Design of Guggul Lipid Loaded Chitosan Nanoparticles Using Box-Behnken Design – An Evaluation Study

Aim: Guggul lipid, a lipophillic antihyperlipidemic moiety, undergoes extensive first-pass metabolism and has low bioavailability. In order to address this limitation, guggul lipid loaded chitosan nanoparticles (GNPs) were designed, optimized and processed by 3factor 3level BoxBehnken design (BBD). Methodology: A 3-factor 3-level BBD was employed to investigate combined influence of formulation variables on percent entrapment efficiency (EE) and percent drug release (DR) of GNPs prepared by ionic gelation method. The generated polynomial equation was validated and desirability function was utilized for optimization. Optimized GNPs were evaluated for physicochemical, morphological, release characteristics, solid state characterization and in-vitro cell line studies. Results: Amounts of chitosan, sodium tripolyphosphate and guggul were selected as independent variables had variably influenced EE and DR. Optimized GNPs were produced with an average Original Research Article Murthy et al.; JPRI, 33(18): 53-67, 2021; Article no.JPRI.66721 54 size of 96.5 nm, electro kinetic potential of -15.4 mV, EE of 92.98% and DR of 95.12% in 24 h with sustained release. Physicochemical and in-vitro characterization revealed existence of guggul in amorphous form in GNPs without interaction and exhibited sustained release profile following first order with Higuchi kinetics. GNPs possessed lipase inhibition activity with IC50 value of 14.72 μg/ml and better viability against various cell lines with CTC50 values (256.24 to 321.27 μg/ml). Conclusions: Design and optimization of GNPs by BBD proved to be an effective and promising approach. High entrapment of guggul followed controlled release were the outcomes of GNPs prepared by ionic gelation with improved cell viability.


INTRODUCTION
Hyperlipidaemia is a heterogeneous disorder characterized by increased flux of free fatty acids raised triglycerides (TGs), low-density lipoprotein-cholesterol (LDL) and apolipoprotein B (apoB) levels. Reduced plasma high-density lipoprotein (HDL) cholesterol is due to metabolic effects or dietary and lifestyle habits [1]. Consumption of high fat diet (intake of fat and cholesterol exceeding 40% of total calories uptake), junk food and changes in lifestyle habits are the main reasons behind increasing prevalence of hyperlipidemia and associated disorders [2]. Hyperlipidemia, in turn, may lead to diabetes mellitus, fatty liver, cerebral infarction, hemiplegia and various cardiovascular disorders (CVDs) that accounts for one third of total deaths worldwide by 2020 [3][4][5].
Several synthetic moieties such as statins, fibrates, bile acid sequesterants, nicotinic acid derivatives, cholesterol absorption inhibitors and lipase inhibitors are prescribed to treat hyperlipidemia widely. However, the therapeutic efficacy of some of the above agents is limited by serious side effects viz., myopathy, rhabdomyolysis, hepatic toxicity, cardiomyopathy, kidney damage, bloating, flatulence, skin rashes, gallstones, abdominal pain, oily spots, diarrhoea and even death. Additionally, novel potential targets like inhibitors of Acyl-CoA cholesterol acyl transferase (ACAT), microsomal triglyceride transfer protein (MTP), cholesteryl ester transfer protein (CETP) and squalene synthase have also been explored but not yet succeeded [6,7]. Thus, the demand to focus on developing multifunctional natural bioactive compounds such as allicin, guggul, citrus flavonoids, curcumin, green tea, epigallocatechin gallate, and resveratrol as alternative therapeutic agents for treating hyperlipidemia and obesity is ever-increasing [8].
Among these, Guggulsterone, an active principal constituent of guggulipid obtained from the gum resin of traditional Ayurvedic plant Commiphora mukul (guggulu in Sanskrit) is approved in India as an antihyperlipidemic drug and has been used in Ayurvedic medicine since 600 BC to treat a wide variety of ailments including arthritis, obesity, hyperlipidemia and other lipid disorders. It also possesses antiseptic, anti-rheumatic, antiulcer, anti-cancer and anti-inflammatory properties [9][10][11]. Recent research indicated that guggulsterones (E & Z) are antagonists of farnesoid X receptor (FXR), which inhibits CYP7A1, the enzyme responsible for biotransformation of cholesterol, and also inhibits bile acid receptor (BAR), nuclear hormone receptors involved in bile acid regulation and cholesterol metabolism there by directly reduces hepatic cholesterol levels in humans [12]. However the antihyperlipidemic efficacy of the above mentioned natural agents depends of the design of suitable delivery systems.
In recent years nanoencapsulation of plant extracts and herbal drugs have revealed numerous advantages like enhanced solubility, bioavailability and therapeutic response, among others [13]. It also may provide protection from chemical degradation, reduced toxicity, targeted delivery and sustained/controlled action [14]. Among different approaches explored so far, colloidal carriers are of particular interest, especially those made from natural polymers [15]. Among biopolymers, chitosan is one of the most promising coating materials for many bioactive agents due to its nontoxic, biocompatible, and biodegradable characteristics [16,17]. In this context, the present research was aimed to design and optimize guggul loaded chitosan nanoparticles (GNPs) by ionic gelation method, optimized by a Box-Behnken Design (BBD), and to improve the release characteristics and antihyperlipidemic potential.

Drug-excipient Compatibility Studies (FTIR)
Compatibility studies for guggul, chitosan, sodium tripoly phosphate (TPP) and GNPs were studied by Fourier Transform Infrared (FTIR) spectroscopy (Shimadzu 8300E). FTIR spectra of guggul, chitosan, TPP and GNPs were studied by KBr pellet method. Each spectrum of the sample was collected from 32 single average scans at a resolution of 4 cm -1 in the absorption region of 400-4000 cm -1 [18].

Preparation of guggul chitosan nanoparticles
Guggul chitosan nanoparticles (GNPs) were prepared via ionic gelation method. A weighed quantity of chitosan was dissolved in Milli-Q water. Subsequently, a predefined quantity of guggul (dissolved in 0.2 ml of methanol) was added to chitosan solution under stirring. NPs were formed by adding TPP solution at a constant flow rate of 0.5 ml/min under stirring at 600 rpm at room temperature and allowed to crosslink for 3 h. Thereafter, GNP suspension was subjected for ultrasonication (Sonics VC 750) for 15 min, producing nanoparticles with controlled and uniform size. The dispersion was centrifuged at 7500 rpm for 20 min (Remi, Mumbai, India). Sediment was washed, redispersed in 1% lactose as cryoprotectant, lyophilized at -60°C and stored till further use [19,20]. As shown in Table 1, 17 different formulations were prepared.

Box-Behnken Design (3 Factor -3 Level with 5 Center Points)
BBD with statistical model incorporating interactive and polynomial terms were utilized to optimize and assess the responses. In this study, BBD with three factors at three levels were employed to generate quadratic response surface and second order polynomial models to quantify and thereby to optimize the GNPs. Based on preliminary studies, amounts of chitosan (X 1 ), TPP (X 2 ) and drug (X 3 ) were selected as independent variables and evaluated at three different levels: high, medium and low. The percent entrapment efficiency (Y 1 ) and percent drug release at 24h (Y 2 ) selected as responses with applied constraints are described in Table 2. Seventeen runs were generated using the Design Expert 8.0.6 software (State Ease, Inc., Minneapolis, MN). All experiments were performed in randomized manner to eradicate possible sources of bias. The non-linear quadratic model generated by the BBD was: Y = A 0 +A 1 X 1 +A 2 X 2 +A 3 X 3 +A 4 X 1 X 2 +A 5 X 2 X 3 +A 6 X 1 X 3 +A 7 X 1 2 +A 8 X 2 2 +A 9 X 2 Where Y is the measured response associated with each factor-level combination; A 0 is an intercept; A 1 -A 9 are the regression coefficients; A 1 -A 3 are main effects of X 1 -X 3 ; A4-A9 are effects of main factors and E is error term.
Multiple linear regressions and ANOVA were carried out for deciding the significance and influence of each individual factor as well as their interactions on response variables. A checkpoint analysis was performed to confirm the role of the derived polynomial equation and contour plots in predicting the responses. Optimization was performed by the desirability approach [21,22].

Entrapment efficiency (EE)
The amount of guggul entrapped in the GNPs was assessed by an indirect method. Before lyophillization, the nanoparticle suspension was centrifuged at 7500 rpm for 20 min. The clear supernatant was collected, filtered through a membrane filtered disc (0.22 µm) and guggul quantification was performed in an UV spectrophotometer (brand, city) at 254 nm. The EE was calculated according to the following expression: Where W t represents the total amount of guggul used for the preparation of nanoparticles and W s represents the amount of free guggul in supernatant liquid [23].

In-vitro Drug Release Study
The in vitro release profile of guggul from the GNPs was determined using dialysis tubing. Nanoparticles equivalent to 50 mg of guggul were placed in a dialysis bag (MWCO 12,000-14,000 g/mol, pore size of 2.4 nm). The dialysis bags were tied USP apparatus 2 (paddle) and suspended in dissolution vessels containing 500 ml of PBS pH 7.4. Aliquots of 5 ml were withdrawn at 0.5, 1, 2, 3, 4, 6, 8, 12 and 24 h by maintaining sink condition. Further, samples were filtered through a 0.22 μm membrane filter and analysed by UV spectrophotometry (Shimadzu UV-1800) at 254 nm. Three replicates of each experiment were carried out and the cumulative percentage of drug release was calculated. Data obtained from the in vitro release studies were fitted to various kinetic models [24].

Identification of optimum formulation
Numerical optimization technique was adapted for optimizing the formulation variables to obtain desired responses. The (predicted) optimum formulations were prepared in triplicate in order to asses the accuracy of the prediction. Mean values of experimental data were compared against predicted values and percent error was determined. Data obtained were screened to assess the accuracy and suitability of the conditions employed in the preparation of GNPs.

Particle size, Zeta potential and morphology
Particle size distribution and zeta potential of GNPs (optimized formulation) were estimated using Zetasizer (Horiba SZ-100

Differential Scanning Calorimetry
Chemical compatibility of guggul with formulation components and its physical state in GNPs was assessed by differential scanning calorimetry (DSC-60, Shimadzu

X-ray Diffraction Study
The X-ray diffraction (XRD) pattern was recorded for guggul, chitosan and GNPs using an X-ray diffractometer (Horizon, Chennai) at room temperature, with a voltage of 30 kV, 5 mA current and a 4°/min scanning speed. The samples were scanned from 5 to 50° (2θ) with a step size 0.01° and a step interval of 0.1 sec [28].

Cell culture and stimulation
3T3-L1 mouse fibroblasts, NSC-34 (mice motor neuron like) and kidney epithelial cells (Vero cells) were obtained from the National centre for cell sciences (Pune, India). Cells were cultured in dulbecco's modified eagle's medium (DMEM) with 10% (v/v) bovine calf serum (BCS) and 1% (v/v) penicillin-streptomycin. Two days after confluence, the cells were stimulated to differentiate with DMEM containing 10% fetal bovine serum (FBS), 0.5 mM 3-isobutyl-1methylxanthine (IBMX) and insulin (10 μg/ml) for two days. Cells were then maintained in 10% DMEM/ FBS medium with insulin (10 μg/ml) for another two days followed by culturing with a 10% FBS/DMEM medium for an additional four days, where >90% of cells were differentiated 3T3-L1 adipocytes with lipid droplets accumulated. Differentiated 3T3-L1, NSC-34 and Vero cells were then treated with GNPs. The cells were maintained at 37ºC with 5% CO 2 throughout the experiments.

Cell viability -MTT assay
Cells were grown and seeded at a density of 1 × 10 4 cells/well in 96 well plates. Cells were then treated with GNPs and incubated with 3-(4, 5dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium (MTT) solution for 3 h at 37ºC. Supernatants were aspirated, DMSO was added to each well, and then plates were agitated to dissolve the crystal product. Absorbance was measured at 540 nm using a microplate reader (Tecan M200). All experiments were performed in triplicate and mean values were used for calculation of circulating tumor cells (CTC 50 ) as per the following equation All experiments were performed in triplicate and mean values were used for calculation of CTC 50 [29,30].

Lipase Inhibition Studies
In-vitro lipase inhibition of optimized GNPs were performed as per the standard procedures [30].

Lipase suspension
Lipase powder was suspended in a Tris buffer pH 8.4 at a concentration of 5 mg/ml, the suspension was centrifuged and the supernatant was used for enzymatic reaction.

Sample preparation for lipase inhibition assay
Samples of GNPs for lipase inhibition were taken during the dissolution studies. Aliquots (5 ml) taken at specified time intervals were centrifuged at 10,000 rpm for 5 min, the supernatants were filtered and used for the assay.

Methods
In vitro assay of lipase activity was analyzed according to Lee et al. (1993). Enzyme assays were performed in 96-well microplate by adding 78 µl of GNP dissolution sample. After 20 min pre-incubation of inhibitor with lipase in the presence of bile salts, 100 µl of substrate p-nitro phenyl butyrate (p-NPP) were added and the absorbance was measured immediately at 410 nm using a microplate reader. Extent of lipase inhibition of GNPs was compared with guggul [31].

Stability Studies
Stability studies of optimized GNPs were carried out at room temperature (25±2°C), refrigerated condition (4±1°C) and accelerated condition

Statistical Analysis
All experiments were performed in triplicate. The results were given as mean ± standard deviation (SD). Statistical comparisons of data were made with simple analysis of variance (ANOVA) and independent student t-tests. The level of significance was taken as p<0.05.

Experimental Design
In experimental design, the effect of independent variables, i.e. amounts of chitosan (X 1 ), TPP (X 2 ) and guggul (X 3 ) on dependent variables such as % EE (Y 1 ) and % DR (Y 2 ) of 17 formulations was studied and results are depicted in Table 1.

Effect on % Entrapment Efficiency
The %EE of GNPs was ranged between 83.1±0.05% to 96± 0.13% (Table 1 The quadratic model had p values (global) of <0.0001 and F ratio of 58.57 stated that the model was significant for EE. The R 2 value of 0.986 for polynomial equation also indicated a good fit. Hence, this model was used to navigate the design space. All the independent variables X 1 , X 2 and X 3 and their interactive effects significantly affected % EE as shown in Fig. 1.  All data are shown as mean ± S.D; n=6

Fig. 2. 3D response surface (upper line) and contour (bottom line) plots of DR as a function of: (a) and (d) chitosan and TPP amounts; (b) and (e) chitosan and guggul amounts, and; (c) and (f) TPP and guggul amounts
Quadratic model had p values of <0.0001 and F value of 114.75, implying that the model was significant for DR. Predicted and experimental values were justified by correlation coefficient (R 2 ) which was found to be 0.9933 and indicated a good fit.
Response surface plots elucidated the effect of independent variables (X 1 , X 2 and X 3 ) on drug release. The drug release profiles of the different formulations are depicted in Fig. 3. For both dependent variables, parity plots ( Fig. 4a and 4b) show strong agreement between experimentally measured responses and the ones predicted by the models. Correlation coefficients demonstrated good relationship between the observed values and the responses predicted by the models. Normal (percentage) probability plots ( Fig. 4c and 4d) demonstrated that errors were normally distributed, and were independent of each other with homogenous error variance. Residual plots (Fig. 4e and 4f) showed that higher predicted values are associated with higher variance. These data further confirmed the reliability of the quadratic models.

Optimization using desirability approach
Numerical optimization technique based on the desirability approach was employed for optimization of GNPs to achieve maximum entrapment efficiency and desired drug release. Levels of variables (X 1 , X 2 and X 3 ) that produced optimum responses were found to be 1733.94, 1833.97 and 342.69 mg, respectively, with predicted 94.86 ± 0.24% EE and 95.49 ± 0.68% DR and overall desirability of 1. Optimized GNPs yielded EE of 92.98 ± 0.47% and DR of 95.12 ± 0.36% which were very close to predicted values with low percentage of bias. The % yield of optimized GNPs was found to be 85.84%.

FTIR spectroscopy
FTIR spectrum of guggul (Fig. 5a)  (C=O stretching of amide I), 1700.78 cm -1 (C=O), 3421 cm -1 (combined peaks of primary -NH 2 and -OH). FTIR of TPP showed a characteristic peak at 2915 cm -1 (C-H stretching). Most characteristic peaks of guggul and chitosan were retained in spectrum of GNPs, indicating no interaction between integrants.

Particle Size, Zeta Potential and Morphology
Particle size and zeta potential of optimized GNPs were 96.5 nm and -15.4 mV, respectively. Optimized GNPs were found to be spherical, uniformly distributed with slight rough and porous texture (Fig. 5b).

Differential Scanning Calorimetry
Thermograms of chitosan, guggul and GNPs are depicted in Fig. 6a. An endothermic peak of guggul is observed at 92.89°C, retained in thermogram of GNPs.

X-ray Diffraction
XRD of guggul (Fig. 6b) showed a range of sharp peaks at 2θ values from 10.0 to 45.0. Some of the peaks of guggul were disappeared in GNP stating slight amorphization.

Stability Studies
Stability studies data revealed that there were no physicochemical changes of GNPs and no shift in the principal functional groups of guggul as noticed in FTIR spectra (Data not shown). There was no significant difference between the samples stored at stressed conditions in terms of physicochemical integrity, drug content (91.36%) and drug release (89.84-95.12%) of optimized formulation. The developed GNPs were found to be physicochemically stable over a period of three months. The release of guggul from GNP followed First-order with Higuchi kinetics up to 24 h.

In vitro Cell Line Study (MTT Assay)
Optimized GNPs showed better level of cell viability against the three selected cell lines.

Lipase Inhibition
IC 50 values of GNPs and guggul were found to be 14.72 and 20.63 µg/ml, respectively, indicating good inhibition of lipase enzyme.

DISCUSSION
Design and optimization of formulation by using Box Behnken experimental Design (BBD) was a good conceptual approach. BBD depicted the effect of dependent variables on independent variables in a well-designed experiment with lowest number of runs in a systematic manner. BBD also allowed the establishment of optimal parameters by suitable mathematical and graphical representation. Coefficients (A 1 , A 2 and A 3 of equation (1)) with one factor in quadratic polynomial equations were attributed to main effect (EE or DR) of that particular factor, while coefficients with more than one factor were attributed to interaction between those variants. A positive coefficient factor indicated direct relationship between factor and response, whereas negative coefficient indicated converse effect. Response surface graphs were generated using polynomial equations, which represented simultaneous effect of two variables on response parameters by taking one variable at a constant level. Increased EE with increased chitosan (X 1 ) could be attributed to more amount of chitosan that had higher ability to form ionic gel with increased viscosity of dispersion thereby increased diffusional resistance of guggul to move forward into external phase through polymer droplet [16]. Moreover, time required for polymer precipitation decreased at higher polymer concentration and rapidly precipitated on the surface of dispersed phase, which prevented guggul molecules to diffuse out of GNPs across the phase boundary. Increased EE with corresponding increase in TPP (X 2 ) might be due to better crosslinking of chitosan matrix and more chitosan molecules could be participated in ionic gelation process which reconciled more guggul. Guggul (X 3 ) had direct correlation with EE where, more number of guggul molecules/micronized particles interacted with chitosan through electrostatic forces, thereby high proportion of guggul was entrapped in GNPs. Interaction factors X 1 X 2 and X 1 X 3 (equation (4)) were positively related to the EE owing to the above phenomena. However, the reason behind contrary relation of X 2 X 3 with EE was not clear, but might be due to anionic nature of both TPP and guggul that competitively interacted with similar chemical group of chitosan, thereby affecting the cross-linking efficiency.
Drug release was significantly The curvilinear increase in drug release with increased TPP (X 2 , see Fig. 2) could be due to effective cross linking with hydrophilic polymer, where more polyphosphoric ions cross-linked with amino groups on chitosan chains that loaded more guggul within its network and sustained its release from the NPs. Similarly, guggul (X 3 ) showed significant positive effect on drug release where higher amount of guggul resulted in greater entrapment with better release.
As GNPs were prepared by ionic gelation technique wherein, positively charged primary amine groups of chitosan interacted with negatively charged groups of polyanion (TPP). Further TPP, being a crosslinker with multivalent characteristics, helps in maintaining the chemical integrity of both chitosan and guggul in GNPs, which was evident from FTIR spectra. However, a peak at 1740 cm -1 and 1700.78 cm -1 of guggul and chitosan disappeared in the GNPs, due to linkage between phosphoric groups of guggul, TPP and ammonium groups of chitosan. Crosslinked GNPs also showed a peak at 1060.75 cm -1 (P=O) and broader peak between 2941-3443 cm -1 owing to hydrogen bonding between -OH and -NH 2 groups. Moreover, all the characteristic peaks of guggul were retained in GNPs and indicated successful encapsulation of guggul in NPs without any modification. Optimized GNPs were found to be stable with intended particle size, drug content, expected drug release and sufficient electrostatic repulsion with slightly negative electrokinetic potential (-15.4 mV) due to excess TPP ions.
Guggul-loaded nanosized (96.5 nm) entities initially released drug in burst mode owing to weak bonding of drug on huge surface of chitosan nanoparticles and later entangled guggul was released out in controlled mode following First order kinetics of Higuchi model up to 24 h. In addition to this biphasic drug release, chitosan-based nanoparticles can interact with negatively charged mucosal surfaces, resulting in enhanced drug absorption and bioavailability because of much longer residence time in gastrointestinal tract which in turn results in reduction of dosing frequency and hence patient compliance is expected to improve.
An endothermic peak of guggul was retained in thermogram of GNPs inferred that there was no physical and phase transition of drug in the mixture of formulation integrants. Similarly in Xray diffractograms, characteristic peaks of guggul were reduced due to amorphous dilution. Thus, indicated both physical/chemical integrity of stable nanoparticles with promised ionic gelation technique. These stable GNPs were intact against different stress conditions over three months as evident with FTIR spectral elucidation. The optimized GNPs were compatible against 3T3-L1 mouse fibroblasts, mice motor neuron like and Vero cells as evidenced with CTC 50 values. However, these GNPs did not influenced reduction of MTT and hence above cells were viable against them.
Optimized GNPs hydrolyzed the chromogenic ester of lipase effectively as assessed the yellow coloured p-nitrophenol product and thus indicating the viability of GNPs with the cells. Lipase inhibition activity of GNPs will be an additive effect in treating hyperlipidemia/obesity. Therefore, as the optimized GNPs maximized in vitro attributes of drug delivery system such as entrapment, controlled drug release, cell viability, lipase inhibition and physicochemical inertness, these formulations are appropriate delivery systems, with the potential to target the fat and adipose tissues in the treatment of obesity and hyperlipidemia.

CONCLUSION
Guggul loaded chitosan nanoparticles were successfully prepared by ionic gelation and optimized by a 3-factor 3-level BBD. The optimized formulation released guggul over a period of 24 h by following Higuchi kinetics. The optimized GNPs witnessed controlled release with better cell viability, additional lipase inhibition activity and were physicochemically intact against stress conditions over a period of 3 months. Thus, guggul loaded nanoparticles produced by ionic gelation proved as a better alternative to conventional dosage forms provided their high entrapment efficiency, improved drug release, enhanced lipase activity and high compatibility for various cell lines. Therefore, GNPs can be employed as suitable drug delivery platforms for delivering guggul lipid, a natural bioactive compound. Further in vivo and preclinical studies using suitable models are essential to prove the antihyperlipidemic potentials of GNPs.

DISCLAIMER
The products used for this research are commonly and predominantly use products in our area of research and country. There is absolutely no conflict of interest between the authors and producers of the products because we do not intend to use these products as an avenue for any litigation but for the advancement of knowledge. Also, the research was not funded by the producing company rather it was funded by personal efforts of the authors.