http://informahealthcare.com/phd ISSN: 1083-7450 (print), 1097-9867 (electronic) Pharm Dev Technol, Early Online: 1–13 ! 2013 Informa Healthcare USA, Inc. DOI: 10.3109/10837450.2013.769566
RESEARCH ARTICLE
Exploration of statistical experimental design to improve entrapment efficiency of acyclovir in poly (d, l) lactide nanoparticles 1
Institute of Pharmacy, Nirma University, Ahmedabad, India and 2Institute of Pharmacy, Ahmedabad University, Ahmedabad, India
Abstract
Keywords
Objective: In current exploration, systematic attempts have been made to improve the entrapment efficiency of a model hydrophilic drug substance, i.e. acyclovir, in poly (d, l) lactide (PLA) nanoparticles (NPs) using a modified nanoprecipitation technique. Methods: Formulation parameters such as drug to polymer ratio, antisolvent selection, electrolyte (NaCl) addition, pH alteration and temperature were screened to improve the entrapment efficiency of acyclovir in PLA NPs. The temperature of the system (0–5 C), phase volume ratio (1:2), stirring speed (2000 rpm), sonication time (5 min), etc. were kept constant during the preparation of NPs. Drug to polymer ratio and electrolyte addition emerged as critical formulation parameters affecting particle size as well as entrapment efficiency. Hence, in the present investigation a 32 full factorial design was used to investigate the combined influence of two factors, i.e. drug to polymer ratio (X1) and the amount of electrolyte, i.e. NaCl (X2) on particle size (Y1) and entrapment efficiency (Y2). The NPs were also evaluated for drugexcipient compatibility study by employing DSC and FT-IR analysis, whereas in vitro drug release studies were performed using dialysis bag technique in phosphate buffer pH 7.4. Results: Statistically significant models were evolved to predict entrapment efficiency and particle size. The effect of factors X1, X2 and X22 was found to be statistically significant in nature. Response variables, i.e. entrapment efficiency and particle size, were simultaneously optimized using desirability function using Design Expert software. This process allowed the selection of most suitable level of factors to achieve desired level of particle size and entrapment efficiency. The results of multiple linear regression analysis revealed that for obtaining desirable particle size (less than 250 nm) and entrapment efficiency (more than 17%), the NPs should be prepared using 1:3 drug to polymer ratio and 0.04 M NaCl. Acyclovir was found to be compatible with PLA as indicated by DSC and FT-IR studies. The experimental values obtained from the optimized formulation highly agreed with the predicted values. The drug release from the optimized formulation exhibited biphasic pattern and the drug release kinetics was best explained by Weibull model. Conclusion: In conclusion, results of the present study demonstrated that PLA NPs with expected particle size and entrapment efficiency can be obtained by adopting the concept of quality by design.
Factorial design, nanoparticles, optimization, poly (d, l) lactide
Introduction Over the past few years, pharmaceutical and medical research have shown increased interest in the development of colloidal carriers composed of biopolymers because of its considerable merits over the conventional drug delivery system. Colloidal drug carriers are likely to improve the bioavailability of entrapped drug molecules as well as exhibit preferential accumulation of drug to the target organ1. Biodegradable polymeric orthoesters, i.e. poly (d, l) lactide (PLA) and polylactide-co-glycolide (PLGA), have been extensively explored for the controlled release of variety of therapeutic agents. These polymers are also approved by the US Food and Drug Administration (US FDA) for pharmaceutical application, thus will endow good safety to the developed colloidal formulation2,3. Address for correspondence: Dr Sanjeev R. Acharya, Institute of Pharmacy, Nirma University, S.G. Highway, Ahmedabad 382481, India. E-mail:
[email protected]
History Received 11 August 2012 Revised 20 December 2012 Accepted 14 January 2013 Published online 20 February 2013
20 13
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
Prerak J. Patel1, Mukesh C. Gohel2, and Sanjeev R. Acharya1
Colloidal carriers of the aforementioned polymers with varied size i.e. micrometer to nanometer range have been constructed with various methodologies. Amongst the different techniques, process developed by Fessi and coworkers based on the principle of nanoprecipitation was conveniently executed on a laboratory scale and comparatively easy to scale up4. Acyclovir is an antiviral drug with a significant and highly specific activity against herpes viruses and is widely used in the management of variety of ocular and brain disorders5,6. Sodium salt of acyclovir is available as dry powder for reconstitution and administered to the patient via intravenous injection. Its plasma half-life is about 2.5 h, hence it requires frequent administration to achieve therapeutic effect7. Apart from that administration of acyclovir is also repeatedly associated with adverse events such as phlebitis at the injection site as well as transient elevations of serum creatinine following rapid intravenous infusion. To improve therapeutic efficacy of this first line agent against herpes simplex virus infection, it is formulated as controlled release polymeric carriers8. During the development of PLA
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
2
P. J. Patel et al.
nanocarriers containing acyclovir, it was observed that the entrapment of acyclovir i.e. hydrophilic drug substances inside the PLA matrix is very difficult by nanoprecipitation technique because of weak interaction between hydrophobic polymer and hydrophilic drug substances as well as more affinity of acyclovir toward the continuous aqueous phase, which lead to low entrapment of acyclovir in the precipitating PLA particles9–11. Hence proposed research is planned with the objective to increase the entrapment efficiency of acyclovir into PLA nanoparticles (NPs). The entrapment efficiency of acyclovir was modulated by screening different variables such as drug to polymer ratio, antisolvent selection, electrolyte concentration, pH alteration and temperature. Conventional formulation development experiments require a good deal of efforts and time especially when formulation is complex. Pharmaceutical formulation scientists are aiming to develop formulation with desired quality using minimum resources and time. Hence in addition to the art of formulation, the statistical optimization techniques were introduced which indicate relative significance of a number of variables and their interactions simultaneously by exploring relatively small number of representative experimental trials12,13. In this study, factorial design based on response surface method, was adopted to optimize PLA NPs of hydrophilic model drug, i.e. acyclovir. A 32 full factorial design was employed to evaluate the combined influence of drug to polymer ratio and electrolyte addition on entrapment efficiency as well as particle size of PLA nanocarriers. Overall, the concept of quality by design is utilized in the present study for developing acyclovirloaded PLA NPs with improved drug entrapment and controlled particle size.
Methods Materials PLA (inherent viscosity 0.18 dl/g, mol wt 16 000–18 000 Da) was obtained from Durect Corporation (Birmingham, AL). Acyclovir was a gift sample from Zydus Cadila Ltd. (Ahmedabad, India). Pluronic Õ P85 and Tween 20 were obtained as gratis samples from Torrent Research Centre (Ahmedabad, India). Acetone (purity NLT 99% by GC), dichloromethane (purity NLT 99% by GC) and ethanol (purity NLT 99.5% by GC) were procured from S.D. Fine-Chem (Mumbai, India). Double distilled water used was filtered through 0.22-mm filter from Millipore (Mumbai, India). All other reagents were used in this study were of analytical grade. Preparation of NPs Acyclovir-loaded PLA NPs were prepared by nanoprecipitation technique as described by Sanjay & Madaswamy14 and Stephanie et al.15 with minor modification. One hundred milligrams of acyclovir was suspended in a solution of PLA (0.5% w/v, 20 ml) in a blend of dichloromethane and acetone (5:95). The mixture was carefully injected to an antisolvent system (60 ml) consisting of water and ethanol (50:50) mixture with Tween 20 (0.3%v/v) and Pluronic Õ P85 (0.2% w/v) as a stabilizer. The mixture was stirred using blade homogenizer at 2000 RPM for 15 min (Remi Equipments, Mumbai, India) followed by sonication for 5 min at a pulse of 10 s (Trans-o-sonic, Mumbai, India) to precipitate PLA as NPs containing entrapped acyclovir. The resulting nanoparticulate dispersion was stirred for 6 h at room temperature (30–35 C) to evaporate organic solvents. After evaporation of organic solvent acyclovir encapsulated PLA NPs were recovered by centrifugation for 30 min at 15 000 g (Remi Equipments, Mumbai, India) from the dispersion followed by dual washing cycle with distilled water to remove excess stabilizer and free acyclovir. Finally, the nanoparticulate formulation was lyophilized
Pharm Dev Technol, Early Online: 1–13
for 18 h (EIE Instruments Pvt. Ltd, Ahmedabad, India) and stored at 4 C till further use. In the present investigation, variables such as drug to polymer ratio, antisolvent selection, electrolyte addition, pH alteration and temperature were scrutinized as independent factors, where as entrapment efficiency and particle size were chosen as dependent variables. Results of preliminary experiments suggested that drug to polymer ratio and electrolyte concentrations have potential influence on entrapment efficiency and particle size. In the present work, the formulations were prepared at different levels of drug to polymer ratio and electrolyte addition as per 32 full factorial design by keeping all other factors constant. Each batch of formulation was prepared in triplicate using the aforementioned method. Determination of process yield The process yield of PLA NPs containing acyclovir was determined as weight of final product after lyophilization, with respect to the initial total amount of the drug, polymer (PLA) and other solid excipients used for the preparation of NPs. Percentage process yield ¼
Practical yield 100 Theoretical yield
Particle size and size distribution analysis The particle size of acyclovir-loaded PLA NPs was measured by dynamic light scattering technique using a particle size analyzer (Nanotrac, NPA 252, Meerbusch, Germany) at a fixed angle of 180 at 25 C. For particle size measurement, samples of NPs were dispersed in distilled water and gently sonicated for 3 min using water-bath type sonicator. The values of the particle sizes are presented as mean standard deviation (SD) from three replicate samples. The width of the size distributions were characterized by the SPAN value. D90%, D50% and D10% are the mean diameters at which 90%, 50% and 10% (cumulative %) of the NPs are counted and calculated16. SPAN value ¼
D90 D10 100 D50
Zeta potential measurement Zeta potentials of PLA NPs containing acyclovir (different drug to polymer ratio) were measured in distilled water using Zetasizer 3000 HSA (Malvern Instruments, Malvern, UK). Particle shape and morphology analysis The surface topology and morphology of plain PLA nanoparticles and acyclovir-loaded PLA NPs were observed using scanning electron microscope. Dry NPs were dispersed in methanol and the suspension was dropped onto a metal grid for drying, after that dried NPs stub was sputtered with gold using argon plasma to coat surface of NPs for better observation. The stub was fixed into a sample holder under the vacuum chamber and sample was observed at low vacuum (103 Torr)17. Entrapment efficiency and drug loading The entrapment efficiency of acyclovir in the PLA NPs was determined using the procedure described by Tahereh et al.18. Twenty milligrams of lyophilized NPs were dissolved in 10 ml of dichloromethane and acyclovir was completely extracted at room temperature in 10 ml of 0.1 N HCl for 3 h9 closed flask on a mechanical shaker at about 75 rpm. The extraction process was repeated thrice and aliquots were combined followed by filtration through 0.2 mm polytetrafluoroethylene membrane filters and
Design to improve entrapment efficiency of acyclovir in PLA
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
DOI: 10.3109/10837450.2013.769566
analyzed by high-performance liquid chromatography (HPLC) for acyclovir content. The HPLC apparatus was a Jasco model (Jasco Ltd., Tokyo, Japan) equipped with PDA detector and operated at 254 nm for detection of acyclovir. An Inertsil ODS 3 reversed-phase column (250 4.6 mm internal diameter, GL Sciences Inc., Tokyo, Japan) was used at room temperature. The optimized mobile phase was acetontrile: 1% v/v glacial acetic acid (10:90). The flow rate was 1 ml/min. Acyclovir solutions in 0.1 N HCl ranging from 0.5 to 20 mg/ml were used for obtaining the calibration curve. The value of correlation coefficient was 0.9999. The sensitivity of the method was 0.05 mg/ml (refer supplementary material). The entrapment of acyclovir within PLA nanocarriers is expressed as both entrapment efficiency and loading capacity. The entrapment efficiency is the percentage of acyclovir added during the PLA nanocarriers preparation that becomes entrapped within the colloidal carrier, whereas the loading capacity is the amount of acyclovir (mg) per 100 mg of PLA. Percentage entrapment efficiency Amount of drug present in sample 100 ¼ Total amount of drug taken Percentage drug loading Amount of drug present in sample 100 ¼ Total amount of nanoparticles
independent variables on response variable at a glance. The design space may be identified from the overlay plot of particle size and entrapment efficiency. Replicate trials (checkpoint batches) were run to validate the generated model21,22. In vitro drug release studies In the present study, dynamic dialysis bag technique was used to study the release of acyclovir from PLA NPs. The release study of acyclovir from the nanoparticulate formulation was performed in phosphate buffer with pH 7.4, which represents physiological pH. NPs equivalent to 1 mg of acyclovir were placed in a dialysis bag (Himedia, Molecular weight cut-off 12 000 Da, Mumbai, India), and sealed at both the ends with clips. The dialysis bag was immersed in the receptor compartment containing 20 ml of phosphate buffer and stirred continuously at 75 rpm and the medium was maintained at 37 C. The receptor compartment was closed with lid to prevent evaporation of the dissolution medium. At predetermined time intervals, 1 ml samples were withdrawn and centrifuged for 8 min at 15 000 g. The same volume was replaced with fresh dissolution medium. The supernatant was filtered (0.2 mm membrane filters) and the released amount of acyclovir was determined by HPLC analysis23,24. Release kinetics
DSC and FT-IR studies The interactive relationship between acyclovir and PLA was studied using a differential scanning calorimetery (DSC) analysis with a thermal analyzer (Perkin Elmer, Pyris-1 DSC, MA). About 10 acyclovir, PLA, physical mixture of acyclovir with PLA in a ratio 1:3 and optimized acyclovir-loaded PLA NPs were singly sealed in an aluminum pan, and heated at a constant rate of 10 C/min from 30 C to 300 C under the environment of nitrogen19. Fourier transform-infrared spectroscopy (FT-IR) spectrum of acyclovir, PLA polymer and NPs (with or without drug) were obtained by the method of KBr cell on an FT-IR spectrometer (Jasco 6100, Japan) with a resolution of 2 cm1. A total of 16 scans were recorded and their average data have been presented over the range 4000–400 cm1 as spectra20. In addition, NPs with or without drug used for these studies (DSC and FT-IR) were freeze-dried without any additive. Factorial design In factorial design, the numbers of trials to be conducted depend upon the number of independent variables and the levels of independent variables. The response/s is/are measured for each trial and then analyzed with either simple linear (Y ¼ b0 þ b1X1 þ b2X2 þ þ biXi) or interactive (Y ¼ b0 þ b1X1 þ b2X2 þ þ biXi þ b12X1X2 þ þ bijXiXj) or quadratic (Y ¼ b0 þ b1X1 þ b2X2 þ þ biXi þ b12X1X2 þ b11X1X1 þ þ bijXiXj) models. In the present work, a 32 full factorial design was used to investigate the combined influence of two factors, i.e. drug to polymer ratio (X1) and the concentration of electrolyte (X2) on particle size (Y1) and entrapment efficiency (Y2). Table 2 displays the actual and the coded values of the selected independent variables. Table 3 shows the design layout and the outcome of the nine experiments. A statistical model incorporating the interactive and polynomial terms was evolved. Y ¼ b0 þ b1 X1 þ b2 X2 þ b12 X1 X2 þ b11 X12 þ b22 X22
3
ð1Þ
The regression equation was used for drawing response surface and contour plots to visualize the impact of changing the
Release kinetics of plain acyclovir and acyclovir loaded in PLA NPs were determined by processing in vitro drug release data using various kinetic equations such as zero order, first order, Higuchi model and Korsmeyer–Peppas model. The kinetics of drug release was decided from the most appropriate model. The release kinetics is usually determined by considering correlation coefficient (R2) value, Akaike information criterion (AIC) value and model selection criteria (MSC) value25,26.
Results Process yield and entrapment efficiency Acyclovir-loaded PLA NPs were prepared in a single step– modified nanoprecipitation technique. Table 1 depicts the results of process yield, entrapment efficiency, drug loading, particle size, zeta potential and SPAN value. Formulation parameters such as drug to polymer ratio, antisolvent selection, electrolyte addition, pH alteration and temperature were screened to evaluate their influence on different properties of NPs. PLA NPs were prepared at different pH, i.e. 2–3, 6.5–7 and 9–10, to evaluate the effect of pH on encapsulation of acyclovir in PLA NPs. Maximum entrapment efficiency was observed at neutral pH (6.5–7), because acyclovir shows least solubility at this pH due to its amphoteric nature. On the other hand, acyclovir exhibited high solubility at pH 2–3 as well as at pH 9–10 resulted to the lower entrapment of acyclovir in PLA NPs. Lowering of temperature of system reduced the solubility of acyclovir and improved its affinity for PLA matrix. The experimental results shown in Table 1 comply with the theory and hence in the subsequent experimentation, the temperature of the system was kept constant (0–5 C). Different antisolvent systems such as water, ethanol and water:ethanol blend were screened to precipitate PLA from the organic solvent system (DCM:acetone (5:95)). The experimental results revealed that entrapment efficiency of acyclovir in PLA NP matrix was found to be higher with ethanol and water:ethanol (50:50) blend as an external phase. The effect of ratio of solvent (organic) to antisolvent (aqueous phase) system was studied on the entrapment of acyclovir in the PLA NP matrix. In the present work, drug-loaded NPs were
4
P. J. Patel et al.
Pharm Dev Technol, Early Online: 1–13
Table 1. Effect of formulation variables on physicochemical properties of PLA NPs.
Variable pH Temperature Type of antisolvent
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
Drug to polymer ratio
Phase volume ratio (Organic:aqueous phase) Electrolyte concentration (NaCl)
Variable value
% Yield
% Loading efficiency
% Entrapment efficiency
Particle size (nm)
Zeta potential (mV)
SPAN value
2–3 6.5–7.0 9–10 0–5 C 30–35 C Water Ethanol Water:ethanol 1:1 1:2 1:3 1:4 1:4
74.88 2.1 75.70 3.2 71.50 1.9 80.2 2.2 75.7 2.1 70.17 1.9 74.20 2.6 79.50 3.5 69.70 1.4 70.83 2.1 72.25 2.7 72.50 2.0 80.20 3.2
0.46 0.24 1.91 0.35 0.68 0.20 2.37 0.50 1.95 0.45 1.05 0.39 3.09 0.54 2.58 0.32 5.44 0.67 3.52 0.55 3.10 0.49 2.76 0.44 2.39 0.32
2.35 0.86 9.70 1.30 3.21 0.78 12.10 1.05 9.82 1.00 5.17 0.85 13.30 1.35 12.38 1.25 7.07 0.85 6.72 0.82 13.50 0.95 13.24 1.10 12.14 0.79
228.6 10.5 236.1 11.2 207.2 8.8 231.5 6.5 232.8 7.2 235.8 6.9 237.5 8.5 231.9 5.4 315.6 6.8 307.1 10.2 260.8 4.9 280.5 10.1 231.5 5.5
– – – 20.4 1.2 21.0 1.1 19.9 1.4 20.1 1.0 20.3 1.4 19.8 1.2 19.9 1.2 20.1 1.5 20.1 1.1 20.5 1.3
0.66 0.51 0.79 0.70 0.51 0.45 0.65 0.61 0.66 0.43 0.54 0.59 0.70
1:2 0.05 M
82.25 4.1 78.50 2.8
2.76 0.32 3.16 0.35
14.30 1.09 17.91 1.15
257.1 5.9 278.8 4.2
20.2 1.5 21.5 1.5
0.42 0.59
0.1 M
78.80 2.9
3.90 0.41
14.53 0.85
716.6 15.7
21.3 1.5
0.81
prepared employing two different phase volume ratios, i.e. 1:4 and 1:2. Entrapment of acyclovir was found to be higher in PLA NPs prepared using phase volume ratio 1:2 in comparison to phase volume ratio 1:4. This observation is also in accordance with the solubility of acyclovir in the external phase. The effect of drug to polymer ratio (1:1, 1:2, 1:3 and 1:4) on the entrapment efficiency as well as particle size was studied by employing constant amount of PLA (100 mg) with variable drug concentration in the range of 20–100% (w/w). The entrapment efficiency was found to be satisfactory with drug to polymer ratios 1:3 and 1:4; while it was found to be unsatisfactory with ratios 1:1 and 1:2. Figure 1. SEM image of acyclovir-loaded PLA NPs.
Particle size, zeta potential and morphology Particle size is considered as an important parameter as the size of the particle affects its physical stability in colloidal suspension. Apart from that, particle size can also influence its biodistribution pattern as well as adhesion and interaction with the biological membrane. Dynamic light scattering experiments exhibited that particle size of acyclovir-loaded PLA NPs were found in the range of 236.1 4.6–780 21.4 nm. Particle size was also influenced by different variables such as pH of the system and electrolyte concentration. Preparation of PLA NPs at pH less than 2 led to the formation of aggregated NPs, while at neutral pH PLA NPs with least aggregation were prepared. The results of zeta potential measurement (Table 1) indicate that the particles present in formulations exhibited a negative charge with values ranging from 21.5 to 19.1 mV. Compared with plain PLA NPs with zeta potential around 42.2 mV, a decrease in zeta potential was likely to occur with acyclovirloaded PLA NPs (21.5–19.1 mV). The low values of zeta potential (20 mV) revealed that the NP suspensions will remain physically stable. The SEM image (ESEM EDAX XL-30, Philips, the Netherlands, Magnification 100 KX) in Figure 1 shows that many acyclovir-loaded PLA NPs approach spherical geometry with a smooth or slightly irregular surface. Irregular-shaped particles visible in the SEM image may be small aggregates formed during the drying process. Further, the optimized formulation was analyzed for residual solvents using gas chromatography and results of the studies revealed that all used solvents were found to be below the limits as per ICH Q3C(R3) guidelines for residual solvents (refer supplementary material).
DSC and FT-IR analysis DSC thermogram of untreated acyclovir, PLA, physical mixture of acyclovir with PLA in a ratio 1:3 and optimized acyclovirloaded PLA NPs are shown in Figure 2. The figure shows DSC thermogram of acyclovir and exhibits an endothermic peak at 254.93 C corresponding to the melting, immediately followed by an exotherm corresponding to the onset of thermal degradation, which then decomposes exothermically at about 273.91 C as shown in Figure 2(a). DSC thermogram of PLA showed an endothermic peak at 40 C in Figure 2(b), which is in accordance with previous findings reported by Musumeci et al.27. The physical mixture of acyclovir and PLA showed the same thermal behavior as the individual components, i.e. melting endotherm at 40 C and 249.91 C as shown in Figure 2(c). The endothermic peak, representing melting of acyclovir, vanished in the thermogram of acyclovir-encapsulated PLA NPs as shown in Figure 2(d). The FT-IR spectra of acyclovir, PLA, physical mixture of acyclovir with PLA and acyclovir-loaded PLA NPs are shown in Figure 3. The FT-IR spectra of acyclovir depicts the characteristics bands at 3197 cm1, 1638 cm1 and 1340 cm1 due to N–H stretching, C¼O stretching and –C–N stretching, respectively. The FT-IR spectra of PLA (polymer) and blank PLA NPs were found to be same and showed characteristic bands at 3509 cm1 and 1758 cm1 due to O–H stretching and C¼O stretching, respectively. The FT-IR spectra of physical mixture of acyclovir with PLA show all the characteristic bands of acyclovir (i.e. at 1638 cm1 and 1340 cm1 due to C¼O stretching and C–N stretching, respectively), and also two characteristic bands of PLA
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
DOI: 10.3109/10837450.2013.769566
Design to improve entrapment efficiency of acyclovir in PLA
5
Figure 2. DSC thermogram of (a) acyclovir, (b) PLA, (c) physical mixture of acyclovir with PLA and (d) acyclovir-loaded PLA NPs.
Figure 3. Overlay FT-IR spectra of (a) PLA, (b) acyclovir, (c) physical mixture of acyclovir with PLA and (d) acyclovir-loaded PLA NPs.
at 3509 cm1 and 1758 cm1 can be seen. The FT-IR spectra of acyclovir-loaded PLA NPs show the absence of characteristic peak of acyclovir N–H stretching at 3197 cm1. Optimization of nanoparticulate formulation using 32 full factorial design The results of the preliminary experiments revealed that amongst the screened formulation attributes, drug to polymer ratio and electrolyte concentration emerged as critical quality attributes and exhibited considerable influence on the percentage entrapment efficiency and particle size. In the current work, both the independent variables were screened at three different levels as shown in Table 2. Nine batches were prepared at different levels of drug to polymer ratio as well as electrolyte concentration and evaluated for response variables as described in Table 3.
Table 2. Different levels of independent variables selected for the study. Independent variables
Actual value
Coded value
Drug to polymer ratio (X1)
1:4 1:3 1:2 0 0.05 0.1
1 0 1 1 0 1
Electrolyte concentration (M) (X2)
The particle size and percentage entrapment efficiency for the nine batches showed substantial variability, 236–780 nm and 3.76–17.98%, respectively. The broad range in the responses indicates the dependence of response variables on the selected independent variables.
6
P. J. Patel et al.
Pharm Dev Technol, Early Online: 1–13
Table 3. Design layout of independent and dependent variables as per 3 factorial design.
2
Batch code
X1
X2
Particle size (nm)
% Entrapment efficiency
O1 O2 O3 O4 O5 O6 O7 O8 O9
1 1 1 0 0 0 1 1 1
1 0 1 1 0 1 1 0 1
327.9 10.2 340.7 13.5 780 21.4 280.5 8.4 272.5 9.4 716.8 22.3 236.1 4.6 246.8 4.0 680.5 13.7
6.72 0.3 9.21 0.7 7.10 0.8 13.86 0.6 17.98 0.6 14.53 0.3 10.69 0.2 14.30 0.6 3.76 0.7
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
X1 ¼ Drug to polymer ratio; X2 ¼ electrolyte concentration.
The polynomial equations can be used to draw conclusions after considering the magnitude of coefficient and the mathematical sign it carries. Full model for particle size analysis is shown below: YParticle size ¼ 278:63 þ 47:53X1 þ 222:15X2 þ 1:925X1 X2 þ 12:06X12 þ 216:3X22
ð2Þ
2
ðR ¼ 0:9996, F ¼ 1823, p 5 0:05, n ¼ 9Þ The results of ANOVA clearly indicate that at least one of the selected independent variables affect particle size. The polynomial equation for the particle size analysis implies that concentration of electrolyte as well as drug to polymer ratio have positive impact on particle size. Equation (2) clearly reveals that low level of X1 (drug to polymer ratio) and X2 (electrolyte concentration) favors the preparation of NPs. Equation (2) is shown in the form of response surface plot and contour plot in Figure 4(a) and (b), respectively, to visualize the impact of changing the independent variables on particle size. The results of multiple linear regression analysis revealed that drug to polymer ratio (b1), electrolyte concentration (b2) and the polynomial term, i.e. b22, contribute significantly (p50.05) to the prediction of particle size and hence these terms are retained in the reduced model. YParticle size ¼ 278:63 þ 46:95X1 þ 218:15X2 þ 216:3X22 R2 ¼ 0:9989, F ¼ 1532, p 5 0:05, n ¼ 9
ð3Þ
Similarly, the full model for percentage drug entrapment efficiency (% EE) is shown below: Y%EE ¼ 17:23 0:95X1 1:81X2 þ 1:82X1 X2 5:13X12 5:19X22 R2 ¼ 0:9616, F ¼ 4:17, p 5 0:05, n ¼ 9 ð4Þ The results of ANOVA clearly indicate that at least one of the selected independent variables affect percentage entrapment efficiency. Equation (4) indicates that both the independent variables, i.e. drug to polymer ratio (X1) and electrolyte concentration (X2), have negative impact on percentage drug entrapment efficiency. The polynomial equation clearly reveals that low level of drug to polymer ratio (X1) and low level of electrolyte concentration (X2) appear to favor the preparation of NPs with higher entrapment of acyclovir. Equation (4) is shown in the form of response surface plot and contour plot in Figure 5(a) and (b), respectively, to visualize the impact of changing the independent variables on percentage entrapment efficiency. The results of the multiple linear regression test revealed that electrolyte concentration (b2) and polynomial terms b11 as well as b22 contribute significantly (p50.05) to the prediction of percentage drug
entrapment efficiency and hence these terms are retained in the reduced model: Y%EE ¼ 17:23 1:81X2 5:13X12 5:19X22
R2 ¼ 0:9076, F ¼ 7:16, p 5 0:05, n ¼ 9
ð5Þ
Multiple linear regression coefficients for particle size and percentage entrapment efficiency are presented in Table 4. Checkpoint batches were prepared to check the predictive ability of regression equation. Tables 5 and 6 show the results of the checkpoint batches for particle size and percentage entrapment efficiency, respectively. Good agreement was observed between the observed and the predicted values. The goal of the present study was to improve the entrapment efficiency of hydrophilic compound, i.e. acyclovir, inside PLA (hydrophobic polymer) NP matrix. Hence in the present work to achieve desired particle size with maximum entrapment of acyclovir, both the response variables were required to optimize simultaneously. The arbitrary selected desired level of particle size was below 250 nm and percentage entrapment efficiency was selected more than 17%. The desirability zone was determined using Design Expert software as shown in Figure 6. Figure 6 shows the acceptable range of independent variables that could meet the stated requirements. From the desirability region, drug to polymer ratio was optimized at 1:3 level and electrolyte concentration was chosen at 0.04 M. The optimized values of variables yielded particle size of 249 1.5 nm and percentage entrapment efficiency of acyclovir about 18.05 0.8% as shown in Table 7. In vitro release studies and release kinetics Release of acyclovir from the PLA NPs was determined using the dialysis bag diffusion technique and release kinetics was established for the optimized nanoparticulate formulation. The release profile of acyclovir from PLA NPs was observed as initial phase of rapid drug release followed by a slower exponential release of the remaining drug over the next 48 h. About 30% of the drug was released within first hour followed by lateral release in a sustained manner over a period of 48 h. The control profile (acyclovir) showed that about 80% of the drug was released within first hour in phosphate buffer with pH 7.4 as shown in Figure 7(b). Release profile of acyclovir was checked at different drug to polymer ratios, but results indicated insignificant difference in the drug release profile for NPs prepared using different drug to polymer ratios (Figure 7a). In vitro drug release data of optimized acyclovir-loaded PLA NPs were fitted to various kinetic equations and the results are presented in Table 8. The release kinetics for acyclovir (control) and for optimized NP batch was determined by considering R2, AIC and MSC. In the present study, release kinetic model was selected based on low AIC and high R2 and MSC values. The release pattern of drug from PLA NPs was derived as biphasic (i.e. initial burst release followed by extended release). The results in Table 8 revealed that plain acyclovir showed best fit for first-order model while the NPs showed best fit for Weibull model.
Discussion Process yield and entrapment efficiency As discussed in the ‘‘Results’’ section, formulation parameters such as drug to polymer ratio, antisolvent selection, electrolyte addition, pH alteration and temperature show influence on different properties of NPs. The pH of the system affects ionization and solubilization of acyclovir in the external phase and hence it has influence on entrapment of acyclovir in NPs.
Design to improve entrapment efficiency of acyclovir in PLA
7
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
DOI: 10.3109/10837450.2013.769566
Figure 4. (a) Response surface plot and (b) contour plot for particle size.
The affinity of acyclovir for PLA is more if acyclovir is present in undissolved form in the system. Maximum entrapment efficiency was observed at neutral pH (6.5–7), because acyclovir shows least solubility at this pH due to its amphoteric nature. On the other hand, acyclovir exhibits high solubility at pH 2–3 as well as at
pH 9–10. At these pH values, ionization of acyclovir leads to poor entrapment of acyclovir in PLA NPs. Results of solubility study of acyclovir at different temperatures indicated that lowering of the temperature of the system reduced the solubility of acyclovir and improved its affinity for
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
8 P. J. Patel et al. Pharm Dev Technol, Early Online: 1–13
Figure 5. (a) Response surface plot and (b) contour plot for % EE.
Design to improve entrapment efficiency of acyclovir in PLA
DOI: 10.3109/10837450.2013.769566
Table 4. Coefficients for particle size and percentage entrapment efficiency (% EE). Response
b0
b1
b2
b12
b11
b22
R2
Particle size % EE
278.63 17.23
47.53 0.95
222.15 1.81
1.925 1.82
12.06 5.13
216.3 5.19
0.9995 0.9616
Table 5. Validation of regression equation by checkpoint batch analysis for particle size.
Sr. no.
Independent variables
Coded level
1
Drug to polymer ratio Electrolyte concentration Drug to polymer ratio Electrolyte concentration
0.5 0.5 0.5 0.5
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
2
Theoretical result
Practical result
% Deviation
253.4 nm
261.8 nm
3.3
429.5 nm
420.8 nm
2.02
Table 6. Validation of regression equation by checkpoint batch analysis for percentage entrapment efficiency.
Sr. no.
Independent variables
Coded level
1
Drug to polymer ratio Electrolyte concentration Drug to polymer ratio Electrolyte concentration
0.5 0.5 0.5 0.5
2
Theoretical result
Practical result
% Deviation
13.63
12.92
5.10
12.30
11.8
4.05
Figure 6. Overlay plot for simultaneous optimization of particle size and % EE.
Table 7. Optimized formulation for acyclovir-loaded PLA NPs. Particle size (nm) Level of independent variables Drug:polymer Electrolyte concentration
1:3 0.04 M
% Entrapment efficiency
Theoretical value
Practical value
Theoretical value
Practical value
253.06
249.1 1.5
17.39
18.05 0.8
9
10
P. J. Patel et al.
Pharm Dev Technol, Early Online: 1–13
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
Figure 7. (a) Effect of different drug to polymer ratios on release of acyclovir in phosphate buffer pH 7.4 and (b) release of acyclovir from optimized formulation in phosphate buffer pH 7.4.
Table 8. Release kinetics of acyclovir in phosphate buffer form formulations. Formulation Release kinetic model Zero order R2 AIC MSC First order R2 AIC MSC Higuchi R2 AIC MSC Korsmeyer–Peepas R2 AIC MSC Hixson–Crowell R2 AIC MSC Weibull R2 AIC MSC
Acyclovir
Acyclovir-loaded PLA NPs
0.5565 50.29 0.78
0.2207 104.16 0.72
0.9979 18.25 4.55
0.8787 78.76 1.58
0.9426 38.03 1.26
0.6907 89.06 0.65
0.9928 26.26 3.22
0.9279 73.89 2.02
0.9848 30.05 2.59
0.6448 90.58 0.51
0.9882 28.81 2.88
0.9933 18.59 4.41
R2 ¼ Correlation coefficient, AIC ¼ Akaike MSC ¼ model selection criterion.
information
criterion,
PLA matrix and hence experiments were designed at constant temperature conditions, i.e. 0–5 C. Acyclovir-loaded PLA NPs were prepared using different antisolvent systems such as water, ethanol and water:ethanol
(50:50) blend. The experimental results revealed that entrapment efficiency of acyclovir in PLA NP matrix was found to be higher with ethanol and water:ethanol (50:50) blend as an external phase. These experimental findings were confirmed by lesser solubility of acyclovir in ethanol as well as in water:ethanol (50:50) blend. The poor entrapment of acyclovir in PLA NP matrix was attributed to fairly good solubility of acyclovir in water. For further experimentation, water:ethanol (50:50) blend was selected as an antisolvent system, as it yielded reasonably high entrapment of acyclovir in PLA NP matrix. The phase volume ratio (organic phase:aqueous phase) also showed influence on entrapment of acyclovir in PLA NPs. Entrapment of acyclovir was found to be higher in PLA NPs prepared using phase volume ratio 1:2 in comparison to phase volume ratio 1:4. This observation is also in accordance with the solubility of acyclovir in the external phase. Experimental run carried out at phase volume ratio 1:4 has higher volume of aqueous phase to solubilize acyclovir as compared to phase volume ratio 1:2 and hence higher entrapment of acyclovir was observed in NPs prepared at phase volume ratio 1:2. Further experiments were carried out at phase volume ratio 1:2, as it yielded NPs with reasonable entrapment of acyclovir in PLA NP matrix. Drug to polymer ratio exhibited considerable influence on entrapment of acyclovir in PLA NPs. The entrapment efficiency was found to be satisfactory with drug to polymer ratios 1:3 and 1:4; while it was found to be unsatisfactory with ratios 1:1 and 1:2. The probable reason for this observation is due to formation of more porous polymer matrix, because penetration of water in the PLA matrix and subsequent leaching of drug solution leads to the formation of porous NPs with lesser drug content. Similar observations were also revealed by Witschi and Lamprecht28,29. The selected drug to polymer ratio was 1:3, i.e. 33% w/w acyclovir, to achieve desirable entrapment efficiency within the PLA NPs.
DOI: 10.3109/10837450.2013.769566
The osmotic gradient between inner and outer phases can also influence entrapment efficiency of acyclovir30,31. Osmotic gradient between inner and outer phases was modulated by addition of an electrolyte, i.e. sodium chloride, which also diminishes the solubility of acyclovir in the outer phase. Good entrapment of acyclovir was achieved when the sodium chloride was added in the concentration range of 0.04–0.05 M.
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
Particle size, zeta potential and morphology Particle size is considered as an important parameter as the size of the particle affects its physical stability in colloidal suspension. Apart from that particle size can also influence its biodistribution pattern as well as adhesion and interaction with the biological membrane. Dynamic light scattering experiments exhibited that particle size of acyclovir-loaded PLA NPs were found in the range of 236.1 4.6–780 21.4 nm. Particle size was also influenced by different variables such as pH of the system and electrolyte concentration. It was observed that system containing higher concentration sodium chloride produced larger aggregated particles. Results of the experiments indicated that addition of sodium chloride to the system altered the osmolarity between inner and outer phases as well as changed the flow mechanics at the interfacial area which led to the formation of less stable quasiemulsion droplets. Han et al.32 reported that aggregated particles were formed due to altered solvent diffusion pattern. Preparation of PLA NPs at pH less than 2 led to the formation of aggregated NPs due to lowered degree of ionization of the free carboxylic acid ends of PLA. While at neutral pH, carboxylic acid groups of PLA are negatively charged, which significantly decreased in number when pH is lowered. Leena et al.33 have reported that particle aggregation increases due to decreased number of charged groups and decreased electric repulsions. Acyclovir-loaded PLA NPs exhibited a negative particle charge with values ranging from 21.5 to 19.1 mV. This negative value of zeta potential was due to the presence of terminal carboxylic groups in PLA. In comparison with plain PLA NPs with zeta potential around 42.2 mV34, a decrease in zeta potential was likely to occur due to a possible ionic interaction between carboxylic terminal groups in the polymer and amino groups in acyclovir. The low values of zeta potential (20 mV) revealed that the NP suspensions will remain physically stable. DSC and FT-IR analysis DSC study was performed to investigate the compatibility of drug with formulation components. Musumeci et al. reported that this aspect could influence the in vitro and in vivo release of the drug from the systems27. The physical mixture of acyclovir and PLA showed the same thermal behavior as the individual components, i.e. melting endotherm at 40 C and 254.93 C indicating that there was no interaction between the drug and the polymer in the solid state. The endothermic peak representing melting of acyclovir was vanished in the thermogram of acyclovir encapsulated PLA NPs. The results suggested that the drug was molecularly dispersed in the PLA matrix and it might exhibit amorphous or disordered crystalline state. FT-IR analysis was carried out to establish compatibility between the drug and the polymer35. The FT-IR spectra of physical mixture of acyclovir with PLA show all the characteristic bands of acyclovir (i.e. at 1638 cm1 and 1340 cm1 due to C¼O stretching and –C–N stretching, respectively), and also two characteristic bands of PLA at 3509 cm1 and 1758 cm1 can be seen. Hence, it can be concluded that PLA and acyclovir do not interact under the experimental conditions. Further, FT-IR spectra
Design to improve entrapment efficiency of acyclovir in PLA
11
of acyclovir-loaded PLA NPs show the absence of characteristic peak of acyclovir –N–H stretching at 3197 cm1, indicating entrapment of acyclovir in the matrix of PLA NPs36,37. Optimization of nanoparticulate formulation using 32 full factorial design Conventional pharmaceutical formulation development approaches involve altering one variable at a time while keeping all the other factors at constant level, but this practice is time consuming and cost intensive. Apart from that the classical technique is not able to give the estimate of interaction between independent variables. The most important point in favor of design of experiments (DOE) is its endorsement by regulators. The complexity of pharmaceutical formulations can be studied by using established statistical tools such as factorial designs. The results of the preliminary experiments revealed that amongst the screened formulation attributes, drug to polymer ratio and electrolyte concentration emerged as critical quality attributes and exhibited considerable influence on the percentage entrapment efficiency and particle size. The particle size and percentage entrapment efficiency for all the prepared batches showed substantial variability, 236–780 nm and 3.76–17.98%, respectively. The broad range in the responses indicates the dependence of response variables on the selected independent variables. The polynomial equation (Equation (2)) for the particle size analysis implies that the concentration of electrolyte as well as drug to polymer ratio have positive impact on particle size. Equation (2) also reveals that low level of X1 (drug to polymer ratio) and X2 (electrolyte concentration) favors the preparation of NPs. Electrolyte concentration has positive effect on particle size as it leads to formation of less stable quasi-emulsion droplets, resulting in the formation of aggregated particles. Equation (4) indicates that both the independent variables, i.e. drug to polymer ratio (X1) and electrolyte concentration (X2), have negative impact on percentage entrapment efficiency. The polynomial equation clearly reveals that low levels of drug to polymer ratio (X1) and electrolyte concentration (X2) appear to favor the preparation of NPs with higher entrapment of acyclovir. Higher concentration of electrolyte leads to the formation of porous particles, which might facilitate leaching of the drug in the surrounding environment. Sodium chloride diminishes the solubility of acyclovir due to its more favorable partition toward hydrophilic environment and hence more concentration of undissolved acyclovir is available for entrapment in the polymer matrix. The maximum drug entrapment was noticed at drug to polymer ratio of 1:3. Formulation prepared using drug to polymer ratio deviating from this level on higher or lower side yielded particle with low entrapment either because of the formation of porous polymer matrix or unavailability of undissolved drug for entrapment inside the polymer matrix. On the other hand, sodium chloride affects osmotic gradient as well as causes reduction in solubility of acyclovir due to preferential distribution of sodium chloride toward aqueous phase, which further enhanced entrapment of acyclovir in PLA NP matrix. Results of checkpoint batch analysis show good agreement between the observed and predicted results. Hence, the evolved model can be utilized as a predictive tool for selecting levels of independent variables to achieve desired output. The goal of present study was to improve entrapment efficiency of hydrophilic compound, i.e. acyclovir, inside PLA (hydrophobic polymer) NP matrix. But at the same time, control of the particle size is critically important as it affects fate of NPs along with its site of accumulation in the body and drug release. Hence, in present work to achieve desired particle size with maximum entrapment of acyclovir, both the response variables were required to optimize simultaneously by desirability function
12
P. J. Patel et al.
using Design Expert software. Experimental data of full factorial design followed by simultaneous optimization method suggested that the constructed model yielded the acceptable levels of independent variables which produced PLA NPs with desired particle size and percentage entrapment of acyclovir.
Pharm Dev Technol, Early Online: 1–13
design was explored in the present study for developing acyclovir-loaded PLA NPs with improved drug entrapment and controlled particle size.
Acknowledgements
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
In vitro release studies and release kinetics Release of acyclovir from the PLA NPs was determined using dialysis bag diffusion technique and release kinetics was established for the optimized nanoparticulate formulation. The release profile of acyclovir from PLA NPs was biphasic. Initial burst release of acyclovir form NPs attributed to drug molecules present near the outer surface of the NPs dissolved instantaneously when it comes in contact with the release medium. The small size of the particle and higher surface area are major factors which influence the burst release effect. Further, lateral release was observed in a sustained manner over a period of 48 h, probably due to slow erosion of the polymer matrix. The control profile (acyclovir) showed that about 80% of the drug was released within first hour. In vitro drug release data of optimized acyclovir-loaded PLA NPs were fitted to various kinetic equations. The release kinetics for acyclovir (control) and for optimized NP batch was determined by considering R2, AIC and MSC. The AIC has been used as a standard tool for selecting optimal kinetic models. The AIC value is a measure of best fitting model based on maximum likelihood, amongst several analyzed models for a given set of data, the model associated with the smallest value of AIC is regarded as best model out of that set of models. Apart from AIC value, MSC value is another statistical criterion for model selection, which is increasing attention in the field of dissolution data modeling. The MSC is a modified reciprocal form of the AIC and has been normalized so that it is independent of the scaling of the data points. Larger value of MSC is a measure for selection of most appropriate model, when compared with all differently analyzed models in the study. Generally, an MSC value of more than two to three indicates a good model fit. By considering above facts in the present study, release kinetic model was selected based on low AIC and high R2 and MSC values. The release pattern of drug from PLA NPs was derived as biphasic (i.e. initial burst release followed by extended release). The results in Table 2 revealed that plain acyclovir showed best fit for first-order model, while the NPs showed best fit for Weibull model25,26,38. Conclusion The present investigation demonstrates that statistical design and optimization technique can be successfully employed in the development of polymeric NPs with predictable particle size and percentage drug entrapment efficiency. The drug excipient compatibility was established by performing DSC and FT-IR studies. From the findings of the screening experiments, we have identified the most influential factors, i.e. acyclovir to PLA ratio and sodium chloride concentration, which have significant effect on particle size and percentage entrapment efficiency of drug. Subsequently, full factorial design was also employed using screened vital factors to design PLA NPs with controlled particle size and improved entrapment efficiency of acyclovir. The results of statistical experimental design were expressed as mathematical models, contour and response surface plot. Further, various checkpoint batch analyses were performed to validate the generated mathematical equations as a predictive tool to develop PLA NPs with desired particle size and entrapment efficiency of drug. Optimized batch was identified by simultaneous optimization of multiple responses using Design Expert software. In nutshell, the concept of quality by
The authors are grateful to the Department of Science & Technology, Government of India, for providing financial assistance to support the present research work.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.
References 1. Gao S, Frederik DH, Ebbe JBN, et al. The effect of chemical modification and nanoparticle formulation on stability and diodistribution of siRNA in mice. Mol Ther 2009;17:1225–33. 2. Acharya S, Sahoo SK. PLGA nanoparticles containing various anticancer agents and tumor delivery by EPR effect. Adv Drug Del Rev 2011;63:170–83. 3. Avnesh K, Yadav SK, Yadav SC. Biodegradable polymeric nanoparticles based drug delivery systems. Colloids Surf B Biointerfaces 2010;75:1–18. 4. Fessi H, Puisieux F, Devissaguet JP, et al. Nanocapsule formation by interfacial deposition following solvent displacement. Int J Pharm 1989;55:R1–4. 5. Jain SP, Shah S, Rajadhyaksha N, et al. Twice a day ocular inserts of acyclovir by melt extrusion technique. Ind J Pharm Sci 2007; 69:562–7. 6. David B, Hermione L, Leena M. Oral acyclovir suppression after neonatal herpes. N Engl J Med 2012;366:90–1. 7. Miranda P, Blum MR. Pharmacokinetics of Acyclovir after intravenous and oral administration. J Antimicrob Chemother 1983; 12:29–37. 8. Kamel AO, Awad GA, Geneidi AS, Mortada ND. Preparation and characterization of acyclovir nanoparticles by double emulsion technique. Egy J Biomed Sci 2007;23:218–36. 9. Claudia G, Claudio B, Adriana M, et al. Influence of preparation conditions on acyclovir-loaded poly-d,l-lactic acid nanospheres and effect of PEG coating on ocular drug bioavailability. Pharm Res 2003;20:584–90. 10. Ugo B, Eric A, Eric D. Development of a nanoprecipitation method intended for the entrapment of hydrophilic drugs into nanoparticles. Eur J Pharm Sci 2005;24:67–75. 11. Amany OK, Gehanne ASA, Ahmed SG, Nahed DM. Preparation of intravenous stealthy acyclovir nanoparticles with increased mean residence time. AAPS PharmSciTech 2009;10:1427–36. 12. Hao J, Xinsheng F, Yanfang Z, et al. Development and optimization of solid lipid nanoparticle formulation for ophthalmic delivery of chloramphenicol using a Box-Behnken design. Int J Nanomed 2011;6:683–92. 13. Mira EG, Egea1 MA, Souto EB, et al. Optimizing flurbiprofenloaded NLC by central composite factorial design for ocular delivery. Nanotechnology 2011;22:0957–4484. 14. Sanjay S, Madaswamy SM. Preparation and characterization of nanoparticles containing an atypical antipsychotic agent. Nanomedicine 2007;2:233–40. 15. Stephanie S, Joseph TD, Ulrich SS. Nanoprecipitation and nanoformulation of polymers: from history to powerful possibilities beyond poly(lactic acid). Soft Matter 2011;7:1581–8. 16. Reis PC, Neufeld RJ, Ribeiro AJ, Viega F. Design of insulin loaded alginate nanoparticles: influence of the calcium ion on polymer gel matrix properties. Chem Ind Chem Eng Quart 2006;12:47–52. 17. Reddy LH, Murthy RSR. Pharmacokinetics and biodistribution studies of doxorubicin loaded poly(butylcyanoacrylate) nanoparticles synthesized by two different techniques. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2004;148:161–6. 18. Tahereh SJK, Solmaz E, Mehdi EM, et al. Improved drug loading and antibacterial activity of minocycline-loaded PLGA nanoparticles prepared by solid/oil/water ion pairing method. Int J Nanomed 2012;7:221–34.
Pharmaceutical Development and Technology Downloaded from informahealthcare.com by 202.131.98.170 on 07/15/13 For personal use only.
DOI: 10.3109/10837450.2013.769566
19. Lv QZ, Yu AH, Xi YW, et al. Development and evaluation of penciclovir loaded solid lipid nanoparticles for topical delivery. Int J Pharm 2009;372:191–8. 20. Wang JL, Wang R, Li LB. Preparation and properties of hydroxycamptothecin-loaded nanoparticles made of amphiphilic copolymer and normal polymer. J Colloid Interface Sci 2009; 336:808–13. 21. Bolton S. Pharmaceutical statistics. 3rd ed. New York: Marcel Dekker Inc;1990:326–54. 22. Montgomery DC. Design and analysis of experiments. 5th ed. New York, NY: Wiley & Sons; 2001. 23. Roberta C, Manuela D, Andrea C, et al. Enhanced antiviral activity of Acyclovir loaded into b-cyclodextrin-poly(4-acryloylmorpholine) conjugate nanoparticles. J Control Rel 2009;137:116–22. 24. Amra H, Martin Z, Gottfried R, Claudia V. Chitosan-tripolyphosphate nanoparticles as a possible skin drug delivery system for acyclovir with enhanced stability. J Pharm Pharmacol 2009;61:1609–16. 25. Kakran M, Sahoo NG, Li L, Judeh Z. Dissolution of artemisinin/ polymer composite nanoparticles fabricated by evaporative precipitation of nanosuspension. J Pharm Pharmacol 2010;62:413–21. 26. Zhang Y, Huo M, Zhou J, et al. DDSolver: an add-in program for modeling and comparison of drug dissolution profiles. AAPS J 2010;12:263–71. 27. Musumeci T, Ventura CA, Giannone I, et al. PLA/PLGA nanoparticles for sustained release of docetaxel. Int J Pharm 2006; 325:172–9. 28. Witschi C, Doelker E. Influence of the microencapsulation method and peptide loading on poly(lactic acid) and poly(lactic-co-glycolic acid) degradation during in vitro testing. J Control Release 1998;51:327–341. 29. Lamprecht A, Ubrich N, Hombreiro PM, et al. Influences of process parameters on nanoparticle preparation performed by a double
Design to improve entrapment efficiency of acyclovir in PLA
30.
31.
32.
33.
34.
35.
36.
37.
38.
13
emission pressure homogenization technique. Int J Pharm 2000; 196:177–82. Uchida T, Yoshida K, Nakada Y. Preparation and characterization of polylactic acid microspheres containing water-soluble anesthetics with small molecular weight. Chem Pharm Bull 1997;45:513–17. Freytag T, Dashevsky A, Tillman L, et al. Improvement of the encapsulation efficiency of oligonucleotide-containing biodegradable microspheres. J Control Release 2000;69:197–207. Han K, Lee KD, Gao ZG, Park JS. Preparation and evaluation of poly(L-lactic acid) microspheres containing rhEGF for chronic gastric ulcer healing. J Control Release 2001;75:259–69. Leena P, Johanna A, Samuli H, et al. Improved entrapment efficiency of hydrophilic drug substance during nanoprecipitation of poly(l) lactide nanoparticles. AAPS PharmSciTech 2004;5:1–6. Govender T, Stolnik S, Garnett MC, et al. PLGA nanoparticles prepared by nanoprecipitation drug loading and release studies of water soluble drug. J Control Release 1999;57:171–85. Sullivan CO, Birkinshaw C. In vitro degradation of insulin-loaded poly(n-butylcyanoacrylate) nanoparticles. Biomaterials 2004; 25:4375–82. Simeonova M, Ivanova G, Enchev V, et al. Physicochemical characterization and in vitro behavior of daunorubicin-loaded poly(butylcyanoacrylate) nanoparticles. Acta Biomater 2009;5:2109–21. Duan JH, Zhang YD, Chen W, et al. Cationic polybutyl cyanoacrylate nanoparticles for DNA delivery. J Biomed Biotechnol 2009;149:254–62. Mayer BX, Mensik C, Krishnaswami S, et al. Pharmacokineticpharmacodynamic profile of systemic nitric oxide-synthase inhibition with L-NMMA in humans. Br J Clin Pharmacol 1999;47:539–44.
