Microsoft Word - 164.docx CHEMICAL ENGINEERING TRANSACTIONS VOL. 56, 2017 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Jiří Jaromír Klemeš, Peng Yen Liew, Wai Shin Ho, Jeng Shiun Lim Copyright © 2017, AIDIC Servizi S.r.l., ISBN 978-88-95608-47-1; ISSN 2283-9216 Optimisation of Orthosiphon Stamineus Loaded onto Nanostructured Lipid Carrier using D Optimal Mixture Design Siti Hasyimah Suhaimi, Rosnani Hasham*, Nur Ayshah Rosli Institute of Bioproduct Development, Universiti Teknologi Malaysia rosnani@ibd.utm.my In this study, the formulation of Orthosiphon stamineus (OS) loaded onto nanostuctured lipid carrier was prepared using melt emulsification homogenisation technique. Glyceryl monostearate and triglyceride were used as solid and liquid lipids respectively. D-optimal mixture design was employed to optimise the formulation of OS loaded into nanostructured lipid carrier. The independent variables were OS extract, solid lipid and liquid lipid, while the dependent variables were particle size, polydispersity index (PDI) and encapsulation efficiency. Multiple correlation coefficient (R2) obtained for particle size, PDI and encapsulation efficiency was 0.9404, 0.9138 and 0.8754. All three models are significant and the lack of fit for all dependent variables was not significant. Solid lipid was the most influential factor for particle size. For PDI, OS was the dominant factor. Meanwhile liquid lipid is the most influential factor toward the encapsulation efficiency. The optimum formulation of OS loaded nanostructured lipid carrier is 4 % of OS, 1 % of solid lipid and 5 % of liquid lipid. 1. Introduction Orthosiphon stamineus (OS) or also known as misai kucing has been used widely to treat hypertension (Azizan et al., 2012), renal calculus (Adam et al., 2009) and diabetes (Rao et al., 2014). Tezuka et al., (2002) found that active compounds such as terpenoids and polyphenols are exist in OS. The presence of polyphenolic contents in OS act as health benefits of OS. Akouwah et al. (2004) reported that sinensetin, rosmarinic acid and eupatorin are presence in the leaves of OS. OS was found to have chemical compounds which exhibit anti-obesity effect such as terpenoids, polyphenols, sterols, orthosiphols, caffeic acid, ursolic acid and siphonols (Son et al., 2011). Human skin consists of epidermis, dermis and hypodermis. Epidermis or also known as stratum corneum is located at the outermost layer of the skin. This stratum corneum acts as a barrier; therefore, most topical products cannot penetrate it. Nanostructured lipid carrier (NLC) is a carrier-based topical and transdermal drug delivery which overcomes the limitation of solid lipid nanoparticle (SLN). NLC may increase the maximum amount of active ingredient being loaded compared to SLN. The production of NLC involves both solid and liquid lipids that exhibit low toxicity (Muller et al., 1997). Ultrasonication method was used in this study. Ultrasonic dispersion provides good alternative for laboratory scale because it involves low cost of apparatus and rapid nature of production (Schwarz, 2012). Oral administration is more toxic than topical administration. Oral aid transports the medicine through the blood stream impacting the entire body along with the area in pain, while topical aid transports the medicine directly to the affected area. Numerous slimming cream and pills are available in the market and easily obtained. However, their efficacies and safety are uncertain. In this research, OS is chosen as a replacement to synthetic drug. Experimental design is a method devised to develop and optimise the formulation of drugs and give the desired formulation. Main objective to design an experiment is to have a valid result at minimum effort, time and resources. In this research, D-optimal mixture design was employed in order to optimise the formulation of OS loaded into NLC. The D-optimal mixture design was used widely in product formulation of cosmetic industry (Borhan et al., 2014). In this study, the formulation of OS loaded into nanostructured lipid carrier was designed specifically for topical application. DOI: 10.3303/CET1756191 Please cite this article as: Suhaimi S.H., Hasham R., Rosli N.A., 2017, Optimization of orthosiphon stamineus loaded onto nanostructured lipid carrier using d optimal mixture design, Chemical Engineering Transactions, 56, 1141-1146 DOI:10.3303/CET1756191 1141 2. Materials and methods 2.1 Materials White variety of OS was purchased from Ethno Resources Sdn Bhd, Sungai Buloh., glyceryl monostearate, triglyceride from Making Cosmetic (USA), Tween 80 and soy lecithin were purchased from Sigma Aldrich, distilled and deionised water. 2.2 Preparation of OS extract OS extract was prepared following a method from Aisha et al. (2014). The OS ethanolic extract was prepared by maceration method. OS was added to 96 % ethanol and mixed continuously using magnetic stirrer for 48 h. Next, it was filtered and rotavapored at 60 ºC. Finally, the sample was freeze-dried. 2.3 Preparation of OS loaded into nanostructured lipid carrier Method for preparation of OS loaded into NLC was employed based on Rosli et al. (2014) with slight modification. Lipid and aqueous phases were prepared separately. Lipid phase was made from glyceryl monostearate and triglyceride while aqueous phase was produced through a combination of water, tween 80 and soy lecithin. Both phases were heated individually. Next aqueous phase was added to lipid phase and mixed well. After that, OS was incorporated into the mixture. The mixture was homogenised using IKA Ultra Turrax® Homogenizer at 11,000 rpm for one minute. The acquired pre-emulsion was ultasonicated using probe sonicator (Fisher Scientific, FB705) for 20 minutes. The NLC was cooled in iced water bath. 2.4 Determinations of Particles Size and Polydispersity Index (PDI) The determinations of particle size and PDI were performed using Malvern Zetasizer Nano S (Malvern instrument, UK). All samples were diluted using deionised water at 1 : 9 ratios respectively. The purpose of dilution was to prevent back-scattering phenomena (Ng et al., 2014). Each measurement was done in triplicate. 2.5 Encapsulation Efficiency (EE) The encapsulation efficiency of OS loaded into NLC was measured based on the method reported by Barras et al. (2013) with slight modifications. Sephadex gel-50 was used to separate the NLC suspension. The concentration of suspension before and after filtration was analysed using HPLC. The encapsulation efficiency was calculated according to the Eq(1): EE (%) = 2 1 n n × 100 % (1) Where; n1 = amount of encapsulated OS, n2 = total amount of OS in the total suspension 2.6 Experimental Design D-optimal mixture design from design expert software version 7.1.5 (STAT-EASE Inc., Minneapolis, USA) was used to find out the optimum formulation of OS loaded into NLC as well as to investigate the effect of independent variables to dependent variables. The independent variables are OS, solid lipid and liquid lipid as listed in Table 1. While the dependent variables are particle size, PDI and EE. The level of independent variables were based on the preliminarily study conducted. Table 1: Summary for independent variable Factor Variable Units Level of Variable Lower limit Upper limit A OS % 1 4 B Solid lipid % 1 3 C Liquid lipid % 3 8 3. Results and discussion 3.1 Model fitting The effect of three independent variables namely active, solid lipid and liquid lipid on the particle size, PDI and encapsulation efficiency was investigated using D-optimal mixture design as shown in Table 2. 1142 Table 2: Results of dependent variables Run Particle size (d, nm) PDI EE (%) 1 90.21 0.135 97.00 2 159.0 0.153 94.70 3 134.0 0.120 97.22 4 140.0 0.143 98.45 5 119.4 0.133 94.60 6 89.48 0.132 97.50 7 128.1 0.115 98.10 8 139.0 0.119 99.10 9 147.5 0.162 90.40 10 138.3 0.114 98.72 11 159.0 0.114 98.70 12 106.0 0.149 95.10 13 116.0 0.132 97.41 14 131.2 0.123 98.80 15 141.0 0.157 92.80 16 147.8 0.157 92.80 Analysis of variance (ANOVA) was used to determine the best fitted model for all the independent variables. The parameters include the adjusted multiple correlation coefficient (Adjusted R2), multiple correlation coefficient (R2), lack of fit, regression (P value), and regression (F value). ANOVA for each dependent variables are shown in Table 3, 4 and 5. Eq(2), (3) and (4) were analysed based on their magnitude and sign of regression coefficient. Higher value of coefficient of independent variable resulted in greater influence on the response. Positive sign of regression coefficient implied that the independent variable was directly proportional to response, while negative sign of regression coefficient indicated that the independent variable was inversely proportional to the response (Yunus et al., 2011). Particle size = 3.36A + 370.22B + 149.28C - 20.69AB + 118.63AC - 387.26BC (2) PDI = 0.81A + 0.30B + 0.11C - 0.26AB - 0.070AC - 0.16BC (3) EE = 98.36A + 88.07B + 98.83C - 10.51AB - 5.03AC + 15.46BC (4) The distribution of nanoparticles was influenced by particle size since small particle resulted in narrow PDI. Eq(2) shows that all independent variables were directly proportional to the particle size and the amount of solid lipid had the biggest influence towards the particle size. Rosli et al. (2014) reported that an increase in lipid concentration led to higher viscosity of sample and lesser dispersion energy available per unit lipid in high concentration of solid lipid which results in higher particle size. In this study, it was found that as the concentration of active, solid lipid and liquid lipid was increased, the particle size increased as well. Uner (2006) found that higher active concentration contributed to higher particle size of nanoparticles. In addition, Emami et al. (2012) also discovered that changing the active content from 5 to 10 may increase the size of nanoparticles. Rosli et al. (2014) found that particle size of nanoparticle was increased as the concentration of solid lipid being increased in the Zingiber zerumbet loaded into nanostructured lipid carrier formulation, possibly due to less energy dispersion per unit lipid available. Table 3 shows ANOVA for particle size. The model is significant (p < 0.05) and the lack of fit is not significant. The adjusted R2 is 0.8409 and R2 is 0.9404. Polydispersity index (PDI) implies particle size homogeneity. The range of PDI is usually between 0 and 1. It was reported that the closer the value of PDI to zero, the higher the homology of particle (Joshi and Patravale, 2008). From Eq(3), it was found that the amount of active being added presented the biggest effect to PDI. The interaction between A and B, A and C, and B and C gave negative value of PDI. The ANOVA for PDI is shown in Table 4. The model is significant (p <0.05) and the lack of fit is not significant. The adjusted R2 is 0.8706 and R2 is 0.9138. Based on Eq(4), all independent variables were directly proportional to the encapsulation efficiency. Liquid lipid contributed the greatest effect on encapsulation efficiency. This finding was in agreement with Yuan et al. (2007) who found that by increasing liquid lipid such as oleic acid, an increase in encapsulation efficiency of the nanostructured lipid carrier was obtained. The interaction between solid and liquid lipids administered positive sign hence implying that it was directly proportional to the encapsulation efficiency. In this research, it was discovered that increasing the amount of liquid lipid gave higher value of encapsulation efficiency. Liquid 1143 lipid played an important role in the encapsulation efficiency since any addition of liquid lipid deformed the perfect crystal formed by solid lipid nanoparticles hence providing more space to fit the active. The encapsulation efficiency was increased (Muller et al., 2007). As for ANOVA analysis, the model is significant (p < 0.05) and the lack of fit is not significant. The adjusted R2 is 0.8131 and R2 is 0.8754. Table 3: Analysis of variance (ANOVA) for particle size Source Sum of square dF Mean Square F-value P-value Significance Model 6,399.04 5 1,279.81 31.58 < 0.0001 Significant Linear Mixture 3,706.35 2 1,853.18 45.73 < 0.0001 AB 1.89 1 1.89 0.047 0.8332 AC 342.91 1 342.91 8.46 0.0156 BC 774.62 1 774.62 19.12 0.0014 Residual 405.22 10 40.52 Lack of fit 136.01 5 27.20 0.51 0.7642 Not significant Pure error 269.22 5 53.84 Cor total 6,804.26 15 R2 0.9404 R2 (Predicted) 0.9107 R2 (Adjusted) 0.8409 15 Table 4: Analysis of variance (ANOVA) for PDI Source Sum of square dF Mean Square F-value P-value Significance Model 3.769×10-3 5 7.539×10-4 21.19 < 0.0001 Significant Linear Mixture 3.406×10-3 2 1.703×10-3 47.87 < 0.0001 AB 3.026×10-4 1 3.026×10-4 8.51 0.0154 AC 1.200×10-4 1 1.200×10-4 3.37 0.0961 BC 1.396×10-4 1 1.396×10-4 3.92 0.0757 Residual 3.557×10-4 10 3.557×10-5 Lack of fit 2.417×10-4 5 4.835×10-5 2.12 0.2145 Not significant Pure error 1.140×10-4 5 2.280×10-5 Cor total 4.125×10-3 15 R2 0.9138 R2 (Predicted) 0.7886 R2 (Adjusted) 0.8706 Table 5: Analysis of variance (ANOVA) for encapsulation efficiency Source Sum of square dF Mean Square F-value P-value Significance Model 81.06 5 16.21 14.05 0.0003 Significant Linear Mixture 66.92 2 33.46 29.01 < 0.0001 AB 0.49 1 0.49 0.42 0.5301 AC 0.62 1 0.62 0.53 0.4818 BC 1.23 1 1.23 1.07 0.3254 Residual 11.53 10 1.15 Lack of fit 8 5 1.60 2.26 0.1953 Not significant Pure error 3.53 5 0.71 Cor total 92.59 15 R2 0.8754 R2 (Predicted) 0.7224 R2 (Adjusted) 0.8131 1144 3.2 Verification of the models The comparison between experimental and predicted values of the response was conducted in order to check the adequacy of the equation. Results obtained (Figure 6) show good agreement with the predicted values. Table 6: Experimental and predicted values of the response Independent variable Dependent variables A (%) B (%) C (%) Y1 Y’1 Y2 Y’2 Y3 Y’3 4 1 5 90.204 88.570±1.187 0.1348 0.135±0.007 97.340 98.10±1.101 Y’1: Experimental value for particle size Y’2: Experimental value for PDI Y’3: Experimental value for encapsulation efficiency 4. Conclusion The optimum formulation of OS loaded onto NLC can be obtained using D-optimal mixture design. Multiple correlation coefficient (R2) obtained for particle size, PDI and encapsulation efficiency was 0.9404, 0.9138 and 0.8754. All three models are significant and the lack of fit for all three responses was not significant. 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