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Preparation and optimization of haloperidol loaded solid lipid nanoparticles by Box–Behnken design
j o u r n a l o f p h a r m a c y r e s e a r c h 7 ( 2 0 1 3 ) 5 5 1 e5 5 8
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Original Article
Preparation and optimization of haloperidol loaded solid lipid nanoparticles by BoxeBehnken design Mohd Yasir a,b,*, U.V.S. Sara c a
Department of Pharmacy, Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India Department of Pharmaceutics, ITS Pharmacy College, Ghaziabad 201206, Uttar Pradesh, India c College of Pharmaceutical Sciences, RKGIT, Ghaziabad, 201201, Uttar Pradesh, India b
article info
abstract
Article history:
Background: The aim of this investigation was to design and evaluate solid lipid nano-
Received 25 March 2013
particles (SLNs) of haloperidol.
Accepted 28 May 2013
Method: A modified solvent emulsification diffusion technique was used to produce Halo-
Available online 18 July 2013
peridol loaded solid lipid nanoparticles. A 3-factor, 3-level BoxeBehnken design was applied to study the effect of independent variables (factors) i.e. drug to lipid ratio (A),
Keywords:
surfactant concentration (B) and stirring speed (C) on dependent variables (responses) i.e.
BoxeBehnken design
particles size (Y1), entrapment efficiency (Y2), and drug loading (Y3). 3-D surface response
Haloperidol
plots were drawn and optimized formulation was selected based on desirability factor.
Solid lipid nanoparticles
Results: The results of optimized formulation showed average particle size of 115.1 nm,
Solvent emulsification diffusion
entrapment efficiency of 71.56%, and drug loading of 26.35%. Morphologically, particles
technique etc
were spherical in shape with smooth surfaces and uniform distribution. Conclusion: Thus, the current study can be useful for the successful design, development and optimization of SLNs for haloperidol using a 3-factor, 3-level BoxeBehnken design. Copyright ª 2013, JPR Solutions; Published by Reed Elsevier India Pvt. Ltd. All rights reserved.
1.
Introduction
Haloperidol is a dopamine inverse agonist of the typical antipsychotic class of medications. It is a butyrophenone derivative. Chemically, it is 4-[4-(4-chlorophenyl)-4-hydroxy-1piperidyl]-1-(4-fluorophenyl)-butan-1-one. Its mechanism of action is mediated by blockade of D2 dopamine receptors in brain.1 Though haloperidol is absorbed after oral dosing, there is a first pass metabolism leading to a reduced bioavailability of the drug (50% oral tablets & liquid). After oral drug delivery, the drug first gets distributed systemically and a
small portion is able to reach the brain through the blood due to first past effect. Some side effects are associated with oral administration. SLNs were introduced in 1991, offer attractive drug delivery systems with lower toxicity, compared to polymeric systems that combine the advantages of polymeric nanoparticles, fat emulsions, and liposomes. They are used for both hydrophilic and lipophilic drugs trapped in biocompatible lipid core and surfactant at the outer shell. They offer good tolerability & biodegradability, lack of acute and chronic toxicity of the carrier, scalability to large scale priduction.2 Moreover, the production
* Corresponding author. Department of Pharmaceutics, ITS Pharmacy College, Ghaziabad 201206, Uttar Pradesh, India. Tel.: þ91 9761131206; fax: þ91 01232 225380. E-mail address: [email protected] (M. Yasir). 0974-6943/$ e see front matter Copyright ª 2013, JPR Solutions; Published by Reed Elsevier India Pvt. Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jopr.2013.05.022
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process can be modulated for desired drug release and protection of entrapped drug against chemical/enzymatic degradation. Therefore, they are considered to be, better alternative than liposomes, microemulsions, nanoemulsions, polymeric nanoparticles, self emulsifying drug delivery systems.3 In the present research work, haloperidol loaded solid lipid nanoparticles were prepared by modified solvent emulsification diffusion technique. The formulation was optimized by using 3-factor, 3-level BoxeBehnken design. The optimized formulation was evaluated for various parameters like particle size analysis, Polydispersity index, zeta potential, entrapment efficiency, drug loading capacity, SEM analysis etc. To optimize the production of these SLNs, a statistically experimental design methodology was employed properly. After selecting the critical variables affecting particle size, entrapment efficiency, and drug loading, the response surface methodology of the BoxeBehnken design (version 8.0.7.1, Stat-Ease, Inc., Minneapolis, Minnesota, USA), using a threefactor, three-level, was employed to optimize the level of particle size, entrapment efficiency, and drug loading variables. The BoxeBehnken design is one of the most efficient designs of response surface experimental methodology to study the effect of formulation components on responses for exploring quadratic response surfaces and the second-order polynomial model.4
2.
Materials and methods
2.1.
Materials
Haloperidol was received as a gift sample from Vamsi Labs Ltd. Solapur, Maharashtra, (India). lipid was purchased from Loba Chemie, Mumbai (India). All other solvents and chemicals used were of analytical grade. Water was distilled and filtered before use through a 0.22 mm nylon filter.
2.2.
Preparation of SLNs
In a preliminary laboratory study, various factors like drug to lipid ratio (1:2e1:4), surfactant concentration (Tween 80, 1e2% w/v), chloroform: ethanol ratio (1:1, 2.5% v/v) as the solvent of the drug and lipids, homogenization time (30 min), stirring time (2 h) & stirring speed (2000e3000 rpm), sonication time 5 min were fixed and their effect on particle size, entrapment efficiency were determined. The design matrix was built by the statistical software package, Design-Expert (version 8.0.7.1, Stat-Ease, Inc., Minneapolis, Minnesota, USA), and Table 1 shows the factors and their respective levels. In this study, all of the experiments were performed in triplicate and the averages were considered as the response. Haloperidol loaded SLNs were prepared by a slight modification of the previously reported solvent emulsification diffusion technique.5 Accurately weighed lipid (100 mg) was dissolved in a 2.5 ml (2.5% v/v) mixture of ethanol and chloroform (1:1) as the internal oil phase. Drug (50 mg, drug to lipid ratio 1:2) was dispersed in the above solution. This organic phase was then poured drop by drop into a homogenizer tube containing 22.5 ml of 1.625% (w/v) aqueous solution of Tween 80, as the
Table 1 e Variables and their levels in BoxeBehnken design. Variables Independent variables A ¼ Drug to lipid ratio B ¼ Surfactant (%) C ¼ Stirring speed (RPM)
Level of variables Low (1) 1:2 1 2000
Dependent variables Y1 ¼ Particles size Y2 ¼ % Entrapment efficiency Y3 ¼ % Drug loading
Medium (0) 1:3 1.5 2500
High (þ1) 1:4 2 3000 Goals Minimize Maximize Maximize
external aqueous phase and homogenized for 30 min at 3000 rpm (Remi Instruments Pvt. Ltd, India) to form primary emulsion (o/w). The above emulsion was poured into 75 ml of ice-cold water (2e3 C) containing 1.625% (w/v) surfactant and stirred to extract the organic solvent into the continuous phase and for proper solidification of SLNs. The stirring was continued for 2.5 h at 3000 rpm to get SLNs. The SLNs dispersion was sonicated for 5 min (1 cycle, 100% amplitude, Bandelin sonoplus, Germany) to get SLNs dispersion of uniform size. The dispersion was then centrifuged at 18,000 rpm for 20 min (Remi Instruments Pvt, Ltd, India) to separate the solid lipid material containing the drug. This was then redispersed in 1.625% (w/v) of an aqueous surfactant mixture of Tween 80 and sonicated for 5 min to obtain the SLNs.
2.3.
Experimental design
According to BoxeBehnken design, a total number of 17 experiments, including 12 factorial points at the midpoints of the edges of the process space and five replicates at the centre point for estimation of pure error sum of squares, were performed to choose the best model among the linear, two-factor interaction model and quadratic model due to the analysis of variance (ANOVA) F-value.6 The obtained P-value less than 0.05 is considered statistically significant. From the preliminary screening test, it was found that the drug to lipid ratio (A), Surfactant concentration (B), Stirring speed (C ), had a significant effect on the particle size (Y1), entrapment efficiency (Y2) and drug loading (Y3) of SLNs. Therefore, by fixing the homogenization time (30 min), stirring time (2 h) and sonication time (5 min), selected variables (A), (B), and (C ) were studied at three different levels as low (1), medium (0), and high (þ1). The coded (factors) and actual values (responses) of the variables are given in Table 2. The following second-order polynomial equation can be used to draw conclusion after considering the magnitude of coefficient and mathematical sign it carries i.e. positive or negative. Y ¼ b0 þ b1 A þ b2 B þ b3 C þ b11 A2 þ b22 B2 þ b33 C2 þ b12 AB þ b13 AC þ b23 BC Where Y was predicted response(s), b0 was an intercept, b1, b2, and b3 were linear coefficients, b11, b22, and b33 were squared coefficients and quadratic term, b12, b13, and b23 were interaction coefficients, and A, B, and C were independent variables, which were selected based on the results from a
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Table 2 e Observed responses for 17 runs of haloperidol SLNs according to BoxeBehnken design (n [ 3, mean ± SD). Formulation code H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17
Drug to lipid ratio
Surfactant conc. (%)
Stirring speed (rpm)
Particle size (nm)
Entrapment efficiency (%)
Drug loading (%)
1:2 1:4 1:2 1:4 1:2 1:4 1:2 1:4 1:3 1:3 1:3 1:3 1:3 1:3 1:3 1:3 1:3
1 1 2 2 1.5 1.5 1.5 1.5 1 2 1 2 1.5 1.5 1.5 1.5 1.5
2500 2500 2500 2500 2000 2000 3000 3000 2000 2000 3000 3000 2500 2500 2500 2500 2500
239.76 2.33 260.65 4.89 177.97 2.97 208.95 2.56 232.32 6.67 222.74 4.36 146.34 3.59 207.67 4.32 300.98 2.54 252.12 3.87 257.32 5.65 172.90 4.61 193.98 6.87 195.87 3.16 195.12 1.12 194.67 3.23 194.52 1.33
62.76 1.32 69.87 0.56 62.67 1.03 66.66 1.98 64.08 0.45 68.50 0.78 69.65 0.89 76.83 1.32 59.89 1.74 61.43 2.17 69.23 0.98 65.32 1.37 69.00 1.63 67.90 1.48 66.90 1.73 67.23 1.06 68.00 0.69
23.88 1.35 14.87 1.09 23.85 1.84 14.28 1.43 24.24 0.47 14.62 0.69 25.82 2.45 16.11 1.64 16.64 1.84 16.99 1.53 18.89 1.65 17.88 2.23 18.87 0.89 18.45 1.81 18.23 1.30 16.30 1.37 18.47 0.83
preliminary study. To evaluate the fitness of the model, predicted R2 and adjusted R2 were evaluated.
responses were compared with the predicted responses and percent error was calculated. A linear regression plots between actual and predicted responses were plotted.7
2.4. Optimization of data and validation of response surface methodology (RSM)
2.5. Determination of particle size, polydispersity index, and zeta potential
Different batches were prepared with different independent variables at different levels and responses, like particles size, % entrapment efficiency and % drug loading were obtained. The data was substituted to design expert software and polynomial equations were obtained. The models were evaluated in terms of statistically significant coefficients and R2 values. 3-D surface plots were used to assess the relationship between the variables and the responses. The criterion for selection of optimum formulations was based on the highest possible value of % entrapment efficiency (Y2), and % drug loading (Y3) and smallest value of particles size (Y1) (Table 1). Finally, four optimized formulations were selected as check point to validate RSM. These formulations were again prepared and evaluated for responses. The resulting observed
All samples were diluted in 1:10 ratio with deionized water to get optimum counts. Average particle size, polydispersity index (PDI) and zeta potential were measured by photon correlation spectroscopy (PCS; Zetasizer, HAS 3000; Malvern Instruments, Malvern, UK). Measurements were carried out with an angle of 90 at 25 C.8
2.6. Determination of entrapment efficiency and drug loading A fixed quantity of SLNs dispersion (10 ml) was taken in a centrifuge tube and centrifuged at 18,000 rpm for 20 min at room temperature (Remi Instruments Pvt. Ltd, India), the lipid portion
Table 3 e Regression analysis for particles size, entrapment efficiency and drug loading. Factor
Particles size CE
Intercept A B C AB AC BC A2 B2 C2 Lack of fit values F-value P-value
194.83 12.95 28.36 25.48 2.25 17.73 3.86 10.77 37.77 18.20 0.24 0.8618
Entrapment efficiency
P-value 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
CE 67.81 2.84 0.71 3.39 0.78 0.69 1.36 1.74 4.06 0.22 0.099 0.9563
P-value 0.0001 0.0160 0.0001 0.0436 0.0662 0.0036 0.0008 0.0001 0.5073
Drug loading CE 18.43 4.83 0.16 0.68 0.14 0.21 0.34 1.6 0.81 0.19 3.64 0.1221
P-value 0.0001 0.1314 0.0002 0.3258 0.1613 0.0372 0.0001 0.0004 0.8887
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was isolated, and the absorbance of the drug in the supernatant was determined spectrophotometrically at lmax 247.5 nm (Shimadzu 1800, Japan).9 The % drug loading and % entrapment efficiency were calculated by using the following equation: % Drug loading ¼ ðWt Ws Þ 100=ðWt Ws Þ þ WL % Entrapment efficiency ¼ ðWt Ws Þ 100=Wt Where Wt is the total weight of drug used, Ws weight of drug in the supernatant, and WL is the weight of the lipid used in preparing the SLNs.
2.7.
Scanning electron microscopy (SEM)
This technique was used to investigate the morphology of the particles. The SLNs sample was observed in the form of aqueous dispersion using Quanta 200 ESEM (FEI, USA) (magnification: 24000; accelerating voltage: 10 kV) at 25 2 C.7
For three factors, the BoxeBehnken design offers some advantage in requiring a fewer number of runs over the composite central, three-level full factorial designs. In full factorial designs, as number of factors increase there is increase in number of trial runs exponentially, such as 33 ¼ 27, but with BoxeBehnken design optimization can be completed with 17 experiments with five centre point. As it is shown in Tables 2 and 3, Y1, Y2, and Y3 were fitted with a quadratic model and insignificant lack of fit (P > 0.05). The positive sign of the factors represent a synergistic effect on the response, while a negative sign means an antagonist relationship. Phrases composed of two factors indicate the interaction terms and phrases with second-order factors stand for the nonlinear relationship between the response and the variable. The second-order polynomial equation relating the response of particle size (Y1) is given below: Y1 ¼ þ194:83 þ 12:95A 28:36B 25:48C þ 2:25AB þ 17:73AC
3.
Results and discussion
On the bases of results obtained in the preliminary screening studies, two levels of each independent variable were decided.
3:86BC 10:47A2 þ 37:77B2 þ 18:20C2
(1)
The model F-value of 7288.58 implied that the model is significant ( p < 0.0001). The ‘Lack of Fit F-value’ of 0.24 implied that the Lack of Fit is not significant ( p ¼ 0.8618).
Fig. 1 e (a)e(c) 3-D surface response plots showing relative effects of different process parameters on particle size.
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As Table 3 shows, the ANOVA test indicates that A, B, C, AB, BC, AC, A2, B2 and C2 are significant model terms. Positive coefficients of A, AB, AC, B2 & C2 in equation (1) indicate the synergistic effect on particle size while negative coefficients of B, C, BC & A2 indicate the antagonistic effect on particle size. The “Pred R Squared” of 0.9996 is in reasonable agreement with the “Adj R-Squared” of 0.9998, indicating the adequacy of the model to predict the response of particle size. The ‘Adeq Precision’ of 345.975 indicated an adequate signal. Therefore, this model is used to navigate the design space. The 3-D surface plots for particle size are shown in Fig. 1. An increase in particle size from 239.76 nm (H1) to 260.65 nm (H2) was observed on increasing the drug to lipid ratio from 1:2 to 1:4 (Table 2). This was probably caused by the aggregation of particles because of the concentration of surfactant was constant and not enough to form a protective layer on each particle10 . A decrease in particle size from 193.98 nm (H13) to172.9 nm (H12) was observed on increasing surfactant concentration (up to certain limit) and stirring
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speed. The probable mechanism of this behaviour could be that as the particle size decrease on increasing stirring speed, the surface area increase. For stabilization of SLNs, the surfactant forms a coating layer so that lipid nanoparticles do not coalesce.5 The second-order polynomial equation relating the response of % entrapment efficiency (Y2) is given below: Y2 ¼ þ67:81 þ 2:84A 0:71B 3:39C 0:78AB þ 0:69AC 1:36BC þ 1:74A2 4:06B2 þ 0:22C2
(2)
The model F-value of 69.33 implied that the model is significant ( p < 0.0001). The ‘Lack of Fit F-value’ of 0.099 implied that the Lack of Fit is not significant ( p ¼ 0.9563). As Table 3 shows, the ANOVA test indicates that A, B, C, AB, BC, A2 and B2 are significant model terms. Positive coefficients of A, AC, A2 & C2 in equation (2) indicate the synergistic effect on % entrapment efficiency, while negative coefficients of B, C, AB, BC, & B2 indicate the antagonistic effect on % entrapment efficiency.
Fig. 2 e (a)e(c) -3-D surface response plots showing relative effects of different process parameters on percent entrapment efficiency.
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The “Pred R Squared” of 0.9716 is in reasonable agreement with the "Adj R-Squared" of 0.9746, indicating the adequacy of the model to predict the response of entrapment efficiency. The ‘Adeq Precision’ of 34.30 indicated an adequate signal. Therefore, this model is used to navigate the design space. The 3-D surface plots for % entrapment efficiency are shown in Fig. 2. The effect of drug to lipid ratio on % entrapment efficiency depends on the extent of drug solubility in lipid. An increase in % entrapment efficiency from 62.76 (H1) to 69.87 (H2) was observed on increasing the drug lipid ratio from 1:2 to 1:4 (Table 2). This is due to large amount of lipid present for drug entrapment. On further increasing drug to lipid ratio the entrapment efficiency decreased (data not shown). This is due to expulsion of drug from particle surface.11 A decrease in % entrapment efficiency from 69.00 (H13) to 65.32 (H12) was observed on increasing surfactant concentration and stirring speed (Table 2). The probable mechanism of this behaviour could be that as the particle size decrease on increasing stirring speed, the surface area increase. As the surfactant increase at a constant amount of lipid, the surface of the formed SLNs is too small to adsorb all surfactant molecules, which will result in the formation of micellar solution of the drug. Hence, the solubility of the drug in water phase will be
increased. Therefore, the drug could partition from SLNs into the formed micelles in the water phase during stirring or washing time.12 The second-order polynomial equation relating the response of % drug loading (Y3) is given below: Y3 ¼ þ18:43 4:83A 0:16B þ 0:68C 0:14AB 0:21AC 0:34BC þ 1:6A2 0:81B2 0:019C2
(3)
The model F-value of 323.46 implied that the model is significant ( p < 0.0001). The ‘Lack of Fit F-value ‘of 3.64 implied that the Lack of Fit is not significant ( p ¼ 0.1221). As Table 3 shows, the ANOVA test indicates that A, C, AB, BC, A2 and B2 are significant model terms. Positive coefficients of C& A2 in equation (3) indicate the synergistic effect on % drug loading, while negative coefficients of A, B, AB, BC, AC, B2 & C2 indicate the antagonistic effect on % drug loading. The “Pred R Squared” of 0.9709 is in reasonable agreement with the “Adj R-Squared” of 0.9945, indicating the adequacy of the model to predict the response of drug loading. The ‘Adeq Precision’ of 57.304 indicated an adequate signal. Therefore, this model is used to navigate the design space. The 3-D surface plots for % drug loading are shown in Fig. 3.
Fig. 3 e (a)e(c)-3-D surface response plots showing relative effects of different process parameters on percent drug loading.
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Table 4 e Point prediction check point for optimization, actual value, experimental value and % error (n [ 3, mean ± SD). Formulation code
Composition of optimized formulation
OH1
1:00:1
OH2
0.75:00:1
OH3
0.75:0.25:1
OH4
1:0.25:1
Response
Actual value
Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3
146.34 3.59 69.65 0.89 25.82 2.45 156.77 3.95 68.35 2.67 23.47 0.57 150.25 5.76 70.43 2.56 23.97 0.95 140.49 2.97 70.65 1.78 26.01 1.23
Predicted value 148.17 69.10 25.68 158.12 70.13 23.85 153.05 69.41 23.48 138.36 69.01 25.65
2.35 2.45 0.95 4.67 1.89 1.32 3.98 1.02 0.88 5.21 1.34 1.2
% Error 1.25 0.79 0.54 0.86 2.6 1.62 1.86 1.45 2.04 1.51 2.32 1.38
Table 5 e Further optimization of OH4 formulation by increasing stirring time (from time 2e2.5 h), (n [ 3, mean ± SD). Independent factors Drug to lipid ratio 1:2 Results
Responses Surfactant conc. (%)
Stirring speed (rpm)
Particle size (nm)
Entrapment efficiency (%)
Drug loading (%)
1.625
3000
115.1 2.78 Acceptable
71.56 0.1.56 Acceptable
26.35 0.56 Acceptable
The effect of drug to lipid ratio on % drug loading is concentration dependent. A decrease in % drug loading from 25.82 (H7) to 16.11 (H8) was observed on increasing the drug to lipid ratio from 1:2 to 1:4 (Table 2) while stirring speed also have positive effect on % drug loading.
3.1. Optimization of data and validation of response surface methodology (RSM) Four formulations (OH1eOH4) were selected from point prediction software of design expert and their responses i.e. particle size, entrapment efficiency and drug loading were evaluated. The composition of all optimum check point formulations, their actual and predicted values for the responses and the % prediction error are shown in Table 4. The low value of % prediction error assures the validity of generated equations and thus depicts the domain of applicability of RSM
Fig. 4 e Particle size distribution curve of optimized formulation (OPH) of SLNs.
model. Finally, the optimum values of drug to lipid ratio 1:2, surfactant concentration 1.625% w/v and stirring speed 3000 were selected. The optimized formulation (OH4) was further optimized by varying stirring time from 2 h to 2.5 h while maintaining all factors constant. A further decrease in particle size from 140.49 nm (OH4) to 115.1 nm (OPH) was observed on increasing the stirring time from 2 to 2.5 h while % drug entrapment and % drug loading were not significantly affected (Table 5).
3.2. Determination of particle size, polydispersity index and zeta potential A particle size, size distribution & zeta potential curve of optimized formulation (OPH) are shown in Figs. 4 and 5 respectively. The average particle size, PDI and zeta potential were found to be115.1 nm, 0.409 and 16.7 mV respectively.
Fig. 5 e Zeta potential curve of optimized formulation (OPH) of SLNs.
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Acknowledgement The authors express their gratitude to Vamsi labs ltd. Solapur, Maharashtra, India for providing gift sample Haloperidol. The authors are thankful to the management of ITS pharmacy college, Muradnagar, Ghaziabad, UP, (India), for providing the facilities to carry out the research work.
references
Fig. 6 e SEM image of optimized formulation (OPH) of SLNs.
3.3. Determination of entrapment efficiency and drug loading The entrapment efficiency and drug loading of optimized formulation (OPH) were found to be 71.56% and 26.35% respectively.
3.4.
Scanning electron microscopy
The Morphology of optimized SLNs was roughly spherical in shape (Fig. 6).
4.
Conclusion
In this study, the haloperidol loaded SLNs were designed and prepared by the solvent emulsification diffusion technique. The SLNs were optimized using the 3-level 3-factor BoxeBehnken statistical design. The optimized formulation (OPH) exhibited particle size115.1 nm, entrapment efficiency 71. 56% and drug loading 26.35%. The Morphology of optimized SLNs was roughly spherical in shape.
Conflicts of interest All authors have none to declare.
1. Dalila MB, Raquel CSB, Nardeli B, et al. Haloperidol-loaded polysorbate-coated polymeric nanocapsules increase its efficacy in the antipsychotic treatment in rats. Eur J Pharm Biopharm. 2011;77:332e336. 2. Muller RH, Mader K, Gohla S. Solid lipid nanoparticles (SLN) for controlled drug deliveryda review of the state of the art. Eur J Pharm Biopharm. 2000;50:161e177. 3. Wissing A, Kayser O, Muller RH. Solid lipid nanoparticles for parenteral drug delivery. Adv Drug Deliv Rev. 2004;56: 1257e1272. 4. Sanjay KM, Shruti C, Sushma T, et al. Chitosanesodium alginate nanoparticles as submicroscopic reservoirs for ocular delivery: formulation, optimisation and in vitro characterisation. Eur J Pharm Biopharm. 2008;68:513e525. 5. Alok PS, Shailendra KS, Shubhini AS. SLN approach for nose-to-brain delivery of alprazolam. Drug Deliv Transpl Res. 2012;2:498e507. 6. Govender S, Pillay V, Chetty DJ. Optimisation and characterisation of bioadhesive controlled release tetracycline microspheres. Int J Pharm. 2005;306:24e40. 7. Maurya DP, Yasmin S, Aqil M, et al. Formulation and optimization of alkaline extracted ispaghula husk microparticles of isoniazid e in vitro and in vivo assessment. J Microencapsul. 2011;28(6):472e482. 8. Jores K, Mehnert W, Drechsler M, et al. Investigations on the structure of solid lipid nanoparticles (SLN) and oilloaded solid lipid nanoparticles by photon correlation spectroscopy, field-flow fractionation and transmission electron microscopy. J Control Release. 2004;95(2):217e227. 9. Venkateswarlu V, Manjunath K. Preparation, characterization and in vitro release kinetics of clozapine solid lipid nanoparticles. J Control Release. 2004;95:627e638. 10. Pandita D, Ahuja A, Velpandian T, et al. Characterization and in vitro assessment of paclitaxel loaded lipid nanoparticles formulated using modified solvent injection technique. Pharmazie. 2009;64:301e310. 11. Ghada A, Rania HF. Diazepam-loaded solid lipid nanoparticles: design and characterization. AAPS Pharm Sci Tech. 2009;10:211e219. 12. Waree T, Watcharaphoron T, Pinyupa P. Formulation and characterization of curcuminoids loaded solid lipid nanoparticles. Int J Pharm. 2007;337:299e306.
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journal homepage: www.elsevier.com/locate/jopr
Original Article
Preparation and optimization of haloperidol loaded solid lipid nanoparticles by BoxeBehnken design Mohd Yasir a,b,*, U.V.S. Sara c a
Department of Pharmacy, Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India Department of Pharmaceutics, ITS Pharmacy College, Ghaziabad 201206, Uttar Pradesh, India c College of Pharmaceutical Sciences, RKGIT, Ghaziabad, 201201, Uttar Pradesh, India b
article info
abstract
Article history:
Background: The aim of this investigation was to design and evaluate solid lipid nano-
Received 25 March 2013
particles (SLNs) of haloperidol.
Accepted 28 May 2013
Method: A modified solvent emulsification diffusion technique was used to produce Halo-
Available online 18 July 2013
peridol loaded solid lipid nanoparticles. A 3-factor, 3-level BoxeBehnken design was applied to study the effect of independent variables (factors) i.e. drug to lipid ratio (A),
Keywords:
surfactant concentration (B) and stirring speed (C) on dependent variables (responses) i.e.
BoxeBehnken design
particles size (Y1), entrapment efficiency (Y2), and drug loading (Y3). 3-D surface response
Haloperidol
plots were drawn and optimized formulation was selected based on desirability factor.
Solid lipid nanoparticles
Results: The results of optimized formulation showed average particle size of 115.1 nm,
Solvent emulsification diffusion
entrapment efficiency of 71.56%, and drug loading of 26.35%. Morphologically, particles
technique etc
were spherical in shape with smooth surfaces and uniform distribution. Conclusion: Thus, the current study can be useful for the successful design, development and optimization of SLNs for haloperidol using a 3-factor, 3-level BoxeBehnken design. Copyright ª 2013, JPR Solutions; Published by Reed Elsevier India Pvt. Ltd. All rights reserved.
1.
Introduction
Haloperidol is a dopamine inverse agonist of the typical antipsychotic class of medications. It is a butyrophenone derivative. Chemically, it is 4-[4-(4-chlorophenyl)-4-hydroxy-1piperidyl]-1-(4-fluorophenyl)-butan-1-one. Its mechanism of action is mediated by blockade of D2 dopamine receptors in brain.1 Though haloperidol is absorbed after oral dosing, there is a first pass metabolism leading to a reduced bioavailability of the drug (50% oral tablets & liquid). After oral drug delivery, the drug first gets distributed systemically and a
small portion is able to reach the brain through the blood due to first past effect. Some side effects are associated with oral administration. SLNs were introduced in 1991, offer attractive drug delivery systems with lower toxicity, compared to polymeric systems that combine the advantages of polymeric nanoparticles, fat emulsions, and liposomes. They are used for both hydrophilic and lipophilic drugs trapped in biocompatible lipid core and surfactant at the outer shell. They offer good tolerability & biodegradability, lack of acute and chronic toxicity of the carrier, scalability to large scale priduction.2 Moreover, the production
* Corresponding author. Department of Pharmaceutics, ITS Pharmacy College, Ghaziabad 201206, Uttar Pradesh, India. Tel.: þ91 9761131206; fax: þ91 01232 225380. E-mail address: [email protected] (M. Yasir). 0974-6943/$ e see front matter Copyright ª 2013, JPR Solutions; Published by Reed Elsevier India Pvt. Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jopr.2013.05.022
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process can be modulated for desired drug release and protection of entrapped drug against chemical/enzymatic degradation. Therefore, they are considered to be, better alternative than liposomes, microemulsions, nanoemulsions, polymeric nanoparticles, self emulsifying drug delivery systems.3 In the present research work, haloperidol loaded solid lipid nanoparticles were prepared by modified solvent emulsification diffusion technique. The formulation was optimized by using 3-factor, 3-level BoxeBehnken design. The optimized formulation was evaluated for various parameters like particle size analysis, Polydispersity index, zeta potential, entrapment efficiency, drug loading capacity, SEM analysis etc. To optimize the production of these SLNs, a statistically experimental design methodology was employed properly. After selecting the critical variables affecting particle size, entrapment efficiency, and drug loading, the response surface methodology of the BoxeBehnken design (version 8.0.7.1, Stat-Ease, Inc., Minneapolis, Minnesota, USA), using a threefactor, three-level, was employed to optimize the level of particle size, entrapment efficiency, and drug loading variables. The BoxeBehnken design is one of the most efficient designs of response surface experimental methodology to study the effect of formulation components on responses for exploring quadratic response surfaces and the second-order polynomial model.4
2.
Materials and methods
2.1.
Materials
Haloperidol was received as a gift sample from Vamsi Labs Ltd. Solapur, Maharashtra, (India). lipid was purchased from Loba Chemie, Mumbai (India). All other solvents and chemicals used were of analytical grade. Water was distilled and filtered before use through a 0.22 mm nylon filter.
2.2.
Preparation of SLNs
In a preliminary laboratory study, various factors like drug to lipid ratio (1:2e1:4), surfactant concentration (Tween 80, 1e2% w/v), chloroform: ethanol ratio (1:1, 2.5% v/v) as the solvent of the drug and lipids, homogenization time (30 min), stirring time (2 h) & stirring speed (2000e3000 rpm), sonication time 5 min were fixed and their effect on particle size, entrapment efficiency were determined. The design matrix was built by the statistical software package, Design-Expert (version 8.0.7.1, Stat-Ease, Inc., Minneapolis, Minnesota, USA), and Table 1 shows the factors and their respective levels. In this study, all of the experiments were performed in triplicate and the averages were considered as the response. Haloperidol loaded SLNs were prepared by a slight modification of the previously reported solvent emulsification diffusion technique.5 Accurately weighed lipid (100 mg) was dissolved in a 2.5 ml (2.5% v/v) mixture of ethanol and chloroform (1:1) as the internal oil phase. Drug (50 mg, drug to lipid ratio 1:2) was dispersed in the above solution. This organic phase was then poured drop by drop into a homogenizer tube containing 22.5 ml of 1.625% (w/v) aqueous solution of Tween 80, as the
Table 1 e Variables and their levels in BoxeBehnken design. Variables Independent variables A ¼ Drug to lipid ratio B ¼ Surfactant (%) C ¼ Stirring speed (RPM)
Level of variables Low (1) 1:2 1 2000
Dependent variables Y1 ¼ Particles size Y2 ¼ % Entrapment efficiency Y3 ¼ % Drug loading
Medium (0) 1:3 1.5 2500
High (þ1) 1:4 2 3000 Goals Minimize Maximize Maximize
external aqueous phase and homogenized for 30 min at 3000 rpm (Remi Instruments Pvt. Ltd, India) to form primary emulsion (o/w). The above emulsion was poured into 75 ml of ice-cold water (2e3 C) containing 1.625% (w/v) surfactant and stirred to extract the organic solvent into the continuous phase and for proper solidification of SLNs. The stirring was continued for 2.5 h at 3000 rpm to get SLNs. The SLNs dispersion was sonicated for 5 min (1 cycle, 100% amplitude, Bandelin sonoplus, Germany) to get SLNs dispersion of uniform size. The dispersion was then centrifuged at 18,000 rpm for 20 min (Remi Instruments Pvt, Ltd, India) to separate the solid lipid material containing the drug. This was then redispersed in 1.625% (w/v) of an aqueous surfactant mixture of Tween 80 and sonicated for 5 min to obtain the SLNs.
2.3.
Experimental design
According to BoxeBehnken design, a total number of 17 experiments, including 12 factorial points at the midpoints of the edges of the process space and five replicates at the centre point for estimation of pure error sum of squares, were performed to choose the best model among the linear, two-factor interaction model and quadratic model due to the analysis of variance (ANOVA) F-value.6 The obtained P-value less than 0.05 is considered statistically significant. From the preliminary screening test, it was found that the drug to lipid ratio (A), Surfactant concentration (B), Stirring speed (C ), had a significant effect on the particle size (Y1), entrapment efficiency (Y2) and drug loading (Y3) of SLNs. Therefore, by fixing the homogenization time (30 min), stirring time (2 h) and sonication time (5 min), selected variables (A), (B), and (C ) were studied at three different levels as low (1), medium (0), and high (þ1). The coded (factors) and actual values (responses) of the variables are given in Table 2. The following second-order polynomial equation can be used to draw conclusion after considering the magnitude of coefficient and mathematical sign it carries i.e. positive or negative. Y ¼ b0 þ b1 A þ b2 B þ b3 C þ b11 A2 þ b22 B2 þ b33 C2 þ b12 AB þ b13 AC þ b23 BC Where Y was predicted response(s), b0 was an intercept, b1, b2, and b3 were linear coefficients, b11, b22, and b33 were squared coefficients and quadratic term, b12, b13, and b23 were interaction coefficients, and A, B, and C were independent variables, which were selected based on the results from a
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Table 2 e Observed responses for 17 runs of haloperidol SLNs according to BoxeBehnken design (n [ 3, mean ± SD). Formulation code H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17
Drug to lipid ratio
Surfactant conc. (%)
Stirring speed (rpm)
Particle size (nm)
Entrapment efficiency (%)
Drug loading (%)
1:2 1:4 1:2 1:4 1:2 1:4 1:2 1:4 1:3 1:3 1:3 1:3 1:3 1:3 1:3 1:3 1:3
1 1 2 2 1.5 1.5 1.5 1.5 1 2 1 2 1.5 1.5 1.5 1.5 1.5
2500 2500 2500 2500 2000 2000 3000 3000 2000 2000 3000 3000 2500 2500 2500 2500 2500
239.76 2.33 260.65 4.89 177.97 2.97 208.95 2.56 232.32 6.67 222.74 4.36 146.34 3.59 207.67 4.32 300.98 2.54 252.12 3.87 257.32 5.65 172.90 4.61 193.98 6.87 195.87 3.16 195.12 1.12 194.67 3.23 194.52 1.33
62.76 1.32 69.87 0.56 62.67 1.03 66.66 1.98 64.08 0.45 68.50 0.78 69.65 0.89 76.83 1.32 59.89 1.74 61.43 2.17 69.23 0.98 65.32 1.37 69.00 1.63 67.90 1.48 66.90 1.73 67.23 1.06 68.00 0.69
23.88 1.35 14.87 1.09 23.85 1.84 14.28 1.43 24.24 0.47 14.62 0.69 25.82 2.45 16.11 1.64 16.64 1.84 16.99 1.53 18.89 1.65 17.88 2.23 18.87 0.89 18.45 1.81 18.23 1.30 16.30 1.37 18.47 0.83
preliminary study. To evaluate the fitness of the model, predicted R2 and adjusted R2 were evaluated.
responses were compared with the predicted responses and percent error was calculated. A linear regression plots between actual and predicted responses were plotted.7
2.4. Optimization of data and validation of response surface methodology (RSM)
2.5. Determination of particle size, polydispersity index, and zeta potential
Different batches were prepared with different independent variables at different levels and responses, like particles size, % entrapment efficiency and % drug loading were obtained. The data was substituted to design expert software and polynomial equations were obtained. The models were evaluated in terms of statistically significant coefficients and R2 values. 3-D surface plots were used to assess the relationship between the variables and the responses. The criterion for selection of optimum formulations was based on the highest possible value of % entrapment efficiency (Y2), and % drug loading (Y3) and smallest value of particles size (Y1) (Table 1). Finally, four optimized formulations were selected as check point to validate RSM. These formulations were again prepared and evaluated for responses. The resulting observed
All samples were diluted in 1:10 ratio with deionized water to get optimum counts. Average particle size, polydispersity index (PDI) and zeta potential were measured by photon correlation spectroscopy (PCS; Zetasizer, HAS 3000; Malvern Instruments, Malvern, UK). Measurements were carried out with an angle of 90 at 25 C.8
2.6. Determination of entrapment efficiency and drug loading A fixed quantity of SLNs dispersion (10 ml) was taken in a centrifuge tube and centrifuged at 18,000 rpm for 20 min at room temperature (Remi Instruments Pvt. Ltd, India), the lipid portion
Table 3 e Regression analysis for particles size, entrapment efficiency and drug loading. Factor
Particles size CE
Intercept A B C AB AC BC A2 B2 C2 Lack of fit values F-value P-value
194.83 12.95 28.36 25.48 2.25 17.73 3.86 10.77 37.77 18.20 0.24 0.8618
Entrapment efficiency
P-value 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
CE 67.81 2.84 0.71 3.39 0.78 0.69 1.36 1.74 4.06 0.22 0.099 0.9563
P-value 0.0001 0.0160 0.0001 0.0436 0.0662 0.0036 0.0008 0.0001 0.5073
Drug loading CE 18.43 4.83 0.16 0.68 0.14 0.21 0.34 1.6 0.81 0.19 3.64 0.1221
P-value 0.0001 0.1314 0.0002 0.3258 0.1613 0.0372 0.0001 0.0004 0.8887
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was isolated, and the absorbance of the drug in the supernatant was determined spectrophotometrically at lmax 247.5 nm (Shimadzu 1800, Japan).9 The % drug loading and % entrapment efficiency were calculated by using the following equation: % Drug loading ¼ ðWt Ws Þ 100=ðWt Ws Þ þ WL % Entrapment efficiency ¼ ðWt Ws Þ 100=Wt Where Wt is the total weight of drug used, Ws weight of drug in the supernatant, and WL is the weight of the lipid used in preparing the SLNs.
2.7.
Scanning electron microscopy (SEM)
This technique was used to investigate the morphology of the particles. The SLNs sample was observed in the form of aqueous dispersion using Quanta 200 ESEM (FEI, USA) (magnification: 24000; accelerating voltage: 10 kV) at 25 2 C.7
For three factors, the BoxeBehnken design offers some advantage in requiring a fewer number of runs over the composite central, three-level full factorial designs. In full factorial designs, as number of factors increase there is increase in number of trial runs exponentially, such as 33 ¼ 27, but with BoxeBehnken design optimization can be completed with 17 experiments with five centre point. As it is shown in Tables 2 and 3, Y1, Y2, and Y3 were fitted with a quadratic model and insignificant lack of fit (P > 0.05). The positive sign of the factors represent a synergistic effect on the response, while a negative sign means an antagonist relationship. Phrases composed of two factors indicate the interaction terms and phrases with second-order factors stand for the nonlinear relationship between the response and the variable. The second-order polynomial equation relating the response of particle size (Y1) is given below: Y1 ¼ þ194:83 þ 12:95A 28:36B 25:48C þ 2:25AB þ 17:73AC
3.
Results and discussion
On the bases of results obtained in the preliminary screening studies, two levels of each independent variable were decided.
3:86BC 10:47A2 þ 37:77B2 þ 18:20C2
(1)
The model F-value of 7288.58 implied that the model is significant ( p < 0.0001). The ‘Lack of Fit F-value’ of 0.24 implied that the Lack of Fit is not significant ( p ¼ 0.8618).
Fig. 1 e (a)e(c) 3-D surface response plots showing relative effects of different process parameters on particle size.
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As Table 3 shows, the ANOVA test indicates that A, B, C, AB, BC, AC, A2, B2 and C2 are significant model terms. Positive coefficients of A, AB, AC, B2 & C2 in equation (1) indicate the synergistic effect on particle size while negative coefficients of B, C, BC & A2 indicate the antagonistic effect on particle size. The “Pred R Squared” of 0.9996 is in reasonable agreement with the “Adj R-Squared” of 0.9998, indicating the adequacy of the model to predict the response of particle size. The ‘Adeq Precision’ of 345.975 indicated an adequate signal. Therefore, this model is used to navigate the design space. The 3-D surface plots for particle size are shown in Fig. 1. An increase in particle size from 239.76 nm (H1) to 260.65 nm (H2) was observed on increasing the drug to lipid ratio from 1:2 to 1:4 (Table 2). This was probably caused by the aggregation of particles because of the concentration of surfactant was constant and not enough to form a protective layer on each particle10 . A decrease in particle size from 193.98 nm (H13) to172.9 nm (H12) was observed on increasing surfactant concentration (up to certain limit) and stirring
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speed. The probable mechanism of this behaviour could be that as the particle size decrease on increasing stirring speed, the surface area increase. For stabilization of SLNs, the surfactant forms a coating layer so that lipid nanoparticles do not coalesce.5 The second-order polynomial equation relating the response of % entrapment efficiency (Y2) is given below: Y2 ¼ þ67:81 þ 2:84A 0:71B 3:39C 0:78AB þ 0:69AC 1:36BC þ 1:74A2 4:06B2 þ 0:22C2
(2)
The model F-value of 69.33 implied that the model is significant ( p < 0.0001). The ‘Lack of Fit F-value’ of 0.099 implied that the Lack of Fit is not significant ( p ¼ 0.9563). As Table 3 shows, the ANOVA test indicates that A, B, C, AB, BC, A2 and B2 are significant model terms. Positive coefficients of A, AC, A2 & C2 in equation (2) indicate the synergistic effect on % entrapment efficiency, while negative coefficients of B, C, AB, BC, & B2 indicate the antagonistic effect on % entrapment efficiency.
Fig. 2 e (a)e(c) -3-D surface response plots showing relative effects of different process parameters on percent entrapment efficiency.
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The “Pred R Squared” of 0.9716 is in reasonable agreement with the "Adj R-Squared" of 0.9746, indicating the adequacy of the model to predict the response of entrapment efficiency. The ‘Adeq Precision’ of 34.30 indicated an adequate signal. Therefore, this model is used to navigate the design space. The 3-D surface plots for % entrapment efficiency are shown in Fig. 2. The effect of drug to lipid ratio on % entrapment efficiency depends on the extent of drug solubility in lipid. An increase in % entrapment efficiency from 62.76 (H1) to 69.87 (H2) was observed on increasing the drug lipid ratio from 1:2 to 1:4 (Table 2). This is due to large amount of lipid present for drug entrapment. On further increasing drug to lipid ratio the entrapment efficiency decreased (data not shown). This is due to expulsion of drug from particle surface.11 A decrease in % entrapment efficiency from 69.00 (H13) to 65.32 (H12) was observed on increasing surfactant concentration and stirring speed (Table 2). The probable mechanism of this behaviour could be that as the particle size decrease on increasing stirring speed, the surface area increase. As the surfactant increase at a constant amount of lipid, the surface of the formed SLNs is too small to adsorb all surfactant molecules, which will result in the formation of micellar solution of the drug. Hence, the solubility of the drug in water phase will be
increased. Therefore, the drug could partition from SLNs into the formed micelles in the water phase during stirring or washing time.12 The second-order polynomial equation relating the response of % drug loading (Y3) is given below: Y3 ¼ þ18:43 4:83A 0:16B þ 0:68C 0:14AB 0:21AC 0:34BC þ 1:6A2 0:81B2 0:019C2
(3)
The model F-value of 323.46 implied that the model is significant ( p < 0.0001). The ‘Lack of Fit F-value ‘of 3.64 implied that the Lack of Fit is not significant ( p ¼ 0.1221). As Table 3 shows, the ANOVA test indicates that A, C, AB, BC, A2 and B2 are significant model terms. Positive coefficients of C& A2 in equation (3) indicate the synergistic effect on % drug loading, while negative coefficients of A, B, AB, BC, AC, B2 & C2 indicate the antagonistic effect on % drug loading. The “Pred R Squared” of 0.9709 is in reasonable agreement with the “Adj R-Squared” of 0.9945, indicating the adequacy of the model to predict the response of drug loading. The ‘Adeq Precision’ of 57.304 indicated an adequate signal. Therefore, this model is used to navigate the design space. The 3-D surface plots for % drug loading are shown in Fig. 3.
Fig. 3 e (a)e(c)-3-D surface response plots showing relative effects of different process parameters on percent drug loading.
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Table 4 e Point prediction check point for optimization, actual value, experimental value and % error (n [ 3, mean ± SD). Formulation code
Composition of optimized formulation
OH1
1:00:1
OH2
0.75:00:1
OH3
0.75:0.25:1
OH4
1:0.25:1
Response
Actual value
Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3
146.34 3.59 69.65 0.89 25.82 2.45 156.77 3.95 68.35 2.67 23.47 0.57 150.25 5.76 70.43 2.56 23.97 0.95 140.49 2.97 70.65 1.78 26.01 1.23
Predicted value 148.17 69.10 25.68 158.12 70.13 23.85 153.05 69.41 23.48 138.36 69.01 25.65
2.35 2.45 0.95 4.67 1.89 1.32 3.98 1.02 0.88 5.21 1.34 1.2
% Error 1.25 0.79 0.54 0.86 2.6 1.62 1.86 1.45 2.04 1.51 2.32 1.38
Table 5 e Further optimization of OH4 formulation by increasing stirring time (from time 2e2.5 h), (n [ 3, mean ± SD). Independent factors Drug to lipid ratio 1:2 Results
Responses Surfactant conc. (%)
Stirring speed (rpm)
Particle size (nm)
Entrapment efficiency (%)
Drug loading (%)
1.625
3000
115.1 2.78 Acceptable
71.56 0.1.56 Acceptable
26.35 0.56 Acceptable
The effect of drug to lipid ratio on % drug loading is concentration dependent. A decrease in % drug loading from 25.82 (H7) to 16.11 (H8) was observed on increasing the drug to lipid ratio from 1:2 to 1:4 (Table 2) while stirring speed also have positive effect on % drug loading.
3.1. Optimization of data and validation of response surface methodology (RSM) Four formulations (OH1eOH4) were selected from point prediction software of design expert and their responses i.e. particle size, entrapment efficiency and drug loading were evaluated. The composition of all optimum check point formulations, their actual and predicted values for the responses and the % prediction error are shown in Table 4. The low value of % prediction error assures the validity of generated equations and thus depicts the domain of applicability of RSM
Fig. 4 e Particle size distribution curve of optimized formulation (OPH) of SLNs.
model. Finally, the optimum values of drug to lipid ratio 1:2, surfactant concentration 1.625% w/v and stirring speed 3000 were selected. The optimized formulation (OH4) was further optimized by varying stirring time from 2 h to 2.5 h while maintaining all factors constant. A further decrease in particle size from 140.49 nm (OH4) to 115.1 nm (OPH) was observed on increasing the stirring time from 2 to 2.5 h while % drug entrapment and % drug loading were not significantly affected (Table 5).
3.2. Determination of particle size, polydispersity index and zeta potential A particle size, size distribution & zeta potential curve of optimized formulation (OPH) are shown in Figs. 4 and 5 respectively. The average particle size, PDI and zeta potential were found to be115.1 nm, 0.409 and 16.7 mV respectively.
Fig. 5 e Zeta potential curve of optimized formulation (OPH) of SLNs.
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Acknowledgement The authors express their gratitude to Vamsi labs ltd. Solapur, Maharashtra, India for providing gift sample Haloperidol. The authors are thankful to the management of ITS pharmacy college, Muradnagar, Ghaziabad, UP, (India), for providing the facilities to carry out the research work.
references
Fig. 6 e SEM image of optimized formulation (OPH) of SLNs.
3.3. Determination of entrapment efficiency and drug loading The entrapment efficiency and drug loading of optimized formulation (OPH) were found to be 71.56% and 26.35% respectively.
3.4.
Scanning electron microscopy
The Morphology of optimized SLNs was roughly spherical in shape (Fig. 6).
4.
Conclusion
In this study, the haloperidol loaded SLNs were designed and prepared by the solvent emulsification diffusion technique. The SLNs were optimized using the 3-level 3-factor BoxeBehnken statistical design. The optimized formulation (OPH) exhibited particle size115.1 nm, entrapment efficiency 71. 56% and drug loading 26.35%. The Morphology of optimized SLNs was roughly spherical in shape.
Conflicts of interest All authors have none to declare.
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