative effi cacy test
(challenge test) of the European Pharmacopeia (EP). The results of this
study were analyzed with the help of Design Expert® software. The
authors (Rahali et al., 2009) were able to formulate topical emulsions in
accordance with the requirements of the EP.

Simultaneous study of the infl uences of different factors for emulsion
systems is diffi cult, due to the problems of complicated nonlinear
correlations. The artifi cial neural networks (ANN) technique seems to
provide a useful tool for solving these problems. Gašperlin et al. (1998)
investigated the infl uence of different ratios of individual components on
the viscoelastic behavior of semisolid lipophilic emulsion systems using
this technique. The creams were prepared according to a preliminary
experimental design (mixture design). ANN involved 3 input, 12 hidden,
and 9 output neurons. The input neurons were the contents of the
particular emulsion components (silicone surfactant Abil® WE 09,

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Computer-aided applications in pharmaceutical technology

purifi ed water, white petrolatum) and the output neurons were the
measured values of dynamic rheological parameter, tan δ , at different
time intervals. Tan δ could be predicted using a neural network model
and the results have shown a great degree of reliability. Similarly, a neural
network model could be used for prediction of the complex dynamic
viscosity of these semi- solid w/o emulsions (Gašperlin et al., 2000).

Other authors (Kumar et al., 2011) applied the ANN model to optimize
the fatty alcohol concentration in the formulation of an o/w emulsion.
Emulsion was composed of purifi ed water, liquid paraffi n, sodium lauryl
sulfate, and fatty lauryl alcohol. Predictions from ANNs are accurate and
allow quantifi cation of the relative importance of the inputs. Furthermore,
by varying the network topology and parameters, it was possible to
obtain output values (zeta potential, viscosity, conductivity, and particle
size) that were close to experimental values. The ANN model predicted
results and the actual output values were compared. An R2 value of 0.84
for the model suggested adequate modeling, which is supported by the
correlation coeffi cient value of 0.9445.

Multiple (or double) emulsions are even more complex dispersion
systems, also known as ‘emulsions of emulsions’. The most common
multiple emulsions are of the w/o/w type, although, for some specifi c
applications, o/w/o emulsions can also be prepared. Usually, the multiple
emulsions have been produced in a two-s tep-production process: the fi rst
one for production of the primary emulsion, and the second for production
of the multiple emulsions.

In w/o/w emulsions, oil globules, containing small droplets of water,
are dispersed in an aqueous continuous phase. The advantages of these
types of emulsion systems are relatively high entrapment capacity for
hydrophilic compounds, protection of the encapsulated substances
towards degradation, the ability to introduce incompatible substances
into the same system, and sustained release of active substance. These
characteristics make them potentially interesting for application in
pharmaceutics and cosmetics. However, in practice, signifi cant problems
may arise because of their thermodynamic instability and strong tendency
for coalescence, fl occulation, and creaming. The stability of w/o/w
emulsion may be affected by a number of factors, including the method
of preparation, osmotic balance between the internal and external water
phase, phase volumes ratio, type, and concentration of the emulsifi ers.

In the work of Onuki et al. (2004), formulation optimization of the
w/o/w multiple emulsion incorporating insulin was performed, based on
statistical methods such as the orthogonal experimental design and the
response surface evaluation. As model formulations, 16 types of emulsions

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Computer-aided formulation development

were prepared according to the orthogonal experimental design. To
optimize the formulation, the infl uences of fi ve factors on characteristics
of the emulsion were evaluated fi rst. Inputs were amounts of gelatin,
insulin, oleic acid, volume ratio of the outer water phase, and agitation
time of the second emulsifi cation process. Outputs were inner droplet
size, viscosity, stability, and pharmacological effect. Based on Analysis of
Variance (ANOVA), it was concluded that the most predominant
contribution of all causal factors was the volume ratio of the outer water
phase. As for the optimization study, the optimum formulation with
respect to pharmacological hypoglycemic effect in rats and stability of
emulsion was estimated using a simultaneous optimization technique, in
which a multivariate spline interpolation (MSI) was incorporated. A two-
factor composite second-o rder spherical experimental design was used to
select model formulation. The data measured for the model formulations
were analyzed by a computer program (dataNESIA, Yamatake, Tokyo,
Japan). The authors (Onuki et al., 2004) reported that the optimum
formulation had pharmacological activity and stability as high as a
pharmaceutical formulation.

The second emulsifi cation step could be critical for the production of
multiple emulsions. For this reason, Lindenstruth and Müller (2004)
examined the second emulsifi cation step in the formulation of w/o/w
multiple emulsions. Unvaried primary w/o emulsion, with diclofenac
sodium as the active ingredient in the inner water phase, was used during
the investigation. In the second step, a central composite design was used,
and the process parameters pressure and temperature were varied. The
multiple droplet size and the encapsulation rate of totally 10 emulsions
were determined after 1, 3, and 5 homogenization cycles, to investigate
the infl uence of process parameters. For statistical analysis, the Statistica®
program was used. It was shown that the pressure and temperature, as
process parameters in the second step, infl uenced the size of multiple
droplets in the w/o/w multiple emulsion. Further experiments with
different w/o emulsions resulted in w/o/w multiple emulsions with
different encapsulation rates of diclofenac sodium.

In the work of Wei et al. (2008), formulation optimization of emulsifi ers
for preparing w/o/w multiple emulsions was performed in respect of
stability by using the ANN technique. The emulsifi ers used were sorbitan
monooleate (Span 80) and polysorbate 80 (Tween 80). The stability of
multiple emulsions was expressed by the percentage of reserved emulsion
volume of freshly prepared sample after centrifugation. Individual
properties of multiple emulsions, such as droplet size, phase angle δ ,
viscosity of the primary, and the multiple emulsions were also considered.

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A back propagation (BP) network was well trained by experimental data
pairs and then used as an interpolating function to estimate the stability
of emulsions of different formulations. It was found that multiple w/o/w
emulsions could be prepared by using mixtures of Span 80 and Tween 80
with different mass ratio as both lipophilic and hydrophilic emulsifi ers.
The stability is sensitive to the mixed HLB numbers and concentration of
the emulsifi ers. By feeding the ANN with 39 pairs of experimental data,
the ANN was well trained and could predict the infl uences of several
formulation variables on the immediate emulsions stability. The validation
test indicated that the immediate stability of the emulsions predicted by
the ANN was in good agreement with measured values. The ANN
therefore could be a powerful tool for rapid screening for emulsifi er

Different optimization techniques, for example experimental design
using response surface modeling or ANN method, could be used to
determine the optimal cosmetic formulations, such as depilatory cream
or an o/w emulsion vehicle for a permanent hair dye (Moulai Mostefa
et al., 2006; Balfagon et al., 2010).

2.3 Application of computer- aided
techniques in development of
microemulsion drug carriers
Microemulsions are thermodynamically stable and optically isotropic
transparent colloidal systems consisting of water, oil, and surfactant.
Although they are clear, low viscous liquids, the different types of
microstructures are identifi ed (i.e. w/o, o/w, and bicontinuous), all
organized on the level below 100 nm. The microstructure of
microemulsions is determined by physicochemical properties and
concentrations of the constituents. Such unique systems, as well as their
water- free preconcentrates, so- called self- microemulsifying oil/surfactant
mixtures, are of increasing interest as potential drug delivery vehicles
with long- term stability, considerable capacity for drug solubilization,
and great potential for bioavailability enhancement (Fanun, 2009).
Development of such carriers requires a complex strategy balancing all
relevant aspects. In systems consisting of water, oil, and tenside(s), a
diverse range of colloidal systems and coarse dispersions may form (e.g.
emulsions, microemulsions, micelles, lyotropic liquid crystals), depending
on physicochemical properties and quantitative ratios of constituents and

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Computer-aided formulation development

temperature. Microemulsions and self- microemulsifying drug delivery
systems (SMEDDS) form only in well balanced mixtures of the selected
excipients and within the specifi c concentration ranges of the constituents
at given temperatures and pressures (i.e. the microemulsion area). The
analysis of the infl uence of formulation variables on the area of
microemulsion systems is usually performed within the phase behavior
studies. Pharmaceutically applicable microemulsions consist of fi ve
(surfactant, cosurfactant, oil, water, and drug) or more components.
Complete phase behavior differentiation in such multicomponent
mixtures requires a large number of experiments (Alany et al., 2009;
Friberg and Aikens, 2009). Furthermore, characterization of a
microstructure is a diffi cult task, due to its dynamic character as well as
nanoscale organization (Tondre, 2005).

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-f orming systems
(Richardson et al., 1996, 1997; Alany et al., 1999; Agatonovic-Kustrin
and Alany, 2001; Agatonovic-Kustrin et al., 2003; Mendyk and
Jachowicz, 2007; Djekic et al., 2008). The pioneer studies of Richardson
et al. (1996, 1997) demonstrated the use of ANNs to identify the physico-
chemical properties of the cosurfactant with relevance for microemulsion
formation in the four-c omponent system lecithin, (surfactant)/isopropyl
myristate (oil)/triple distilled water/cosurfactant. The different types of
cosurfactants (i.e. short- and medium- chain alcohols, amines, acids, and
ethylene glycol monoalkyl ethers) were employed. The BP feed-f orward
algorithm of learning and four computed cosurfactant molecule properties
(molecular volume (v), areas for its head group (a ψ ) and hydrophobe (aφ ),
and computed octanol/water logP value), were selected. The output was
presence (+1) or absence (−1) of microemulsion formation in a particular

The data required for ANN training and testing were extracted from
the pseudo-t ernary diagrams generated previously by Aboofazeli et al.
(1994), together with the additional data from four pseudo ternary phase
diagrams constructed at a fi xed weight ratio of surfactant-t o-cosurfactant
1:1. The trained ANN (the in- house software YANNI) with the
fi nal architecture involving 6 input neurons, a single hidden layer of
14 neurons, and 1 output neuron, was shown to be highly successful in
predicting phase behavior for the investigated systems from the computed
values of v, a ψ , a φ , and logP, achieving mean success rates of 96.7 and
91.6% for training and test data, respectively. These investigations
pointed to the potential of the trained ANN to screen out cosurfactants

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Computer-aided applications in pharmaceutical technology

considering only the molecule features and gave an idea for more
general networks, trained with data on systems involving other oils,
surfactants, and surfactant- to-cosurfactant ratios. In a related study,
Agatonovic-Kustrin and Alany (2001) estimated the infl uence of
the cosurfactant on phase behavior of the fi ve- component systems
(ethyl oleate (oil)/a mixture of sorbitan monolaurate and polyoxyethylene
20 sorbitan monooleate (surfactant+cosurfactant)/deionized water/
n- a lcohols (1-propanol, 1-butanol, 1-hexanol, and 1-octanol) or
1,2-alkanediols (1,2-propandiol, 1,2-pentanediol, 1,2-hexanediol,
and 1,2-octanediol)(cosurfactant)). A supervised network with a
multilayer perceptron (MLP) architecture with a BP learning rule (Neural
Networks®, StatSoft Inc, Tulsa, USA), was used to correlate phase
behavior of the investigated systems with cosurfactant descriptors
(inputs), which were preselected by a genetic algorithm (GA) (Pallas 2.1,
Compu Drug Int., San Francisco, USA and ChemSketch 3.5 freeware,
ACD Inc., Toronto, Canada).

The most successful MLP ANN model, with two hidden layers
comprising 14 and 9 neurons, predicted the phase behavior for a new set
of cosurfactants with 82.2% accuracy for the microemulsion region.
Alany et al. (1999) presented the fi rst report describing the utility of
ANNs in predicting phase behavior of the four component system
(ethyl oleate (oil)/sorbitan monolaurate (primary surfactant)/
polyoxyethylene 20 sorbitan monooleate (secondary surfactant)/
deionized water) regarding the components ratio. The BP training
algorithm was selected. The training and testing data were extracted
from several pseudo- ternary triangles, which represented the cuts through
the phase tetrahedron. The inputs were percentages of oil and water and
HLB values of the surfactants blend. The outputs were the corresponding
systems (o/w emulsion, w/o emulsion, microemulsion, and liquid
crystals). The trained MLP (ANNs simulator software, NNMODEL
Version 1.404, Neural Fusion), with 1 hidden neuron, was tested on
validation data and an accuracy of 85.2 to 92.9% was estimated,
depending on the output critical values used for the classifi cation. The
low error rate demonstrated the success in employing ANNs to predict
phase behavior of quaternary systems.

The fundamental goal in SMEDDS development is to optimize the
surfactant/cosurfactant/oil mixture, in order to achieve suffi cient drug
solubility and infi nite dilutability with water phase. However, there is a
risk of disturbing the thermodynamic stability on dilution with the
subsequent drug precipitation (Kyatanwar et al., 2010). The study of
Mendyik and Jachowicz (2006) describes the development of the system

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Computer-aided formulation development

of 11 ANN models suitable for further prediction of phase behavior of
microemulsion- forming systems analyzing only the properties and
contents of the components. The ANN models were developed by
digitalization of phase diagrams of 118 systems, published in the relevant
literature. The inputs were the concentrations of the constituents
(surfactant, cosurfactant, oil, water) and their molecule features (ionic
strength of the water phase, HLB value of the surfactant, density of the
oil, etc.). The outputs confi rmed whether the microemulsion is present or
not for a particular quantitative and qualitative composition. The ANN-
based optimization technique was extended with Neuro-Fuzzy Modeling
(NFM). There have been varied numbers of hidden layers of the MLP
network (from 1 to 5) and up to 100 nodes in neuro-f uzzy systems, by
using neural networks simulator Nets2004 written by the authors. After
the architecture search step, 10 of the best ANNs were selected to become
the expert committee . In addition, a so- called second- order ANN was
employed to combine outputs of the above-m entioned ANNs and to
produce the fi nal decision of the system. The developed expert system
was applied in selection of the surfactant/cosurfactant/water mixtures
(microemulsion preconcentrates) with the high capacity for water
solubilization. The performance of the system was estimated on 77% of
properly classifi ed data records.

Novel investigations pointed to the signifi cance of biocompatible
SMEDDS with the reduced risk for drug precipitation on water dilution,
stabilized with the nonionic surfactants of polyglycolized glyceride types
such as Labrasol® and Labrafi l®, Gelucire®, Cremophor®, and Plurol®
groups (Fanun, 2011). Djekic et al., (2011) evaluated the infl uence of the
cosurfactant type, the relative content of the cosurfactant (expressed as a
surfactant- to-cosurfactant mass ratio (Km )), and the oil phase content
(expressed as an oil-t o-surfactant/cosurfactant mixture mass ratio (O/
SCoS)) on the water solubilization capacity (Wm ax , %w/w) of Labrasol®
(PEG-8 caprylic/capric glycerides)/cosurfactant/isopropyl myristate/water,
by application of ANN modeling. The cosurfactants were commercial
nonionic tensides: Plurol® Isostearique (polyglyceryl-6 isostearate);
Cremophor® RH40 (PEG-40 hydrogenated castor); Solubilisant gamma®
2421 ((Octoxynol-12 (and) Polysorbate 20)); and Solubilisant gamma®
2429 (Octoxynol-12 (and) Polysorbate 20 (and) PEG-40 Hydrogenated
castor oil). The Km values were 4:6, 5:5, and 6:4. The SCoS/O values were
varied from 1:9 to 9:1. The water solubilization limit was detected by
titrating the O/SCoS mixtures with water. The results were used to generate
the inputs and output for ANN training. The inputs were Km and SCoS/O
values. The output was the water solubilization limit (Wm ax , % w/w). The

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Computer-aided applications in pharmaceutical technology

appropriate selection of network architecture was the milestone in
utilization of ANNs. A Generalized Regression Neural Network (GRNN),
MLP, and Radial Basis Function (RBF) ANN architectures (Statistical
Neural Networks, StatSoft, Inc., Tulsa, OK, USA) were used throughout
the study. In the presence of the Plurol® Isostearique cosurfactant, a feed-
forward GRNN comprising four layers (the fi rst layer had 2 input units,
the second layer had 27 hidden units, the third layer had 2 units, and the
fourth layer had 1 output unit), was characterized by the generalization
ability of 99.1%. When Cremophor® RH40 was used as a cosurfactant, a
MLP network with 4 layers was generated with the prediction ability of
92% for training data set, 93% for validation data set, and 92% for test
data set. In systems with Solubilisant gamma® 2421 and Solubilisant
gamma® 2429, satisfactory results were achieved with the RBF network.
The ANN models provided a deeper understanding and prediction of a
water solubilization limit for any combination of surfactant concentration
and oil concentration in their mixture, within the investigated range.
Learned networks were used for modeling, simulation, and optimization
of the microemulsion area boundary by testing experimental points in
experimental fi elds; searching for the optimal solutions; and presenting
response surfaces (or contour plots). Response surfaces presenting the
infl uence of the surfactant concentration in the surfactant/cosurfactant
mixture and the oil concentration in the mixture with tensides on the
water solubilization limit, pointed to the maximum performance in the
presence of Cremophor® RH40 at high SCoS/O ratios (SCoS/O >7:3)
within the investigated Km range. Such mixtures would be the most
promising regarding the self-m icroemulsifi cation phenomenon. The
combination of the titration method for phase behavior data collection
with i n silico data modeling, demonstrated in this study, is a particularly
useful approach in development of SMEDDS, which allows to follow
dilution of self-m icroemulsifying concentrate with the aqueous phase in a
continuous manner.

The study of Podlogar et al. (2008) demonstrated that ANN modeling
could be effective in minimizing the experimental efforts characterizing
complex structural features of microemulsions. Two evolutionary ANNs
(Yao, 1991) have been constructed by introducing GA to the feed- forward
ANN, one being able to predict the type of microemulsion from its
composition and the second to predict the type of microemulsion from
the differential scanning calorimetry (DSC) curve. The components of the
microemulsion- forming system were isopropyl myristate (oil),
polyoxyethylene (20) sorbitan monopalmitate (Tween® 40) (surfactant),
glyceryl caprylate (Imwitor® 308) (cosurfactant), and twice distilled

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Computer-aided formulation development

water. The type of microemulsion microstructure (i.e. o/w, bi- continuous,
w/o) was differentiated by measuring the freezing peak of the water in
DSC thermograms. The data pool used to train both ANNs included the
composition of 170 microemulsion samples and DSC curves. To
determine the type of microemulsion from its composition, a feed-
forward network was programmed, with the fi nal architecture involving
4 input neurons, a single hidden layer of 12 neurons, and 5 output
neurons. To determine the type of microemulsion from its DSC curve, a
second feed-f orward ANN with 1 hidden layer was constructed,
containing 100 input neurons, a single layer of 5 hidden neurons, and
5 output neurons. Both ANNs showed an accuracy of 90% in predicting
the type of microemulsion from the previously untested compositions.

2.4 Conclusion
A nonlinear mathematical approach comprising experimental design,
neural networks, GAs, and/or neuro-f uzzy logic represents a promising
tool for i n silico modeling of formulation procedures in development of
emulsion and (micro)emulsion drug carriers. Although i n silico
formulation is not a substitute for laboratory experiments, the results of
current efforts clearly demonstrated a potential to shorten the time
necessary to fi nd optimal quantitative and qualitative composition. Also,
this strategy is capable of generating new potential (micro)emulsion
forming systems. The upcoming step would be application of such
methodology as a tool to correlate composition/structure characteristics
with the biopharmaceutical profi les of (micro)emulsion drug delivery
systems, which is encouraging for their future development.

2.5 References
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‘ Investigations into the formation and characterization of phospholipid
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Agatonovic-Kustrin , S . and A lany , R .G. (2 001 ) ‘ Role of genetic algorithms and
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Computer-aided applications in pharmaceutical technology

Agatonovic-Kustrin , S. , Glass, B.D. , Wisch , M.H., and A lany, R.G. ( 2003 )
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Alany , R .G. , A gatonovic-Kustrin , S . , R ades, T ., and Tucker , I .G. (1 999 ) ‘ Use of
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Alany , R .G. , E l Maghraby, G.M.M. , K rauel-Goellner, K . , and G raf, A . (2 009 )
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Gašperlin , M. , Tušar, L. , Tušar, M. , Kristl , J. , and Šmid-Korbar , J. ( 1998 )
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Kumar , K.J. , Panpalia , G.M. , and P riyadarshini, S. ( 2011 ) ‘A pplication of
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