1
1
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
Dr. Vikas Rana
Punjabi University, Patiala
Paper Coordinator Principal Investigator
Dr. Vijaya Khader
Former Dean, Acharya N G Ranga Agricultural University
Content Writer
Prof. Farhan J Ahmad Jamia Hamdard, New Delhi Paper No. : 08Novel Drug Delivery Systems II
Module No : 23 Ratio Optimization of different components for microemulsion
Development Team
Dr. SushamaTalegaonkar Jamia Hamdard, New Delhi
Content Reviewer
Dr. Vikas Rana
Punjabi University, Patiala Prof. Ashok Kumar Tiwary Punjabi University, Patiala
2
2
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
Description of Module Subject Name Pharmaceutical Sciences
Paper Name Novel Drug Delivery Systems II Module
Name/Title
Ratio Optimization of different components for microemulsion
Module Id 23
Pre-requisites
Objectives To study ratio optimization techniques employed to develop stable microemulsion based drug delivery system.
Keywords Microemulsion, phase diagram, pseudoternary diagram, ternary diagram, QbD, Experimental design approach
Content Reviewer
Dr. Vijaya Khader Dr. MC Varadaraj
Prof A K Tiwarey Punjabi University, Patiala
3
3
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
1.0 Introduction
The mixing of two immiscible liquids (such as water and oil) using emulsifying agent has been the matter of investigation since years. This is because fixing the proportion of two components and varying the third component was a difficult task to optimize emulsion. In addition there are some pharmaceutical mixtures where almost all components are fixed except one vary, but with a narrow range. These techniques most of the times leads to misleading results. However. microemulsions are isotropic systems, which are difficult to prepare then routine mixing techniques, this is probably due to formulation components are to be mixed in a specific process that involve spontaneous interactions among the constituent molecules.
Moreover, microemulsion are thermodynamically stable systems that are broadly categorized into [1] water-in-oil (w/o) microemulsions [2] oil-in-water (o/w) microemulsions, and [3] bicontinuous microemulsions. The literature revealed many attempts for the formulation techniques for microemulsions. These techniques are mainly pseudo ternary diagram construction and titration method. Irrespective of the type of microemulsion systems, microemulsions can be developed easily by mixing the oil phase with surfactant and co-surfactant phase. Aqueous phase is added dropwise and slowly to the mixture of oil-surfactant- cosurfactant part. Due to the thermodynamic stability of microemulsions, they immediately form micelle without the input of external energy into the system. Thus, this module highlights the ratio optimization techniques [i.e phase diagram method and QbD method] employed to develop stable microemulsion system.
2.0 Ratio Optimization techniques: Phase diagram
The main technique followed for the optimization of three components of a microemulsion is ternary phase diagrams. However, the increases in components of microemulsion to more than four are studied by pseudo ternary phase diagrams.
Phase diagrams represents the equilibrium between the different phases formed, when the three components mixed at different proportions, as a function of Temperature, particle size, clarity, etc. Ternary phase diagram is a technique that provides the phase behaviour of the microemulsion system. Ternary phase diagram is a Gibb’s triangle, in which each side of a triangle represents one component of microemulsion with 100-0% concentration in the increment of 10%. For the optimization of more than four components of a microemulsion system, binary mixtures [like oil/drug or surfactant/cosurfactant] are taken one of the ordinates of triangle and pseudo ternary phase diagram is constructed.
2.1 Advantages of Phase diagram
4
4
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
They are quick result oriented, reasonably accurate, economical and precise due to limited number of experimental trial batches. The technique provide the true picture of the phase boundary between the polyphasic and monophasic region, the different types o/w, w/o and bicontinuous microemulsion. However, the main disadvantage is that the monophasic region with in different w/o, o/w and bicontinuous microemulsion region cannot be identified from the phase diagram which is constructed on the basis of titration method without further characterization.
2.1 How to read overall composition of point X on Phase diagram ?
To read overall composition of arbitrary point X marked on a ternary phase diagram, a parallel line technique is useful. For this purpose, draw lines passing through X, and parallel to each of the sides (AB, BC and CD) (Figure -1). A parallel line A’C’ parallel to line AC passing through point X is constructed that intersect Line BC at C’ and AB at A’. Similarly, parallel lines A’B’ and B’C’ parallel to AB and BC, respectively are constructed (Figure 1). The two intersecting points A’ and B’ on line AB divide the line in to three sections. The first section BB’ is the proportion of A in X. The second section B’A’ is the proportion of C and the third section A’A is the proportion of B in X.
2.2 Construction of Phase Diagram by water titration method.
Phase diagram is constructed to obtain the components with their concentration ranges that could possible provide large existence area of microemulsion. Once the microemulsion components are selected, ternary /pseudo phase diagram is constructed to define the extent and nature of the mi croemulsion regions. To produce such diagrams, vials containing oil and surfactant(S)/surfactant- cosurfactant (Smix)/surfactant-drug/Smix-drug of different composition are prepared at room temperature. For each phase diagram, the ratio of oil to the S/Smix was 9:1, 8:2, 7:3, 6:4, 5:5, 4:6, 3:7, 2:8, 1:9 (v/v). Water was added drop wise to each vial under vigorous stirring. After equilibrium, the samples were visually checked and determined as being clear microemulsions. No heating was done during the preparation. Phase diagrams were constructed using software. The area of the monophasic region was used as a tool for the selection of suitable surfactant to cosurfactant ratio for respective drugs.
2.3 Interpretations of Phase diagrams
The Phase diagram provides appropriate concentration range (lower to higher) of oil/surfactant or Smix/water phase that give microemulsion region. The best combination is one that gives maximum microemulsion region. In addition, a factors associated with microemulsions is the presence of various textures like oil droplets in water, water droplets in oil, bicontinuous, lamellar mixtures, etc., which are formed by altering the environmental factors such as salinity, temperature, etc. Such
5
5
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
differences in the structure of microemulsion originate due to the composition of the system. Phase study greatly helps to evaluate such phase differences that exist in the region. One feature of microemulsions is that these structures are interchangeable.
Preparing phase diagram enables us to find aqueous dilutability and range of components of a microemulsion that have a capability to form a monophasic region (Figure 2). The presence of different structures as classified by Winsor (Winsor, 1948) is unique feature of microemulsion. Winsor I (o/w), Winsor II (w/o), Winsor III (bicontinuous or middle phase microemulsion) and Winsor IV systems are formed by altering the curvature of interface with the help of different factors such as salinity, temperature, etc.
Where, Type I revealed surfactant-rich water phase (lower phase) that coexists with surfactant-poor oil phase (Winsor I),
Draw lines passing through X, and parallel to each of the sides.
A”
C”
X
A”
C”
X
B”
A”
6
6
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
A”
C”
X
B”
A”
B” C”
A”
C”
X
B”
A”
B” C”
A component C component
B component
Figure 1: Steps to locate composition of point X on ternary phase diagram.
Type II is surfactant-rich oil phase (the upper phase) that coexists with surfactant- poor water phase (Winsor II),
Type III represents the surfactant rich middle phase which coexists with both water (lower) and oil (upper) surfactant-poor phases (Winsor III) and
Type IV is a single phase homogeneous mixture.
Based upon the composition, these can be of various types viz., water-in-oil (W/O) or oil-in-water (O/W) type or Lamellar or bicontinuous, hexagonal and reverse hexagonal, etc. (Figure 3).
7
7
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion Water
Surfactant/ Smix
Oil
Micelle
WII WIV
WI WIII
W/O microemulsion
O/w microemulsion
Winsor Classification
Figure 2: Phase diagram representing different regions as per winsor classification.
8
8
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
Winsor Classification
Winsor-I
Oil Rich Area
Microemulsion
surfactant-rich water phase
surfactant-poor
oil phase Microemulsion
Water Rich Area
Winsor-II
surfactant-poor Water phase surfactant-rich oil phase
Winsor-III
Oil Rich Area
Microemulsion
Water Rich Area
surfactant-poor oil phase
surfactant-poor Water phase
surfactant-rich middle phase
Winsor-IV
Microemulsion
single phase homogeneous mixture
Figure 3: Winsor classification of different microemulsion
2.4 Ratio Optimization techniques: Quality by design approach
Schwatoz and Connor (1996) says the word optimize usually implies to make as perfect, effective or functional as possible. In a statement given by Doornbos and Haan, (1995) that defined optimization as a way to find those values of controllable independent variables that yield the most desired value of the dependent variable (the objective). Overall, the Optimization has been defined as the systematic approach for achieving the best process and the formulation within the given area of restrictions.The systematic approach ‘‘formulation by design’’ based on the salient principles of Design of experiment and quality by design, provides rational understanding of the plausible interaction(s) among the variables and helps in selecting ‘‘the best’’ formulation with minimal expenditure of time, effort and developmental cost. Quality by Design principles have been utilized in the product development of every industry as recently accepted by the U.S. Food and Drug Administration as a vehicle for the transformation of how drugs are discovered, developed, and commercially manufactured. ICH guidelines Q8 (on Pharmaceutical Development), Q9 (on Quality Risk Management), and Q10 (on Pharmaceutical
9
9
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
Quality System) provide some guidelines for manufacturers to implement Quality by Design into their own operations. The theme of Quality by Design methodology provides complete information on diverse optimization aspects organized in a five- step sequence shown in figure 4.
Variables and Responses in QbD
It is evident that several factors pertaining to formulation ingredients and processing variables are involved in the development of a pharmaceutical formulation. The formulation variables are independent whereas, process variables are directly under the control of the formulator. On the other hand, the dependent variables, are the responses of an experiment or characteristics of the finished drug delivery product. These are usually, a direct function of the independent variables.
The part of variable space with in which an optimization study is conducted i s termed as experimental domain. The term ‘factor’ is used to represent independent variables influencing a response Non influential variables are not normally called factors, except when they fall under the unexplored area of the experimental domain. The
‘levels’ of the factors are the values assigned to the factor. For example, 100, 200 and 300 mg represents the low level, intermediate level and high level, respectively, for a given factor.
During initial studies, it is advised to first study the possible input variables of a system to investigate if they can be justified to be factors. This process is called
“screening” of influential variables. An input variable identified as a factor increases the chance of success, while an input variable that is not a factor has no consequence. An input variable falsely identified as a factor unduly increases the effort and cost. On the contrary, if an input variable is a factor but is not recognized, it leads to an erroneous picture and a true optimum may be missed.
A response is interpreted either as an outcome of an experiment or the set of outcomes of experiments arranged according to some design or a mathematical relationship between the controllable factors and the magnitude of the outcome
The main purpose of QbD is to carry out experimentation to determine the relationship (usually in the form of a mathematical model) between factors acting on the system (the system being a process or a product or both). The generated information is then used for achieving the aims of the project. These designs not only minimize the number of experiments but also optimize the objectives as quickly and as surely as possible with the best possible precision, while respecting various restrictions that have been imposed as a part of the strategy. QbD are based on the principles of randomization (the manner of allocating treatments to the experimental units), replication (the number of units provided for each treatment and error control choosing appropriate type of experimental units and their grouping to increase the
10
10
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
precision). Coding (transformation) is essential in almost all QbD as it allows easier calculation of coefficients, orthogonality of effects, easier calculation of the coefficient variances, depiction of effects and interaction using signs (+) or (-) and apportioning equal significance to each axis.
Figure 4: Five-step sequence for Quality by Design/Formulation by Design proposed by Singh et al., (2011).
Several methods for optimization can be broadly categorized in to two classes.
Step II: Selection of dependent variables
11
11
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
(A) Simultaneous optimization, where the experimentation is completed before the optimization takes place and
(B) Sequential optimization, where the experimentation continues as the optimization study proceeds.
In simultaneous optimization methods, responses are recorded for a set of experiments carried out in a systematic way. The interaction effects are then predicted. An area of space defined with in the upper and lower limits of independent variable depicting the relationship of these variables to the measured response is called ‘response surface modeling’. The response equation derived by factorial design estimate main effects and interactions but this design at two levels has an inherent assumption of linearity. The response equation will be more reliable if it contai ns terms that reflect curvature. However, increasing the number of levels and factors in a full factorial design increases the number of experiments.
Table 1 highlights application of QbD approach in the development of microemulsion.
High-throughput formulation screening (HTFS)
The designs of PCMs require substantial time, manpower, and amounts of candidate compounds for the following reasons:
1) PCMs is a multicomponent system (Lipid, hydrophilic surfactant, lipophilic surfactant, co-surfactant etc.) and the concentration of each component has to screen
2) It is difficult to dispense small amounts of viscous/semisolid surfactants or powdered compounds in preparing the formulations.
These factors make it difficult to design PCM formulations for drug development (particularly at the drug discovery stage) where time, manpower, and amounts of compounds are limited. High-throughput formulation screening is the rapid method to prepare PCMs formulations and evaluate their emulsion properties (microemulsion particle size, phase stability, Turbidity, refractive index) are required in developing a PCMs (Yi et al., 2008; Ratanabanangkoon et al., 2008; Sakai et al., 2012; Kuentz et al., 2012; Caldwell et al., 2014). High-throughput formulation screening can be efficiently and rapidly used to screen formulations and it can be an effective way to design PCMs formulations.
In one of the study, PCM- High-throughput formulation screening system was used to screen the excipients for preparing physically stable emulsion screening. PCM formulations and the most suitable hydrophilic surfactant/lipophilic surfactant combination, which formed the largest PCM area on its corresponding phase diagram, were selected by PCM– High-throughput formulation screening system with minimal manpower (one person) and compound consumption (0.2 mg/formulation).
12
12
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
PCM– High-throughput formulation screening system enabled rapid and efficient selections of PCM formulations and the most suitable hydrophilic surfactant/lipophilic surfactant combination for PCM (Sakai et al., 2009). High-throughput formulation screening system that enabled to rapidly and efficiently select PCM formulations has been developed to investigate the applicability of the High-throughput formulation screening system to PCM designs. A poorly soluble drug (Nilvadipine), oil (Sefsol- 218), 11 hydrophilic surfactants and 10 lipophilic surfactants were used.
Formulations were prepared by choosing High-throughput formulation screening system. A hydrophilic surfactant with the largest number of PCM formulations was selected. In the selected hydrophilic surfactant system, a lipophilic surfactant with the largest number of PCM formulations was selected. Total 2455 formulations were prepared during this system and microemulsion with particle si ze of 33.6 nm was prepared (Sakai et al., 2010).
Selection of active variables/factors and optimization of microemulsion
The ternary phase diagrams provide information on microemulsion region. This is considered as a experimental area or experimental domai n. Some of the examples of different experimental domains are shown in Figure 5 and 6. This region has been selected and is used for identifying factors, which significantly influence the response. In these designs, each factor is presented at 2 levels (upper limit and lower limit) or 3 Levels (upper limit, middle and lower limit), etc . The variables may be either qualitative or quantitative. The lower middle and upper levels may be represented, respectively, as -1, 0 and +1. Multiple linear regressions between factors with their coded variables (+ or -) and response (Y) will give following equation (for 7 variables):
Y = b0 + b1 X1 + b2 X2 + b3 X3 + b4 X4 + b5 X5 + b6 X6 + b7 X7
Where b1, b2…b7 are the coefficients of the factors X1, X2…X7, respectively. b0 is a constant representing the true or theoretical response. To make these calculations easier, now we have availability of computer softwares e.g Design Expert, etc. to do this tedious work.
13
13
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
Figure 5: Square experimental domain ( 22 factorial design with centre point)
Figure6: Spherical experimental domain (Central composite design)
Example: Simplex latex design for the formulation optimization of microemulsion
(Source: http://www.itl.nist.gov/div898/handbook/pri/section5/pri542.htm)
Consider a three component of a microemulsion [(Oil phase (X1) Surfactant (X2) and Cosurfactant (X3) ]. The number of equally spaced levels (already selected from ternary phase diagram) for each component is four i.e for X1 the levels are 0, 0.333, 0.667, 1 and similarly for X2 and X3. The ten experimental trial with their experimental domain as per simplex lattice design is summarized as follows.
Simplex Lattice Design INDEPENDENT VARIABLES
X1 X2 X3
0 0 1
0 0.667 0.333
0 1 0
0.333 0 0.667
0.333 0.333 0.333 0.333 0.6667 0
0.667 0 0.333
DEPENDENT VARIABLES
These variables are Y1, Y2, Y3...
These variables are parameters on the bases of which microemulsion is evaluated. For example particle size, zeta potential, thermodynamic stability, self emulsification time, etc. The multiple linear regression using computer softwares is then employed to achieve correlation between dependent and independent variables.
14
14
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
0.667 0.333 0
1 0 0
EXPERIMENTAL DOMAIN
15
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
Table 1: Some of the Optimization techniques used in the development of microemulsions/nanoemulsions
Sr. No. Formulation Design (LevelFactor)
Independent variables (Factors)
Dependent variables
(Responses) Statistics Infe rence Referenc e
1. Lovastatin
Face- centered cubic design
Nikkol-HCO50 and Lutrol-F127
Globule size, liquefaction time, emulsification time,
MDT, dissolution efficiency and permeation parameter
Design Expert® ver.
8.0.7.1 One- way ANOVA
Systematic quality by design showed improved in vitro/in
vivo performance
Beg et al., 2014
2. Fenofibrate Box-Behnken design (33)
Surfactants/oil, cosurfactant/surfactant
and percentage of cosolvent
Droplet size and cumulative percentage drug
released
ANOVA and multiple correlation
(R2) tests.
Use of HLB combined with response surface
methodology
Bahloul et al.,
2014
3. Fenofibrate
Box–
Behnken design
(33)
Amounts of Labrafil M 1944 CS (lipid), Labrasol (Surfactant),
and Capryol PGMC (Cosurfactant)
Droplet size, cumulative percentage of drug released in 30 min and equilibrium
solubility of fenofibrate
ANOVA and Lack of
fit test
X1:10, X2:77.7 and X3:20 (w/w) Fine agreement existed
between the predicted and observed results.
Oral bioavailability enhanced 3.6- fold
enhanced
Lee et al., 2013
4. Curcumin Box-Behnken design (33)
Gelucire 44/14 (mg);
Labrasol (mg) Vit. E TPGS (mg)
Solubility of Curcumin (mg/g), Droplet size upon
dilution (nm)
Design Expert® 8.0.4
software ANOVA and
Graphical optimization method
helped in finding the design space to get
Pawar and Vavia,
2012
16
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
linear model, quadratic
model
LBOFs with desired properties.
5. Apigenin
Simplex lattice experiment
design
Concentrations of surfactant (S), co-
surfactant (CoS) and oil (O)
Solubility of apigenin and the mean particle
size
NA
60% surfactant (Cremophor®EL), 30%
co-surfactant (Transcutol®HP) and
10% oil (Capryol™
90)
Zhao et al., 2013
6. Fenofibrate Box-Behnken design (33)
amounts
of Capryol PGMC (X1), 7.Tween 20 (X2), and
Transcutol HP (X3).
Y1: Droplet size (nm); Y2:
Equilibrium solubility (mg/g); Y3:
Cumulative percentage of flurbiprofen released
in 15 min (%)
Analysis of Variance and
Lack of Fit Tests
BBD demonstrated its effectiveness in
optimizing the SNEDDS formulation and in understanding the
effects of formulation variables on the
performance of SNEDDS
Marasini et al.,
2012
7. Valsartan Box-Behnken design (33)
Amount of oil (X1), surfactant (X2) and co-
surfactant (X3)
Particle size (nm);
Polydispersity index, Dissolution after
15 min (%);
Equilibrium solubility (mg/g)
ANOVA test, lack of fit test
and correlation coefficient
(R2).
BBD facilitated in the better understanding of
inherent relationship of formulation variables
with the responses
Poudel et al., 2012
8. Carvedilol
Central composite design (CCD)
Cremophor EL as surfactant(X1) and
Transcutol HP as cosurfactant (X2)
Percentage of drug released in 5 min (Q5 min), mean
dissolution time,
Multiple linear regression
analysis
Nano globule size:
46–475 nm.
Singh et al., 2011
17
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion emulsification time,
and droplet size.
9. Estradiol
D-optimal mixture
model
IPM, Distilled water, Ethanol
Size (nm), PI, Viscosity (cps × 103),
Q24h (:g/cm2) Flux[:g/(cm2 h)] Leg
time (h)
Design- Expert software.
ANOVA test, lack of fit test
and correlation coefficient
individual characteristics of the
estradiol- loaded microemulsions were significantly affected by
the formulation factors and their interactions.
Tsai et al., 2011
10.
Betahistine dihydrochlori
de
D-optimal design
Drug loading, concentration of lipophilic surfactant,
concentration of co-surfactant and
wax type
Encapsulation efficiency ), pellet
diameter, and the percentage drug released at 1, 6 and
12 h
Design- Expert® software, Predicted R2,
adjusted R2, PRESS,
19.95% drug loading, 9.95%
Span® 80, 0.25%
Capmul® using glyceryl tripalmitate optimized.
Shamma et al.,
2012
11. Lacidipine D-optimal mixture
oil phase (a mixture of Labrafil®/Capmul®),
the surfactant X2 (a mixture of Cremophor®/Tween®
80) and the co- surfactant (TP)
Droplet size, light absorbance, optical clarity, drug release and emulsification
efficiency
ANOVA
Optimized formulation of Lacidipine showed a significant increase in
dissolution rate
Basaliou s et al., 2010
12. Quercetin Simplex
lattice design
Concentrations of surfactant, cosurfactant
and oil
Solubility of Quercetin and the
mean droplet size
Matlab Software
Oil (7%), surfactant (48%) and cosurfactant
(45%). simplex lattice method can accurately predict the experiment
Gao et al., 2009
18
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
results.
13. Genistein Box-Behnken design (33)
Amount of added oil;
Amount of added surfactant; Amount of added cosurfactant;
Particle size;
Turbidity;
Dissolution after 5 min;
Dissolution after 30 min
ANOVA
Understanding the effect of formulation variables on the rapid
dissolution of drug
Zhu et al. 2009
14. Gemfibrozil
Box–
Behnken experimental
design
Cremophor® EL, Capmul® MCM-C8,
and lemon essential oil
Visual characteristics (emulsification efficacy), turbidity,
droplet size, polydispersity
index and drug release
Design- Expert® version 7.1.6
software (quadratic, linear and two
factors interactions
Cremophor® EL (32.43%), Capmul®
MCM-C8 (29.73) and lemon essential oil
(21.62%)
Villar et al., 2012
15. Lu 28-179 free base
D-Optimal Design
Akoline MCM and medium-
chain triglyceride mixture (2:1, w/w), a mixture of Cremophor EL: Tween 80 (1:1, w/w), and PEG 200
Particle size (nm), Solubility
(mg/g), Weight change
32% RH (%), Weight change 54% RH (%)
Design expert software
package quadratic
model by cross- validated correlation coefficient
mathematical/statistical approach that can be used to obtain a superior
experimental mixture design
Holm et al., 2006
16. CoQ10
Face- centered cubic design
Amount of colloidal silicon dioxide (X1),
magnesium stearate mixing time (X2), and compression
Drug release at 1, 2, 4, 6, 8h, hardness
(kg), flowability index, friability (%)
X-STAT®, polynomial equaction, ANOVA
Optimizion of controlled release
formulation of SMEDDS
Nazzal, and Khan,
2006
19
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion force (X3).
20
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion 3.0 Thermodynamic aspects of microemulsion
The thermodynamic treatment of conventional emulsion preparation has been explained by Reiss (1975), if it is a simple two phase system. For such systems, the free energy of a system will be a direct function of the energy needed to create a new surface between the two phases, hence:
∆G = ƩNiπ~ri2σπ
Where, ∆G = the free energy associated with the process (Ignoring the free energy of mixing), ~r is the interracial energy and N = the number of droplets of radius r. The two phases will tend to separate with time due to reduction in the interracial area between the two phases and hence this reduces the free energy of the system. Conventional emulsifying agents such as surfactants develop a layer around the emulsion particles and hence minimize the interfacial energy and act as a barrier to coalescence. It is interesting to know that the separation of phases is just being delayed, although these emulsions are thermodynamically unstable. Further, self-emulsification takes place with the greater change in entropy for the dispersion than the energy required to increase the surface area of the dispersion. The free energy of conventional emulsion is directly proportional to the energy required to create a new surface between the oil and water phases. The two phases of the conventional emulsion forced to separate w ith time to reduce the interfacial area and hence the free energy of the systems. The routine emulsifying agents that stabilize emulsions resulting from aqueous dilution by forming a single layer around the droplets of emulsion that decreased the interfacial energy and thus forming a barrier to coalescence. On the other hand, emulsification occurs spontaneously with microemulsion because the free energy required to form the microemulsion is either low and positive or negative.
In the case of self-emulsifying systems, the free energy needed to prepare emulsion is very low, positive or actually negative indicated by thermodynamically spontaneous formation. This reflects the high specificity of the proportions of oil and surfactant required for self emulsifica tion. This may also be observed from the development of the liquid crystalline phase that may be expected to be highly dependent on the proportions of surfactant, oil and water. The free energy needed in microemulsion formation is dependent on the extent to which surfactant lowers the surface tension of the water-oil interface and the change in entropy of the system such that:
ΔGm =ΔG1+ΔG2+ΔG3- TΔS
Where, ΔGm is the free energy change for microemulsion formation, ΔG1 denotes free energy change due to increase in total surface area, ΔG2 is the free energy change due to interaction between droplets, ΔG3 = free energy change due to adsorption of surfactant at the oil/water interface from bulk oil or water, ΔS = increase in entropy due to dispersion of oil as droplets.
In other way, we can write it as following:
ΔGm = γΔA-TΔS
21
Pharmaceutical sciences
Novel Drug Delivery Systems II
Ratio Optimization of different components for microemulsion
Where, ΔGm = Free energy of emulsion/microemulsion formulation, γ = Surface tension of the oil- water interface, ΔA = Change in surface area on microemulsification, ΔS = Change in entropy of the system and T = Temperature.
Thermodynamic theory takes into account the entropy of droplets and thermal fluctuations at the interface as important parameters leading to interfacial bending instability (Figure 5). Initially, researchers proposed that a negative value of γ was needed for the microemulsion to be formed. It is now identified that that positive value of γ at all times, it is very small, and is offset by the entropic component. The dominant favorable entropic contribution is the increased dispersion entropy arising from the mixing of one phase in to the other changing itself to large numbers of small droplets.
Thus, a negative free energy of formation is achieved at lowest surface tension that is accompanied by significant favorable entropic change. In such cases, microemulsification is spontaneous and the resulting dispersion is thermodynamically stable. Later it was shown that accumulation of the surfactant and co-surfactant at the interface results in a decrease in chemical potential generating an additional negative free energy change called as dilution effect. This theory explained the role of co- surfactant and salt in a microemulsion formed with ionic surfactants. The co-surfactant produces an additional dilution effect and decreases interfacial tension further.
Figure 5: Role of surface free energy in the development of microe mulsion (Source: Ruckenstein,
& Chi, 1960; Stability of microemulsions).
