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Preparation and optimization of chitosan nanoparticles and magnetic chitosan nanoparticles as delivery systems using Box–Behnken statistical design
Journal of Pharmaceutical and Biomedical Analysis 80 (2013) 141–146
Contents lists available at SciVerse ScienceDirect
Journal of Pharmaceutical and Biomedical Analysis journal homepage: www.elsevier.com/locate/jpba
Preparation and optimization of chitosan nanoparticles and magnetic chitosan nanoparticles as delivery systems using Box–Behnken statistical design Hamideh Elmizadeh a,∗ , Mohammadreza Khanmohammadi a , Keyvan Ghasemi a , Gholamreza Hassanzadeh b , Marjan Nassiri-Asl c , Amir Bagheri Garmarudi d a
Department of Chemistry, Faculty of Science, IKIU, Qazvin, Iran Department of Anatomy, Tehran University of Medical Science, Tehran, Iran c Department of Pharmacology, School of Medicine, Qazvin University of Medical Sciences, Qazvin, Iran d Department of Chemistry & Polymer Laboratories, Engineering Research Institute, Tehran, Iran b
a r t i c l e
i n f o
Article history: Received 4 October 2012 Received in revised form 21 February 2013 Accepted 26 February 2013 Available online 18 March 2013 Keywords: Chitosan nanoparticles Experimental design Particle size optimization DR-FTMIR spectrometry Tacrine
a b s t r a c t Chitosan nanoparticles and magnetic chitosan nanoparticles can be applied as delivery systems for the anti-Alzheimer drug tacrine. Investigation was carried out to elucidate the influence of process parameters on the mean particle size of chitosan nanoparticles produced by spontaneous emulsification. The method was optimized using design of experiments (DOE) by employing a 3-factor, 3-level Box–Behnken statistical design. This statistical design is used in order to achieve the minimum size and suitable morphology of nanoparticles. Also, magnetic chitosan nanoparticles were synthesized according to optimal method. The designed nanoparticles have average particle size from 33.64 to 74.87 nm, which were determined by field emission scanning electron microscopy (FE-SEM). Drug loading in the nanoparticles as drug delivery systems has been done according to the presented optimal method and appropriate capacity of drug loading was shown by ultraviolet spectrophotometry. Chitosan and magnetic chitosan nanoparticles as drug delivery systems were characterized by Diffuse Reflectance Fourier Transform Mid Infrared spectroscopy (DR-FTMIR). © 2013 Elsevier B.V. All rights reserved.
1. Introduction Nanoparticles have become an important area of research in the field of drug delivery because they have the ability to deliver a wide range of drugs to different areas of the body at appropriate times [1]. Polymers used to form nanoparticles can be two types, hydrophobic and hydrophilic. Nanoparticles based on hydrophilic polymers such as chitosan are appropriate candidates for drug delivery systems [2–4], because they have the advantage of prolonged circulation in blood, which could facilitate extravasation and passive targeting [5]. It is worth mentioning that hydrophilic polymer based nanostructures with particle size less than 100 nm avoid opsonization [6]. Chitosan, poly [-(1-4)-linked-2-amino-2-deoxy-d-glucose], is a polymer similar to cellulose which is obtained from the deacetylation of chitin. Chitosan has many significant biologic and chemical properties: it is biodegradable, biocompatible, bioactive, bioadhesive, nontoxic, nonimmunogenic, antibacterial and poly cationic
∗ Corresponding author. Tel.: +98 2813780040; fax: +98 2813780040. E-mail address: [email protected] (H. Elmizadeh). 0731-7085/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jpba.2013.02.038
[7]. These properties render chitosan a very attractive material as a drug delivery carrier. Different methods have been employed to prepare chitosan micro/nanoparticles. Selection of any of the methods depends on factors such as particle size requirements, thermal and chemical stability of the active agent, stability of the final product and residual toxicity associated with the final product [8]. Tacrine (THA, 9-amino-1,2,3,4-tetrahydroacridine), used in the treatment of Alzheimer’s disease, is known to induce hepatotoxicity, the mechanisms of which remain to be fully established [9]. Magnetic nano- and micron-sized spheres are widely used in biomedical applications such as diagnostics, magnetic separation and carriers for targeted drug delivery [10]. Magnetic particles are usually made of magnetite (Fe3 O4 ), maghemite (␥-Fe2 O3 ), cobalt ferrite (Fe2 CoO4 ). Magnetite conjugated with specific drugs has some limitations, e.g. difficult drug release control and low drug loading capacity. To resolve these problems, many researchers have used suitable biodegradable polymers, such as chitosan, poly lactides, poly (alkylcyanoacrylate), poly (lactide-coglycolides), polyglycolides, polyanhydrides or polyorthoesters [11]. The polymeric magnetic nanoparticles contain a magnetic nucleus and a biodegradable polymer shell. The magnetic core is used for delivery of a carrier to a specific site under external
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magnetic fields, and the polymer matrix for drug loading transports the drug and releases it during biodegradation. DOE’s have been frequently applied to the synthesis and development of drug delivery systems, considering their advantages such as reduction in the number of experiments that need to be performed [12]. There are several papers which used DOE for optimization of nanoparticles containing nano/micro chitosan [13,14]. In this work, Box–Behnken statistical design is used to achieve the minimum size and a suitable morphology of chitosan nanoparticles as a novel strategy compared to previous studies. Also, this paper describes how to use output of optimization for the synthesis of magnetic chitosan nanoparticles. Loading of drug on the nanoparticles as drug delivery system has been studied in accordance with the presented optimal method. After synthesis of chitosan and magnetic chitosan nanoparticles based on outputs of optimal method, ultraviolet spectrophotometry at 240 nm was applied to determine the drug loading capacity. Also, particle size and morphology were determined by FE-SEM. Chitosan and magnetic chitosan nanoparticles were characterized by DR-FTMIR. 2. Materials and methods 2.1. Materials The drug tacrine (9-amino-1,2,3,4-tetrahydroacridine), Glutaraldehyde solution and Span 80 were from Sigma (St. Louis, MO). Linseed oil was from Aldrich. Chitosan was from Suvchem (India). Nano Fe3 O4 with particle size 30 nm was from Neutrino Company (China). All other chemicals and reagents used were of analytical grade. 2.2. Instruments and software The particle size and morphology of synthesized nano materials were determined using a field emission scanning electron microscope (FE-SEM, 15 kV, model 54160, Hitachi, Japan). The UV–vis spectrophotometer was from Camspec Co. (model, M350). UV–vis spectra were recorded in 210–400 nm spectral region with a data point spacing of 1 nm. Mid-infrared spectrometer (Nicolet, Madison, WI, USA) equipped with diffuse reflection sampling cell with a DTGS detector and a CsI beam splitter was employed. FT-IR spectra were recorded in 400–4000 cm−1 spectral region with a data point spacing of 3.85 cm−1 . For digitization of the spectra, WINFIRST software version 3.57 was used. Sizes of nanoparticles from FE-SEM images were determined by Microstructure Measurement software called Nahamin Pardazan Asia version 1.0. This software uses image processing technology to estimate the average size of nanoparticles according to the captured image. Optimization process was done by Design-Expert software Version 7.0.0, Stat-Ease, Inc., and Minneapolis, MN, USA. 2.3. Methods 2.3.1. Optimizing the chitosan nanoparticles synthesis method using Box–Behnken design method Chitosan nanoparticles were prepared by spontaneous emulsification method [15]. In order to optimize the method of chitosan nanoparticles preparation, DOE was employed using Box–Behnken response surface methodology. Box–Behnken experimental design as a response surface method can be used to evaluate the relationship between the studied parameters and their effects on the size of chitosan nanoparticles. Three factors (Span 80 (%) (X1 ), NaCl (%) (X2 ), and glutaraldehyde saturated toluene (GST) (mL) (X3 )), 3-level Box–Behnken design on the measured response (particle
Table 1 Variables used in Box–Behnken experimental design. Independent variables Span 80 (v/v) NaCl (w/v)% GST (ml)
Symbol
Levels
X1 X2 X3
−1 2 0.5 2
0 6 1.5 4
1 10 2.5 6
Dependent variable
Unit
Constraints
Y = particle size
nm
Minimize
size (Y)) were established for this optimization procedure. The role of these factors in modified spontaneous emulsification method includes the amount of NaCl in aqueous solution as an electrolyte (0.5–2.5%), the amount of Span 80 in linseed oil as a surfactant (2–10%) and the amount of GST in emulsification step as a chemical cross-linking agent (2–6 mL). The factor levels were coded (−1, 0, 1). The Box–Behnken design generated 15 experiments, the code ranges of levels of the independent variables are shown in Table 1. The design matrix in the results coded and predicted by the model is shown in Table 2. Variance analysis is shown in Table 3. Variance analysis was used in order to determine the importance of each factor in the model and select the appropriate model for the optimization. 2.3.2. Synthesis of chitosan nanoparticles Chitosan (100 mg) and required quantities of sodium chloride (according to Table 2) were dispersed in glacial acetic acid (3%, 20 mL) and stirred continuously for 2 h to obtain chitosan gel. Then the solution was kept overnight to obtain clear chitosan gel. In the next step, chitosan gel (5 mL) was added dropwise into linseed oil (10 mL) containing required quantities of Span 80 (according to Table 2) under magnetic stirring for 30 min at room temperature. Then acetone (5 mL) was added drop by drop (2 mL/min) with a micropipette to the system. The system was maintained under stirring for 1 h while covered with aluminum foil, and then, the beaker was kept open, resulting in precipitation of the polymer due to the evaporation of acetone with subsequent formation of smaller spheres suspended in oil phase. Then required quantities of GST (according to Table 2) were added to the system slowly and stirred for 2 h continuously to solidify and stabilize the spheres. The nanoparticles suspension was obtained with centrifugation at 5000 rpm for 30 min, being washed three times, each time with toluene and acetone. Finally the product was dried at room temperature to obtain a free flowing, fine powder. A second degree
Table 2 Box–Behnken experimental design in various runs and the responses obtained. Run no
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Independent variables
Dependent variables
X1
X2
X3
Y = particle size (nm)
0 0 −1 −1 1 0 1 0 −1 1 1 0 0 −1 0
0 1 0 −1 0 −1 1 1 1 −1 0 0 0 0 −1
0 −1 −1 0 −1 −1 0 1 0 0 1 0 0 1 1
41.00 74.87 36.30 42.25 51.52 37.60 68.72 73.33 58.71 33.64 41.38 38.50 36.09 65.30 39.15
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Table 3 The analysis of variance table for particle size as the response (Y). Source
Sum of squares
df
Mean square
Model X1 –Span 80 X2 –NaCl X3 –GST X1 X2 X1 X3 X2 X3 X1 2 X2 2 X3 2 Residual Lack of fit Pure error Cor total b 2 R c Adj R2
2965.63 6.66 1890.81 44.50 86.67 382.98 2.38 20.28 366.00 221.84 118.77 106.71 12.05 3084.40 0.96 0.89
9 1 1 1 1 1 1 1 1 1 5 3 2 14
329.51 6.66 1890.81 44.50 86.67 382.98 2.38 20.28 366.00 221.84 23.75 35.57 6.02
a b c
F-value
p-Valuea (Prob > F)
13.87 0.28 79.59 1.87 3.64 16.12 0.10 0.85 15.40 9.33
0.00 0.61 0.00 0.22 0.11 0.01 0.76 0.39 0.01 0.02
Significant
5.90
0.14
Not significant
Significant at 0.05 level. Correlation coefficient of model. Adjusted correclation coefficient.
polynomial model for this response is as follows: y = ˇ0 + ˇ1 X1 + ˇ2 X2 + ˇ3 X3 + ˇ12 X1 X2 + ˇ13 X1 X3 + ˇ23 X2 X3 + ˇ11 X12 + ˇ22 X22 + ˇ33 X32
(1)
where Y = measured response associated with each factor level combination; ˇ0 = intercept; ˇ1 to ˇ33 are regression coefficients computed from the observed experimental values of Y from experimental runs; and X1 , X2 and X3 are the coded levels of independent variables. By using this equation, it is possible to evaluate appropriately the linear, quadratic and interactive effects of the independent variables on the responses. The statistical analysis of the data through regression model and plotting the response surface graphs were carried out using Design-Expert. The analysis of variance (ANOVA) includes a full analysis of variance, prediction equations, and case statistics. ANOVA helps to determine which model, if any, is significant to choose. ANOVA evaluated the significance of each coefficient by p-value (less than 0.05) through Fisher’s test. The predicted values and the experimental parameters were evaluated by the correlation coefficient and the adjusted correlation coefficient. In order to find the optimized formulations, in all experimental regions both numerical and grid searches were employed by considering the constraints in which the particle size is in its minimum level. The experimental responses were compared with the predicted values (obtained from the equation) to evaluate the precision of the model. 2.3.3. Preparation of chitosan nanoparticles containing tacrine drug The chitosan nanoparticles containing tacrine were prepared using the optimal synthesis method provided by the experimental design. Chitosan gel (5 mL) was taken in a beaker (100 mL) and the drug tacrine was dissolved in chitosan gel (drug-to-polymer ratios of 1:1) under magnetic stirring. Then, this gel was added dropwise into linseed oil (10 mL), which contained optimum quantity of Span 80, under magnetic stirring for 30 min at room temperature. Then, acetone (5 mL) was added drop by drop (2 mL/min) with a micropipette. The other steps of synthesis process were in accordance with the preparation of chitosan nanoparticles. 2.3.4. Preparation of magnetic chitosan nanoparticles The magnetic chitosan nanoparticles of drug tacrine were prepared using optimal synthesis method provided by the experimental design. Chitosan gel (5 mL) was taken in a beaker (100 mL) and the drug tacrine was dissolved in chitosan gel (drug-to-polymer
ratio of 1:1) under magnetic stirring. Then a required quantity of magnetite nanoparticles (30 nm, Drug-to-polymer to nano magnetic ratio = 1:1:2.57) was dispersed in required quantity of ethanol (50%, v/v) by ultrasonic probe system (Hielscher ultrasound technology UP200H) and it was added to chitosan-drug solution. The mixture was homogenized (5000 rpm) using a homogenizer (T 25 digital ULTRA-TURRAX® ). The resulting mixture was added dropwise into linseed oil (10 mL) containing optimum quantity of Span 80 under magnetic stirring for 30 min at room temperature. Then optimum quantity of GST was added to the system slowly by a micropipette and stirred for 6 h continuously. The magnetic nanoparticles were separated by centrifugation (10,000 rpm) for 30 min, washed three times by petroleum ether and acetone, and dried at room temperature. Having omitted the drug, the same procedure was applied for preparation of free magnetic chitosan nanoparticles. 2.3.5. Determination of tacrine content in chitosan and magnetic chitosan nanoparticles In order to determine the drug-loading capacity of chitosan nanoparticles, 50 mg of drug loaded nanoparticles with drug-topolymer ratio of 1:1 were dissolved in 20 ml of mixture of 0.1 N HCl and ethanol (1:1, v/v) being stirred for 24 h. It was then centrifuged at 5000 rpm for 30 min and the drug content in the supernatant was analyzed by UV spectrophotometer at 240 nm against dummy nanoparticles, which had also been prepared as reagent blank and treated similarly to the drug-loaded nanoparticles. The same procedure was applied for determination of drug-loading capacity of magnetic chitosan nanoparticles [16]. The percentage of drug loading was calculated using this formula [17]: % drug loading =
Actual drug content × 100 Theoretical drug content
3. Results and discussion 3.1. Effect of variables on the size of chitosan nanoparticles Experimental design can predict the shared effect of three variables. Since the quadratic model has three independent variables, one variable is considered fixed at the middle level. Finally, three diagrams of surface plot were prepared and the forms are shown in Fig. 1. Surface plot of average particle size of chitosan nanoparticles as a function of some variables such as NaCl–Span 80 in constant amount of GST is shown in Fig. 1(a). Thus, the particle size
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Fig. 1. 3D response surface plots for mean particle size analysis (a) response surface plot showing effect of the amount of Span 80 (X1 ) and NaCl (X2 ) on mean particle size (nm) (Y). (b) Response surface plot showing effect of the amount of Span 80 (X1 ) and GST (X3 ) on mean particle size (nm) (Y). (c) Response surface plot showing effect of the amount of NaCl (X2 ) and GST (X3 ) on mean particle size (nm) (Y).
is increased by increasing NaCl percentage. Particles are minimized by increasing the percentage (v/v) of Span 80 in linseed oil. Fig. 1(c) shows that the particle size will be of minimum values if the average amount of GST has been applied. Polynomial coefficients and variance analysis in Table 3 show that X1 and X2 have decreasing and increasing effects on the particle size, respectively. It was found that X3 initially decreases the particle size, but then, it has an increasing effect on the particle size. Thus, effects of different factors in the synthesis process of chitosan nanoparticles were described as follows: Presence of NaCl stabilizes the emulsions (w/o). Stability of the emulsion particles depends on the electrostatic hindrance of the particles which were created in the overlying layers and also due to steric hindrance. Increasing the amount of NaCl resulted in increased diameter of nanoparticles. Also, the presence of NaCl accelerated the coagulation of the formed polymer nanospheres [18]. Although the presence of salt is necessary, increasing it has a detrimental effect on the formation of chitosan nanoparticles. As a result, for finding a suitable range of salt concentration, several percentages from 0 to 2.5 were tested practically. Results showed that concentrations below 0.45% cause agglomeration in particle size. This effect is illustrated in Fig. 2(a). Percentage (v/v) of Span 80 in oil phase is another factor that has been investigated in the system; Span 80 is in the emulsification system as a non-ionic surfactant which plays an important role in emulsion stability of water in oil which reduces surface tension between the aqueous phase and oil phase. On the other hand, the presence of aqueous phase droplets is stabilized in oil phase droplets, thus the conclusion of the aqueous phase is prevented if the surfactant concentration is increased reasonably. Another effective factor is the amount of GST
as a cross link agent in the system. As specified in the experimental design and its associated graph, increasing the concentrations had some favorable effects, but caused aggregation of nanoparticles and increased particle size. 3.2. Optimization In the steps mentioned above, the synthesis methods were optimized. In accordance with the factors affecting chitosan nanoparticle synthesis method and the change ranges, and also, in accordance with the selected method, the best conditions for optimal synthesis of chitosan nanoparticles (minimum particle size) were introduced; In accordance with Box–Behnken experimental design, optimum amount of NaCl (0.52%), Span 80 (10%) and GST (4.86 mL) were used for the synthesis method. The optimized response was 28.46 nm. After reviewing the effective factors, optimal synthesis method was introduced, and according to the section described in the experimental part, chitosan nanoparticles containing the tacrine drug and magnetic chitosan nanoparticles containing the tacrine drug were synthesized based on the optimized method (as described in Section 2). 3.3. Particle size analysis The particle size and morphology of synthetic chitosan nanoparticles were determined using FE-SEM. The size of optimized chitosan nanoparticles, chitosan nanoparticles containing the tacrine drug, and magnetic chitosan nanoparticles containing the tacrine drug obtained in this experiment were measured using FESEM and the results are shown in Fig. 2(b). The average particle
H. Elmizadeh et al. / Journal of Pharmaceutical and Biomedical Analysis 80 (2013) 141–146
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Fig. 2. (a) FE-SEM images showing the effect of different NaCl concentrations on the agglomeration of chitosan particles; a(1) 0%, a(2) 0.25%, a(3) 0.35%, a(4) 0.45%. (b) FE-SEM images of optimized chitosan nanoparticles b(1), chitosan nanoparticles containing the tacrine drug b(2), and magnetic chitosan nanoparticles containing the tacrine drug b(3); b(1) 29.2 ± 1.8 nm, b(2) 32.23 ± 3.8 nm, b(3) 44.10 ± 3.82 nm.
sizes were 29.2 ± 1.8 nm, 32.23 ± 3.8 nm, 44.10 ± 3.82 nm, respectively. These results show that sizes of nanoparticles are suitable enough as compared to the values predicted by the model. Clearly, some differences are seen in the practical results which are due to errors in model prediction. As shown in Table 3, pure error of the model is about 12.05 and prediction capability is close to 0.96. Synthetic nanoparticles were all spherical with appropriate morphology. Another reason for this difference is the presence of the drug tacrine and nano Fe3 O4 in synthesized nanoparticles containing the tacrine drug, due to which their particle sizes were greater than those of pure chitosan nanoparticles. 3.4. DR-FTMIR analysis DR-FTMIR analysis of synthesized chitosan nanoparticle and magnetic chitosan nanoparticles are shown in Fig. 3(a). DRIFT spectrums were taken from synthesized chitosan nanoparticle samples. Chitosan nanoparticle spectrum has free amine, hydroxyl and ether functional group. The peak at 3446 cm−1 corresponds to stretching vibrations of hydroxyl group in chitosan, also 2921 and 2867 cm−1 (C H stretching vibrations). The stretching vibrations of C O are found at 1088 cm−1 and 1022 cm−1 [19]. Amino groups are located 1300–1700 cm−1 . Peak area in 590–700 cm−1 is related
to the stretching vibration of Fe O group. Also, functional group of chitosan nanoparticle is observed in magnetic chitosan nanoparticles peak, except the area which is covered by the high absorption of magnetic nanoparticles.
3.5. Determination of tacrine content in chitosan and magnetic chitosan nanoparticles Drug loading in micro/nanoparticulate systems can be done by two methods: during the preparation of particles (incorporation), and after the formation of particles (incubation). In these systems, drug is physically embedded into the matrix or adsorbed onto the surface [8]. Yield percentage and drug loading percentage of chitosan nanoparticles that were synthesized according to the optimum method were 90% and 13.4 ± 0.51%, respectively. Also, Yield percentage and drug loading percentage of magnetic chitosan nanoparticles that were synthesized according to optimum method were 80.70% and 10 ± 0.71%, respectively. Also, UV absorption spectra of Real drug tacrine content in chitosan nanoparticles and magnetic chitosan nanoparticles are shown in Fig. 3(b). The solvents applied in UV spectrophotometry were described in Section 2.3.5.
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Fig. 3. (a) The DR-FTMIR spectra of chitosan nanoparticles (1), and magnetic chitosan nanoparticles (2). (b) UV absorption spectra of real drug tacrine content in chitosan nanoparticles (3), and magnetic chitosan nanoparticles (4).
4. Conclusion The Preparation and optimization of chitosan and magnetic chitosan nanoparticles as delivery systems for the anti-Alzheimer drug tacrine were the goals of this study. Some parameters such as the amount of NaCl in aqueous solution as an electrolyte, amount of Span 80 in linseed oil as a surfactant, and the amount of GST in emulsification step as a chemical cross-linking agent can affect the sizes of these particles. Box–Behnken design was used to statistically optimize parameters and evaluate the main effects of the independent variables on the particle size. Chitosan and magnetic chitosan nanoparticles were analyzed by DR-FTMIR. Drug loading in the nanoparticles as drug delivery systems has been done according to the presented optimal method and appropriate capacity of drug loading was shown by ultraviolet spectrophotometry. In this method, a proper yield percentage and suitable drug loading were observed. References [1] M.L. Hans, A.M. Lowman, Biodegradable nanoparticles for drug delivery and targeting, Curr. Opin. Solid State Mater. Sci. 6 (2002) 319–327.
[2] E. Lee, J. Lee, I.H. Lee, M. Yu, H. Kim, S.U. Chae, S. Jon, Conjugated chitosan as a novel platform for oral delivery of paclitaxel, J. Med. Chem. 51 (2008) 6442–6449. [3] A. Trapani, J. Sitterberg, U. Bakowsky, T. Kissel, The potential of glycol chitosan nanoparticles as carrier for low water soluble drugs, Int. J. Pharm. 375 (2009) 97–106. [4] Y. Zhang, M. Huo, J. Zhou, D. Yu, Y. Wu, Potential of amphiphilically modified low molecular weight chitosan as a novel carrier for hydrophobic anticancer drug: synthesis, characterization, micellization and cytotoxicity evaluation, Carbohydr. Polym. 77 (2009) 231–238. [5] U. Gaur, S.K. Sahoo, T.K. De, P.C. Ghosh, A.N. Maitra, P.K. Ghosh, Biodistribution of fluoresceinated dextran using novel nanoparticles evading reticuloendothelial system, Int. J. Pharm. 202 (2000) 1–10. [6] E. Allemann, R. Gurny, E. Deolker, Drug loaded nanoparticles: preparation methods and drug targeting issues, Eur. J. Pharm. Biopharm. 39 (1993) 173–191. [7] Y. Boonsongrit, A. Mitrevej, B.W. Mueller, Chitosan drug binding by ionic interaction, Eur. J. Pharm. Biopharm. 62 (2006) 267–274. [8] S.A. Agnihotri, N.N. Mallikarjuna, T.M. Aminabhavi, Recent advances on chitosan-based micro- and nanoparticles in drug delivery, J. Control. Release 100 (2004) 5–28. [9] M. Galisteo, M. Rissel, O. Sergent, M. Chevanne, J. Cillard, A. Guillouzo, D. Lagadic-Gossmann, Hepatotoxicity of tacrine: occurrence of membrane fluidity alterations without involvement of lipid peroxidation, J. Pharmacol. Exp. Ther. 294 (2000) 160–167. [10] A. Ito, M. Shinkai, H. Honda, T. Kobayashi, Medical application of functionalized magnetic nanoparticles, J. Biosci. Bioeng. 100 (2005) 1–11. [11] J. Yang, S.B. Park, H.G. Yoon, Y.M. Huh, S. Haam, Preparation of poly caprolactone nanoparticles containing magnetite for magnetic drug carrier, Int. J. Pharm. 324 (2006) 185–190. [12] J. Hao, X. Fang, Y. Zhou, J. Wang, F. Guo, F. Li, X. Peng, Development and optimization of solid lipid nanoparticle formulation for ophthalmic delivery of chloramphenicol using a Box–Behnken design, Int. J. Nanomed. 6 (2011) 683–692. [13] T. Gazori, M. Khoshayand, E. Azizi, P. Yazdizade, A. Nomani, I. Haririan, Evaluation of alginate/chitosan nanoparticles as antisense delivery vector: formulation, optimization and in vitro characterization, Carbohydr. Polym. 77 (2009) 599–606. [14] E. Biro, A.S. Nemeth, T. Feczko, J. Toth, C. Sisak, J. Gyenis, Threestep experimental design to determine the effect of process parameters on the size of chitosan microspheres, Chem. Eng. Process. 48 (2009) 771–779. [15] B. Wilson, M.K. Samanta, K. Santhi, K.P.S. Kumar, M. Ramasamy, M. Pharm, B. Suresh, Chitosan nanoparticles as a new delivery system for the anti-Alzheimer drug tacrine, Nanomedicine 6 (2010) 144–152. [16] B. Wilson, M.K. Samanta, K. Santhi, K.P.S. Kumar, M. Ramasamy, B. Suresh, Significant delivery of tacrine into the brain using magnetic chitosan microparticles for treating Alzheimer’s disease, J. Neurosci. Methods 177 (2009) 427–433. [17] K.P. Dhanya, K. Santhi, S.A. Dhanaraj, C.I. Sajeeth, Formulation and evaluation of chitosan nanospheres as a carrier for the targeted delivery of lamivudine to the brain, Pharm. Globale. 2 (2011) 1–5. [18] F. Ito, H. Fujimori, H. Honnami, H. Kawakami, K. Kanamura, K. Makino, Control of drug loading efficiency and drug release behavior in preparation of hydrophilicdrug-containing monodisperse PLGA microspheres, J. Mater. Sci. Mater. Med. 21 (2010) 1563–1571. [19] M.D. Dhanaraju, R.M. Kumar, P. Nithya, J.V.V.N. Kishan, G. Thirumurugan, Controlled delivery of anti retroviral drug loaded chitosan cross linked microspheres, Arch. Appl. Sci. Res. 1 (2009) 279–286.
Contents lists available at SciVerse ScienceDirect
Journal of Pharmaceutical and Biomedical Analysis journal homepage: www.elsevier.com/locate/jpba
Preparation and optimization of chitosan nanoparticles and magnetic chitosan nanoparticles as delivery systems using Box–Behnken statistical design Hamideh Elmizadeh a,∗ , Mohammadreza Khanmohammadi a , Keyvan Ghasemi a , Gholamreza Hassanzadeh b , Marjan Nassiri-Asl c , Amir Bagheri Garmarudi d a
Department of Chemistry, Faculty of Science, IKIU, Qazvin, Iran Department of Anatomy, Tehran University of Medical Science, Tehran, Iran c Department of Pharmacology, School of Medicine, Qazvin University of Medical Sciences, Qazvin, Iran d Department of Chemistry & Polymer Laboratories, Engineering Research Institute, Tehran, Iran b
a r t i c l e
i n f o
Article history: Received 4 October 2012 Received in revised form 21 February 2013 Accepted 26 February 2013 Available online 18 March 2013 Keywords: Chitosan nanoparticles Experimental design Particle size optimization DR-FTMIR spectrometry Tacrine
a b s t r a c t Chitosan nanoparticles and magnetic chitosan nanoparticles can be applied as delivery systems for the anti-Alzheimer drug tacrine. Investigation was carried out to elucidate the influence of process parameters on the mean particle size of chitosan nanoparticles produced by spontaneous emulsification. The method was optimized using design of experiments (DOE) by employing a 3-factor, 3-level Box–Behnken statistical design. This statistical design is used in order to achieve the minimum size and suitable morphology of nanoparticles. Also, magnetic chitosan nanoparticles were synthesized according to optimal method. The designed nanoparticles have average particle size from 33.64 to 74.87 nm, which were determined by field emission scanning electron microscopy (FE-SEM). Drug loading in the nanoparticles as drug delivery systems has been done according to the presented optimal method and appropriate capacity of drug loading was shown by ultraviolet spectrophotometry. Chitosan and magnetic chitosan nanoparticles as drug delivery systems were characterized by Diffuse Reflectance Fourier Transform Mid Infrared spectroscopy (DR-FTMIR). © 2013 Elsevier B.V. All rights reserved.
1. Introduction Nanoparticles have become an important area of research in the field of drug delivery because they have the ability to deliver a wide range of drugs to different areas of the body at appropriate times [1]. Polymers used to form nanoparticles can be two types, hydrophobic and hydrophilic. Nanoparticles based on hydrophilic polymers such as chitosan are appropriate candidates for drug delivery systems [2–4], because they have the advantage of prolonged circulation in blood, which could facilitate extravasation and passive targeting [5]. It is worth mentioning that hydrophilic polymer based nanostructures with particle size less than 100 nm avoid opsonization [6]. Chitosan, poly [-(1-4)-linked-2-amino-2-deoxy-d-glucose], is a polymer similar to cellulose which is obtained from the deacetylation of chitin. Chitosan has many significant biologic and chemical properties: it is biodegradable, biocompatible, bioactive, bioadhesive, nontoxic, nonimmunogenic, antibacterial and poly cationic
∗ Corresponding author. Tel.: +98 2813780040; fax: +98 2813780040. E-mail address: [email protected] (H. Elmizadeh). 0731-7085/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jpba.2013.02.038
[7]. These properties render chitosan a very attractive material as a drug delivery carrier. Different methods have been employed to prepare chitosan micro/nanoparticles. Selection of any of the methods depends on factors such as particle size requirements, thermal and chemical stability of the active agent, stability of the final product and residual toxicity associated with the final product [8]. Tacrine (THA, 9-amino-1,2,3,4-tetrahydroacridine), used in the treatment of Alzheimer’s disease, is known to induce hepatotoxicity, the mechanisms of which remain to be fully established [9]. Magnetic nano- and micron-sized spheres are widely used in biomedical applications such as diagnostics, magnetic separation and carriers for targeted drug delivery [10]. Magnetic particles are usually made of magnetite (Fe3 O4 ), maghemite (␥-Fe2 O3 ), cobalt ferrite (Fe2 CoO4 ). Magnetite conjugated with specific drugs has some limitations, e.g. difficult drug release control and low drug loading capacity. To resolve these problems, many researchers have used suitable biodegradable polymers, such as chitosan, poly lactides, poly (alkylcyanoacrylate), poly (lactide-coglycolides), polyglycolides, polyanhydrides or polyorthoesters [11]. The polymeric magnetic nanoparticles contain a magnetic nucleus and a biodegradable polymer shell. The magnetic core is used for delivery of a carrier to a specific site under external
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magnetic fields, and the polymer matrix for drug loading transports the drug and releases it during biodegradation. DOE’s have been frequently applied to the synthesis and development of drug delivery systems, considering their advantages such as reduction in the number of experiments that need to be performed [12]. There are several papers which used DOE for optimization of nanoparticles containing nano/micro chitosan [13,14]. In this work, Box–Behnken statistical design is used to achieve the minimum size and a suitable morphology of chitosan nanoparticles as a novel strategy compared to previous studies. Also, this paper describes how to use output of optimization for the synthesis of magnetic chitosan nanoparticles. Loading of drug on the nanoparticles as drug delivery system has been studied in accordance with the presented optimal method. After synthesis of chitosan and magnetic chitosan nanoparticles based on outputs of optimal method, ultraviolet spectrophotometry at 240 nm was applied to determine the drug loading capacity. Also, particle size and morphology were determined by FE-SEM. Chitosan and magnetic chitosan nanoparticles were characterized by DR-FTMIR. 2. Materials and methods 2.1. Materials The drug tacrine (9-amino-1,2,3,4-tetrahydroacridine), Glutaraldehyde solution and Span 80 were from Sigma (St. Louis, MO). Linseed oil was from Aldrich. Chitosan was from Suvchem (India). Nano Fe3 O4 with particle size 30 nm was from Neutrino Company (China). All other chemicals and reagents used were of analytical grade. 2.2. Instruments and software The particle size and morphology of synthesized nano materials were determined using a field emission scanning electron microscope (FE-SEM, 15 kV, model 54160, Hitachi, Japan). The UV–vis spectrophotometer was from Camspec Co. (model, M350). UV–vis spectra were recorded in 210–400 nm spectral region with a data point spacing of 1 nm. Mid-infrared spectrometer (Nicolet, Madison, WI, USA) equipped with diffuse reflection sampling cell with a DTGS detector and a CsI beam splitter was employed. FT-IR spectra were recorded in 400–4000 cm−1 spectral region with a data point spacing of 3.85 cm−1 . For digitization of the spectra, WINFIRST software version 3.57 was used. Sizes of nanoparticles from FE-SEM images were determined by Microstructure Measurement software called Nahamin Pardazan Asia version 1.0. This software uses image processing technology to estimate the average size of nanoparticles according to the captured image. Optimization process was done by Design-Expert software Version 7.0.0, Stat-Ease, Inc., and Minneapolis, MN, USA. 2.3. Methods 2.3.1. Optimizing the chitosan nanoparticles synthesis method using Box–Behnken design method Chitosan nanoparticles were prepared by spontaneous emulsification method [15]. In order to optimize the method of chitosan nanoparticles preparation, DOE was employed using Box–Behnken response surface methodology. Box–Behnken experimental design as a response surface method can be used to evaluate the relationship between the studied parameters and their effects on the size of chitosan nanoparticles. Three factors (Span 80 (%) (X1 ), NaCl (%) (X2 ), and glutaraldehyde saturated toluene (GST) (mL) (X3 )), 3-level Box–Behnken design on the measured response (particle
Table 1 Variables used in Box–Behnken experimental design. Independent variables Span 80 (v/v) NaCl (w/v)% GST (ml)
Symbol
Levels
X1 X2 X3
−1 2 0.5 2
0 6 1.5 4
1 10 2.5 6
Dependent variable
Unit
Constraints
Y = particle size
nm
Minimize
size (Y)) were established for this optimization procedure. The role of these factors in modified spontaneous emulsification method includes the amount of NaCl in aqueous solution as an electrolyte (0.5–2.5%), the amount of Span 80 in linseed oil as a surfactant (2–10%) and the amount of GST in emulsification step as a chemical cross-linking agent (2–6 mL). The factor levels were coded (−1, 0, 1). The Box–Behnken design generated 15 experiments, the code ranges of levels of the independent variables are shown in Table 1. The design matrix in the results coded and predicted by the model is shown in Table 2. Variance analysis is shown in Table 3. Variance analysis was used in order to determine the importance of each factor in the model and select the appropriate model for the optimization. 2.3.2. Synthesis of chitosan nanoparticles Chitosan (100 mg) and required quantities of sodium chloride (according to Table 2) were dispersed in glacial acetic acid (3%, 20 mL) and stirred continuously for 2 h to obtain chitosan gel. Then the solution was kept overnight to obtain clear chitosan gel. In the next step, chitosan gel (5 mL) was added dropwise into linseed oil (10 mL) containing required quantities of Span 80 (according to Table 2) under magnetic stirring for 30 min at room temperature. Then acetone (5 mL) was added drop by drop (2 mL/min) with a micropipette to the system. The system was maintained under stirring for 1 h while covered with aluminum foil, and then, the beaker was kept open, resulting in precipitation of the polymer due to the evaporation of acetone with subsequent formation of smaller spheres suspended in oil phase. Then required quantities of GST (according to Table 2) were added to the system slowly and stirred for 2 h continuously to solidify and stabilize the spheres. The nanoparticles suspension was obtained with centrifugation at 5000 rpm for 30 min, being washed three times, each time with toluene and acetone. Finally the product was dried at room temperature to obtain a free flowing, fine powder. A second degree
Table 2 Box–Behnken experimental design in various runs and the responses obtained. Run no
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Independent variables
Dependent variables
X1
X2
X3
Y = particle size (nm)
0 0 −1 −1 1 0 1 0 −1 1 1 0 0 −1 0
0 1 0 −1 0 −1 1 1 1 −1 0 0 0 0 −1
0 −1 −1 0 −1 −1 0 1 0 0 1 0 0 1 1
41.00 74.87 36.30 42.25 51.52 37.60 68.72 73.33 58.71 33.64 41.38 38.50 36.09 65.30 39.15
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Table 3 The analysis of variance table for particle size as the response (Y). Source
Sum of squares
df
Mean square
Model X1 –Span 80 X2 –NaCl X3 –GST X1 X2 X1 X3 X2 X3 X1 2 X2 2 X3 2 Residual Lack of fit Pure error Cor total b 2 R c Adj R2
2965.63 6.66 1890.81 44.50 86.67 382.98 2.38 20.28 366.00 221.84 118.77 106.71 12.05 3084.40 0.96 0.89
9 1 1 1 1 1 1 1 1 1 5 3 2 14
329.51 6.66 1890.81 44.50 86.67 382.98 2.38 20.28 366.00 221.84 23.75 35.57 6.02
a b c
F-value
p-Valuea (Prob > F)
13.87 0.28 79.59 1.87 3.64 16.12 0.10 0.85 15.40 9.33
0.00 0.61 0.00 0.22 0.11 0.01 0.76 0.39 0.01 0.02
Significant
5.90
0.14
Not significant
Significant at 0.05 level. Correlation coefficient of model. Adjusted correclation coefficient.
polynomial model for this response is as follows: y = ˇ0 + ˇ1 X1 + ˇ2 X2 + ˇ3 X3 + ˇ12 X1 X2 + ˇ13 X1 X3 + ˇ23 X2 X3 + ˇ11 X12 + ˇ22 X22 + ˇ33 X32
(1)
where Y = measured response associated with each factor level combination; ˇ0 = intercept; ˇ1 to ˇ33 are regression coefficients computed from the observed experimental values of Y from experimental runs; and X1 , X2 and X3 are the coded levels of independent variables. By using this equation, it is possible to evaluate appropriately the linear, quadratic and interactive effects of the independent variables on the responses. The statistical analysis of the data through regression model and plotting the response surface graphs were carried out using Design-Expert. The analysis of variance (ANOVA) includes a full analysis of variance, prediction equations, and case statistics. ANOVA helps to determine which model, if any, is significant to choose. ANOVA evaluated the significance of each coefficient by p-value (less than 0.05) through Fisher’s test. The predicted values and the experimental parameters were evaluated by the correlation coefficient and the adjusted correlation coefficient. In order to find the optimized formulations, in all experimental regions both numerical and grid searches were employed by considering the constraints in which the particle size is in its minimum level. The experimental responses were compared with the predicted values (obtained from the equation) to evaluate the precision of the model. 2.3.3. Preparation of chitosan nanoparticles containing tacrine drug The chitosan nanoparticles containing tacrine were prepared using the optimal synthesis method provided by the experimental design. Chitosan gel (5 mL) was taken in a beaker (100 mL) and the drug tacrine was dissolved in chitosan gel (drug-to-polymer ratios of 1:1) under magnetic stirring. Then, this gel was added dropwise into linseed oil (10 mL), which contained optimum quantity of Span 80, under magnetic stirring for 30 min at room temperature. Then, acetone (5 mL) was added drop by drop (2 mL/min) with a micropipette. The other steps of synthesis process were in accordance with the preparation of chitosan nanoparticles. 2.3.4. Preparation of magnetic chitosan nanoparticles The magnetic chitosan nanoparticles of drug tacrine were prepared using optimal synthesis method provided by the experimental design. Chitosan gel (5 mL) was taken in a beaker (100 mL) and the drug tacrine was dissolved in chitosan gel (drug-to-polymer
ratio of 1:1) under magnetic stirring. Then a required quantity of magnetite nanoparticles (30 nm, Drug-to-polymer to nano magnetic ratio = 1:1:2.57) was dispersed in required quantity of ethanol (50%, v/v) by ultrasonic probe system (Hielscher ultrasound technology UP200H) and it was added to chitosan-drug solution. The mixture was homogenized (5000 rpm) using a homogenizer (T 25 digital ULTRA-TURRAX® ). The resulting mixture was added dropwise into linseed oil (10 mL) containing optimum quantity of Span 80 under magnetic stirring for 30 min at room temperature. Then optimum quantity of GST was added to the system slowly by a micropipette and stirred for 6 h continuously. The magnetic nanoparticles were separated by centrifugation (10,000 rpm) for 30 min, washed three times by petroleum ether and acetone, and dried at room temperature. Having omitted the drug, the same procedure was applied for preparation of free magnetic chitosan nanoparticles. 2.3.5. Determination of tacrine content in chitosan and magnetic chitosan nanoparticles In order to determine the drug-loading capacity of chitosan nanoparticles, 50 mg of drug loaded nanoparticles with drug-topolymer ratio of 1:1 were dissolved in 20 ml of mixture of 0.1 N HCl and ethanol (1:1, v/v) being stirred for 24 h. It was then centrifuged at 5000 rpm for 30 min and the drug content in the supernatant was analyzed by UV spectrophotometer at 240 nm against dummy nanoparticles, which had also been prepared as reagent blank and treated similarly to the drug-loaded nanoparticles. The same procedure was applied for determination of drug-loading capacity of magnetic chitosan nanoparticles [16]. The percentage of drug loading was calculated using this formula [17]: % drug loading =
Actual drug content × 100 Theoretical drug content
3. Results and discussion 3.1. Effect of variables on the size of chitosan nanoparticles Experimental design can predict the shared effect of three variables. Since the quadratic model has three independent variables, one variable is considered fixed at the middle level. Finally, three diagrams of surface plot were prepared and the forms are shown in Fig. 1. Surface plot of average particle size of chitosan nanoparticles as a function of some variables such as NaCl–Span 80 in constant amount of GST is shown in Fig. 1(a). Thus, the particle size
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Fig. 1. 3D response surface plots for mean particle size analysis (a) response surface plot showing effect of the amount of Span 80 (X1 ) and NaCl (X2 ) on mean particle size (nm) (Y). (b) Response surface plot showing effect of the amount of Span 80 (X1 ) and GST (X3 ) on mean particle size (nm) (Y). (c) Response surface plot showing effect of the amount of NaCl (X2 ) and GST (X3 ) on mean particle size (nm) (Y).
is increased by increasing NaCl percentage. Particles are minimized by increasing the percentage (v/v) of Span 80 in linseed oil. Fig. 1(c) shows that the particle size will be of minimum values if the average amount of GST has been applied. Polynomial coefficients and variance analysis in Table 3 show that X1 and X2 have decreasing and increasing effects on the particle size, respectively. It was found that X3 initially decreases the particle size, but then, it has an increasing effect on the particle size. Thus, effects of different factors in the synthesis process of chitosan nanoparticles were described as follows: Presence of NaCl stabilizes the emulsions (w/o). Stability of the emulsion particles depends on the electrostatic hindrance of the particles which were created in the overlying layers and also due to steric hindrance. Increasing the amount of NaCl resulted in increased diameter of nanoparticles. Also, the presence of NaCl accelerated the coagulation of the formed polymer nanospheres [18]. Although the presence of salt is necessary, increasing it has a detrimental effect on the formation of chitosan nanoparticles. As a result, for finding a suitable range of salt concentration, several percentages from 0 to 2.5 were tested practically. Results showed that concentrations below 0.45% cause agglomeration in particle size. This effect is illustrated in Fig. 2(a). Percentage (v/v) of Span 80 in oil phase is another factor that has been investigated in the system; Span 80 is in the emulsification system as a non-ionic surfactant which plays an important role in emulsion stability of water in oil which reduces surface tension between the aqueous phase and oil phase. On the other hand, the presence of aqueous phase droplets is stabilized in oil phase droplets, thus the conclusion of the aqueous phase is prevented if the surfactant concentration is increased reasonably. Another effective factor is the amount of GST
as a cross link agent in the system. As specified in the experimental design and its associated graph, increasing the concentrations had some favorable effects, but caused aggregation of nanoparticles and increased particle size. 3.2. Optimization In the steps mentioned above, the synthesis methods were optimized. In accordance with the factors affecting chitosan nanoparticle synthesis method and the change ranges, and also, in accordance with the selected method, the best conditions for optimal synthesis of chitosan nanoparticles (minimum particle size) were introduced; In accordance with Box–Behnken experimental design, optimum amount of NaCl (0.52%), Span 80 (10%) and GST (4.86 mL) were used for the synthesis method. The optimized response was 28.46 nm. After reviewing the effective factors, optimal synthesis method was introduced, and according to the section described in the experimental part, chitosan nanoparticles containing the tacrine drug and magnetic chitosan nanoparticles containing the tacrine drug were synthesized based on the optimized method (as described in Section 2). 3.3. Particle size analysis The particle size and morphology of synthetic chitosan nanoparticles were determined using FE-SEM. The size of optimized chitosan nanoparticles, chitosan nanoparticles containing the tacrine drug, and magnetic chitosan nanoparticles containing the tacrine drug obtained in this experiment were measured using FESEM and the results are shown in Fig. 2(b). The average particle
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Fig. 2. (a) FE-SEM images showing the effect of different NaCl concentrations on the agglomeration of chitosan particles; a(1) 0%, a(2) 0.25%, a(3) 0.35%, a(4) 0.45%. (b) FE-SEM images of optimized chitosan nanoparticles b(1), chitosan nanoparticles containing the tacrine drug b(2), and magnetic chitosan nanoparticles containing the tacrine drug b(3); b(1) 29.2 ± 1.8 nm, b(2) 32.23 ± 3.8 nm, b(3) 44.10 ± 3.82 nm.
sizes were 29.2 ± 1.8 nm, 32.23 ± 3.8 nm, 44.10 ± 3.82 nm, respectively. These results show that sizes of nanoparticles are suitable enough as compared to the values predicted by the model. Clearly, some differences are seen in the practical results which are due to errors in model prediction. As shown in Table 3, pure error of the model is about 12.05 and prediction capability is close to 0.96. Synthetic nanoparticles were all spherical with appropriate morphology. Another reason for this difference is the presence of the drug tacrine and nano Fe3 O4 in synthesized nanoparticles containing the tacrine drug, due to which their particle sizes were greater than those of pure chitosan nanoparticles. 3.4. DR-FTMIR analysis DR-FTMIR analysis of synthesized chitosan nanoparticle and magnetic chitosan nanoparticles are shown in Fig. 3(a). DRIFT spectrums were taken from synthesized chitosan nanoparticle samples. Chitosan nanoparticle spectrum has free amine, hydroxyl and ether functional group. The peak at 3446 cm−1 corresponds to stretching vibrations of hydroxyl group in chitosan, also 2921 and 2867 cm−1 (C H stretching vibrations). The stretching vibrations of C O are found at 1088 cm−1 and 1022 cm−1 [19]. Amino groups are located 1300–1700 cm−1 . Peak area in 590–700 cm−1 is related
to the stretching vibration of Fe O group. Also, functional group of chitosan nanoparticle is observed in magnetic chitosan nanoparticles peak, except the area which is covered by the high absorption of magnetic nanoparticles.
3.5. Determination of tacrine content in chitosan and magnetic chitosan nanoparticles Drug loading in micro/nanoparticulate systems can be done by two methods: during the preparation of particles (incorporation), and after the formation of particles (incubation). In these systems, drug is physically embedded into the matrix or adsorbed onto the surface [8]. Yield percentage and drug loading percentage of chitosan nanoparticles that were synthesized according to the optimum method were 90% and 13.4 ± 0.51%, respectively. Also, Yield percentage and drug loading percentage of magnetic chitosan nanoparticles that were synthesized according to optimum method were 80.70% and 10 ± 0.71%, respectively. Also, UV absorption spectra of Real drug tacrine content in chitosan nanoparticles and magnetic chitosan nanoparticles are shown in Fig. 3(b). The solvents applied in UV spectrophotometry were described in Section 2.3.5.
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Fig. 3. (a) The DR-FTMIR spectra of chitosan nanoparticles (1), and magnetic chitosan nanoparticles (2). (b) UV absorption spectra of real drug tacrine content in chitosan nanoparticles (3), and magnetic chitosan nanoparticles (4).
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