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Copyright © 2014 by American Scientific Publishers All rights reserved. Printed in the United States of America

Science of Advanced Materials Vol. 6, pp. 1–13, 2014 (www.aspbs.com/sam)

Solid Lipid Nanoparticles for Oral Delivery of Decitabine: Formulation Optimization, Characterization, Stability and Ex-Vivo Gut Permeation Studies Yub Raj Neupane, Manish Srivastava, Suman Gyenwalee, Nafees Ahmad, Kriti Soni, and Kanchan Kohli∗ Department of Pharmaceutics, Faculty of Pharmacy, Jamia Hamdard University, New Delhi 110062, India

ABSTRACT

1. INTRODUCTION Chemically, DCB (4-amino-1-(2-deoxy--D-erythropentofuranosyl)-1, 3, 5-triazin-2(1H)-one) is an analogue of natural nucleoside 2 -deoxycytidine which was first synthesized in early 1960s (Fig. 1).1 2 It is an off white solid crystalline powder with molecular formula of C8 H12 N4 O4 and a molecular weight of 228.21 g/mol which is available as Dacogen™ Dacogen™ contains 50 mg of DCB in lyophilized powder intended for injection. It was first approved for the treatment of patients with MDS (Myelodysplastic Syndrome) in the United States on May, 2006.3 The oral bioavailability of the DCB is very low (3.9–14%).4 It is unstable in acidic condition and has been reported to be metabolized by the enzyme cytidine deaminase present in liver. However, at neutral pH it is stable ∗

Author to whom correspondence should be addressed. Email: [email protected] Received: xx xx xxxx Accepted: xx xx xxxx

Sci. Adv. Mater. 2014, Vol. 6, No. xx

for 7 days at 4 C, for 96 hrs at 20 C, and for 21 hrs at 37 C.5 6 In alkaline solution DCB undergoes rapid and reversible opening of the 5-azacytosine ring followed by irreversible decomposition. On the other hand, in acidic condition the glycosidic bond of aza-nucleoside is cleaved which is one of the major cause for the low oral bioavailability of the drug.6 Colloidal drug delivery systems such as solid lipid nanoparticles (SLNs), lipid drug conjugates (LDCs) and nanostructured lipid carrier (NLCs) are very good examples of lipid nanoparticles which have successfully incorporated many drugs and protected them from physiological degradation.7 SLNs, first introduced in 1991, are novel lipid nanocarriers which represent system alternatives to traditional colloidal drug carriers, such as polymeric nanoparticles, polymeric microparticles, nanoemulsions, liposomes and others.8 Additionally, SLNs allow controlled drug release and drug targeting, change the distribution pattern of the drug in the body, increases intestinal permeability, increase drug loading, solubility

1947-2935/2014/6/001/013

doi:10.1166/sam.2014.2133

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The aim of current study was to develop solid lipid nanoparticles (SLNs) as novel lipid nanocarriers for oral delivery of Decitabine (DCB) using the cold homogenization technique. Box-Behnken design (33 ) with 17 experimental runs was used to achieve optimal formulations. The optimized batch was characterized by combining particle size distribution analysis, Z-potential, TEM, EE, DL, and DLS measurements, with rheological in-vitro drug release, accelerated stability, and ex-vivo gut permeation studies. The optimized batch revealed SLNs with spherical morphology with TEM-determined particle size of 136.6 ± 2.35 nm. Z-potential and %EE were found to be −31.34 ± 0.67 mV and 58.89% ±0.78, respectively. The in-vitro release study showed burst release at the initial stage, followed by sustained release over the subsequent 24 hrs in the intestinal medium. Thus, obtained data were further analyzed using release kinetic models, which revealed Higuchi matrix as the best fit model. The ex-vivo gut permeation study proved that the SLNs prepared by using lipid and surfactants allowed a nearly 4-fold increment in the permeation of the drug present in the formulation from the intestine, as compared to plain drug solution. Moreover, SLNs prepared with solid lipid and surfactants held high potential for entrapping DCB, showing better prospects for the oral delivery of DCB. KEYWORDS: Decitabine, Cold Homogenization Technique, Response Surface Methodology, Solid Lipid Nanoparticle, Ex-Vivo Gut Permeation Study.

Solid Lipid Nanoparticles for Oral Delivery of Decitabine

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2. EXPERIMENTAL SECTION

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Fig. 1. Chemical structure of decitabine.

and bioavailability, avoid biotoxicity, circumvent use of organic solvents as carriers, and protect drug against chemical degradation.9 10 It has been clearly reported that the drug incorporated into lipid shows very good permeability from the intestine because lipids and lipophilic excipients act as very good permeation enhancer for drug from the gastrointestinal tract thus increasing the oral bioavailability of the drug.11 Lipids enhance the solubilization of the drugs in the intestinal milieu and form micelles by incorporating in them. Moreover, they reduce the first pass metabolism of the drugs by transporting them through lymphatic route by altering enterocyte based drug transport to systemic circulation.12 SLNs can be produced by various methods, such as high shear homogenization, solvent evaporation, injection and ultrasonication.13 14 All these lipid nanoparticles are becoming one of the most accepted drug delivery system for both lipophilic and hydrophilic drugs because of easy to produce and scaled up. Most advantageously, the use of lipid excipients is generally regarded as safe (GRAS), and avoids the use of organic solvents.15 From extensive literature review, it can be concluded that SLNs can increase the oral bioavailability of many drugs along with protection from gastric degradation, increase intestinal permeability and offer all other aforementioned advantages.16–21 Literature research revealed no reports on SLN and other oral formulation system of DCB. The objective of the present study was to develop a novel SLNs for the oral delivery of DCB using cold homogenization technique. The prepared SLNs were optimized to identify the key independent variables influencing dependent variables (particle size, polydispersity index and entrapment efficiency) by constructing a Box-Behnken design (33 with 17 experimental runs. Moreover, systematic characterization of the optimized SLN batch was carried out by several techniques (particle size distribution determination, zeta potential, TEM, EE, DL, DSC, rheological and in-vitro drug release studies). Shelf life of DCB was determined with the help of Arrhenius equation. An ex-vivo gut permeation study for the comparison of the permeability of the drug present in the formulation with plain drug solution was carried out. 2

2.1. Materials DCB was obtained from Dabur Research Foundation, India. Precirol ATO 5 (glyceryl palmitostearate), Compritol 888 ATO (Glyceryl behenate), Gelucire 50/13 [lauroyl macrogolglycerides (polyoxylglycerides)], Gelucire 44/14 [Lauroyl macrogolglycerides (Polyoxylglycerides)], and Cutena CP (Cetyl palmitate) were kindly provided by Gattefosse (Mumbai, India). Glyceryl monostearate was provided by CDH (Bombay); stearic acid was provided by Qualikem Fine Chemicals. Tween 80 (SD Fine Chem), Poloxamer 188 (BASF) and Solutol HS 15 (BASF) were used as surfactants. Deionized water (Milli-Q) was obtained from the laboratory. Ammonium acetate (Merck, Bombay), sodium bisulfite (Merck, Bombay), and ethanol (HPLC Grade, SRL, Mumbai) were used as received. Amicon Ultra Centrifugal Filter Units (Ultra-15, MWCO 10 KDa) were purchased from Sigma-Aldrich. 2.2. Excipients Screening Selection of excipients for the formulation of SLN was done on the basis of both solubility analysis and compatibility studies. Solid lipid was selected by performing the solubility analysis of drug in various solid lipids. To this aim, 1 g of individual lipid was kept in a glass vial and melted at 5–10 C above its melting point. The glass vial was kept in water bath shaker (Nirmal International, Delhi) and maintained at the aforementioned temperature. A small quantity of the drug was added slowly and continuously until the solution was fully saturated. The saturation point was determined by observing the loss of transparency upon addition of drug to the molten lipid. After 48 hrs the vial was taken out and the supernatant was transferred into another vial. The vial was kept in ice bath to form solid mass. The solid mass was scrapped out and dissolved in ethanol and then analyzed by using UV spectrophotometer (Shimadzu, Japan) at 230 nm. The amount of the drug dissolved in the solid lipid was determined from the calibration curve of DCB in ethanol (data not shown). The compatibility studies were carried out for physical mixtures of drug and lipid which had the highest drug solubility by DSC (Pyris 6 DSC, Perkin Elmer) and FTIR (Shimadzu, Japan) analysis to prove the absence of interaction between drug and the selected lipid (data not shown). 2.3. Preparation of DCB Loaded SLN DCB loaded SLNs were prepared by the cold highpressure homogenization technique22 with slight modification, using a pressure cell homogenizer (Stansted, UK). 0.6% w/v of DCB was dispersed in melted Precirol ATO 5 (4–8% w/v) at 5–10 C above the melting point. The molten lipid drug mixture was immediately solidified in dry ice. The dried mixture was powdered using mortar Sci. Adv. Mater., 6, 1–13, 2014

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and pestle. The powdered mixture was then dispersed in cold aqueous solution containing (3–5% w/v) surfactants, namely. Polaxomer 188, Tween 80 and Solutol HS 15 (2:1:2) ratio. Cold aqueous surfactants solution containing lipid drug mixture was then mixed using Diax 9000 (Heidolph, Germany) at 5400 rpm for 12 min to produce coarse pre-dispersion. Finally, the pre-dispersion was homogenized using the pressure cell homogenizer under cold condition at pressure of 750 and 1500 bar for 12–20 cycles to produce SLN dispersion.

Y = A0 + A1 X1 + A2 X2 + A3 X3 + A12 X1 X2 + A13 X1 X3 +A23 X2 X3 + A11 X12 + A22 X22 + A33 X32

(1)

where Y is the response of the dependent variables associated with each factor level combination; A0 is an intercept; A1 to A33 are the regression coefficients of the respective variables, X1 , X2 and X3 are the coded levels of the independent variables that were selected on the basis of experiments, Xi , Xi Xi and Xi2 (i = 1, 2 or 3) represents the linear effect, the interactions and quadratic effect respectively.23–25 The independent variables selected were % w/v of lipid concentration (X1 ), % w/v of surfactant concentration (X2 , and the number of homogenization cycles (X3 which were represented by −1, 0, +1, analogous to the low, middle, and high levels, respectively (see Table I). As dependent variables, the particle size (Y1 , polydispersity index (Y2 , and entrapment efficiency (Y3 were taken for the study. 2.5. HPLC Analysis The analysis of DCB was done by using in-house developed and validated HPLC method. The chromatographic system consists of Shimadzu (Japan), Binary pump with UV detector. Ammonium acetate (0.01 M) was used as mobile phase. C18: 250 × 46 mm particle size 5 m, (Agilent, Switzerland) was used as an analytical column. Class-VP software was used to obtain data and chromatogram. 20 l of sample was injected, flow rate was maintained at 1 mL/min, and detection of DCB was done at a wavelength of 230 nm. Sodium bisulphite (0.1 M) Sci. Adv. Mater., 6, 1–13, 2014

Factors Independent variables X1 = Lipid concentration (% w/v) X2 = Surfactant concentration (% w/v) X3 = Number of homogenization cycle

Coded levels Low level (−1)

Medium level (0)

High level (+1)

4

6

8

3

4

5

12

16

20

Dependent variables Y1 = Particle size (nm) Y2 = Poly dispersity index (PDI) Y3 = Entrapment efficiency (%w/w)

Constraints Optimum (100–200 nm) Minimum Maximum

Note: nm = nanometer, %w/v = percentage weight/volume.

solution was used as diluent which acts as a stabilizer. The LOD and LOQ of the method was LOD = 1.92 ug/mL and LOQ = 5.82 ug/mL respectively. The retention time for the DCB was 12.015 min. 2.6. Characterization of DCB Loaded SLN 2.6.1. Particle Size and Poly Dispersity Index Particle size and PDI were measured by Dynamic Light Scattering (DLS) technique using Zetasizer, (Nano-ZS, Malvern Instruments) and analyzed by “DTS nano” software at 25 C at the detection angle of 174 . All the formulations were properly diluted about 250 times with distilled water followed by vigorous shaking of the formulations to obtain about 100–250 kilocounts per second for measurements. Average particle size (d.nm) and PDI were recorded. 2.6.2. Zeta Potential Zeta potential measures the charge distribution on the surface of the SLN. It was measured using Zetasizer, (Nano-ZS, Malvern Instruments) and analyzed by “DTS nano” software. The applied field strength was 20 V cm−1 . It is an indicator of the physical stability of the particles; values higher than ±30 mV are considered as optimum values. 2.6.3. Rheological Study The rheological behavior (viscosity) of the optimized SLN was measured using R/S Plus Rheometer (Brookfield Engineering Laboratories, Inc., Middleboro, MA, USA) having spindle of # C 50-1 at 25 ± 0.5 C without any dilution of the formulation. The data and rheograms were monitored using RHEO3000 software. About 0.5 ml of the formulation was used for the viscosity measurement. Controlled stress rate was applied to the formulation to determine the flow behavior of the SLN with change in the speed of the spindle up to 60 sec. 3

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2.4. Experimental Design Box–Behnken statistical experimental design with 3factors 3-levels and 17 experimental runs was selected for the optimization study. This design is suitable for investigating quadratic response surfaces and for constructing second-order polynomial models using Design Expert (Version 8.0.7.1, Stat-Ease Inc., MN, U.S.A.). Experimental design consists of a set of points lying at the midpoint of each edge and the replicated center point of a multidimensional cube, which enables the optimization process with a small number of experimental runs. The polynomial equation which was generated for the experimental design is given as Eq. (1):

Table I. Variables and their levels in the Box-Behnken design.

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2.6.4. Transmission Electron Microscopy (TEM) Surface morphology and size of the formulation were observed by transmission electron microscopy. The diluted sample of SLN was negatively stained with phosphotungstic acid and was dried after applying it on carbon coated grid. The dried slide was observed in a transmission electron microscope (JOEL 2100F) operating at 200 KV.

35–345 C with a heating rate of 10 C per min in an inert environment of nitrogen gas with flow rate of 20 ml/min. The samples were kept in hermetic pan made-up of aluminum and empty aluminum pan was used as a reference. Thermograms were recorded for powdered DCB and lyophilized optimized SLN formulation for the evaluation of both stability and compatibility.

2.6.5. Entrapment Efficiency and Drug Loading Quantitative determination of un-entrapped drug in the SLN was performed by using above described HPLC method. The concentration of un-entrapped drug in the SLN was determined using Amicon Ultra Centrifugal Filter Units (Ultra-15, MWCO 10 KDa, Sigma Aldrich) which consisted of a detachable donor (upper) compartment having a semi permeable membrane and sample recovery chamber (outer chamber) at the base. 1 ml of the SLN suspension was diluted with 4 ml of diluent (Sodium bisulphite solution) to dissolve any un-dissolved drug particles and was kept in the upper compartment of the Ultra centrifuge tube and centrifuged at 10000 rpm for 20 min. The SLN along with encapsulated drug remained in the upper chamber and the aqueous phase with un-entrapped drug moved into the sample recovery chamber through filter membrane at the base. The amount of the un-entrapped drug from the sample recovery chamber was determined using above described HPLC method. The entrapment efficiency (EE) and drug loading (DL) were determined using the following Eqs. (2) and (3) respectively.26

2.7. Drug Content Determination The amount of the drug present in the weighed amount of the formulation was determined by dissolving the optimized formulation in measured volume of ethanol and was stirred in the vortex mixer. The solution was further diluted with diluent to get proper concentration range. The resultant solution was filtered through 0.25 m membrane filter and was analyzed for drug content using above described HPLC method.

Entrapment Efficiency (% w/w) = Amount of drug added in the SLN −Amount of un-entrapped drug ×Amount of drug added in the SLN−1 ×100 (2) Drug Loading (% w/w) = Amount of drug added in the SLN −Amount of un-entrapped drug ×Amount of the lipid added−1 ×100

(3)

2.6.6. Lyophilization of DCB Loaded SLN Lyophilization of the SLN nanoparticle was performed by using mannitol as cryoprotectant in the ratio of 3% to total formulation. After addition of mannitol, gentle mixing was done to ensure proper mixing of the mannitol with the formulation. The sample was frozen at −20 C for 24 hrs after mixing. The frozen sample was lyophilized using freeze dryer (Heto Dry, Winner) for 24 hrs. The lyophilized sample was subjected for DSC analysis. 2.6.7. Differential Scanning Calorimetry (DSC) DSC (Pyris 6 DSC, Perkin Elmer Software Pyris series) study was carried out over a temperature range of 4

2.8. In-Vitro Drug Release Study The in-vitro release study of the DCB loaded SLN and DCB solution was performed in phosphate buffer (PBS) (pH 7.4) by using Dialysis bag with molecular weight cutoff 12000 kDa (Sigma Aldrich). The membrane was activated prior to use as per the procedure provided by Sigma Aldrich. The activated membrane was soaked in the dissolution medium for 24 hrs. 5 ml of the DCB loaded SLN and solution was placed in pre-activated dialysis bag, tying both of its ends with thread. Dialysis bag was kept inside the basket of USP XXIV dissolution apparatus I (DS 8000, Labindia, India) containing 500 ml of PBS. Dissolution study was carried out at 100 rpm maintaining a temperature of 37 ± 0.5 C. 5 ml of the sample was withdrawn periodically and replaced with an equal volume of fresh PBS. The samples were filtered and analyzed for DCB content using HPLC method as described above. 2.9. Stability Study According to ICH Q1A (R2) and Determination of Shelf Life Stability study of the drug loaded SLN were carried out as per the guidelines given in the ICH Q1A (R2).27 SLN dispersion was stored under 25 C ± 2 C/65% RH ± 5% RH and 40 C ± 2 C/75% RH ± 5% RH for the period of 6 months in Thermolab TH 90S. Samples at the predetermined time intervals (0, 3 and 6 months) were withdrawn and analyzed for its colloidal behaviour (formation of precipitate, phase separation, redispersibility), particle size, and drug content. The drug content after each time intervals was determined by above described HPLC procedure. A graph was plotted between log% drug remaining vs time (days). The degradation rate constant (k) was determined from the following Eq. (4), where the slope of the curve was determined form the graph. Slope = −K/2303

(4)

where, K is the degradation rate constant. Sci. Adv. Mater., 6, 1–13, 2014

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The shelf life of the formulation at 25 C was calculated by calculating the time required to degrade 10% of the drug in the formulation from the following Eq. (5). t10% = 2303/K × log100/90

(5)

where t10% is the time required to degrade 10% of the drug in the formulation.

APC = F /A × C0 cm min−1

(6)

where F is the permeation flux (g/min) obtained from the slope of the graph plotted between cumulative amounts of the drug permeated through the sac against time (min), A is surface area of the barrier membrane (7.85 cm2 taking length of sac 5 cm and assuming they have cylindrical shape) and C0 is initial concentration of the drug in the mucosal medium.

3. RESULTS AND DISCUSSION 3.1. Excipient Screening From the solubility analysis of DCB on various solid lipids (Precirol ATO 5, Compritol 888 ATO, Gelucire 50/13, Gelucire 44/14, Cutena CP, Glyceryl monostearate and Stearic acid), Precirol ATO5 was found to guarantee maximum DCB solubility and was hence selected for the formulation of DCB loaded SLN. A three-surfactants mixture (Polaxomer 188, Tween 80 and Solutol HS 15 Sci. Adv. Mater., 6, 1–13, 2014

3.2. Data Analysis by Design Expert Software The selected independent variables were observed to significantly influence the observed responses for Y1 particle size (122.6–299.8 nm), Y2 polydispersity index (0.221–0.403) and Y3 entrapment efficiency (30.34– 62.23%), as depicted in Table II. For each response Y1 , Y2 , and Y3 , mathematical model fit analysis and polynomial equations accounting for the main effect, and interaction factors were determined based on estimation of statistical parameters. In addition, each response was checked for sequential p-value, goodness of fit and difference between adjusted and predicted R2 -values.29 Data shows that all the responses fit to the quadratic model. The individual p-values of each term for each response were calculated using ANOVA. The intercept and the coefficients of each term of the quadratic equation generated by the software are given in Eqs. (7)–(9), where the negative or positive sign before the coefficients of each factor indicates the decreasing or increasing effect on the response due to that factor.30 Three-dimensional model graphs and contour plots were plotted in response surface analysis for depicting the effects of each independent variables: lipid concentration, surfactant concentration and number of homogenization cycles for the response of the each dependent variables: particle size, polydispersity index and entrapment efficiency are shown in Figures 2–4. These 3D response surface plots and contour plots were used to study the interaction effects of two independent variables on the dependent variables while keeping a third factor as a constant.31 From these plots the qualitative effects of each independent variable on each dependent variable can be easily predicted. 3.3. Effects of Independent Variables on Particle Size (Y 1 ) The particle size varied from 122.6 nm for SLN-2 to 299.8 nm for SLN-10 as per the result shown in Table II. The particle size was clearly influenced by all three independent variables. The mathematical relationship 5

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2.10. Ex-Vivo Gut Permeation Study Non-Everted Gut sac permeation study was conducted according to the protocol No. 907 approved by Jamia Hamdard University, India. For this study, over night fasted male albino rats weighing 200–250 gm (n = 3) were selected and sacrificed using anesthetic ether. A nearly 5 cm long ileum was taken, flushed with normal saline, and then kept in the Krebs solution with continuous aeration using aerator. This experiment was conducted according to the procedure described and instrument shown in our previous paper.28 In this experiment, 1 mL of plain drug solution was filled in the sac with the help of injection tube in the mucosal side and both the ends were ligated tightly. Then, the sac containing drug solution was kept in the glass assembly containing 100 mL Krebs solution with continuous aeration for 2 hrs using an aerator. At different time intervals 2 mL of the solution was collected from the serosal side and replenished with fresh Krebs solution to maintain sink condition. The collected samples were filtered and analyzed using HPLC method for the determination of permeated drug. Similarly, the experiment was repeated with the SLN formulation and analyzed for the drug content. The amount of the drug permeated through the intestinal sac was expressed in the form of an apparent permeability coefficient (APC), which was calculated from the following Eq. (6):

(2:1:2) ratio) was selected on the basis of the particle size and stability of placebo SLN dispersion formulated with different surface active agents (data not shown). The concentration of lipid and surfactants are specified in the production method and optimization (see Experimental Section). Compatibility studies between physical mixture of lipid and DCB were carried out by DSC and FTIR analysis. DSC analysis showed signals of both lipid and DCB at their respective melting point, proving that there was no interaction between them. FTIR analysis also proved that there was no interaction between them, showing peaks of the major functional group of DCB in the fingerprint region (data not shown).


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Table II. Observed and predicted values of particle size (Y1 , polydispersity index (Y2 , and % encapsulation efficiency (Y3 , of formulations made according to the Box-Behnken design. Particle size (nm) Y1 Formulation code

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SLN-1 SLN-2 SLN-3 SLN-4 SLN-5 SLN-6 SLN-7 SLN-8 SLN-9 SLN-10 SLN-11 SLN-12 SLN-13 SLN-14 SLN-15 SLN-16 SLN-17

PDI Y2

% EE Y3

Observed

Predicted

Observed

Predicted

Observed

Predicted

273.4 122.6 227.6 247.8 187.9 162.4 194.3 157.6 210.2 299.8 137.2 141.5 144.7 140.2 135.4 146.7 139.3

272.2 112.1 205.0 225.6 189.3 171.3 218.4 136.1 220.6 290.8 139.6 139.6 139.6 161.4 139.6 169.9 139.6

0.255 0.367 0.234 0.265 0.267 0.378 0.256 0.403 0.238 0.227 0.241 0.253 0.259 0.221 0.238 0.401 0.248

0.258 0.361 0.240 0.268 0.261 0.367 0.259 0.402 0.231 0.226 0.245 0.257 0.251 0.223 0.241 0.403 0.241

62.23 30.34 34.98 58.67 36.35 46.63 32.39 45.69 53.39 47.43 57.25 51.49 54.37 50.41 50.28 61.23 47.34

60.98 33.37 37.20 63.61 36.95 48.71 33.62 45.38 61.52 49.21 48.96 48.96 48.96 52.54 48.96 60.98 48.96

between independent variables and particle size for SLN is given by Eq. (7): Particle Size Y1 = +13962 + 1821X1 − 2335X2 − 3606X3 − 480X1X2 + 1038X1X3 + 4110X2X3 + 3650X12 + 4973X22 + 065X32

(7)

The main effects of X1 , X2 , and X3 represent the average result of changing one variable at a time from its low level to its high level. The most significant factor governing the size of particles is the lipid concentration (X1 ; p = 00795, F -value 4.202444), while the surfactant concentration and number of homogenization cycle exerted lower effect (X2 ; P = 00340, F -value 6.907749 and X3 ; p = 00048, F -value 16.47686). From the Eq. (7), it is

Fig. 2. 3D response surface plots and contour plots showing the effects of different variables on the size (nm) of the SLNs (R = particle size; A = % lipid concentration; B = % surfactant concentration; C = No. of homogenization cycles).

6

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clear that the concentration of lipid had increasing effect while surfactant and homogenization cycle reduced the particle size. The interaction terms X1 X2 , X1 X3 , X2 X3 , X12 , X22 and X32 showed how the particle size changes when two variables are simultaneously changed. The effect of interaction terms on particle size can be easily studied in 3D response surface plots and contour plots as shown in Figure 2. 3.4. Effects of Independent Variables on Polydispsersity Index (Y 2 ) PDI measures the heterogeneity of particles in the SLN. The PDI of SLN varies from 0.221 for SLN-14 to 0.403 for SLN-8. A smaller value of PDI is highly desirable in order to have uniform size distribution in the formulation. The mathematical relationship between independent variables and PDI for SLN is given in Eq. (8): PDI Y2 = +025 + 3875X1 − 1500X2 + 0078X3 − 1250X1X2 + 7500X1X3 − 4250X2 X3 + 7600X12 + 4350X22 + 0055X32

(8)

The main effects of X1 , X2 , and X3 represent the average result of changing one variable at a time from its low level to its high level. Increasing the lipid concentration led to increases in the PDI (p < 00001 and F = 5063246) to a significant level, whereas increases in surfactant concentration had a negative effect on PDI (p = 03818 and F = 0870877). However, an increase in Sci. Adv. Mater., 6, 1–13, 2014

number of homogenization cycle led to a slight increase in PDI (p = 07286 and F = 0130496), which may be due to non-uniform reduction in the particle size or to some particle growth induced by high shear forces and kinetic energy during forced passage of particle from the small piston gap of homogenizer. A higher concentration of surfactant produces low PDI, possibly due to production and stabilization of smaller particles. The effect of interaction terms on PDI can be easily studied in 3D response surface plots and contour plots in Figure 3. 3.5. Effects of Independent Variables on Entrapment Efficiency (Y 3 ) The entrapment efficiency of the DCB loaded SLN varied from 30.34% for SLN-2 to 62.23% for SLN-1 as shown in the Table II. A higher value of EE is desirable for the SLN so that a maximum amount of drug can be loaded and a higher amount can be delivered to the target site. The effect of various independent variables on EE can be well explained by the mathematical polynomial of Eq. (9) given below: % EE Y3 = +5113 + 1368X1 + 167X2 − 191X3 − 188X1X2 + 212X1 X3 − 276X2X3 − 326X12 − 046X22 − 088X32

(9)

In the Eq. (9), it is clearly shown that the lipid concentration plays major role in the entrapment efficiency (p < 00001 and F = 1427101). This may be due to the 7

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Fig. 3. 3D response surface plots and contour plots showing the effects of different variables on polydispersity index (PDI) of the SLNs (R = polydispersity index; A = % lipid concentration; B = % surfactant concentration; C = number of homogenization cycles).

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Fig. 4. 3D response surface plots and contour plots showing the effects of different variables on % entrapment efficiency of the SLNs (R = % Entrapment efficiency; A = % lipid concentration; B = % surfactant concentration; C = number of homogenization cycles).

increase in concentration of lipid that solubilizes higher amount of the drug. Increase in surfactant concentration showed increase in EE (p = 01893 and F = 2113257) which may be due to surfactant layer which helps in the protection of drug and prevents leaching of drug from lipid matrix. Increase in number of homogenization cycle showed a slight decrease in EE which may due to high shear forces caused reduction in particle size and ultimately poor entrapment of the drug. Their interaction effect on EE can be easily studied form 3D surface plots and contour plots in Figure 4. 3.6. Optimization and Validation The selection criterion of the SLN formulation was set as per the desired results of a particle size range in between 100 nm to 200 nm with low PDI and maximum EE. The formulations SLN-11, SLN-12, SLN-13, SLN-15 and SLN-17 were within the desired range, and these were the centre points of the Box Behnken model. These were the middle values of all independent variables with compositions 6% w/v of lipid (Precirol ATO5), 4% w/v of surfactant concentration (polaxomer 188, tween 80 and solutol HS15 in 2:1:2 ratio) and 16 cycles of homogenization at pressure of 750 and 1500 bar (8 × 750 and 8 × 1500). Among these formulations, SLN-11 was taken for the further elaboration and characterization of the SLN. 8

3.7. Particle Size, Polydispersity Index and Z-Potential The particle size and PDI of the optimized SLN-11 were 1366 ± 235 nm and 0244 ± 0002 (n = 3), as shown in Figure 5(top panel). The Z-potential was measured as −31.34 ± 067 mV (n = 3), as reported in Figure 5(bottom panel). The DLS technique was used to measure the particle size of these nanoparticles, which works on the principle of the fluctuation of the intensity of scattered light caused by particle movement in the SLN. This technique covers a size range from a few nanometers to about 3 m.32 Particle size distribution is one of the most important parameter to determine the stability of the SLN. PDI is an indicator of the homogeneity of the size distribution in the formulation. The low PDI value is considered as the optimal value as it shows the higher homogeneity. Generally, the Z-potential behavior can be used to predict the physical stability of the nanoparticles.33 34 Z-potential is considered as one of the most relevant measurable parameter for assessing the stability of SLN, since it denotes the overall charges acquired by the particles in a particular medium. It indicates the degree of repulsion between similarly charged particles that are interacting. Values away from the zero are considered as optimum values (±30 mV) to which satisfactory stability of particle in the dispersed medium can be guaranteed (actually, particles will repel each other if they carry high positive or negative value of zeta potential35 ). Sci. Adv. Mater., 6, 1–13, 2014

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corroborated by particle size measurement. This result also showed that the particles were homogeneously separated from each other. Thus, it was clear that both surfactant and lipid combination had produced stable SLNs. A TEM photomicrograph of SLN-11 is given in Figure 6 (bottom panel). Fig. 5. (Top) Particle size and distribution of SLN-11. (Bottom) Z-potential of SLN-11.

3.8. Rheological Study Assessment of the rheological behavior of the pharmaceutical semi-solid dosage form is a very important way to characterize the viscoelastic properties. These viscoelastic properties of the formulations deal with the microstructure and elasticity, and storage stability of the materials. Lipids and surfactants, contained in these semi-solid dosage forms, may lead to gelation during storage, which may be the probable cause for the instability of the product after certain period of time.36 The viscosity of the SLN-11 was found to be 0.3291pas and followed Hershel Bulkley plastic viscosity behavior, as shown in Figure 6(top panel). The Hershel Bulkley yield exponent was 0.9105 as extracted from the plot of Tau [pa] versus D [1/s]. 3.9. Transmission Electron Microscopy TEM imaging showed SLSs with spherical morphology and uniform size distribution. The results were Sci. Adv. Mater., 6, 1–13, 2014

3.10. Entrapment Efficiency and Drug Loading The EE and DL were determined by the above described HPLC method. The % EE of SLN-11 was 58.89% ± 0.78 (n = 3) and % DL was 5.63% ± 0.2784. The % EE for all other formulations are listed in Table II with their actual values and predicted values generated from software. The role of each independent variable on EE is fully described in the optimization procedure. As the lipid concentration increases, % EE also increases and % DL decreases insignificantly. 3.11. Differential Scanning Calorimetry (DSC) DSC thermograms of DCB and DCB loaded SLN are shown in Figure 7. DCB showed melting point of 201.54 C with enthalpy H = 367.358 J/g indicating the crystalline structure of the drug. Precirol ATO5 showed a melting point of 56 C corresponding to the reference literature. However, in the formulated SLN, the sharp peak of the lipid is present at a melting point of 55.58 C with enthalpy (H = 163.891 J/g), whereas the peak of the DCB at around 200 C disappeared as the drug was 9

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Fig. 6. (top) Hershel Bulkley equation followed by SLN-11. (bottom) TEM image of SLN-11.

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Fig. 7. DSC thermograms of Decitabine and Lyophilized SLN-11.

fully dissolved in the lipid matrix. This result consistently proved the stability and compatibility of the drug and excipients along with their superior protection within the solid lipid matrix where the drug existed in molecularly dispersed form. The peak of lipid in SLNs was slightly broader and slightly higher at the melting point than pure lipid peak, which indicated a more stable crystalline form of the lipid.37 One additional peak was observed for the SLNs at melting point of about 160 C which corresponded to the melting point of mannitol which had been used during lyophilization of the SLN as a cryoprotectant. Thus, it is clear that there is no significant interaction between drug and lipids in the formulations developed. 3.12. Drug Content Determination The drug content in optimized formulation SLN-11 was determined as above described procedure and analyzed by using HPLC method given above. The drug was extracted from the formulation using ethanol, as lipid is freely soluble in ethanol, and the solution was filtered and analyzed. The amount of drug present in the SLN-11 was found to 10

have 99.26% ± 1.42 (n = 3). This proved that the drug was stable during the manufacturing process. 3.13. In-Vitro Drug Release Study The release of the drug form SLN-11 showed biphasic behavior with initial burst release followed by a sustained release and steady release profile. The burst release effect was measured as the maximum percentage of drug release in the first hour (22.67% ± 0.892 cumulative % drug release) and after 24 hrs; cumulative % drug release was measured as (72.53% ± 1.23). However, plain drug solution showed maximum release at 4–8 hrs with (96.67% ± 1.45–99.23% ± 1.89) cumulative % drug release. The graph showing cumulative % drug release v/s time profile is shown in Figure 8. Factors that can influence the release of the drug from the SLN are the lipid matrix and its concentration, solubility of the drug in the lipid, partition co-efficient, and particle size.16 The release data was incorporated into various release kinetic models: zero order, first-order, Higuchi matrix, KorsmeyerPeppas, Hixcon–Crowell. The best fit model was determined on the basis of regression co-efficient (R2 value. Sci. Adv. Mater., 6, 1–13, 2014

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In-vitro release and release kinetics models for SLN-11.

The observed best fit model was the Higuchi matrix one, followed by first order model with R2 0.957 and 0.9558, respectively. The Korsmeyer-Peppas model suggests that the equation Mt /M = Kt n , (Mt /M = fraction of the drug release, K = release rate constant; t = release time and n = release exponent) is for the drug release dependent on the shape of the matrix dosage form. n-values 0 to 0.5, 0.5 to 1, 1 and >1 characterize a Fickian diffusion, non Fickian, Zero order and superdiffusion, respectively, for spherical particles.38 We calculated the value of n by plotting the fraction of drug release versus square root of time, where the slope of the curve represents the n value. The obtained value of n = 0167 showed the release behavior is Fickian diffusion. However, for lipid nanoparticles, Higuchi matrix model suggests that the drug release is dissolution-controlled rather than diffusion-controlled. These various kinetic models are given in Figure 8. 3.14. Stability Studies According to ICH Q1A (R2) Samples selected for stability studies at different time intervals were evaluated for their physical and colloidal properties (formation of precipitate, redispersibility, phase separation, particle size, and drug content). The results Table III.

3.15. Ex-Vivo Gut Permeation Study Ex-vivo gut permeation study showed a nearly 4-fold increment in the permeation of the drug form for the formulation SLN-11 than compared with plain drug solution. This result showed that the drug incorporated into the lipid and stabilized with the help of surfactants and their size reduction to the nanometric range showed remarkably better permeation of the drug than the plain solution ultimately resulting in higher concentration delivered to the systemic site. From the linear portion of the plot of the

Stability studies of the formulation SLN-11 at 25 C ± 2 C/60% RH ± 5% RH at predetermined time (n = 3).

Time (Days) 0 90 180

obtained at scheduled time intervals are given in Table III. Results revealed that SLN-11 after 6 months showed no formation of precipitate, no phase separation, particle size of 198 ± 646 nm and PDI 0367 ± 067 (n = 3), and good redispersibility; the drug content was found to be 97.45% ± 2.39 (n = 3) at 25 C. The shelf life of the formulation SLN-11 was found to be 2.087 years at 25 C which was calculated from the above mentioned Eq. (5) with slope being calculated from Figure 9. The effect of temperature on the formulation at 40 C was determined: the drug content after 6 months was found to be 87.62% ± 3.89 (n = 3).

Formation of precipitate

Phase separation

Redispersiblity

Particle size ± SD nm

PDI ± SD

No No No

No No No

Yes Yes Yes

136.6 ± 2.35 158.2 ± 3.56 198 ± 6.46

0.244 ± 0.002 0.278 ± 0.022 0.367 ± 0.67

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Fig. 8.



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4. CONCLUSIONS

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Fig. 9. Stability studies on the SLN-11.

cumulative amount of drug permeated (g) v/s time (min) in Figure 10(top panel), the permeation flux (F ) was calculated. The APC for the plain drug solution and SLN11 were found to be 0356 ∗ 10−4 cm/min and 1436 ∗ 10−4 cm/min, respectively, as shown in Figure 10 (bottom panel). The increment in the permeation of the drug incorporated into the lipid may be due to either one or to a combination of the following factors/processes, such as lipid-stimulated billiary and pancreatic secretions, decreases metabolism and efflux activity, increases intestinal wall permeability, transport of the drug via lymphatic system, and prolongation of the gastrointestinal tract transit time. Particle size also plays very important role on the intestinal tissue uptake of these nanoparticles. It is well documented that particles with size of 100 nm or smaller show significantly greater tissue uptake than mircoparticles.39

DCB loaded SLNs was successfully prepared by the cold homogenization technique which was further optimized by response surface methodology using design expert software. Various parameters of the formulation were characterized; additionally, an ex-vivo gut permeation study was performed. The concentration of the lipid and the number of homogenization cycles play a major role in particle size and entrapment efficiency. Results of TEM analysis corroborated the particle size obtained from DLS measurements. Z-potential showed highly stable formulation and good combination between lipids and surfactants. The result of in-vitro study showed two-stage release behavior with an initial burst release followed by sustained release over 24 hrs. The best fit model for release kinetics was found to be based on the Higuchi matrix. DSC analysis of the lyophilized SLN proved that there was no interaction between lipid and drug in the formulation, and drug was molecularly dispersed in the lipid matrix. Stability studies according to ICH Q1AR2 revealed stable formulation with a shelf life of 2.087 years at 25 C. Ex-vivo gut permeation study of SLN showed a nearly 4-fold increment in the permeation of the drug from the intestine, as compared with plain drug solution performed under similar experimental conditions. In conclusion, our investigation suggest that the developed SLNs are promising prospective carriers for oral delivery of DCB. However, a detailed pharmacokinetic study and bioequivalence studis of DCB loaded SLNs are highly encouraged. Acknowledgments: The authors wish to express their appreciation to Mr. Anurag Tyagi (Gattefosse, India) for providing lipid as gift sample and Dabur Research Foundation, India for providing Decitabine as Gift sample. We gratefully acknowledge Mrs. Srijana Subedi Neupane for her linguistic support during manuscript editing.

References and Notes

Fig. 10. (top) Cumulative amount of drug permeated (g) v/s time; (bottom) APC for plain drug solution and SLN formulation.

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