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Response surface methodology (RSM) was utilized to investigate the influence of the main emulsion composition; mixture of palm and mediumchain triglyceride (MCT) oil (6%–12%
Parkinson’s disease (PD) is one of the central nervous system (CNS) diseases which is expected to rise with increasing lifespan and population demographics in the future. Various types of drugs are used to treat and relieve the symptoms of the disease including levodopa, ropinirole, apomorphine and selegiline. However, levodopa is the “gold standard” for antiparkinsonian therapy and consequently, nearly every PD patient eventually receives this drug [
At present, 95% of all new potential therapeutics has poor pharmacokinetics and biopharmaceutical properties [
Nanoemulsion systems are potential carriers for efficient delivery of drugs across the bloodbrain barrier. In general, they are biocompatible, biodegradable, physically stable (particularly nanoemulsion and microemulsions) and relatively easy to produce on a large scale using proven technology [
Palm oil is yielded from the fruit of the
Response Surface Methodology (RSM) is a tool which consists of mathematical and statistical techniques which are derived from the fit of empirical models to the obtained data from experiments. In order to explain the studied system, linear or square polynomial functions are utilized. Hence, the experimental conditions can be investigated for the optimization study. RSM has been used broadly to develop and optimize new formulations as it can evaluate all potential factors simultaneously. From the experimental design, the influence of the formulation variables on responses can be determined and the effects of factor interaction can be investigated. The advantages of using RSM are reported to be the reduction in the number of experimental runs needed to evaluate multiple variables and the ability of the statistical tool to identify interactions [
In this work, levodopa was selected as the targeted drug to be loaded in the nanoemulsion as it is widely used to treat and relieve the symptoms of PD. To date, levodopa is only available for the patient in tablet form. Therefore, the aims of this work by using RSM were to formulate an optimal novel palmbased nanoemulsion containing levodopa and to evaluate simultaneously the main effects and interaction effects between the factors including composition of oil, lecithin and Cremophor EL as well as the addition rate on the responses; particle size, zeta potential and polydispersity index.
A preliminary study was carried out to evaluate the levels of independent variables. Based on the resultant data, the lower, middle and upper levels of the four independent variables were determined. Levodopa nanoemulsions showed particle size below 200 nm, narrow size distribution and zeta potential of more or less ±25 mV by restraining the range of oil, lecithin, Cremophor EL composition and addition rate at levels of 6%–12%, 1%–3%, 0.5%–1.5% and 2–20 mL/min, respectively.
The variation in the particle size, polydispersity index and zeta potential were predicted by employing response surface methodology as the responses were the function of the emulsion composition and preparation variables of levodopa loaded nanoemulsions.
In this work, the response surface analysis demonstrated that the secondorder polynomial used for particle size has a higher coefficient of determination (
From
The coefficient significance of the quadratic polynomial models was evaluated by using Analysis of Variance (ANOVA). For any of the terms in the models, a large
The variable which exhibited the largest effect on the zeta potential of the nanoemulsion for the linear term was Cremophor EL composition. The other three variables (oil composition, lecithin composition and addition rate) showed insignificant effects. The quadratic terms of Cremophor EL composition, addition rate and oil composition exhibited significant effects on the zeta potential as well. The interaction between oil composition and addition rate showed a significant effect on the zeta potential compared to the other interactions.
For the polydispersity index, the linear and quadratic term of the addition rate had the most significant effect (
In general, there is a high demand in the pharmaceutical industry for the production of nanoemulsions with a smaller droplet size (<1 μm). Due to the nanosized and kinetically stable characteristics, nanoemulsions are very efficient in encapsulating and/or solubilising the drugs and can successfully deliver them to the targeted part of the body. Direct contact of the drug with the body fluids and tissues can be avoided and the drug is released slowly over a prolonged period of time, which may lead to minimization of side effects [
For the optimization of levodopaloaded nanoemulsions, response surface analyses were plotted in three dimensional model graphs. The response surface plots for particle size, zeta potential and polydispersity index which are used to interpret the interaction effect of the variables are presented in
As shown in
The zeta potential is a stability indicative parameter in colloidal systems like submicron emulsions [
Polydispersity index (PI) characterizes the disperse systems with respect to deviation from the average size, and values up to 0.250 are acceptable for parenteral emulsions [
By increasing the addition rate (11–20mL/min) and composition of Cremophor EL (0.85%–1.5%
By using DesignExpert software, the desirability function was probed to acquire an optimized formulation. An optimum levodopa nanoemulsion is that with smallest particle size, lowest polydispersity index and highest zeta potential. The response surface and contour plot were used to visualize the interaction between the independent variables. By investigating the interaction effect between the independent variables and evaluating the optimization constraints, the optimum levodopa nanoemulsion was prepared with a composition of 7.14% oil, 2.2% lecithin, 1.24% Cremophor EL, and an addition rate of 5.5 mL/min. Based on the optimum formulation, the predicted values of particle size, zeta potential and polydispersity index are 104.04 nm, −29.18 mV and 0.136, respectively.
Experimental and predicted values of the responses were compared to check the adequacy of the response surface equations. The optimized formulation of levodopaloaded nanoemulsion has a particle size of 109.63 nm, zeta potential of −31.06 and polydispersity index of 0.174. As displayed in
For parenteral emulsions, the droplet size and polydispersity index (PI) are important physicochemical parameters since large particle sizes are clinically unacceptable due to emboli formation [
Palm oil was purchased from Sime Darby Jomalina Sdn Bhd, Malaysia. The composition of palm oil is 44.3% palmitic acid, 38.7% oleic acid, 10.5% linoleic acid, 4.6% stearic acid, 1.0% myristic acid, and 0.9% other material which can be considered as impurities. Mediumchain Triglyceride oil was purchased from PharmD Sendirian Berhad, Malaysia. Pure soy bean lecithin with 70% phosphatidylcholine (Lipoid S 75) was purchased from Lipoid GmbH, LudwigshafenGermany. Polyethylene glycol 400 (PEG 400) was purchased from Merck, USA. Cremophor EL, a nonionic surfactant with pH of 6.0 to 8.0 was purchased from SigmaAldrich, France. Glycerol was purchased from JT Baker, USA. Levodopa was purchased from Noveltek Lifescienceco, Limited, Hong Kong, China. Water was deionised by MilliQ filtration system.
Nanoemulsions were formulated using a mixture of palm and MCT oil containing levodopa as dispersed oil phase and MiliQ water as the continuous aqueous phase. Lecithin was dissolved in the oil phase containing a mixture of palm oil with MCT oil (1:1) at 55 °C for 30 min. Levodopa was added to the oil phase and stirred. PEG 400, Cremophor EL and glycerol were dissolved in the deionised water. The preparation was continued by adding the oil phase dropwise to the aqueous solution with continuous stirring using the overhead stirrer (IKA^{®} RW 20 Digital, Nara, Japan) at 300 rpm. The mixture was preemulsified with a high shear homogenizer (Kinematica, Lucerne, Switzerland) at 4000 rpm. The preemulsification step was performed for 5 min and repeated three times. The resultant coarse emulsion was subjected to a high pressure homogenizer (Gea Niro Soavi S.p.a) for 10 cycles at 800 bars.
A fourfactor CCD was utilized to study the effect of oil composition (6%–12%
The optimum condition of the independent variables was ascertained by conducting Response Surface Methodology to predict the variation of material compositions as well as preparation conditions. The optimal composition and conditions of preparation of levodopaloaded nanoemulsions were chosen based on the condition of attaining minimum particle size (
Where
Quantitative comparison between the theoretical prediction and obtained experimental values was made to validate the models. In addition, the percentage of the calculated value was also determined. The predicted error is the difference between the experimental value and the predicted value per predicted value [
Dynamic light scattering was used to analyze the particle size and polydispersity index of the nanoemulsion by using Malvern Nano ZS90, Malvern, UK. The measurement was performed at a scattering angle of 173° at 25 °C. The nanoemulsions were diluted with deionised water to the required concentration. Then the diluted emulsions were placed in the cuvette. The count rate was maintained between 100 and 300 kcps.
Dynamic light scattering was used to measure the zeta potential of the nanoemulsion by using Malvern Nano ZS90, Malvern, UK. The measurement was carried out at a scattering angle of 173° at 25 °C. The nanoemulsions were diluted with deionised water to the required concentration. A folded capillary electrophoresis cell was used to measure the zeta potential. The count rate was maintained between 100 and 300 kcps.
After preparation of the Levodopacontaining nanoemulsion based on the optimized formulation, the nanoemulsion was observed over a period of 6 months at 4 °C or until instability was observed at room temperature, 25 °C. The particle size, polydispersity index and zeta potential were evaluated.
The current study showed that Response Surface Methodology is a beneficial tool for carrying out the optimization study of levodopa nanoemulsion formulations. The variation of the average particle size, zeta potential and polydispersity index were predicted by employing second order polynomial regression. Generally, the linear effect of Cremophor EL had a significant effect (
The authors gratefully acknowledge the financial support from the Ministry of Science, Technology and Innovation of Malaysia (MOSTI) under the Nanotechnology Top Down (NND) project number 5489103 and National Science Fellowship (NSF) grant for the scholar, Syafinaz Zainol.
Response surface plots showing the interaction effects of (
Response surface plot showing the interaction effects of oil composition and addition rate on response R_{2}, zeta potential.
Response surface plot showing the interaction effects of Cremophor EL composition and addition rate on response
Stability of the optimized levodopa nanoemulsion upon storage at 4 °C as a function of particle size and polydispersity index over storage of 6 months.
The experimental data obtained for the three responses.
Experiment Number  

1  89.49  23.10  0.14 
2  101.87  25.40  0.12 
3  100.90  24.10  0.12 
4  145.40  28.93  0.23 
5  101.85  30.42  0.14 
6  101.00  23.10  0.13 
7  93.54  36.80  0.16 
8  104.70  1.19  0.18 
9  107.23  2.76  0.15 
10  133.25  26.70  0.13 
11  103.47  7.67  0.20 
12  96.88  33.80  0.19 
13  119.70  35.45  0.22 
14  106.70  28.70  0.16 
15  186.30  25.45  0.13 
16  91.39  36.45  0.15 
17  100.48  26.35  0.14 
18  107.93  29.65  0.15 
19  145.50  18.30  0.16 
20  99.87  37.00  0.10 
21  108.95  23.05  0.16 
22  117.50  30.90  0.32 
23  113.80  32.00  0.16 
24  84.68  36.10  0.17 
25  122.80  26.33  0.19 
26  105.10  23.37  0.14 
27  88.68  35.88  0.16 
28  99.50  20.23  0.13 
29  99.47  29.43  0.11 
30  87.89  26.10  0.19 
Regression coefficients, adjusted and probability values for the final reduced models.
Regression Coefficient  

A^{0}  102.958  24.802  0.140 
A  −3.83  1.938  −0.004 
B  −10.623  1.2  −0.0013 
C  0.896  5.727  −0.009 
D  7.206  −1.245  0.039 
A^{2}  −0.155  2.36  0.0017 
B^{2}  5.896  −0.609  −0.0008 
C^{2}  0.783  −2.622  0.00051 
D^{2}  0.855  2.564  0.024 
AB  −10.3  0.643  −0.0069 
AC  −2.081  −1.436  −0.0011 
AD  −6.749  3.825  −0.008 
BC  2.559  1.628  0.0039 
BD  2.674  −2.052  −0.0067 
CD  −1.429  −0.885  −0.0157 
0.976  0.909  0.963  
0.922  0.671  0.882  
Regression ( 
<0.0001  0.0284  0.0003 
Lack of Fit ( 
0.0514  0.0711  0.2241 
A_{0} is constant, A, B, C and D are the linear, quadratic and interaction coefficients of the quadratic polynomial coefficient.
Analysis of variance (ANOVA) of regression coefficient of the fitted quadratic equation.
Variables  



 
Main effects  A  3.393  0.0986  1.087  0.3277  0.606  0.4564 
B  78.41  <0.0001  0.417  0.5366  0.201  0.6648  
C  0.558  0.474  9.492  0.0151  9.242  0.014  
D  12.028  0.0071  0.449  0.5218  58.543  <0.0001  
 
Quadratic effects  A^{2}  0.019  0.8929  5.527  0.0466  0.404  0.541 
B^{2}  27.606  0.0005  0.368  0.561  0.102  0.7567  
C^{2}  0.487  0.5027  6.823  0.031  0.034  0.8573  
D^{2}  0.581  0.4655  6.522  0.034  75.862  <0.0001  
 
Interaction effects  AB  49.143  <0.0001  0.24  0.6377  3.611  0.0898 
AC  2.006  0.1904  1.193  0.3065  0.091  0.7703  
AD  21.101  0.0013  8.467  0.0196  5.114  0.0501  
BC  3.034  0.1155  1.535  0.2505  1.155  0.3104  
BD  3.034  0.1021  2.438  0.1571  3.418  0.0975  
CD  3.034  0.3562  0.454  0.5195  18.856  0.0019 
A: Composition of oil; B: Composition of Lecithin, C: Composition of Cremophor EL; D: Addition rate.
The predicted and observed response values for the optimized nanoemulsion.
Response  Predicted  Observed 

104.04  109.63  
−29.18  −31.06  
0.136  0.174 
Composition of oil and aqueous phase formulated nanoemulsion.
Materials  Amount ( 

Palm Oil  5 
MCT Oil  5 
Lecithin  3 
Levodopa  0.9 
 
Polyethylene glycol 400  0.45 
Cremophor EL  0.4 
Glycerol  2 
Deionised water q.s  100 
Levels of independent variables in central composite design (CCD).
Independent variables  Coded Levels  

 
Axial (−α)  Low  Centre  High  Axial (+α)  
Palm oil: MCT oil (1:1) (%, 
3  6  9  12  15 
Lecithin (%, 
0  1  2  3  4 
Cremophor EL (%, 
0  0.5  1  1.5  2 
Addition rate (mL/min)  −7  2  11  20  29 
The matrix of central composite design (CCD).
Experiment Number  Blocks  A  B  C  D 

1 
Block 1  9  2  1  11 
2  Block 1  12  3  0.5  20 
3  Block 1  12  3  1.5  2 
4  Block 1  6  3  0.5  2 
5  Block 1  6  1  0.5  20 
6  Block 1  12  1  1.5  20 
7 
Block 1  9  2  1  11 
8  Block 1  6  3  1.5  20 
9  Block 1  12  1  0.5  2 
10  Block 1  6  1  1.5  2 
11  Block 2  6  3  1.5  2 
12 
Block 2  9  2  1  11 
13  Block 2  12  3  1.5  20 
14  Block 2  6  1  1.5  20 
15  Block 2  12  1  1.5  2 
16  Block 2  12  3  0.5  2 
17 
Block 2  9  2  1  11 
18  Block 2  6  3  0.5  20 
19  Block 2  12  1  0.5  20 
20  Block 2  6  1  0.5  2 
21 
Block 3  9  2  1  11 
22  Block 3  9  2  0  11 
23  Block 3  15  2  1  11 
24  Block 3  9  2  1  −7 
25 
Block 3  9  2  1  11 
26  Block 3  9  4  1  11 
27  Block 3  9  2  2  11 
28  Block 3  9  0  1  11 
29  Block 3  3  2  1  11 
30  Block 3  9  2  1  29 
A: Composition of oil; B: Composition of Lecithin, C: Composition of Cremophor EL; D: Addition rate.
Center point.