An analytical method of RAS was developed for its quantification  in phosphate buffer saline (PBS) pH 7.4 using a double beam UV Visible Spectrophotometer (JASCO V 630). A stock solution (I) of RAS was prepared by dissolving 100 mg RAS in 100 ml PBS pH 7.4 to obtain a concentration of 1000 µg/ml. Further, stock solution (II) was prepared by taking 10 ml of stock solution (I) and diluting it to 100 ml PBS pH 7.4 to obtain a concentration of 100 µg/ml. Aliquots of 2, 6 ml from stock solution (II) and 1.4, 1.8, 2.2, 2.6 ml from stock solution (I) were withdrawn respectively and volume was made up to 10 ml to get concentrations of 20, 60, 100, 140, 180, 220 and 260 µg/ml. These solutions were analyzed using UV-visible Spectrophotometer at 271 nm and absorbances were recorded. The procedure was followed in triplicate to minimize the chances of error.
To select the factors and their experimental ranges (process variables & excipient having major influence on PNPs) beginning trials were done by characterized EE & PS. Polymer concentration (A), stabilizer concentration (B) and sonication time (C) were found to be the major factors affecting efficiency of PNPs. These factors and their ranges were fitted in factorial design to give formulations with their best possible combinations.
A Box-Behnken design was selected for statistical optimization of formulation parameters by
investigating effects of factors on encapsulation efficiency (A), particle size (B), drug release (C) of RAS loaded nanoparticles. It was used to determine optimal condition of all factors by estimating experimental conditions having minimum variability using analysis of variance (ANOVA) (18-20). The factors for design were selected on the basis of literature and preliminary trials performed and used at three different levels. The levels of factors and responses are coded as shown in Table 1.
|Independent||Agent||Codes||Unit||-1 (Low)||0 (Central)||+1 (High)|
The PLGA nanoparticles (PNPs) of RAS were prepared using “Double Emulsion Method” . The procedure was carried out in two stages by preparing primary and secondary emulsions. The first step i.e. primary emulsion was prepared by dissolving of drug (10 mg) in aqueous phase (1 ml) and adding it to the solution of poly-(d, l-lactide-co-glycolide) (PLGA in 4ml dichloromethane) and sonicated (5 minutes) in a probe sonicator on an ice bath for complete preparation of the emulsion of the two phases. Further, preparation of secondary emulsion was done by adding primary emulsion to a known concentration of aqueous solution (16 ml) of PVA and again sonicated for a specified amount of time on an ice bath.
The samples were analyzed for physicochemical interaction between drug and polymer by FT-IR analysis. The surface morphology of drug loaded PNPs was analyzed using Transmission Electron Microscopy (TEM). For TEM analysis, a drop of nanoparticles dispersion diluted with distilled water (dH2O) was negatively stained with phosphor-tungstic acid (2% w/v) and placed on a 400-mesh TEM copper grid coated with carbon film and dried at room temperature. Then, the slide was examined under transmission electron microscope using TEM CM-10 Philips, operating at 60-80 KV at different magnifications.
The entrapment efficiency (EE) of drug loaded PNPs-1 to 20 (Table 2) was determined by “Indirect Method” by estimating total amount of free drug present [21-22]. In this, aqueous dispersion of drug loaded nanoparticles was centrifuged at 13400 rpm for half an hour and supernatant was withdrawn carefully without disturbing the cake left at bottom of Eppendorf.
Preparation of dialysis Bag
The dialysis tubing was cut into small pieces of desired length and immersed & soaked in warm dH2O (250 ml) for half an hour. After that, they were immersed in sodium sulphide (0.3%, 250 ml) and boiled for 5 min. The tubings were then rinsed thoroughly with warm dH2O and immersed in sulphuric acid (2%, 250 ml) and boiled again for 5 min. The dialysis tubings were rinsed thoroughly again with fresh warm dist. H2O for 10 min to get rid of any chemical residue. Afterwards, dH2O was decanted and tubings were submerged completely in freshly prepared phosphate buffer saline (PBS pH 7.4 250 ml). The tubings were stored at 4oC till 24 hrs before use.
In vitrorelease study was performed in PBS pH 7.4 for 48 hrs over drug solution as control and nanoparticles dispersion (equivalent to 2 mg drug contained in dialysis bag was placed in a beaker containing 50 ml of fresh PBS pH 7.4 kept on a magnetic stirrer at 37°C). At fixed time 0.5, 1, 2, 4, 6, 12, 18, 24, 36 and 48 hrs intervals; 5 ml samples were withdrawn from receptor compartment and same volume of fresh PBS pH 7.4 was replaced in receptor medium immediately after each withdrawal to maintain sink condition. The samples were diluted sufficiently and analyzed using UV Visible Spectrophotometer at 271 nm.
The drug release kinetic of was determined by fitting the release data in kinetic models; Zero, First, Higuchi order models Korsmeyer-Pappas, Hixson Crowell and assessing the line of best fit. The mechanism of drug release from formulation was investigated by incorporating 60% release data of Fopt in equations of kinetic model to calculate exponent value of n from slope of straight line graph and interpreted release mechanism.
The stability study of optimized formulation was performed as per ICH guidelines Q1A (R2) [25-27]. The optimized formulation packed in an ambered colored tightly closed glass vial was stored at normal and accelerated storage conditions i.e. 5 ± 3°C and 25±2°C/60±5% RH respectively for two months and analyzed after specific time intervals 0, 15, 30 and 60 days for any change in physical and chemical characteristics.
As revealed by preliminary trials, polymer, stabilizer concentration and sonication time were found to be the critical parameters for PNPs and thus, were taken into consideration as independent variables (factors) and were coded as A, B, C respectively. Total 20 experimental runs/trials (formulations) were generated by design expert with all the possible combinations of factors, as shown in Table 1. Based on design, formulations were prepared using double emulsion method and analyzed in terms of encapsulation efficiency (EE), particle size (PS) and drug release (DR) as dependent variables and were coded as Y1, Y2& Y3 respectively (Table 2).
A three factor, three levels Box-Behnken statistical experimental design as the RSM requires 20 runs. The total 20 runs with triplicate center points were generated and the responses so observed are shown in (Table 2). All the responses obtained for 20 formulations prepared were simultaneously fitted to first order, second order and quadratic models using Design Expert. The best-fitted model was quadratic and the comparative values of R2, SD and %CV are given in along with the regression equation generated for each response. All statistically significant (p < 0.05) coefficients are included in the equations. A positive value represents an effect that favors the optimization, while a negative value indicates an inverse relationship between factors and the response.
Response 1 (Y1): Entrapment efficiency
The model purports the following polynomial equation for entrapment efficiency of PNPs Y1=43.66+0.64A+2.65B+2.67-4.20AB-4.40AC-4.40BC+10.62A2-3.83B2-1.93C2 Where Y1 is the entrapment efficiency of PNPs, A is the polymer conc., B is the stabilizer conc. and C is the sonication time. The model F-value of 56.18 implies that the model is significant (p < 0.0001). The lack of fit F-value of 1.56 implies that the lack of fit is not significant. In this case X1X2, X1X3& X2X3 are significant model terms and X12 had a more pronounced effect on entrapment efficiency of PNPs than any other parameters. The predicted Rsquared of 0.8844 is in reasonable agreement with the adjusted R-squared of 0.9632. Adequate precision is within desirable limit. A ratio of 31.576 indicates an adequate signal. Therefore this model can be used to navigate the design space. The contour plots (Figure 2) which showed the effect of different independent variables on entrapment efficiency of PNPs (Y1). The EE of PNPs for all batches was found to be in the range of 35.10-62.10%.
The following polynomial equation was projected by the model for particle size of the PNPs.
Y2=380.90+36.34A+11.85B-72.62C-6.76AB-18.79AC+2.99BC+83.75A2-16.00B2-39.25C2 Where Y2 is the particle size of the PNPs.; the negative coefficients for the sonication time show that the particlesize decreased with increase in sonication time. In totality, the model is significant (F-value = 71.85; p < 0.0001). The lack of fit is 0.49 not significant relative to pure error. The predicted R-square of 0.9572 is in reasonable agreement with the adjusted R-square of 0.9711; i.e. the difference is less than 0.2. Adequate Precision measures of 32.272 indicate it’s an adequate signal. It was observed that response Y2 i.e. PS was significantly affected by altering the polymeric conc. (A; Figure 3 showed the effect of different independent variables on PS).
stabilizer was increased, the size of PNPs first decrease then increased due to increased viscosity of the medium as already discussed. So the size decreased due to the enhanced interfacial stabilization while it increases due to the increased aqueous phase viscosity. Therefore optimum amount of conc. plays an important role, which was found to be level 1 (1.2%). Reverse effect was observed for sonication time, particle size decreased with increase in sonication time (C) i.e. level 1 (5 min)
The model proposed constant, the regression coefficients and the statistical parameters for each
response variable, as follows: Y3=97.53+7.000E-003A-0.21B-0.22C+0.44AB+0.34AC-0.28BC-2.33A2+0.74B2+0.21C2; Where Y3 is the drug release from PNPs. This model was found to be significant (F-value = 33.49; p < 0.0001). The lack of fit is not significant (F-value = 2.86) relative to the pure error. The predicted and adjusted R-squared values (0.8799 and 0.9390) are in reasonable agreement. Adequate precision of 20.147 indicates an adequate signal to navigate the design space. The DR of PNPs was found to be in the range of 94.70-98.21% (Figure 4) after 48 hrs of study via dialysis sac method.
Two dimensional contour plots were prepared for all the three responses shown in (Figure 2, 3 & 4) on Y1, Y2 & Y3. These plots are used for studying the interaction effects of the factors on the responses as well as are useful in studying the effects of two factors on the response at one time. For validation of RSM results, the experimental values of the response were compared with the anticipated values. The linear correlation plots drawn between the predicted and experimental values demonstrated high values of R2 for all three responses. The R2 value (Table3) for response Y1, Y2 and Y3 was observed to be 0.9806, 0.9848, and 0.9679, respectively. It indicates excellent goodness of fit at p < 0.0001. Thus, the low magnitudes of error as well as the significant values of R2 in the present investigation prove the high predictive ability of the RSM. The plots between actual and predicted value are shown in (Figure 5).
From the above discussion it is clear that, best results were concluded with consideration of all the levels of factors and relating them with the response so generated. The optimized formulation was selected on the basis of independent variable i.e. %EE, PS and DR, obtained for all 20 trials. When the values of all the above mentioned factors were compared, trial number 7 obtained the most appropriate results and hence was selected as optimized formulation
The TEM analysis of Fopt showed formation of homogenous, smooth, spherical and well-formed nanoparticles. No aggregation was observed between particles, indicating their integrity as discrete entities. Therefore, it could be concluded that the integrity of PNPs formed by double emulsion were good indicating suitability of method for preparation of PNPs.
The encapsulation efficiency (EE) of Fopt was determined by “indirect method” and was found to be 62.10 ± 1.1. The particle size (PS) and polydispersity index (PDI) of Fopt determined by Particle size analyzer (Malvern Zeta SizerNano ZS, UK) as depicted from (Figure 7a). The PS of Fopt was found to be 338.60 ± 13 nm with PDI of 0.191 (observed range 0.193 to 0.187), indicating a narrow size distribution and good homogeneity of PNPs. As depicted from (Figure 7b) the zeta potential of Fopt was found to be -35.9 (observed range was -34.8 to -37.1), indicating better physical stability of formulation as particles with ζ-potential more than +30 mV and lower than -30 mV are considered to be stable.
The in vitro drug release profile of control (drug solution) was rapid, consistent and completed within 6 hrs (97.78 ± 1.55%) whereas Fopt showed a sustained release profile (94.70 ± 0.28%) for 48 hrs, indicating sustained release behavior of PNPs over free drug. The Fopt have shown biphasic release pattern of RAS, consisting of an initial burst effect of 6 hrs followed by a sustained release phase up to 48 hrs (Figure 8). The initial burst effect is characterized by 43.97 ± 1.22% release of drug within 6 hrs, attributed to dissolution and diffusion of drug adhered on or located near the surface of PNPs or entrapped poorly in polymer matrix.
The samples of Fopt at both storage conditions were observed for physical changes at specific time interval during stability studies and results are summarized in Table 4. No physical change was observed in Fopt during stability study at normal and accelerated storage conditions of temperature and humidity, indicating good physical stability of formulation in terms of color, odour, particle aggregation and redispersibility of nanoparticles in water. Moreover, the Fopt was observed for chemical changes in terms of encapsulation efficiency and drug content at accelerated storage conditions and results are summarized in Table 5. The change in drug content observed at accelerated storage conditions i.e. 25+2°C/60+5% RH was plotted against time using software, Sigma Plot 13.0; calculated shelf life of Fopt was found to be 362.53 (Figure 9).
None to declare
The authors are grateful and thankful to Chairman and Management of PDMREA, PDM University; P. D. M. College of Pharmacy, B’garh for supported & provided facilities for carried out research work.
- Davie CA. “A review of Parkinson’s disease”. British Medical Bulletin 86.1 (2008): 109–27.
- Dauer W and Przedborski S. “Parkinson’s Disease”. Neuron 39.6 (2003): 889–909.
- Wright Willis A., et al. “Geographic and ethnic variation in Parkinson disease: A population-based study of us medicare beneficiaries”. Neuroepidemiology 34.3 (2010): 143–51.
- Riederer P and Laux G. “MAO-inhibitors in Parkinson’s disease”. Experimental Neurobiology 20.1 (2011): 1-17.
- Lecht S., et al. “Rasagiline - A novel MAO B inhibitor in Parkinson’s disease therapy”. Therapeutics and Clinical Risk Management 3.3 (2007): 467–474.
- Mittal D., et al. “Brain targeted nanoparticulate drug delivery system of rasagiline via intranasal route”. Drug Delivery 23.1 (2014): 1–10.
- Garbayo E., et al. “Drug development in parkinson’s disease: from emerging molecules to
innovative drug delivery systems”. Drug Delivery 76.3 (2003): 272-278.
- Mandel S., et al. “Mechanism of neuroprotective action of the anti-Parkinson drug rasagiline and its derivatives”. Brain Research Reviews 48.2 (2005): 379–387.
- Wilczewska AZ., et al. “Nanoparticles as drug delivery
systems”. Pharmacological Reports 64.5 (2012): 1020-1037.
- Marin E., et al. “Critical evaluation of biodegradable polymers used in nanodrugs”. International Journal of Nanomedicine 8 (2013):3071–3090.
- Mohanraj V., et al. “Nanoparticles – A Review”. Tropical Journal of Pharmaceutical Research 5.1 (2006): 561–573.
- Gambaryan PY., et al. “Increasing the Efficiency of Parkinson’s Disease Treatment Using a poly (lactic-co-glycolic acid) (PLGA) Based L-DOPA Delivery System”. Experimental Neurobiology 23.3 (2014): 246–252.
- Cheng C.J., et al. “A holistic approach to
targeting disease with polymeric nanoparticles”. Nature Reviews. Drug Discovery 14.4 (2015): 239–247.
- Pahuja R., et al. “Trans-blood brain barrier delivery of dopamine-loaded nanoparticles reverses functional deficits in parkinsonian rats”. ACS Nano 9.5 (2015): 4850–4871.
- Muthu M.S and Singh S. “Studies on biodegradable polymeric nanoparticles of risperidone: in vitro and in vivo evaluation”. Nanomedicine 3.3 (2008): 305–319.
- Esteves M., et al. “Retionic acid-loaded polymeric nanoparticles induce
neuroprotection in a mouse model for Parkinson’s disease”. Neuroscience 3.7 (2015): 1-10.
- Bukka R and Karwa P. ”UV Spectrophotometric Method for the Determination of Rasagiline
Mesylate”. 5.1 (2010): 5–7.
- Acharya S., et al. “Optimization of size controlled poly (lactide-coglycolic acid) nanoparticles using quality by design concept”. Asian Journal of Pharmaceutics 9.3 (2015): 152.
- Aslan N and Cebeci Y. “Application of Box-Behnken design and response surface methodology for modeling of some Turkish coals”. Fuel – Journal 86.1-2 (2007): 90–97.
- Gajra B., et al. “Formulation and optimization of itraconazole polymeric lipid hybrid nanoparticles (Lipomer) using box behnken design”. DARU Journal of Pharmaceutical Sciences 23.3 (2015): 1–15.
- Bohrey S., et al. “Polymeric nanoparticles containing diazepam:
preparation , optimization , characterization , in - vitro drug release and release kinetic study”. Nano Convergence 3.1 (2016): 3–9.
- Kumar A and Sawant K. “Encapsulation of exemestane in polycaprolactone nanoparticles: Optimization, characterization, and release kinetics”. Cancer Nanotechnology 4.4-5 (2013): 57–71.
- Singhvi G and Singh M. “Review: In vitrodrug release characterization models”. International Journal of Pharmaaceutical studies and Research 2.1 (2011): 77-84.
- Barzegar-Jalali M., et al. “Kinetic Analysis of Drug Release from Nanoparticles”. Journal of Pharmaceutical Sciences 11.1 (2008): 167-177.
- ICH Guidelines Q1A (R2), Guidance for Industry Q1A (R2) Stability Testing of New Drug Substances and Products, November 2003, Revision 2.
- Puthli S and Vavia PR. “Stability Studies of Microparticulate System with Piroxicam as Model Drug”. AAPS PharmSciTech 10.3 (2009): 872-880.
- Patel PN., et al. “Development and testing of novel temoxifen citrate loaded chitosan nanoparticles using ionic gelation method”. Der Pharmacia Sinica 2.4 (2011): 17-25