http://informahealthcare.com/ddi ISSN: 0363-9045 (print), 1520-5762 (electronic) Drug Dev Ind Pharm, Early Online: 1–10 ! 2015 Informa Healthcare USA, Inc. DOI: 10.3109/03639045.2015.1024685
RESEARCH ARTICLE
Pharmacokinetic and pharmacodynamic studies of nisoldipine-loaded solid lipid nanoparticles developed by central composite design Drug Dev Ind Pharm Downloaded from informahealthcare.com by UMEA University Library on 04/02/15 For personal use only.
Narendar Dudhipala and Kishan Veerabrahma Department of Pharmaceutical Sciences, Laboratory of Nanotechnology, University College of Pharmaceutical Sciences, Kakatiya University, Warangal, Telangana, India Abstract
Keywords
Objective: Nisoldipine (ND) is a potential antihypertensive drug with low oral bioavailability. The aim was to develop an optimal formulation of ND-loaded solid lipid nanoparticles (ND-SLNs) for improved oral bioavailability and pharmacodynamic effect by using a two-factor, three-level central composite design. Glyceryl trimyristate (Dynasan 114) and egg lecithin were selected as independent variables. Particle size (Y1), PDI (Y2) and entrapment efficiency (EE) (Y3) of SLNs were selected as dependent response variables. Methods: The ND-SLNs were prepared by hot homogenization followed by ultrasonication. The size, PDI, zeta potential, EE, assay, in vitro release and morphology of ND-SLNs were characterized. Further, the pharmacokinetic (PK) and pharmacodynamic behavior of ND-SLNs was evaluated in male Wistar rats. Results: The optimal ND-SLN formulation had particle size of 104.4 ± 2.13 nm, PDI of 0.241 ± 0.02 and EE of 89.84 ± 0.52%. The differential scanning calorimetry and X-ray diffraction analyses indicated that the drug incorporated into ND-SLNs was in amorphous form. The morphology of ND-SLNs was found to be nearly spherical by scanning electron microscopy. The optimized formulation was stable at refrigerated and room temperature for 3 months. PK studies showed that 2.17-fold increase in oral bioavailability when compared with a drug suspension. In pharmacodynamic studies, a significant reduction in the systolic blood pressure was observed, which sustained for a period of 36 h when compared with a controlled suspension. Conclusion: Taken together, the results conclusively demonstrated that the developed optimal ND-SLNs caused significant enhancement in oral bioavailability along with pharmacodynamic effect.
Central composite design, nisoldipine, pharmacodynamics, pharmacokinetics, solid lipid nanoparticles
Introduction Solid lipid nanoparticles (SLNs) are sub-micron colloidal carriers having a size range of 50–1000 nm. These are prepared with physiological lipid and dispersed in water or aqueous surfactant solution. SLNs were developed in the last decade as an alternative system to the existing traditional carriers, i.e. emulsions, liposomes and polymeric nanoparticles1,2. These are related to emulsions, where the liquid lipid, oil is substituted by a solid lipid. SLNs offer unique properties such as small size, large surface area and high drug loading and are attractive for their potential to improve performance of active pharmaceutical ingredients (APIs). The advantages of SLNs include drug targeting, biocompatibility, lower cytotoxicity, drug release modulation and the
Address for correspondence: Prof. Kishan Veerabrahma, Department of Pharmaceutics, Laboratory of Nanotechnology, University College of Pharmaceutical Sciences, Kakatiya University, Warangal, Telangana, India. Tel: +91 08702446259. Fax: +91 08702438844. E-mail: [email protected]
History Received 20 November 2014 Revised 9 February 2015 Accepted 23 February 2015 Published online 1 April 2015
possibility of production on a large industrial scale3. The mechanism proposed for enhancement of bioavailability of poorly water-soluble drugs by use of oral lipids include; promotion of lymphatic transport, which delivers drug directly to the systemic circulation, while avoiding hepatic first-pass metabolism and by increasing gastrointestinal (GI) membrane permeability4,5. Caffeine SLN as hydrogel was reported to improve the efficiency of treatment of cellulite following topical application on skin6. Sometimes a related drug carrier to SLNNLCs was also reported to improve the therapeutic application of meloxicam7. Pharmaceutical formulation developmental study requires a detailed understanding of the relationship between process parameters and quality attributes. In particular, it is necessary to establish a science-based rationale and a design space to identify multidimensional combinations of the many causal factors that determine target quality8. For decades, this task has been attempted through trial and error, supplemented with the previous experience, knowledge and wisdom of the formulator. Optimization of a pharmaceutical formulation or process using this traditional approach involves changing one variable at a time9.
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Response surface methodology (RSM) is an experimental design, in which the factors involved and their relative importance can be assessed and an effective tool for optimizing the process10. RSM is preferred since it can determine the effect of factors on characteristic properties, the best optimal conditions of process and parameters interactions. The main advantage of RSM is to reduce the experimental runs required than would be needed in a full factorial design and it is already widely applied to optimize formulation design. Central composite design (CCD) is a popular form of RSM and is a very useful tool in understanding the interactions among the parameters that have been optimized11. This method is suitable for fitting a quadratic surface, and it helps to optimize the effective parameters with a minimum number of experiments and also to analyze the interaction between the parameters. Nisoldipine (ND), is a 1,4-dihydropyridine derivative calcium channel blocker, used as antihypertensive drug. It has a very poor bioavailability (55%), undergoes extensive first-pass metabolism in gut wall and is known to be a CYP3A substrate12. As ND is a lipophilic drug (LogP-3.1), which can be delivered in the form of SLN by which poor bioavailability, photolysis, hydrolysis and oxidation can be minimized. Previously buccal tablets of ND were reported to improve the oral bioavailabilty13 and no colloidal delivery system was reported enhancing the oral bioavailability along with improved pharmacodynamic effect. Further, in a recent study the design of SLNs improved oral bioavailability along with pharmacodynamic effect of candesartan cilexetil14. This investigation involved the development of optimal SLNs of ND by using CCD, further to study the pharmacokinetic (PK) and pharmacodynamic effects of optimal formulation.
Central composite design In this study, a CCD was used to optimize the formulation variables of ND-SLNs containing two factors and evaluated at three levels. The experimental trials were performed at all 13 possible combinations15. The amount of drug (10 mg) and poloxamer 188 (150 mg) were kept constant. Particle size (Y1), PDI (Y2) and entrapment efficiency (EE) (Y3) were included as responses. The experiments were designed by using DOE software (Version 8.0.7.1, Stat-Ease Inc., Minneapolis, MN) and the layout of the design is shown in Table 1. The DOE software was used to give information not only on the critical values required to achieve the desired response but also the possible interactions of the selected independent variables on the dependent variables11. The response surface method normally approximates the correlation function as a full quadratic equation (Equation (1)) and is based on the experimental design: Y ¼ B0 þ
2 X i¼1
Bi Xi þ
2 X
Bi, j Xi Xj þ
i5j
2 X
Bi, j Xi2 þ E
ð1Þ
i¼1
where Y is a response equation applicable for the particle size, PDI, EE; Xi terms include X1 or X2 and are independent variables ranging from (1 X 1), X1 is the amount of lipid and X2 is the amount of egg lecithin (surfactant). Bi terms are the equation coefficients related to the main factor. E is the experimental error. To perform the statistical data analysis, the Design-Expert Program 8.0.7.1 software (Stat-Ease Inc., Minneapolis, MN) was utilized and analysis of variance (ANOVA) was used to know the significance of the factors and their interactions. Preparation of ND-loaded solid lipid nanoparticles
Materials and methods ND was a kind gift sample from Orchid Labs, Chennai, India. Trimyristin (TM; Dynasan-114; glyceryl trimyristate) purchased from Sigma-Aldrich Chemicals, Hyderabad, India. Egg lecithin E-80 was a gift sample from Lipoid, Goettingen, Germany. Poloxamer-188 was a gift sample from Aurobindo Labs, Hyderabad, India. Methanol, acetonitrile and chloroform were of high performance liquid chromatography (HPLC) grade (Merck, Mumbai, India). Centrisart filters (Molecular weight cut-off 20 000) were purchased from Sartorius, Goettingen, Germany.
ND-loaded solid lipid nanoparticles (ND-SLNs) were prepared by hot homogenization followed by ultrasonication method16. ND (10 mg), lipid and egg lecithin were dissolved in 10 mL of chloroform and methanol (1:1) mixture. The organic solvent was evaporated by rotaevaporator (Heidolph, Schwabach, Germany). Lipid phase was heated at above 5 C of its melting point for drug embedment. The aqueous phase was prepared by dissolving the Poloxamer 188 (150 mg) in double distilled water (Direct Q UV3, Millipore SAS, Molsheim, France) sufficient to produce 10 mL and heated at same temperature as that of molten lipid phase. Hot aqueous phase was added to molten lipid phase and homogenized
Table 1. CCD – selected compositions of variables. Independent variables
Run 1 2 3 4 5 6 7 8 9 10 11 12 13
Dependent/response variables
Factor 1: X1 Dynasan-114 (mg)
Factor 2: X2 Egg lecithin (mg)
Response 1: size of SLNs (Y1) (nm)
Response 2: PDI (Y2)
Response 3: EE (Y3) (%)
ZP (mV)
Assay
79.289* 150 100 150 100 220.71y 150 150 200 150 150 150 200
112.5 59.46* 150 112.5 75 112.5 165.53y 112.5 75 112.5 112.5 112.5 150
98.67 165.70 152.03 140.27 105.20 202.83 159.53 137.70 177.80 136.13 137.50 137.13 214.63
0.221 0.168 0.229 0.286 0.242 0.221 0.274 0.243 0.321 0.259 0.262 0.261 0.253
76.61 88.16 80.74 91.75 89.99 92.30 91.08 89.42 88.50 90.97 88.60 91.96 93.84
21.57 22.77 28.40 28.53 25.33 23.73 29.57 25.03 24.90 24.33 24.03 26.03 29.07
99.39 99.73 98.95 99.41 99.35 99.35 98.60 99.39 99.39 99.46 99.50 99.29 99.56
X1 ¼ amount of dynasan-114 in mg (high level ¼ 200, medium level ¼ 150, low level ¼ 100) and X2 ¼ amount of egg lecithin in mg (high level ¼ 150, medium level ¼ 112.5, low level ¼ 75). *Low level edge of error; yhigh level edge of error.
DOI: 10.3109/03639045.2015.1024685
Pharmacokinetics and pharmacodynamics of nisoldipine solid lipid nanoparticles
(Diax900, Heidolph, Schwabach, Germany) for 5 min at 12 000 rpm, while the system was maintained at 5 C above the melting point of the liquid throughout the homogenization process. The resulted coarse hot oil in water emulsion was ultrasonicated to get nanoemulsion using a 12T probe sonicator (Vibracell, Sonics, Newtown, CT) for 20 min. The nanoemulsion was cooled to room temperature to obtain ND-SLNs. Photon correlation spectroscopy
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Photon correlation spectroscopy (PCS) was used to measure the particle size, PDI and zeta potential (ZP) of all prepared formulations. Zeta sizer (NanoZS90, Malvern Instruments, Worcestershire, UK) was used for performance of PCS. All the samples were diluted with double distilled water to sufficient concentration before measurement and analyzed in triplicate and data were represented in mean ± SD. EE and drug content ND content and EE of ND-SLNs were determined by HPLC. The chromatographic system consisting of a SPD-20AD solvent delivery system containing double reciprocating plunger pump (Shimadzu, Tokyo, Japan), a SPD-20A UV–Visible variable wavelength detector with deuterium lamp using a RP C-18 analytical column (250 mm 4.6 mm i.d., 5 mm; Merck, Mumbai, India) and the mobile phase consisting of acetonitrile–water (80:20) ratio at a flow rate of 1 mL/min was used for the detection of drug at lmax of 237 nm17. To determine EE, centrifugation was performed using Centrisart (Sartorius, Goettingen, Germany) tubes at 8000 rpm for 30 min, which consisted of a filter membrane (molecular weight cut-off 20 000 Da) to separate the ultra filtrate18. The EE of the system was determined by measuring the concentration of free drug in the dispersion medium using HPLC, along with the following formula. EE ¼
Wtotal Wfree 100 Wtotal
where Wtotal was the weight of drug added in the system. For total drug content, about 0.1 mL of formulation was dissolved in chloroform and methanol (1:1) mixture. The final dilution of solution was made with mobile phase. ND content was determined by HPLC. In vitro drug release The in vitro release of ND-SLNs was studied using dialysis method in 0.1 N HCl and phosphate buffer (pH 6.8) in 24 h. Dialysis membrane (Himedia, Mumbai, India; pore size 2.4 nm and molecular weight cut-off between 12 000 and 14 000) was soaked overnight in double distilled water prior to the release studies. The experimental unit had donor and receptor compartments. Donor compartment consisted of a boiling tube which was cut open at one end and tied with dialysis membrane at the other end into which 1 mL of SLN dispersion was taken for release study. Receptor compartment consisted of a 250 mL beaker which was filled with 100 mL release medium and the temperature was maintained at 37 ± 0.5 C. At 0.25, 0.5, 1, 2, 3, 4, 6, 8, 10, 12 and 24 h time points, 2 mL samples were withdrawn from receiver compartment and replenished with the same volume of release medium. The collected samples were suitably diluted and analyzed by UV– Visible Spectrophotometry (SL-210, ELICO, Hyderabad, India). Statistical analysis of the data and validation of the model In this study, evaluation of the quality of fit of the model was performed using DOE software. Polynomial models including
3
linear, interaction and quadratic terms were generated for all the response variables using multiple linear regression analysis. The best fit model was selected based on comparison of several statistical parameters, including the coefficient of determination (R2), adjusted R2 and coefficient of variation (CV) provided by DOE software15. Further, ANOVA was used to identify significant effects of factors on response regression coefficients. The F test and p values were also calculated using the software. The relationship between the dependent and independent variables was elucidated using response surface plots (contour and 3D surface). These plots were used to study the effect of various factors at a given time and to predict the dependent response variables at intermediate levels of independent variables. Finally, a numerical optimization technique (desirability approach) and a graphical optimization technique (overlay plots) were used to generate new formulation with desired responses. To validate the chosen experimental design, the responses of experimental values were quantitatively compared with responses of predicted values and percentage relative error was calculated. Stability studies Stability of statistically optimized ND-SLN was studied at room and refrigerated temperature for 3 months. The average size, PDI, ZP, assay and EE were determined periodically after first day, 15 days, 1 month, 2 months and 3 months19. Lyophilization of SLNs Lyophilization (freeze drying) was used for enhancement of stability of SLNs20. The optimized ND-SLNs containing 10% w/v trehalose were prepared and kept in deep freezer at 40 C (Sanyo, Tokyo, Japan) for overnight. The frozen samples were then transferred into freeze-dryer (Lyodel, Delvac Pumps Pvt. Ltd., Chennai, India). Vacuum was applied and sample was subjected to various drying phases for about 48 h to get stable powdered lyophilized product21. Solid-state characterization Differential scanning calorimetry Differential scanning calorimetry (DSC) thermal analysis of ND, TM and physical mixture (PM) in 1:1 ratio of drug and TM, and lyophilized ND-SLNs was performed using 4000 model, Perkin Elmer, Waltham, MA. The instrument was calibrated with indium. All the samples (10 mg) were heated in aluminum pans using dry nitrogen as the effluent gas. The thermograms were obtained in the heating range of 20–200 C and at a rate of 20 C/min. Powder X-ray diffractometry Powder X-ray diffractometer (PXRD) (Multiflex, M/s. Rigaku, Tokyo, Japan) was used for diffraction studies. PXRD studies were performed on the samples by exposing them to nickelfiltered Cu Ka radiation (40 kV, 30 mA) and scanned from 2 to 70 , 2 at a step size of 0.045 and step time of 0.5 s. Samples used for PXRD analysis were ND, lipid (Dynasan-114), PM of drug with lipid (1:1) and lyophilized ND-SLNs. Study of ND-SLN morphology by scanning electron microscopy The morphology of nanoparticles was studied by using scanning electron microscopy (SEM, Hitachi, Tokyo, Japan). The sample of SLN formulation was first adhered to the carbon-coated metallic stub. This was sputter coated with Platinum coating
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machine (JFC-1600 Auto fine coater, JEOL, Tokyo, Japan) and mounted in SEM (JSM-6510LA, JEOL, Tokyo, Japan) for surface analysis. Imaging was carried out in high vacuum22.
Results and discussion
Bioavailability study
Statistical analysis
Study design and sampling schedule
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A single dose bioavailability study was designed in male Wistar rats under fasting conditions. The oral bioavailability of the optimized ND-SLN formulation and suspension was estimated in male Wistar rats with oral dose of 10 mg/kg body weight23. All experimental procedures were reviewed and approved by the institutional animal ethical committee, University College of Pharmaceutical Sciences, Kakatiya University (Warangal, India). Male Wistar rats weighing 200–250 g were taken for the study (six animals per group). Blood samples were withdrawn by retroorbital venous plexus puncture at 0, 0.5, 1, 2, 3, 4, 6, 8, 10, 12 and 24 h post-dose. About 1 mL of blood samples was withdrawn in eppendorf tubes and centrifuged at 3000 rpm for 30 min. The serum was transferred to another eppendorf tube and stored at 20 C until analysis by HPLC. HPLC analysis The HPLC column, Merck C18 (250 mm 4.6 mm), was equilibrated with an eluent mixture of acetonitrile–water (62:38 v/v) at a flow rate of 1 mL/min. The peaks were eluted at 237 nm wavelength without any interference from serum. Extraction procedure To 100 mL of serum, 100 mL of mobile phase and 100 mL of nimodipine solution as internal standard (3 mg/mL) was added and vortexed for 3 min. Then, 25 mL of 0.1 M NaOH was added and vortexed for 2 min. To this 1 mL of ether was added and vortexed for 3 min followed by centrifugation at 5000 rpm for 20 min. Organic phase was separated and evaporated. The residue was reconstituted with 100 mL of mobile phase and 20 mL of this solution was spiked on to the HPLC column24. Estimation of PK parameters and statistical significance The PK parameters such as peak serum concentration (Cmax), time for peak serum concentration (tmax), AUCtotal, biological half-life (t1/2) and mean residence time (MRT) were calculated by using the Kinetica software (version 5.0, Innaphase Corporation, Philadelphia, PA). The values were expressed as mean ± SD. The statistical comparison of data of two samples was performed with unpaired Student t-test using Graph pad prism software (version 5.02.2013, GraphPad Software, San Diego, CA) and p50.01 was considered as statistically significant. Pharmacodynamic study Four groups of male Wistar rats of A, B, C (hypertensive) and D (normal) (each of six), weighing 210–250 g were used in the study and allowed free access to standard laboratory diet and drinking fluid. Drinking fluid consisted of either tap water or 10% fructose solution25,26. The rats were trained to stay in the rat holder in a calm and non-aggressive state during BP measurement. Two weeks later, rats with a minimum mean systolic BP of 140–145 mmHg were selected. Groups C and D served as hypertensive and non-hypertensive controls, respectively. Groups A and B received SLN formulation and a suspension of drug, respectively, at 10 mg/kg orally. Using the tail-cuff method (NIBP, IITC, Woodland Hills, CA), systolic blood pressure (BP) was measured at different time intervals (0, 1, 2, 4, 6, 12, 24, 36, 48, 60, 72, 96, 120, 144 and 168 h) for all groups.
Experimental design optimization and response surface approach
The results of the design of experiments indicated that this system was highly influenced by the amount of lipid and surfactant, which resulted in high drug EE and small particle sizes for the preparation of SLN. From the response surface model, the regression equations (2)–(4) for the dependent variables were obtained using Design Expert software over the range of independent variables from the random order and are shown in Table 1. Y1 ¼ þ177:43 0:095X1 1:76X2 þ 1:33X1 X2 þ 3:17X12 9:88X22 Y2 ¼ þ0:048 þ 1:39X1 þ 1:26X2 3:88X1 X2 4:19X12 7:46X22 Y3 ¼ þ84:62306 þ 0:194X1 0:26X2 þ 1:94X1 X2 þ 1:094X12 1:08X22
ð2Þ
ð3Þ
ð4Þ
The sign and value of the quantitative effect represent the tendency and magnitude of the term’s influence on the response, respectively27. In the regression equation, a positive value indicates an effect that favors the optimization due to synergistic effect, while a negative value indicates an inverse relationship or antagonistic effect between the factor and the response. Regression values represent the quantitative effect of process variables X1, X2 and their influence on the dependent responses Y1, Y2 and Y3. These response data are shown in Table 2. Further, a close observation of data revealed the suitability of response surface quadratic model when compared to linear model and the two-factor (2FI) model. ANOVA for the responses indicated that the quadratic regression model was significant and valid for each of the responses Y1 (p50.0001), Y2 (p50.0001) and Y3 (p50.0001) and hence was appropriate to represent the observed data, respectively (Table 3). The observed R2 values for the dependent responses are 0.9138, 0.8803 and 0.8943 (Table 2). According to Torrades et al.28 when observed value of R2 was at least 0.80, it implied a good correlation and was found in all cases, indicating a good fit by the model. The R2adj values, for size (Y1) is 0.8523, PDI (Y2) is 0.8188 and EE (Y3) is 0.7948, are high and advocated the significance of the model29. The R2Pre values were 0.8012, 0.7945 and 0.7194 for size, PDI and EE, respectively, given by the model, which indicated a correlation between the observed and predicted values. Hence, the response model of this system is better one for the selected responses. Three-dimensional (3D) plots The independent variables, i.e. the amount of lipid (X1) and the amount of surfactant (X2) and their interaction on the particle size (Y1), PDI (Y2) and EE (Y3) as dependent responses were graphically represented by 3D surface plots by using RSM. The effect of the amount of lipid and amount of surfactant on particle size, PDI and EE are represented in Figure 1. When particle size (Y1) was indicated as the response, good correlation was shown between observed and predicted values as revealed by R2 of 0.913 (Table 2). This Y1 was significantly influenced by lipid amount (X1), surfactant amount (X2) and their
Pharmacokinetics and pharmacodynamics of nisoldipine solid lipid nanoparticles
DOI: 10.3109/03639045.2015.1024685
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Table 2. Regression values of the selected responses during optimization. Y1 (Size) Model Linear FI Quadratic
R
2
2
Y2 (PDI)
Adjusted R
Predicted R
0.7515 0.7264 0.8523
0.6127 0.3536 0.8012
0.7929 0.7948 0.9138
p Value
2
2
R
Adjusted R
0.7332 0.7535 0.8943
0.6798 0.6713 0.8188
0.0001
2
Y3 (EE) Predicted R
2
0.5092 0.4339 0.7945
R
2
0.5062 0.6948 0.8803
0.0001
Adjusted R2
Predicted R2
0.4074 0.5930 0.7948
0.0004 0.2720 0.7194
0.0001
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Table 3. ANOVA of optimized quadratic model. Parameter
Source
Size
Model Residual Lack of fit Pure error Model Residual Lack of fit Pure error Model Residual Lack of fit Pure error
EE
PDI
Degree of freedom
Sum of squares
Mean squares
F Value
p Value
5 7 3 5 5 7 3 4 5 7 3 4
12306.42 1160.35 1150.93 9.42 248.35 33.78 25.09 8.68 25.63 3.303 9.64 8.53
2461.28 165.76 383.64 2.36 49.67 4.83 8.36 2.17 3.709 5.73 3.25 2.4
14.85
0.0013
162.83
0.0001
10.29
0.0040
3.85
0.0001
11.46
0.0029
16.07
0.0107
interactive term (X1X2) and polynomial model of lipid concentration X12 with a p50.0001. Magnitude of the positive coefficient (177.4) of the term is suggesting that elevated levels of lipid and surfactant concentrations in the formulation could increase the particle size drastically. The size of lipid nanoparticles which is highly dependent on lipid concentration can be explained in terms of tendency of lipid to coalesce at high lipid concentration, also, in an SLN formulation, when increasing the solid lipid content, leading to higher surface tension and thus higher particle size. Figure 1(A) and (D) show the response surface model for particle size in response to the investigated factors. The surfactant and lipid levels had positive influence on particle size and the results are inconsistent with the conclusion of other investigators30. For the 13 formulations, the various factor combinations resulted in varied EE of ND from 76.61% to 93.84%. From Figure 1(C) and (F), there was a linear relationship between the amount of lipid and EE of ND. Quantitative estimation of the significant models indicated that lipid concentration had the prime influence on the EE for its large positive coefficient (0.194), suggesting that increasing the amount of lipid increased the EE and the amount of surfactant has little effect on the EE in the formulation (Equation (4)). Increase in the amount of lipid results in increasing the EE. This might be due to the formation of micelles at increased lipid concentration resulting in, sufficient spare space to accommodate more drug. This tendency could be attributed to the limited water solubility of ND and high lipophilicity31. Particle size distribution of SLN formulation in aqueous medium can be measured by PDI in a multimodal distribution. From Figure 1(B) and (E), the effect of formulation type and process variables on PDI can be evaluated. When the amounts of lipid and surfactant were altered from lower to higher levels, no significant changes in PDI were noticed, but at low levels of lipid amount alone, a marginal increasing trend in PDI was observed. This result implied that lipid concentration showed a promising effect on the PDI, which was confirmed by a statistical ANOVA result. However, lipid amount exhibited significant
positive influence on particle size at higher levels of the lipid, as revealed by the large and positive coefficient value (X12 ). It might be interpreted by the fact that lower lipid concentration and higher surfactant concentration resulted in a narrower particle size distribution. Optimization of independent variable and validation After analyzing the polynomial equations, depicting the dependent and independent variables, a further optimization and validation process by means of the Design Expert software was undertaken with desirable characteristics to probe the optimal formula solution of SLN. This depended on the prescriptive criteria of maximum EE, minimum particle size and PDI. The composition of optimum formulation was determined as 100 mg of lipid and 75 mg of surfactant, which fulfilled the requirements of optimization. At these levels, the predicted values of Y1 (particle size), Y2 (PDI) and Y3 (EE) were 102.34 nm, 0.235 and 89.37%, respectively. Therefore, in order to confirm the predicted model, a new batch of SLNs according to the optimal formulation factor levels was prepared. The observed optimized formulation had EE of 89.84 ± 0.52%, particle size of 104.4 ± 2.13 nm, and PDI of 0.241 ± 0.02, which were in good agreement with the predicted values. A comparison between these observed results and theoretical predictions indicated the reliability of CCD in predicting a desirable SLN formulation. In vitro release studies In vitro release of ND from ND-SLNs was studied in 0.1 N HCl (pH 1.2) and in phosphate buffer (pH 6.8) by using dialysis method. In 0.1 N HCl (pH 1.2), the cumulative percentage of release from all the 13 formulations ranged from 44.25 ± 0.53% to 84.38 ± 0.58% for a period of 24 h (Figure 2). In phosphate buffer (pH 6.8), the cumulative percentage of release from 13 formulations ranged from 45.16 ± 0.16% to 86.14 ± 0.68% (Figure 2) in 24 h. No significant difference was observed in drug release in phosphate buffer (pH 6.8) and 0.1 N HCl, at 24 h in each one of the formulations due to pH independent solubility of ND.
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Figure 1. Contour plots showing the interactive effects of (i) amount of lipid (X1) and amount of surfactant (X2) on particle size (Y1) (A and D); (ii) amount of lipid (X1) and amount of surfactant (X2) on PDI (Y2) (B and E); and (iii) amount of lipid (X1) and amount of surfactant (X2) on EE (Y3), and corresponding response surface plots (C and F).
The release profiles of SLN formulations exhibited a typical biphasic pattern with an initial rapid phase followed by a slow phase in phosphate buffer. The initial rapid phase could be due to the burst release of drug. A possible explanation is a short diffusion path due to enrichment of drug in the outer region of SLN or drug deposition on the solid surface3.
forces would increase32 the particle agglomeration and this might be a reason for increase in the particle size of the SLN formulation.
Stability studies
The purity of drug and the status of lipids in the SLN formulation were determined by DSC study. DSC thermograms of pure drug, pure lipid, physical mixture (PM) of drug and (1:1) and statistically optimized lyophilized SLN formulation are shown in Figure 3(a). The DSC thermogram of ND showed a sharp melting endothermic peak at 151.52 C. The peak was observed at its reported melting point (150–155 C) which indicated the purity of drug. The pure lipid (Dynasan-114) showed a sharp endothermic peak at the melting point of 59.57 C. In case of PM, very broader drug endothermic peak at 145.6 C (slight shift in the melting point) and lipid at 57.69 C. The relevant drug peak was not visible in the lyophilized formulation, which might be due to insufficient amount of drug in the lyophilized product. Alternatively, the drug might be uniformly dispersed at molecular level in the lipid matrix loosing the complete crystal structure33.
The stability of the statistically optimized SLN formulation was determined by monitoring the physical appearance, particle size, PDI, ZP, assay and EE of ND after storage at refrigerated and room temperature for a period of 3 months. At definite time intervals, we could not notice any signs of drug crystallization in SLN formulation. Further, no drastic increase in particle size was observed when stored at refrigerated and room temperature for a period of 3 months (Table 4). However, relatively a less change in size was noticed, in samples stored at either refrigerated or room temperature. However, this minor size changes were not reflected in PDI of samples during storage. Further, no significant reduction in the EE (p40.05) at either refrigerated or room temperature was observed. This could be due to the stable nature of lipid matrix formed during ND-SLNs preparation.
Solid-state characterization Differential scanning calorimetry
Lyophilization The optimized SLN formulation was freeze dried with 10% trehalose and resulted in freeze dried SLN powder. Upon reconstitution, increase in size, PDI and ZP were noticed. Due to removal of water in freeze drying process, particle attractive
Powder X-ray diffractometry Powder-XRD patterns of ND showed sharp peaks at 2 scattered angles of 11.28 , 12.43 , 18.97 , 22.69 , 25.24 , 27.42 , 44.05 and 64.41 , these were indicating the crystalline nature of drug
Pharmacokinetics and pharmacodynamics of nisoldipine solid lipid nanoparticles
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DOI: 10.3109/03639045.2015.1024685
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Figure 2. In vitro drug release from ND-SLNs in 0.1 N HCl and in pH 6.8 phosphate buffer (mean ± SD, n ¼ 3).
Table 4. Effect of storage at refrigerated and room temperature on size, PDI, ZP, assay and EE of statistically optimized ND-SLN formulation (mean ± SD, n ¼ 3). Size (nm) Time 1 day 15 days 30 days 60 days 90 days
PDI
ZP (mV)
Assay (%)
At 4 C
At 25 C
At 4 C
At 25 C
At 4 C
At 25 C
104.8 ± 1.43 106.3 ± 0.72 108.7 ± 0.60 111.1 ± 1.04 114.7 ± 1.69
104.4 ± 1.10 109.4 ± 0.85 121.7 ± 1.17 123.9 ± 1.65 126.6 ± 0.55
0.230 ± 0.015 0.244 ± 0.013 0.248 ± 0.007 0.250 ± 0.009 0.256 ± 0.006
0.240 ± 0.008 0.254 ± 0.006 0.268 ± 0.008 0.278 ± 0.004 0.282 ± 0.002
24.40 ± 1.11 25.50 ± 1.25 25.17 ± 0.55 25.60 ± 0.53 25.47 ± 0.50
22.77 ± 0.35 23.73 ± 0.32 23.30 ± 0.75 22.63 ± 0.83 22.53 ± 1.45
(Figure 3b). These characteristic peaks of ND existed in PMs. These drug crystalline peaks were absent in the sample submitted after lyophilization. This indicated that the drug was not in crystalline form after lyophilization of SLN. Intensity of pure lipid peaks was also decreased in the lyophilized samples. This reduced intensity indicated the decreased crystallinity of lipid. The change in crystallinity of lipid and drug would influence the release of ND from nanoparticles6. This reduction in crystallinity was noticed in DSC analysis also. Morphology of SLN using SEM SLN formulation was studied for surface morphology at 200, 15 K and 45 K magnification using SEM. The morphology of nanoparticles was found to be nearly spherical in shape, exhibited
At 4 C
EE (%)
At 25 C
At 4 C
99.49 ± 0.01 99.42 ± 0.02 89.41 ± 0.51 99.38 ± 0.02 98.97 ± 0.04 89.11 ± 0.4 99.41 ± 0.03 99.39 ± 0.61 89.34 ± 0.6 99.43 ± 0.04 99.4 ± 0.06 89.31 ± 0.9 99.4 ± 0.05 99.3 ± 0.04 88.91 ± 0.7
At 25 C 89.12 ± 0.62 88.92 ± 0.73 88.3 ± 0.61 89.01 ± 0.63 87.93 ± 0.61
polydispersity and possessed smooth surface with increasing tendency in particle size due to lyophilization process, as a follow-up of the agglomeration phenomenon, shown in Figure 432,19. PK study ND has poor oral absorption due to hepatic first-pass metabolism and poor aqueous solubility (BCS class-II). This study focused to investigate the feasibility of SLN for improved oral delivery of ND. The PK parameters of ND in individual rats for statistically optimized SLN formulation and suspension were calculated by non-compartmental estimations using Kinetica 2000 software. The PK parameters, AUCtotal, tmax, Cmax, MRT and t1/2 were calculated for optimized SLN formulation and compared with that
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Figure 3. Solid-state characterization of (A) drug, (B) excipient, (C) mixture and (D) lyophilized SLN formulation by DSC (a) and powder X-ray diffraction (b).
Figure 4. SEM images of optimized SLNs at different magnifications showing nearly spherical particles.
Table 5. Consolidated pharmacokinetic parameters of statistically optimized ND-SLN and suspension formulation in rats (n ¼ 6).
Parameter
SLN**** Mean ± SD
Suspension Mean ± SD
Cmax (mg/mL) tmax (h) AUCtot (mg/mLh) thalf (h) MRT (h)
12.55 ± 0.60 4.00 ± 0.00 96.15 ± 3.92 17.23 ± 1.80 16.50 ± 1.76
7.53 ± 0.13 3.00 ± 0.00 44.13 ± 2.90 8.27 ± 1.10 9.73 ± 0.65
****p50.0001 compared with suspension administration (mean ± SD, n ¼ 6). Figure 5. PK profiles of ND in rat serum following oral administration of optimized SLN formulation and suspension formulation (mean ± SD; n ¼ 6).
Pharmacokinetics and pharmacodynamics of nisoldipine solid lipid nanoparticles
DOI: 10.3109/03639045.2015.1024685
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Table 6. Pharmacodynamic (antihypertensive) effects of optimized ND-SLN and control suspension formulation after oral administration (mean ± SD, n ¼ 6). Mean systolic BP (mmHg) Untreated rats
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Time (h) Initial 1 2 4 6 12 24 36 48 60 72 96 120 144 168
Treated rats
Normal
Control
Suspension
ND-SLN
93.87 ± 3.56 92.23 ± 2.01 92.18 ± 2.20 94.95 ± 4.37 93.88 ± 3.94 95.78 ± 2.68 96.15 ± 3.65 97.12 ± 4.02 95.64 ± 1.54 93.57 ± 4.76 95.27 ± 4.21 96.39 ± 4.58 94.93 ± 3.63 94.21 ± 2.82 94.57 ± 3.56
149.11 ± 2.08 148.11 ± 2.08 146.93 ± 1.94 146.53 ± 1.53 146.4 ± 1.79 146.4 ± 1.86 145.5 ± 1.95 144.1 ± 1.96 139.6 ± 1.64 129.46 ± 0.92 123.36 ± 1.97 114.71 ± 2.88 109.81 ± 1.09 109.05 ± 0.93 108.53 ± 0.79
149.78 ± 3.26* 128.70 ± 1.37* 108.18 ± ± 1.03* 117.73 ± 3.09* 127.53 ± 1.64* 135.71 ± 3.09* 139.3 ± 0.94* 140.33 ± 1.47 132.48 ± 1.24 124.61 ± 1.12 122.08 ± 1.46 117.58 ± 2.49 110.93 ± 1.38 109.58 ± 2.16 109 ± 1.34
149.79 ± 2.28** 136.8 ± 2.20k 129.45 ± 0.69** 126.95 ± 1.23** 125.375 ± 1.23** 123.13 ± 1.72** 113.65 ± 2.16# 106.76 ± 2.34# 121.52 ± 2.62 126.7 ± 2.19 123.16 ± 2.13 116.46 ± 2.68 111.48 ± 1.58 109.87 ± 0.89 108.42 ± 1.41
Control, hypertension-induced rats; normal, normal male Wistar rats (non-hypertensive rats); ND-SLN, ND solid lipid nanoparticles. *p50.05 with respect to either control or normal. **p50.05 with respect to either control or normal. k p50.05 with respect to normal. #p50.05 with respect to control only.
of suspension. The statistical comparison of data was performed using the Student unpaired t-test at a significance level of p50.01. The serum concentration versus time profiles following single dose administration of ND-SLN formulation and suspension are shown in Figure 5 and Table 5. The pertinent PK parameters were calculated. From this, the higher Cmax for SLN formulation (12.55 ± 0.6 mg/mL) with respect to suspension (7.53 ± 0.13 mg/mL) was statistically significant at p50.0001. However, the time to reach the peak concentration was comparable to that of control. The AUCtotal, which denoted the extent of absorption, was also significantly (p50.0001) higher for SLN formulation (96.15 ± 3.92 mg/mLh) compared to suspension (44.13 ± 2.90 mg/mLh). The t1/2 and MRT were higher for SLN formulation because of slower elimination rate of ND from SLN formulation34. SLN formulation showed 2.17-fold improvement in oral bioavailability when compared to a suspension. Due to the nanosize of the SLNs the effective surface would increase influencing the adhesion to GI tract. Consequently, there is increased contact time of the SLN particles. In addition, the phosphatidylcholine and poloxamer could alter the permeability characters of the GI membrane. The fatty acid chains present in the lipids of SLNs that improve the uptake by lymphatic transport. The higher the chain length, greater the extent of lymphatic transport. This lymphatic transport minimizes the first-pass effect of the drug4. Overall, the improved oral bioavailability of SLNs could be due to the contribution of individual and/or combined mechanisms known till now.
ND-SLN formulation resulted in a gradual decrease of BP, with the maximum effect observed at 24 h (p50.05) and further continued for 36 h. In the hypertensive control group, there was no decrease in the systolic BP observed up to 72 h after the hypertension induction due to effect of fructose. In normal rat group, normal systolic BP was observed. From PK studies in rats increased AUCtotal, t1/2 and MRT of SLN (more than two times) in comparison to suspension were observed and this clearly indicated that the SLN had released the drug gradually over a period of 36 h. Oral ND suspension acted quickly (2 h) and drastically, but then its effect dropped off after 24 h, where as the optimal SLN formulation could not decrease the BP greatly in the initial phase, when compared with the suspension form. Since the administration of ND-SLN resulted in sustained and continued drug release for 24 h and beyond (from in vitro release studies), the designed SLNs were able to control the hypertension throughout 36 h period. We could find an increased systolic BP in rat models fed with fructose-rich diet even after changing or shifting the diet to normal. This is in consistent with the earlier reports26. Obviously, the obtained SLN statistically optimized formulation was capable of surmounting the shortcomings of oral administration of ND, such as low bioavailability and high firstpass metabolism. Further, it becomes a clinical advantage in controlling the hypertension slowly, steadily and for extended period by designing the drugs in SLN formulation.
Pharmacodynamic study
ND-SLNs were prepared and optimized by using RSM. The solidstate characterization revealed the transformation of crystalline state of the drug to amorphous state. The components were found to be compatible during DSC-excipient studies. Controlled release profiles were obtained by incorporating ND into the solid matrix of Dynasan114-based lipid nanoparticles. The PK studies were carried out in rats, which showed 2.17-fold improvement in bioavailability and reduction in systolic BP up to 36 h from SLN formulation. This confirmed the potential of SLN as a suitable carrier for oral delivery of ND. Nevertheless, to extrapolate the
In addition, the antihypertensive effect of SLN formulation was studied in comparison to a suspension in the rat model. The hypertension was induced in rats by 10% oral fructose solution. The systolic BP was measured and results are given in Table 6. The oral administration of ND suspension significantly (p50.05) controlled the hypertension initially, with the maximum effect at 2 h, but afterwards, the BP raised gradually until it was same as the initial value at 24 h. By contrast, the oral administration of
Conclusion
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findings, PK and PD studies in humans are necessary to confirm the improved oral delivery of ND.
Drug Dev Ind Pharm, Early Online: 1–10
16.
Acknowledgements Mr. Dudhipala Narendar acknowledges the UGC, New Delhi, India for BSR fellowship to carry out this research work. We thank Dr. P. Govardhan, Vaagdevi College of Pharmacy, Warangal, for allowing us to use the NIBP equipment.
Declaration of interest
17.
18.
19.
The authors report no conflicts of interest. The authors are alone responsible for the content and writing of this paper.
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RESEARCH ARTICLE
Pharmacokinetic and pharmacodynamic studies of nisoldipine-loaded solid lipid nanoparticles developed by central composite design Drug Dev Ind Pharm Downloaded from informahealthcare.com by UMEA University Library on 04/02/15 For personal use only.
Narendar Dudhipala and Kishan Veerabrahma Department of Pharmaceutical Sciences, Laboratory of Nanotechnology, University College of Pharmaceutical Sciences, Kakatiya University, Warangal, Telangana, India Abstract
Keywords
Objective: Nisoldipine (ND) is a potential antihypertensive drug with low oral bioavailability. The aim was to develop an optimal formulation of ND-loaded solid lipid nanoparticles (ND-SLNs) for improved oral bioavailability and pharmacodynamic effect by using a two-factor, three-level central composite design. Glyceryl trimyristate (Dynasan 114) and egg lecithin were selected as independent variables. Particle size (Y1), PDI (Y2) and entrapment efficiency (EE) (Y3) of SLNs were selected as dependent response variables. Methods: The ND-SLNs were prepared by hot homogenization followed by ultrasonication. The size, PDI, zeta potential, EE, assay, in vitro release and morphology of ND-SLNs were characterized. Further, the pharmacokinetic (PK) and pharmacodynamic behavior of ND-SLNs was evaluated in male Wistar rats. Results: The optimal ND-SLN formulation had particle size of 104.4 ± 2.13 nm, PDI of 0.241 ± 0.02 and EE of 89.84 ± 0.52%. The differential scanning calorimetry and X-ray diffraction analyses indicated that the drug incorporated into ND-SLNs was in amorphous form. The morphology of ND-SLNs was found to be nearly spherical by scanning electron microscopy. The optimized formulation was stable at refrigerated and room temperature for 3 months. PK studies showed that 2.17-fold increase in oral bioavailability when compared with a drug suspension. In pharmacodynamic studies, a significant reduction in the systolic blood pressure was observed, which sustained for a period of 36 h when compared with a controlled suspension. Conclusion: Taken together, the results conclusively demonstrated that the developed optimal ND-SLNs caused significant enhancement in oral bioavailability along with pharmacodynamic effect.
Central composite design, nisoldipine, pharmacodynamics, pharmacokinetics, solid lipid nanoparticles
Introduction Solid lipid nanoparticles (SLNs) are sub-micron colloidal carriers having a size range of 50–1000 nm. These are prepared with physiological lipid and dispersed in water or aqueous surfactant solution. SLNs were developed in the last decade as an alternative system to the existing traditional carriers, i.e. emulsions, liposomes and polymeric nanoparticles1,2. These are related to emulsions, where the liquid lipid, oil is substituted by a solid lipid. SLNs offer unique properties such as small size, large surface area and high drug loading and are attractive for their potential to improve performance of active pharmaceutical ingredients (APIs). The advantages of SLNs include drug targeting, biocompatibility, lower cytotoxicity, drug release modulation and the
Address for correspondence: Prof. Kishan Veerabrahma, Department of Pharmaceutics, Laboratory of Nanotechnology, University College of Pharmaceutical Sciences, Kakatiya University, Warangal, Telangana, India. Tel: +91 08702446259. Fax: +91 08702438844. E-mail: [email protected]
History Received 20 November 2014 Revised 9 February 2015 Accepted 23 February 2015 Published online 1 April 2015
possibility of production on a large industrial scale3. The mechanism proposed for enhancement of bioavailability of poorly water-soluble drugs by use of oral lipids include; promotion of lymphatic transport, which delivers drug directly to the systemic circulation, while avoiding hepatic first-pass metabolism and by increasing gastrointestinal (GI) membrane permeability4,5. Caffeine SLN as hydrogel was reported to improve the efficiency of treatment of cellulite following topical application on skin6. Sometimes a related drug carrier to SLNNLCs was also reported to improve the therapeutic application of meloxicam7. Pharmaceutical formulation developmental study requires a detailed understanding of the relationship between process parameters and quality attributes. In particular, it is necessary to establish a science-based rationale and a design space to identify multidimensional combinations of the many causal factors that determine target quality8. For decades, this task has been attempted through trial and error, supplemented with the previous experience, knowledge and wisdom of the formulator. Optimization of a pharmaceutical formulation or process using this traditional approach involves changing one variable at a time9.
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Drug Dev Ind Pharm, Early Online: 1–10
Response surface methodology (RSM) is an experimental design, in which the factors involved and their relative importance can be assessed and an effective tool for optimizing the process10. RSM is preferred since it can determine the effect of factors on characteristic properties, the best optimal conditions of process and parameters interactions. The main advantage of RSM is to reduce the experimental runs required than would be needed in a full factorial design and it is already widely applied to optimize formulation design. Central composite design (CCD) is a popular form of RSM and is a very useful tool in understanding the interactions among the parameters that have been optimized11. This method is suitable for fitting a quadratic surface, and it helps to optimize the effective parameters with a minimum number of experiments and also to analyze the interaction between the parameters. Nisoldipine (ND), is a 1,4-dihydropyridine derivative calcium channel blocker, used as antihypertensive drug. It has a very poor bioavailability (55%), undergoes extensive first-pass metabolism in gut wall and is known to be a CYP3A substrate12. As ND is a lipophilic drug (LogP-3.1), which can be delivered in the form of SLN by which poor bioavailability, photolysis, hydrolysis and oxidation can be minimized. Previously buccal tablets of ND were reported to improve the oral bioavailabilty13 and no colloidal delivery system was reported enhancing the oral bioavailability along with improved pharmacodynamic effect. Further, in a recent study the design of SLNs improved oral bioavailability along with pharmacodynamic effect of candesartan cilexetil14. This investigation involved the development of optimal SLNs of ND by using CCD, further to study the pharmacokinetic (PK) and pharmacodynamic effects of optimal formulation.
Central composite design In this study, a CCD was used to optimize the formulation variables of ND-SLNs containing two factors and evaluated at three levels. The experimental trials were performed at all 13 possible combinations15. The amount of drug (10 mg) and poloxamer 188 (150 mg) were kept constant. Particle size (Y1), PDI (Y2) and entrapment efficiency (EE) (Y3) were included as responses. The experiments were designed by using DOE software (Version 8.0.7.1, Stat-Ease Inc., Minneapolis, MN) and the layout of the design is shown in Table 1. The DOE software was used to give information not only on the critical values required to achieve the desired response but also the possible interactions of the selected independent variables on the dependent variables11. The response surface method normally approximates the correlation function as a full quadratic equation (Equation (1)) and is based on the experimental design: Y ¼ B0 þ
2 X i¼1
Bi Xi þ
2 X
Bi, j Xi Xj þ
i5j
2 X
Bi, j Xi2 þ E
ð1Þ
i¼1
where Y is a response equation applicable for the particle size, PDI, EE; Xi terms include X1 or X2 and are independent variables ranging from (1 X 1), X1 is the amount of lipid and X2 is the amount of egg lecithin (surfactant). Bi terms are the equation coefficients related to the main factor. E is the experimental error. To perform the statistical data analysis, the Design-Expert Program 8.0.7.1 software (Stat-Ease Inc., Minneapolis, MN) was utilized and analysis of variance (ANOVA) was used to know the significance of the factors and their interactions. Preparation of ND-loaded solid lipid nanoparticles
Materials and methods ND was a kind gift sample from Orchid Labs, Chennai, India. Trimyristin (TM; Dynasan-114; glyceryl trimyristate) purchased from Sigma-Aldrich Chemicals, Hyderabad, India. Egg lecithin E-80 was a gift sample from Lipoid, Goettingen, Germany. Poloxamer-188 was a gift sample from Aurobindo Labs, Hyderabad, India. Methanol, acetonitrile and chloroform were of high performance liquid chromatography (HPLC) grade (Merck, Mumbai, India). Centrisart filters (Molecular weight cut-off 20 000) were purchased from Sartorius, Goettingen, Germany.
ND-loaded solid lipid nanoparticles (ND-SLNs) were prepared by hot homogenization followed by ultrasonication method16. ND (10 mg), lipid and egg lecithin were dissolved in 10 mL of chloroform and methanol (1:1) mixture. The organic solvent was evaporated by rotaevaporator (Heidolph, Schwabach, Germany). Lipid phase was heated at above 5 C of its melting point for drug embedment. The aqueous phase was prepared by dissolving the Poloxamer 188 (150 mg) in double distilled water (Direct Q UV3, Millipore SAS, Molsheim, France) sufficient to produce 10 mL and heated at same temperature as that of molten lipid phase. Hot aqueous phase was added to molten lipid phase and homogenized
Table 1. CCD – selected compositions of variables. Independent variables
Run 1 2 3 4 5 6 7 8 9 10 11 12 13
Dependent/response variables
Factor 1: X1 Dynasan-114 (mg)
Factor 2: X2 Egg lecithin (mg)
Response 1: size of SLNs (Y1) (nm)
Response 2: PDI (Y2)
Response 3: EE (Y3) (%)
ZP (mV)
Assay
79.289* 150 100 150 100 220.71y 150 150 200 150 150 150 200
112.5 59.46* 150 112.5 75 112.5 165.53y 112.5 75 112.5 112.5 112.5 150
98.67 165.70 152.03 140.27 105.20 202.83 159.53 137.70 177.80 136.13 137.50 137.13 214.63
0.221 0.168 0.229 0.286 0.242 0.221 0.274 0.243 0.321 0.259 0.262 0.261 0.253
76.61 88.16 80.74 91.75 89.99 92.30 91.08 89.42 88.50 90.97 88.60 91.96 93.84
21.57 22.77 28.40 28.53 25.33 23.73 29.57 25.03 24.90 24.33 24.03 26.03 29.07
99.39 99.73 98.95 99.41 99.35 99.35 98.60 99.39 99.39 99.46 99.50 99.29 99.56
X1 ¼ amount of dynasan-114 in mg (high level ¼ 200, medium level ¼ 150, low level ¼ 100) and X2 ¼ amount of egg lecithin in mg (high level ¼ 150, medium level ¼ 112.5, low level ¼ 75). *Low level edge of error; yhigh level edge of error.
DOI: 10.3109/03639045.2015.1024685
Pharmacokinetics and pharmacodynamics of nisoldipine solid lipid nanoparticles
(Diax900, Heidolph, Schwabach, Germany) for 5 min at 12 000 rpm, while the system was maintained at 5 C above the melting point of the liquid throughout the homogenization process. The resulted coarse hot oil in water emulsion was ultrasonicated to get nanoemulsion using a 12T probe sonicator (Vibracell, Sonics, Newtown, CT) for 20 min. The nanoemulsion was cooled to room temperature to obtain ND-SLNs. Photon correlation spectroscopy
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Photon correlation spectroscopy (PCS) was used to measure the particle size, PDI and zeta potential (ZP) of all prepared formulations. Zeta sizer (NanoZS90, Malvern Instruments, Worcestershire, UK) was used for performance of PCS. All the samples were diluted with double distilled water to sufficient concentration before measurement and analyzed in triplicate and data were represented in mean ± SD. EE and drug content ND content and EE of ND-SLNs were determined by HPLC. The chromatographic system consisting of a SPD-20AD solvent delivery system containing double reciprocating plunger pump (Shimadzu, Tokyo, Japan), a SPD-20A UV–Visible variable wavelength detector with deuterium lamp using a RP C-18 analytical column (250 mm 4.6 mm i.d., 5 mm; Merck, Mumbai, India) and the mobile phase consisting of acetonitrile–water (80:20) ratio at a flow rate of 1 mL/min was used for the detection of drug at lmax of 237 nm17. To determine EE, centrifugation was performed using Centrisart (Sartorius, Goettingen, Germany) tubes at 8000 rpm for 30 min, which consisted of a filter membrane (molecular weight cut-off 20 000 Da) to separate the ultra filtrate18. The EE of the system was determined by measuring the concentration of free drug in the dispersion medium using HPLC, along with the following formula. EE ¼
Wtotal Wfree 100 Wtotal
where Wtotal was the weight of drug added in the system. For total drug content, about 0.1 mL of formulation was dissolved in chloroform and methanol (1:1) mixture. The final dilution of solution was made with mobile phase. ND content was determined by HPLC. In vitro drug release The in vitro release of ND-SLNs was studied using dialysis method in 0.1 N HCl and phosphate buffer (pH 6.8) in 24 h. Dialysis membrane (Himedia, Mumbai, India; pore size 2.4 nm and molecular weight cut-off between 12 000 and 14 000) was soaked overnight in double distilled water prior to the release studies. The experimental unit had donor and receptor compartments. Donor compartment consisted of a boiling tube which was cut open at one end and tied with dialysis membrane at the other end into which 1 mL of SLN dispersion was taken for release study. Receptor compartment consisted of a 250 mL beaker which was filled with 100 mL release medium and the temperature was maintained at 37 ± 0.5 C. At 0.25, 0.5, 1, 2, 3, 4, 6, 8, 10, 12 and 24 h time points, 2 mL samples were withdrawn from receiver compartment and replenished with the same volume of release medium. The collected samples were suitably diluted and analyzed by UV– Visible Spectrophotometry (SL-210, ELICO, Hyderabad, India). Statistical analysis of the data and validation of the model In this study, evaluation of the quality of fit of the model was performed using DOE software. Polynomial models including
3
linear, interaction and quadratic terms were generated for all the response variables using multiple linear regression analysis. The best fit model was selected based on comparison of several statistical parameters, including the coefficient of determination (R2), adjusted R2 and coefficient of variation (CV) provided by DOE software15. Further, ANOVA was used to identify significant effects of factors on response regression coefficients. The F test and p values were also calculated using the software. The relationship between the dependent and independent variables was elucidated using response surface plots (contour and 3D surface). These plots were used to study the effect of various factors at a given time and to predict the dependent response variables at intermediate levels of independent variables. Finally, a numerical optimization technique (desirability approach) and a graphical optimization technique (overlay plots) were used to generate new formulation with desired responses. To validate the chosen experimental design, the responses of experimental values were quantitatively compared with responses of predicted values and percentage relative error was calculated. Stability studies Stability of statistically optimized ND-SLN was studied at room and refrigerated temperature for 3 months. The average size, PDI, ZP, assay and EE were determined periodically after first day, 15 days, 1 month, 2 months and 3 months19. Lyophilization of SLNs Lyophilization (freeze drying) was used for enhancement of stability of SLNs20. The optimized ND-SLNs containing 10% w/v trehalose were prepared and kept in deep freezer at 40 C (Sanyo, Tokyo, Japan) for overnight. The frozen samples were then transferred into freeze-dryer (Lyodel, Delvac Pumps Pvt. Ltd., Chennai, India). Vacuum was applied and sample was subjected to various drying phases for about 48 h to get stable powdered lyophilized product21. Solid-state characterization Differential scanning calorimetry Differential scanning calorimetry (DSC) thermal analysis of ND, TM and physical mixture (PM) in 1:1 ratio of drug and TM, and lyophilized ND-SLNs was performed using 4000 model, Perkin Elmer, Waltham, MA. The instrument was calibrated with indium. All the samples (10 mg) were heated in aluminum pans using dry nitrogen as the effluent gas. The thermograms were obtained in the heating range of 20–200 C and at a rate of 20 C/min. Powder X-ray diffractometry Powder X-ray diffractometer (PXRD) (Multiflex, M/s. Rigaku, Tokyo, Japan) was used for diffraction studies. PXRD studies were performed on the samples by exposing them to nickelfiltered Cu Ka radiation (40 kV, 30 mA) and scanned from 2 to 70 , 2 at a step size of 0.045 and step time of 0.5 s. Samples used for PXRD analysis were ND, lipid (Dynasan-114), PM of drug with lipid (1:1) and lyophilized ND-SLNs. Study of ND-SLN morphology by scanning electron microscopy The morphology of nanoparticles was studied by using scanning electron microscopy (SEM, Hitachi, Tokyo, Japan). The sample of SLN formulation was first adhered to the carbon-coated metallic stub. This was sputter coated with Platinum coating
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machine (JFC-1600 Auto fine coater, JEOL, Tokyo, Japan) and mounted in SEM (JSM-6510LA, JEOL, Tokyo, Japan) for surface analysis. Imaging was carried out in high vacuum22.
Results and discussion
Bioavailability study
Statistical analysis
Study design and sampling schedule
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A single dose bioavailability study was designed in male Wistar rats under fasting conditions. The oral bioavailability of the optimized ND-SLN formulation and suspension was estimated in male Wistar rats with oral dose of 10 mg/kg body weight23. All experimental procedures were reviewed and approved by the institutional animal ethical committee, University College of Pharmaceutical Sciences, Kakatiya University (Warangal, India). Male Wistar rats weighing 200–250 g were taken for the study (six animals per group). Blood samples were withdrawn by retroorbital venous plexus puncture at 0, 0.5, 1, 2, 3, 4, 6, 8, 10, 12 and 24 h post-dose. About 1 mL of blood samples was withdrawn in eppendorf tubes and centrifuged at 3000 rpm for 30 min. The serum was transferred to another eppendorf tube and stored at 20 C until analysis by HPLC. HPLC analysis The HPLC column, Merck C18 (250 mm 4.6 mm), was equilibrated with an eluent mixture of acetonitrile–water (62:38 v/v) at a flow rate of 1 mL/min. The peaks were eluted at 237 nm wavelength without any interference from serum. Extraction procedure To 100 mL of serum, 100 mL of mobile phase and 100 mL of nimodipine solution as internal standard (3 mg/mL) was added and vortexed for 3 min. Then, 25 mL of 0.1 M NaOH was added and vortexed for 2 min. To this 1 mL of ether was added and vortexed for 3 min followed by centrifugation at 5000 rpm for 20 min. Organic phase was separated and evaporated. The residue was reconstituted with 100 mL of mobile phase and 20 mL of this solution was spiked on to the HPLC column24. Estimation of PK parameters and statistical significance The PK parameters such as peak serum concentration (Cmax), time for peak serum concentration (tmax), AUCtotal, biological half-life (t1/2) and mean residence time (MRT) were calculated by using the Kinetica software (version 5.0, Innaphase Corporation, Philadelphia, PA). The values were expressed as mean ± SD. The statistical comparison of data of two samples was performed with unpaired Student t-test using Graph pad prism software (version 5.02.2013, GraphPad Software, San Diego, CA) and p50.01 was considered as statistically significant. Pharmacodynamic study Four groups of male Wistar rats of A, B, C (hypertensive) and D (normal) (each of six), weighing 210–250 g were used in the study and allowed free access to standard laboratory diet and drinking fluid. Drinking fluid consisted of either tap water or 10% fructose solution25,26. The rats were trained to stay in the rat holder in a calm and non-aggressive state during BP measurement. Two weeks later, rats with a minimum mean systolic BP of 140–145 mmHg were selected. Groups C and D served as hypertensive and non-hypertensive controls, respectively. Groups A and B received SLN formulation and a suspension of drug, respectively, at 10 mg/kg orally. Using the tail-cuff method (NIBP, IITC, Woodland Hills, CA), systolic blood pressure (BP) was measured at different time intervals (0, 1, 2, 4, 6, 12, 24, 36, 48, 60, 72, 96, 120, 144 and 168 h) for all groups.
Experimental design optimization and response surface approach
The results of the design of experiments indicated that this system was highly influenced by the amount of lipid and surfactant, which resulted in high drug EE and small particle sizes for the preparation of SLN. From the response surface model, the regression equations (2)–(4) for the dependent variables were obtained using Design Expert software over the range of independent variables from the random order and are shown in Table 1. Y1 ¼ þ177:43 0:095X1 1:76X2 þ 1:33X1 X2 þ 3:17X12 9:88X22 Y2 ¼ þ0:048 þ 1:39X1 þ 1:26X2 3:88X1 X2 4:19X12 7:46X22 Y3 ¼ þ84:62306 þ 0:194X1 0:26X2 þ 1:94X1 X2 þ 1:094X12 1:08X22
ð2Þ
ð3Þ
ð4Þ
The sign and value of the quantitative effect represent the tendency and magnitude of the term’s influence on the response, respectively27. In the regression equation, a positive value indicates an effect that favors the optimization due to synergistic effect, while a negative value indicates an inverse relationship or antagonistic effect between the factor and the response. Regression values represent the quantitative effect of process variables X1, X2 and their influence on the dependent responses Y1, Y2 and Y3. These response data are shown in Table 2. Further, a close observation of data revealed the suitability of response surface quadratic model when compared to linear model and the two-factor (2FI) model. ANOVA for the responses indicated that the quadratic regression model was significant and valid for each of the responses Y1 (p50.0001), Y2 (p50.0001) and Y3 (p50.0001) and hence was appropriate to represent the observed data, respectively (Table 3). The observed R2 values for the dependent responses are 0.9138, 0.8803 and 0.8943 (Table 2). According to Torrades et al.28 when observed value of R2 was at least 0.80, it implied a good correlation and was found in all cases, indicating a good fit by the model. The R2adj values, for size (Y1) is 0.8523, PDI (Y2) is 0.8188 and EE (Y3) is 0.7948, are high and advocated the significance of the model29. The R2Pre values were 0.8012, 0.7945 and 0.7194 for size, PDI and EE, respectively, given by the model, which indicated a correlation between the observed and predicted values. Hence, the response model of this system is better one for the selected responses. Three-dimensional (3D) plots The independent variables, i.e. the amount of lipid (X1) and the amount of surfactant (X2) and their interaction on the particle size (Y1), PDI (Y2) and EE (Y3) as dependent responses were graphically represented by 3D surface plots by using RSM. The effect of the amount of lipid and amount of surfactant on particle size, PDI and EE are represented in Figure 1. When particle size (Y1) was indicated as the response, good correlation was shown between observed and predicted values as revealed by R2 of 0.913 (Table 2). This Y1 was significantly influenced by lipid amount (X1), surfactant amount (X2) and their
Pharmacokinetics and pharmacodynamics of nisoldipine solid lipid nanoparticles
DOI: 10.3109/03639045.2015.1024685
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Table 2. Regression values of the selected responses during optimization. Y1 (Size) Model Linear FI Quadratic
R
2
2
Y2 (PDI)
Adjusted R
Predicted R
0.7515 0.7264 0.8523
0.6127 0.3536 0.8012
0.7929 0.7948 0.9138
p Value
2
2
R
Adjusted R
0.7332 0.7535 0.8943
0.6798 0.6713 0.8188
0.0001
2
Y3 (EE) Predicted R
2
0.5092 0.4339 0.7945
R
2
0.5062 0.6948 0.8803
0.0001
Adjusted R2
Predicted R2
0.4074 0.5930 0.7948
0.0004 0.2720 0.7194
0.0001
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Table 3. ANOVA of optimized quadratic model. Parameter
Source
Size
Model Residual Lack of fit Pure error Model Residual Lack of fit Pure error Model Residual Lack of fit Pure error
EE
PDI
Degree of freedom
Sum of squares
Mean squares
F Value
p Value
5 7 3 5 5 7 3 4 5 7 3 4
12306.42 1160.35 1150.93 9.42 248.35 33.78 25.09 8.68 25.63 3.303 9.64 8.53
2461.28 165.76 383.64 2.36 49.67 4.83 8.36 2.17 3.709 5.73 3.25 2.4
14.85
0.0013
162.83
0.0001
10.29
0.0040
3.85
0.0001
11.46
0.0029
16.07
0.0107
interactive term (X1X2) and polynomial model of lipid concentration X12 with a p50.0001. Magnitude of the positive coefficient (177.4) of the term is suggesting that elevated levels of lipid and surfactant concentrations in the formulation could increase the particle size drastically. The size of lipid nanoparticles which is highly dependent on lipid concentration can be explained in terms of tendency of lipid to coalesce at high lipid concentration, also, in an SLN formulation, when increasing the solid lipid content, leading to higher surface tension and thus higher particle size. Figure 1(A) and (D) show the response surface model for particle size in response to the investigated factors. The surfactant and lipid levels had positive influence on particle size and the results are inconsistent with the conclusion of other investigators30. For the 13 formulations, the various factor combinations resulted in varied EE of ND from 76.61% to 93.84%. From Figure 1(C) and (F), there was a linear relationship between the amount of lipid and EE of ND. Quantitative estimation of the significant models indicated that lipid concentration had the prime influence on the EE for its large positive coefficient (0.194), suggesting that increasing the amount of lipid increased the EE and the amount of surfactant has little effect on the EE in the formulation (Equation (4)). Increase in the amount of lipid results in increasing the EE. This might be due to the formation of micelles at increased lipid concentration resulting in, sufficient spare space to accommodate more drug. This tendency could be attributed to the limited water solubility of ND and high lipophilicity31. Particle size distribution of SLN formulation in aqueous medium can be measured by PDI in a multimodal distribution. From Figure 1(B) and (E), the effect of formulation type and process variables on PDI can be evaluated. When the amounts of lipid and surfactant were altered from lower to higher levels, no significant changes in PDI were noticed, but at low levels of lipid amount alone, a marginal increasing trend in PDI was observed. This result implied that lipid concentration showed a promising effect on the PDI, which was confirmed by a statistical ANOVA result. However, lipid amount exhibited significant
positive influence on particle size at higher levels of the lipid, as revealed by the large and positive coefficient value (X12 ). It might be interpreted by the fact that lower lipid concentration and higher surfactant concentration resulted in a narrower particle size distribution. Optimization of independent variable and validation After analyzing the polynomial equations, depicting the dependent and independent variables, a further optimization and validation process by means of the Design Expert software was undertaken with desirable characteristics to probe the optimal formula solution of SLN. This depended on the prescriptive criteria of maximum EE, minimum particle size and PDI. The composition of optimum formulation was determined as 100 mg of lipid and 75 mg of surfactant, which fulfilled the requirements of optimization. At these levels, the predicted values of Y1 (particle size), Y2 (PDI) and Y3 (EE) were 102.34 nm, 0.235 and 89.37%, respectively. Therefore, in order to confirm the predicted model, a new batch of SLNs according to the optimal formulation factor levels was prepared. The observed optimized formulation had EE of 89.84 ± 0.52%, particle size of 104.4 ± 2.13 nm, and PDI of 0.241 ± 0.02, which were in good agreement with the predicted values. A comparison between these observed results and theoretical predictions indicated the reliability of CCD in predicting a desirable SLN formulation. In vitro release studies In vitro release of ND from ND-SLNs was studied in 0.1 N HCl (pH 1.2) and in phosphate buffer (pH 6.8) by using dialysis method. In 0.1 N HCl (pH 1.2), the cumulative percentage of release from all the 13 formulations ranged from 44.25 ± 0.53% to 84.38 ± 0.58% for a period of 24 h (Figure 2). In phosphate buffer (pH 6.8), the cumulative percentage of release from 13 formulations ranged from 45.16 ± 0.16% to 86.14 ± 0.68% (Figure 2) in 24 h. No significant difference was observed in drug release in phosphate buffer (pH 6.8) and 0.1 N HCl, at 24 h in each one of the formulations due to pH independent solubility of ND.
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Figure 1. Contour plots showing the interactive effects of (i) amount of lipid (X1) and amount of surfactant (X2) on particle size (Y1) (A and D); (ii) amount of lipid (X1) and amount of surfactant (X2) on PDI (Y2) (B and E); and (iii) amount of lipid (X1) and amount of surfactant (X2) on EE (Y3), and corresponding response surface plots (C and F).
The release profiles of SLN formulations exhibited a typical biphasic pattern with an initial rapid phase followed by a slow phase in phosphate buffer. The initial rapid phase could be due to the burst release of drug. A possible explanation is a short diffusion path due to enrichment of drug in the outer region of SLN or drug deposition on the solid surface3.
forces would increase32 the particle agglomeration and this might be a reason for increase in the particle size of the SLN formulation.
Stability studies
The purity of drug and the status of lipids in the SLN formulation were determined by DSC study. DSC thermograms of pure drug, pure lipid, physical mixture (PM) of drug and (1:1) and statistically optimized lyophilized SLN formulation are shown in Figure 3(a). The DSC thermogram of ND showed a sharp melting endothermic peak at 151.52 C. The peak was observed at its reported melting point (150–155 C) which indicated the purity of drug. The pure lipid (Dynasan-114) showed a sharp endothermic peak at the melting point of 59.57 C. In case of PM, very broader drug endothermic peak at 145.6 C (slight shift in the melting point) and lipid at 57.69 C. The relevant drug peak was not visible in the lyophilized formulation, which might be due to insufficient amount of drug in the lyophilized product. Alternatively, the drug might be uniformly dispersed at molecular level in the lipid matrix loosing the complete crystal structure33.
The stability of the statistically optimized SLN formulation was determined by monitoring the physical appearance, particle size, PDI, ZP, assay and EE of ND after storage at refrigerated and room temperature for a period of 3 months. At definite time intervals, we could not notice any signs of drug crystallization in SLN formulation. Further, no drastic increase in particle size was observed when stored at refrigerated and room temperature for a period of 3 months (Table 4). However, relatively a less change in size was noticed, in samples stored at either refrigerated or room temperature. However, this minor size changes were not reflected in PDI of samples during storage. Further, no significant reduction in the EE (p40.05) at either refrigerated or room temperature was observed. This could be due to the stable nature of lipid matrix formed during ND-SLNs preparation.
Solid-state characterization Differential scanning calorimetry
Lyophilization The optimized SLN formulation was freeze dried with 10% trehalose and resulted in freeze dried SLN powder. Upon reconstitution, increase in size, PDI and ZP were noticed. Due to removal of water in freeze drying process, particle attractive
Powder X-ray diffractometry Powder-XRD patterns of ND showed sharp peaks at 2 scattered angles of 11.28 , 12.43 , 18.97 , 22.69 , 25.24 , 27.42 , 44.05 and 64.41 , these were indicating the crystalline nature of drug
Pharmacokinetics and pharmacodynamics of nisoldipine solid lipid nanoparticles
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Figure 2. In vitro drug release from ND-SLNs in 0.1 N HCl and in pH 6.8 phosphate buffer (mean ± SD, n ¼ 3).
Table 4. Effect of storage at refrigerated and room temperature on size, PDI, ZP, assay and EE of statistically optimized ND-SLN formulation (mean ± SD, n ¼ 3). Size (nm) Time 1 day 15 days 30 days 60 days 90 days
PDI
ZP (mV)
Assay (%)
At 4 C
At 25 C
At 4 C
At 25 C
At 4 C
At 25 C
104.8 ± 1.43 106.3 ± 0.72 108.7 ± 0.60 111.1 ± 1.04 114.7 ± 1.69
104.4 ± 1.10 109.4 ± 0.85 121.7 ± 1.17 123.9 ± 1.65 126.6 ± 0.55
0.230 ± 0.015 0.244 ± 0.013 0.248 ± 0.007 0.250 ± 0.009 0.256 ± 0.006
0.240 ± 0.008 0.254 ± 0.006 0.268 ± 0.008 0.278 ± 0.004 0.282 ± 0.002
24.40 ± 1.11 25.50 ± 1.25 25.17 ± 0.55 25.60 ± 0.53 25.47 ± 0.50
22.77 ± 0.35 23.73 ± 0.32 23.30 ± 0.75 22.63 ± 0.83 22.53 ± 1.45
(Figure 3b). These characteristic peaks of ND existed in PMs. These drug crystalline peaks were absent in the sample submitted after lyophilization. This indicated that the drug was not in crystalline form after lyophilization of SLN. Intensity of pure lipid peaks was also decreased in the lyophilized samples. This reduced intensity indicated the decreased crystallinity of lipid. The change in crystallinity of lipid and drug would influence the release of ND from nanoparticles6. This reduction in crystallinity was noticed in DSC analysis also. Morphology of SLN using SEM SLN formulation was studied for surface morphology at 200, 15 K and 45 K magnification using SEM. The morphology of nanoparticles was found to be nearly spherical in shape, exhibited
At 4 C
EE (%)
At 25 C
At 4 C
99.49 ± 0.01 99.42 ± 0.02 89.41 ± 0.51 99.38 ± 0.02 98.97 ± 0.04 89.11 ± 0.4 99.41 ± 0.03 99.39 ± 0.61 89.34 ± 0.6 99.43 ± 0.04 99.4 ± 0.06 89.31 ± 0.9 99.4 ± 0.05 99.3 ± 0.04 88.91 ± 0.7
At 25 C 89.12 ± 0.62 88.92 ± 0.73 88.3 ± 0.61 89.01 ± 0.63 87.93 ± 0.61
polydispersity and possessed smooth surface with increasing tendency in particle size due to lyophilization process, as a follow-up of the agglomeration phenomenon, shown in Figure 432,19. PK study ND has poor oral absorption due to hepatic first-pass metabolism and poor aqueous solubility (BCS class-II). This study focused to investigate the feasibility of SLN for improved oral delivery of ND. The PK parameters of ND in individual rats for statistically optimized SLN formulation and suspension were calculated by non-compartmental estimations using Kinetica 2000 software. The PK parameters, AUCtotal, tmax, Cmax, MRT and t1/2 were calculated for optimized SLN formulation and compared with that
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Figure 3. Solid-state characterization of (A) drug, (B) excipient, (C) mixture and (D) lyophilized SLN formulation by DSC (a) and powder X-ray diffraction (b).
Figure 4. SEM images of optimized SLNs at different magnifications showing nearly spherical particles.
Table 5. Consolidated pharmacokinetic parameters of statistically optimized ND-SLN and suspension formulation in rats (n ¼ 6).
Parameter
SLN**** Mean ± SD
Suspension Mean ± SD
Cmax (mg/mL) tmax (h) AUCtot (mg/mLh) thalf (h) MRT (h)
12.55 ± 0.60 4.00 ± 0.00 96.15 ± 3.92 17.23 ± 1.80 16.50 ± 1.76
7.53 ± 0.13 3.00 ± 0.00 44.13 ± 2.90 8.27 ± 1.10 9.73 ± 0.65
****p50.0001 compared with suspension administration (mean ± SD, n ¼ 6). Figure 5. PK profiles of ND in rat serum following oral administration of optimized SLN formulation and suspension formulation (mean ± SD; n ¼ 6).
Pharmacokinetics and pharmacodynamics of nisoldipine solid lipid nanoparticles
DOI: 10.3109/03639045.2015.1024685
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Table 6. Pharmacodynamic (antihypertensive) effects of optimized ND-SLN and control suspension formulation after oral administration (mean ± SD, n ¼ 6). Mean systolic BP (mmHg) Untreated rats
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Time (h) Initial 1 2 4 6 12 24 36 48 60 72 96 120 144 168
Treated rats
Normal
Control
Suspension
ND-SLN
93.87 ± 3.56 92.23 ± 2.01 92.18 ± 2.20 94.95 ± 4.37 93.88 ± 3.94 95.78 ± 2.68 96.15 ± 3.65 97.12 ± 4.02 95.64 ± 1.54 93.57 ± 4.76 95.27 ± 4.21 96.39 ± 4.58 94.93 ± 3.63 94.21 ± 2.82 94.57 ± 3.56
149.11 ± 2.08 148.11 ± 2.08 146.93 ± 1.94 146.53 ± 1.53 146.4 ± 1.79 146.4 ± 1.86 145.5 ± 1.95 144.1 ± 1.96 139.6 ± 1.64 129.46 ± 0.92 123.36 ± 1.97 114.71 ± 2.88 109.81 ± 1.09 109.05 ± 0.93 108.53 ± 0.79
149.78 ± 3.26* 128.70 ± 1.37* 108.18 ± ± 1.03* 117.73 ± 3.09* 127.53 ± 1.64* 135.71 ± 3.09* 139.3 ± 0.94* 140.33 ± 1.47 132.48 ± 1.24 124.61 ± 1.12 122.08 ± 1.46 117.58 ± 2.49 110.93 ± 1.38 109.58 ± 2.16 109 ± 1.34
149.79 ± 2.28** 136.8 ± 2.20k 129.45 ± 0.69** 126.95 ± 1.23** 125.375 ± 1.23** 123.13 ± 1.72** 113.65 ± 2.16# 106.76 ± 2.34# 121.52 ± 2.62 126.7 ± 2.19 123.16 ± 2.13 116.46 ± 2.68 111.48 ± 1.58 109.87 ± 0.89 108.42 ± 1.41
Control, hypertension-induced rats; normal, normal male Wistar rats (non-hypertensive rats); ND-SLN, ND solid lipid nanoparticles. *p50.05 with respect to either control or normal. **p50.05 with respect to either control or normal. k p50.05 with respect to normal. #p50.05 with respect to control only.
of suspension. The statistical comparison of data was performed using the Student unpaired t-test at a significance level of p50.01. The serum concentration versus time profiles following single dose administration of ND-SLN formulation and suspension are shown in Figure 5 and Table 5. The pertinent PK parameters were calculated. From this, the higher Cmax for SLN formulation (12.55 ± 0.6 mg/mL) with respect to suspension (7.53 ± 0.13 mg/mL) was statistically significant at p50.0001. However, the time to reach the peak concentration was comparable to that of control. The AUCtotal, which denoted the extent of absorption, was also significantly (p50.0001) higher for SLN formulation (96.15 ± 3.92 mg/mLh) compared to suspension (44.13 ± 2.90 mg/mLh). The t1/2 and MRT were higher for SLN formulation because of slower elimination rate of ND from SLN formulation34. SLN formulation showed 2.17-fold improvement in oral bioavailability when compared to a suspension. Due to the nanosize of the SLNs the effective surface would increase influencing the adhesion to GI tract. Consequently, there is increased contact time of the SLN particles. In addition, the phosphatidylcholine and poloxamer could alter the permeability characters of the GI membrane. The fatty acid chains present in the lipids of SLNs that improve the uptake by lymphatic transport. The higher the chain length, greater the extent of lymphatic transport. This lymphatic transport minimizes the first-pass effect of the drug4. Overall, the improved oral bioavailability of SLNs could be due to the contribution of individual and/or combined mechanisms known till now.
ND-SLN formulation resulted in a gradual decrease of BP, with the maximum effect observed at 24 h (p50.05) and further continued for 36 h. In the hypertensive control group, there was no decrease in the systolic BP observed up to 72 h after the hypertension induction due to effect of fructose. In normal rat group, normal systolic BP was observed. From PK studies in rats increased AUCtotal, t1/2 and MRT of SLN (more than two times) in comparison to suspension were observed and this clearly indicated that the SLN had released the drug gradually over a period of 36 h. Oral ND suspension acted quickly (2 h) and drastically, but then its effect dropped off after 24 h, where as the optimal SLN formulation could not decrease the BP greatly in the initial phase, when compared with the suspension form. Since the administration of ND-SLN resulted in sustained and continued drug release for 24 h and beyond (from in vitro release studies), the designed SLNs were able to control the hypertension throughout 36 h period. We could find an increased systolic BP in rat models fed with fructose-rich diet even after changing or shifting the diet to normal. This is in consistent with the earlier reports26. Obviously, the obtained SLN statistically optimized formulation was capable of surmounting the shortcomings of oral administration of ND, such as low bioavailability and high firstpass metabolism. Further, it becomes a clinical advantage in controlling the hypertension slowly, steadily and for extended period by designing the drugs in SLN formulation.
Pharmacodynamic study
ND-SLNs were prepared and optimized by using RSM. The solidstate characterization revealed the transformation of crystalline state of the drug to amorphous state. The components were found to be compatible during DSC-excipient studies. Controlled release profiles were obtained by incorporating ND into the solid matrix of Dynasan114-based lipid nanoparticles. The PK studies were carried out in rats, which showed 2.17-fold improvement in bioavailability and reduction in systolic BP up to 36 h from SLN formulation. This confirmed the potential of SLN as a suitable carrier for oral delivery of ND. Nevertheless, to extrapolate the
In addition, the antihypertensive effect of SLN formulation was studied in comparison to a suspension in the rat model. The hypertension was induced in rats by 10% oral fructose solution. The systolic BP was measured and results are given in Table 6. The oral administration of ND suspension significantly (p50.05) controlled the hypertension initially, with the maximum effect at 2 h, but afterwards, the BP raised gradually until it was same as the initial value at 24 h. By contrast, the oral administration of
Conclusion
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N. Dudhipala & K. Veerabrahma
findings, PK and PD studies in humans are necessary to confirm the improved oral delivery of ND.
Drug Dev Ind Pharm, Early Online: 1–10
16.
Acknowledgements Mr. Dudhipala Narendar acknowledges the UGC, New Delhi, India for BSR fellowship to carry out this research work. We thank Dr. P. Govardhan, Vaagdevi College of Pharmacy, Warangal, for allowing us to use the NIBP equipment.
Declaration of interest
17.
18.
19.
The authors report no conflicts of interest. The authors are alone responsible for the content and writing of this paper.
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