Biodegradable intranasal nanoparticulate drug delivery system of risedronate sodium for osteoporosis

Abstract

Context: Osteoporosis (OP) is the most common metabolic bone disease predominantly found in elderly people. It is associated with reduced bone mineral density, results in a higher probability of fractures, especially of the hip, vertebrae, and distal radius. Worldwide prevalence of OP is considered a serious public health concern.

Objective: The purpose of the present work was to develop and evaluate polymeric nanoparticles (NPs) of risedronate sodium (RIS) for the treatment of OP using intranasal (IN) route in order to reduce peripheral toxic effects.

Materials and methods: Polymeric NPs of RIS were prepared by nanoprecipitation methods. Formulations were developed and evaluated in context to in vitro drug release, ex vivo permeation, in vivo study, and biochemical studies.

Results and discussions: The particles size, entrapment efficiency (EE) (%), and loading capacity (LC) (%) of optimized formulations were found to be 127.84 ± 6.33 nm, 52.65 ± 5.21, and 10.57 ± 1.48, respectively. Release kinetics showed diffusion-controlled, Fickian release pattern. Ex vivo permeation study showed RIS from PLGA-NPs permeated significantly (p < 0.05) through nasal mucosa. In vivo study showed a marked difference in micro-structure (trabeculae) in bone internal environment. Biochemical estimation of treated group and RIS PLGA indicated a significant recovery (p < 0.01) as compared with the toxic group.

Conclusion: Polymeric NPs of RIS were prepared successfully using biodegradable polymer (PLGA). Intranasal delivery showed a good result in in vivo study. Thus PLGA-NPs have great potential for delivering the RIS for the treatment and prevention of OP after clinical evaluation in near future.

Keywords:

• Nanoprecipitation
• osteoporosis
• PLGA
• polymeric nanoparticles
• risedronate sodium

Introduction

Osteoporosis (OP) is a major disease in elderly people and its complications and prevalence are increasing rapidly worldwide. It is associated with reduced bone density, results in a higher probability of fractures, especially of the hip, vertebrae, and distal radius. Professor John A. Kanis, President, international osteoporosis foundation (IOF) has said, “Osteoporosis is a serious threat to women’s health – worldwide one in three women over the age of 50 will suffer a broken bone due to OP (International Osteoporosis Foundation, Switzerland). Yet too many women are unaware of their increased risk after menopause and fail to take preventive measures.” Initially, it was expected that with the rising life span worldwide lesser number of people are suffering from this disease but now due to its prevalence worldwide, osteoporosis is considered a serious public health concern. Currently, it is estimated that over 200 million people worldwide suffer from this disease among which the number of hip fractures throughout this globe will rise from 1.66 million in 1990 to 6.26 million by 2050 (Dhanwal et al., 2011). Postmenopausal osteoporosis (POP) is a common disorder characterized by an increase in bone resorption relative to bone formation, generally in conjunction with an increased rate of bone turnover. The progressive decrease in bone mass leads to an increased susceptibility to fractures, which results in substantial morbidity and mortality. Therefore, there is a need to develop drugs and delivery systems for prevention and treatment of OP.

Current pharmacological treatments for OP have been developed on the basis of existing knowledge of basic bone biology, while the development of novel therapies relies on the exploration of fundamental regulatory mechanisms. The balance between bone resorption and bone formation is maintained through a complex regulatory system of systemic and local factors that act on bone cells, such as calcium-regulating hormones, sex hormones, growth factors, and cytokines. The existing drugs for treatment of osteoporosis are limited in scope, tolerability, and antifracture efficacy (Akesson, 2003). Considerable efforts have been made to optimize the existing drugs and to develop newer ones. Estrogen, Raloxifene, BPs, Salmon Calcitonin, Teriparatide, and Denusumab are US FDA approved drugs for OP. The FDA has also approved synthetic bone graft substitutes which are osteoconductive without growth factors. These grafts consist of calcium phosphates alone or in combination with collagen or other polymers.

Bisphosphonates (BPs) therapy is the standard choice for the treatment of OP. It is highly effective and well tolerated. BPs may be considered as gold standard for OP treatment due to their positive results in clinical studies. BPs are available in both oral and intravenous (IV) formulations. Nitrogen-containing BPs are the therapy of choice for postmenopausal osteoporosis (POP), glucocorticoid-induced osteoporosis (GIO), osteoporosis in men, and Paget’s disease. In addition to the benefits of increased bone mineral density (BMD), the use of BP also has been associated with reduced risk of fracture (Silvermann & Maricic, 2007). One of them is risedronate sodium (RIS) which is approved by USFDA in 1998 for clinical use. RIS is a nitrogen-containing BP which is used to strengthen bone, treat or prevent OP, and treat Paget's disease of bone. Conventionally, RIS is given orally and possesses GI adverse effects like inflammation or damage esophagus (esophagitis or esophageal erosions or ulcers). RIS, an antiresorptive drug widely prescribed for the treatment of OP, is associated with injuries of the upper GIT (Lin, 1996; Li & Kendler., 2004). In order to overcome this limitation, the tablet should be swallowed with a full glass of water, and patients should avoid lying down for at least 30 min to facilitate esophageal passage and decrease mucosal adherence (Lanza et al., 2000). RIS has a short half life (1.5 h) and requires frequent dosing leading to non-compliance to the patients. Oral bioavailability of RIS is only 0.63% due to its gastric first pass effect. Major dose does not show the effect in the body but show the GIT problems (Mitchell et al., 2001). Drug delivery systems (DDS) are designed with rationale of promoting the therapeutic effect of a drug and minimizing their toxic side effects, which is achieved by optimizing the amount and duration of the drug in the vicinity of the target cells, while reducing the drug exposure to non-target cells in view of formulations as well as delivery route also (Deepa et al., 2014). Nam and co workers developed topical formulation of RIS containing RIS 10% w/w and PEG (M_W 400). Their results showed that topically administered RIS had anti-OP effects and even supported new bone formation. Topical RIS application helped to reduce many side effects of orally administered RIS (Nam et al., 2012). Nasr et al. (2011) developed biodegradable microsphere of RIS for bone deposition alveolar targeting. However, the nanoparticulate formulation planned in the present study has sound advantages over these developed delivery systems.

The nasal pathway is an alternative route for non-invasive systemic administration of drugs, although the various barriers restrict pathways of drug molecules. Nanonization of drugs have a great potential to increase solubility and to augment permeability through mucosal barriers. Pharmaceutical technology may help to increase the solubility of active agents and enhance the permeability of molecules with larger molecular mass like peptides via the modulation of the paracellular pathway (Illum, 2004).

The aim of the present study was to develop polymeric NPs of RIS that will be delivered intranasally to avoid first pass metabolism and avoid the distribution to non-targeted site. The present investigation also aims to develop polymeric NPs, as these NPs are expected to offer many advantages over conventional dosage forms, like reduced toxic effects, greater availability of the target site, and constant drug release.

Materials and methods

Materials

RIS, [1-hydroxy-2-(3-pyridinyl) ethylidene] bi's [phosphonic acid] monosodium salt home pentahydrate with a molecular weight of 350.13 g/mol, was received as a gift sample from Jubilant Life Sciences (Noida, Uttar Pardesh, India). Pluronic® was received as a gift sample from BASF, Ludwigshafen, Germany, and PLGA (50:50, Resomer 502 H) was received as a gift sample from Evonik Research (Banglore, India). Potassium dihydrogen phosphate, methanol, sodium hydroxide (NaOH), and 1-octanol were all purchased from S.D. Fine Chemicals, Ltd. (Mumbai, India). Glacial acetic acid was purchased from IOL Chemical Ltd. (Mumbai, India). Methanol and acetonitrile HPLC grade are also procured from S.D. Fine Chemicals, Ltd. (Mumbai, India). Tetrabutyl ammonium hydroxide was purchased from Sisco Research Laboratories Private limited (Mumbai, India) and sodium pyrophosphate was purchased from Central Drug House Pvt. Ltd. (New Delhi, India). Dialysis sacs (mol. wt. cut-off: 12 000 Da, flat with 25 mm, a diameter of 16 mm, a capacity of 60 mlft) was purchased from Sigma Aldrich Chemicals, St. Louis, MO. All reagents were of analytical grade.

Preparation of polymeric NPs

The PLGA-NPs were prepared by modified nanoprecipitation as described by Seju et al. (2011). PLGA NPs were prepared by dissolving PLGA in the organic phase consisting of a non-solvent (chloroform). The drug was dissolved in the aqueous phase because the RIS is highly soluble in the aqueous phase. The organic phase was then injected into the aqueous phase under continuous stirring. Finally, this mixture was added drop wise into the distilled water containing Pluoronic F-68 (Poloxamer 188). Organic solvent was removed by stirring overnight. The NPs were optimized by using three level three factors Box-behnken statistical design. Polymer concentration, stabilizer concentration, and number of homogenation cycles were selected as independent variables based on the preliminary study where as particles size, entrapment efficiency (EE), and LC were selected as dependent variables. Total 17 experimental runs were generated in different compositions as shown in Table 1. The prepared nanoparticulate suspension was analyzed for particle size and surface morphology. The nanoparticulate suspension was centrifuged at 15 000 × g for 60 min at 4 °C, using cooling centrifuge (C24, Remi Centrifuge, Mumbai, India). The supernatant was analyzed by RP-HPLC to calculate the EE (%) and drug loading (%).

Table 1. Independent factors and observed values of the dependent factors (responses) (n = 3).

Independent factors					Dependent factors
Std	Run	A: polymer concentration (mg/ml)	B: stabilizer (%)	C: homogenization no. of cycles	Particle size (Y1) (nm) ± S.D.	% EE (Y2) ± S.D.	% LC (Y3) ± S.D.
12	1	30	1	3	125.98 ± 5.45	50.23 ± 5.23	7.76 ± 1.72
14	2	30	0.55	2	118.54 ± 6.33	51.89 ± 2.73	8.94 ± 1.32
16	3	30	0.55	2	124.49 ± 6.12	51.26 ± 3.65	9.12 ± 0.42
4	4	50	1	2	184.87 ± 7.44	66.43 ± 4.46	3.24 ± 0.36
13	5	30	0.55	2	120.67 ± 3.54	50.65 ± 3.44	8.79 ± 1.16
11	6	30	0.1	3	180.39 ± 4.34	55.87 ± 4.63	13.77 ± 0.68
15	7	30	0.55	2	117.92 ± 2.64	50.98 ± 5.34	9.06 ± 0.64
2	8	50	0.1	2	167.43 ± 5.57	63.23 ± 4.46	5.47 ± 0.94
3	9	10	1	2	94.36 ± 6.45	40.75 ± 5.56	8.59 ± 0.63
10	10	30	1	1	174.19 ± 3.56	58.68 ± 3.64	4.47 ± 0.83
17	11	30	0.55	2	115.51 ± 3.45	50.48 ± 5.37	9.02 ± 0.68
7	12	10	0.55	3	84.86 ± 3.54	33.39 ± 6.43	15.43 ± 0.48
6	13	50	0.55	1	165.98 ± 3.67	61.34 ± 4.58	3.72 ± 0.33
1	14	10	0.1	2	162.52 ± 3.45	46.83 ± 6.45	11.24 ± 0.32
9	15	30	0.1	1	167.75 ± 3.56	59.76 ± 5.74	4.52 ± 1.14
5	16	10	0.55	1	85.83 ± 4.85	32.12 ± 5.66	12.57 ± 0.23
8	17	50	0.55	3	102.39 ± 3.57	43.83 ± 7.32	13.47 ± 0.82

Particle characterization

Particle size and surface morphology

Particle size and particle size distribution (Polydispersity index – PDI) were determined by Zetasizer Nano ZS (Malvern Instruments Ltd, Worcestershire, UK). Measurements were performed using Standard laser 4 mW He–Ne, 633 nm, room temperature (25 °C) at a fixed angle of 90°. The sample volume used for the analysis was kept constant, i.e., 1 ml. The instrument is equipped with appropriate software for analysis of particle size and PDI.

The surface morphology of the prepared NPs was determined for by using scanning electron microscopy (SEM), and the size of the NPs was also confirmed using transmission electron microscopy (TEM). The nanosuspension samples were prepared by dispersing a small amount of NPs into distilled water. A drop of nanosuspension was placed on a paraffin sheet and carbon-coated grid was placed on sample and left for 1 min to allow polymeric NPs to adhere on the carbon substrate. The remaining suspension was removed by adsorbing the drop with the corner of a piece of filter paper. Then the grid was placed on a drop of phosphotungstate for 10 s. The remaining solution was removed by absorbing the liquid with a piece of filter paper and the sample was air dried. The sample was examined by TEM (Morgagni 268D TEM, Boston, MA). Particle size is a major factor for NPs delivering through nasal route. Thus, on the basis of particle size, the formulation was optimized and then their release study was performed. For estimation of drug release from formulation, the validated HPLC method was used.

EE and LC

The EE and LC of polymeric NPs were determined by separation of NPs from the aqueous medium containing non-associated RIS by centrifugation at 15 000 rpm at 4 °C for 45 min. The amount of free RIS in the supernatant was quantified by RP-HPLC at an absorption maximum (λ_max) of 262 nm. The EE and LC of RIS-loaded NPs were calculated as per equations given below with all the measurements were performed in triplicate and averaged. EE was calculated based on the ratio of amount of drug present in the NPs to the amount of drug used in the loading process. The EE and LC of drug-loaded NPs were calculated as per the equations given below. $EE\left(\%\right)=\frac{\text{total}\text{drug}-\text{free}\text{drug}}{\text{total}\text{drug}}\times 100$ LC of the drug-loaded system was also calculated with respect to the yield of the nanoparticles obtained after centrifugation (Calvo et al., 1997). $LC\left(\%\right)=\frac{\text{total}\text{drug}-\text{free}\text{drug}}{\text{nanaoparticles}\text{weight}}\times 100$

Differential scanning calorimetry (DSC) study

DSC analysis of pure RIS, polymers, physical mixtures of drug and polymer, freeze dried polymeric NPs were carried out using Perkin Elmer Pyris 6 DSC (Perkin Elmer, Wellesley MA) calibrated with indium. Sample (5 mg) was placed onto a standard aluminum pan, crimped, and heated from 40 to 400 °C at a heating rate of 10 °C/min with the continuous purging of nitrogen (20 ml/min). An empty sealed pan was used as a reference.

In vitro release studies of nanoparticulate drug delivery system

In vitro release study was performed and release pattern of RIS was determined from RIS-loaded polymeric NPs using dialysis sacs. The RIS-loaded NPs (containing 5 mg of drug) were placed in pretreated dialysis sacs which were immersed into 100 ml of phosphate buffer solution, pH 7.4 and 6.4, at 37 °C magnetically stirred at 50 rpm. At selected time intervals, aliquots were withdrawn from the release medium and replaced with the same amount of phosphate buffer. The samples were analyzed in triplicate using HPLC. The HPLC determination was performed using reverse phase Oyster BDS C₁₈ column system (15 cm × 4.6 mm, particle size 5 μm). The mobile phase was used as 0.005 M tetrabutyl ammonium hydroxide and 0.005 M pyrophosphate sodium (pH 7.0) mixed with acetonitrile in a ratio (75:25, v/v) at a flow rate of 1 ml/min. The peak detection was performed at 262 nm (Kyriakides & Panderi, 2007).

Ex vivo nasal permeation study

Fresh nasal mucosa was carefully removed from the nasal cavity of porcine obtained from the local slaughter house. Tissue samples were inserted in Franz diffusion cells displaying a permeation area of 0.785 cm². The phosphate buffer saline (PBS) pH 7.4 (20 ml) at 37 °C was added to the receptor chamber. To ensure oxygenation and agitation, a mixture of 95% oxygen and 5% carbon dioxide was bubbled through the automated design system. The temperature within the chambers was maintained at 37 °C. After a pre-incubation time of 20 min, pure drug solution and formulation equivalent to 5.0 mg of RIS was placed in the donor chamber (volume: 10 ml) in each case. At predetermined time points, 2 ml samples were withdrawn from the receptor chamber, replacing the sampled volume with PBS pH 7.4 after each sampling, for a period of 24 h. The samples withdrawn were filtered and used for analysis. Blank samples (without RIS) were run simultaneously throughout the experiment to check for any interference. The amount of permeated drug was determined using HPLC.

In vivo study

The study was conducted after getting approval from institutional animal ethics committee and their guidelines for animal handling were followed. Female Wistar rats (aged, 4–5 months), weighing between 200 and 250 g were selected for the study. The animals were housed three per cage at 20–24 °C with free access to food and water with a 12-h light–dark cycle. The rats were divided into five groups, Group I – control, Group II – toxic control, Group III – IV solution, Group IV – IN-NPs, and Group V –IN solution. Three rats for each group were taken.

Induction of osteoporosis

After 7 d of acclimatization, experimental animals (female Wistar rats) were divided into five groups and osteoporosis was induced by administrating Dexona (dexamethasone sodium phosphate) 8 mg/kg body weight (Zydus alidac, 4 mg/2 ml) subcutaneously once a week up to 4 weeks (Banji et al., 2014). Weights of rats were observed during induction of osteoporosis and their treatment.

Biochemical analysis

After 28 d, rats were anaesthetized with diethyl ether and blood was withdrawn from retro orbital plexus and collected into dry test tubes. It was centrifuged at 5000 rpm for 10 min for separation of serum. The collected serum was used for biochemical analysis. Serum alkaline phosphatase, serum calcium level, serum creatinine, SGOT, and SGPT were estimated using standard biochemical kit purchased from Span Diagnostic Ltd. (Surat, India).

Histology of bone internal structure

After 28 d, rats were sacrificed and femur of the rats was separated from the flesh. The bones were removed by dissection and placed 10% formalin, prior to assessment of histology of femur. The bone specimens were washed for 12 h in each of the following series of solutions: 0.01 M PBS containing 5% glycerol, 0.01 M PBS containing 10% glycerol, and 0.01 M PBS containing 15% glycerol. The specimens were then decalcified in EDTA-G solution (14.5 g EDTA, 1.25 g NaOH, and 15 ml glycerol were dissolved in distilled water and the pH was adjusted to pH 7.3. The solution was then made up to 100 ml and stored at 4 °C) for 10–14 d at 4 °C as described by Mori et al. (1988). The EDTA-G solution was replaced every 5 d. Using this protocol, rat femur would normally be fully decalcified in 10 d. After decalcification of bone, the slicing of bone in longitudinal section was done and stained with hematoxylin followed by eosin. The sections of various groups were seen with Motic microscope.

Results

Preparation and characterization of polymeric NPs

PLGA NPs were prepared by the modified nanoprecipitation method. For optimization of various factors Box Benken Design (9.0.2) was used and the parameters were optimized as polymer concentration, concentration of the stabilizer, and number of cycles of HPH. A varying concentration of PLGA and polaxamer 188 was taken on the basis of mean particle size, %EE and %LC of different formulations. Particle sizes ranged from 184.87 ± 4.33 to 77.86 ± 8.67 nm. The developed NPs size was also characterized by transmission electron microscope (TEM) (Figure 1) and their surface morphology was studied using scanning electron microscope (SEM). The TEM and SEM analyses (Figure 2) revealed that the size range was in the range of as found in Malvern Zetasizer (Malvern Inc., Malvern, UK), the surface of NPs was round and spherical in shape. Various process effects were studied using Box Benken Design software. From optimization, these are the equation on which the effects were dependent: the polynomial equation for average particle size (PS), EE, and LC are given as (1) $\begin{array}{l}\text{Particle}\text{size}\left(R_1\right):+119.43+24.14\times A-12.34\times B\\ -12.52\times C+21.40\times AB-15.66\times AC-15.21\times BC\\ -9.72\times A^2+42.59\times B^2+0.061\times C^2\end{array}$(1) (2) $\begin{array}{l}\text{EE}\left(R_2\right):+51.05+10.22\times A-1.20\times B-3.57\times C+2.32\\ \times AB-4.70\times AC-4.70\times AC-1.14\times BC-5.10\times A^2\\ +8.36\times B^2-3.28\times C^2\end{array}$(2) (3) $\begin{array}{l}\text{LC}\left(R_3\right):+8.99-2.74\times A-1.37\times B+3.14\times C+0.11\\ \times AB+1.72\times AC-1.49\times BC+0.91\times A^2-2.76\times B^2\\ +1.40\times C^2\end{array}$(3) where A is the polymer concentration, B is the stabilizer concentration, and C is number of cycles of HPH at 1000 PSI.

Figure 1. Particle size (Zetasizer image) of optimized NPs and their TEM image.

Figure 2. SEM image of developed PLGA-NPs.

The effect of various variables on particle size, EE and LC is also shown in Figures 3–5, respectively.

Figure 3. Effects of polymer concentration, stabilizer concentration, and homogenization cycle on particle size.

Figure 4. Effects of polymer concentration, stabilizer concentration, and homogenization cycle on entrapment efficiency.

Figure 5. Effects of polymer concentration, stabilizer concentration, and homogenization cycle on loading capacity.

EE and LC

The prepared PLGA NPs showed (%) EE 41.66 ± 3.26 to 52.65 ± 5.21 whereas (%) LC 10.57 ± 1.48 to 13.84 ± 1.68 depending on the experimental runs and observed values. PLGA NPs have low EE and LC due to the hydrophilicity of the drug. Negative value with polymer in Equation (3(3) $\begin{array}{l}\text{LC}\left(R_3\right):+8.99-2.74\times A-1.37\times B+3.14\times C+0.11\\ \times AB+1.72\times AC-1.49\times BC+0.91\times A^2-2.76\times B^2\\ +1.40\times C^2\end{array}$(3) ) indicated that on increasing the polymer concentration LC is decreasing. Level of independent and dependent variables are shown in Table 2, the final optimized formulation composition is given in Table 3.

Table 2. Level of independent and dependent variables used in experiments.

	Low level (-1)	Intermediate Level (0)	High level (+1)
Independent variables
Polymer concentration (mg/ml): A	10.00	30.00	50
Stabilizer concentration (%): B	0.10	0.55	1.0
Number of homogenization cycle: C (10 PSI)	1.0	2.00	3.0
Dependent variables
Particle size (nm)	Minimized (≥50<200 nm)
EE (%)	Maximized
LC (%)	Maximized

Table 3. Final optimization formula for PLGA NPs from point prediction (n = 3).

Optimized composition	Response	Actual value, mean ± S.D.	Predicted value
30.0:0.55:2	Y1 (nm)	127.84 ± 6.33	119.426
	Y2 (%)	52.65 ± 5.21	51.052
	Y3 (%)	10.57 ± 1.48	8.986

DSC analysis

DSC thermograms of polymer (PLGA), RIS, physical mixture, and NPs clearly showed endothermic peaks at 48.56 °C, the drug showed an exothermic peak at 275.533 °C, physical mixture showed all peaks of drugs as well as polymers and RIS-loaded NPs did not show any prominent peak of drug as in Figure 6 and Table 4.

Figure 6. DSC thermograms of PLGA (A), RIS (B), physical mixture of PLGA, and RIS (C), and RIS-loaded PLGA NPs (D).

Table 4. DSC thermograms of PLGA, RIS, physical mixtures, and PLGA-NPs.

S. no.	Sample	Endothermic peak (°C)	Inference
1	PLGA	48.566	Small diminished peak, amorphous nature
2	RIS	275.533 (exothermic)	Sharp peak, crystalline
3	Physical mixture	49.236 and 276.254	Peaks of both are visible
4	Drug loaded PLGA-NPs	Not significant	No sharp peak at melting point, drug encapsulated, amorphous

In vitro release study and release kinetics

In vitro release profile of the RIS-loaded NPs of PLGA and pure RIS is shown in Figure 7. Drug release studies of RIS-loaded NPs showed biphasic release profile for PLGA NPs whereas RIS release quickly and not maintained the tailored released as desired. The initial fast release rate was observed followed by sustained release. To ascertain the release profile, authors have applied data to various release kinetic models and calculated co-efficient of correlation (R²). The achieved kinetic profile indicated the release of drug from nanoparticles as a square root of time-dependent process. The maximum value of R² PLGA NPs was found to be 0.913 as shown in Table 5.

Figure 7. In vitro release profile of RIS from RIS-loaded PLGA-NPs and RIS drug solution.

Table 5. Co-efficient of correlation for optimized PLGA-NPs from release kinetics.

	Co-efficient of correlation (R^2)
Release profile model	PLGA-NPs
Zero order release model	0.541
First order release model	0.590
Higuchi model	0.913
Korsmeyer–Peppas model	0.909 (n = 0.519)

Ex vivo permeation of RIS through porcine nasal mucosa

The cumulative amount of drug permeated through the nasal mucosa (Q, μg/cm²) was plotted as a function of time (h). The drug flux (permeation rate) at steady state (J_ss, μg/cm²/h) was calculated from the slope of linear portion of the curve obtained. Permeation from RIS-loaded PLGA-NPs was found to be significantly higher as compared with pure RIS drug solution, due to the nanosizing of the formulations. The cumulative percentage of drug permeated through the nasal mucosa from RIS-loaded PLGA-NPs 34.32 ± 2.64 where as 22.13 ± 1.72 from pure drug solution over 48 h. The comparative permeation study of PLGA-NPs and RIS drug solution is shown in Figure 8.

Figure 8. Comparative permeation study of RIS from drug solution and PLGA-NPs.

In vivo study

Biochemical analysis

Biochemical estimation of various enzymes indicates the pathological conditions of various diseases. These are the major biochemical study which gives conditions of bone disease. Animal groups and administered doses are given in Table 6. The serum level of alkaline phosphatase (ALP) was highest in DEXA (s.c.)-treated group alone as 506.11 ± 14.72 (μ/l) whereas in case of group-I normal control showed 390.55 ± 29.95 (μ/l), but in case group-III as DEXA-treated with IN-NPs have 440.27 ± 11.55 (μ/l). Serum calcium and creatinine levels also showed significant changes as represented in Table 7.

Table 6. Experimental groups and their treatments.

S. no.	Group	No. of animals (N)	Dose, DEXA: 8 mg/kg body weight
1.	Group-I	3	Normal control–normal saline (oral)
2.	Group-II	3	Toxic control (osteoporotic control)–DEXA (S.C.)
3.	Group-III	3	DEXA+ RIS-IV solution
4.	Group-IV	3	DEXA+ RIS-IN-NPs
5.	Group-V	3	DEXA+ RIS-IN solution

Table 7. Effects of formulations on various biochemical parameters in DEXA (8 mg/kg body weight) induced osteoporotic animals (n=3) (mean value ± S.D.).

Experimental groups	ALP (u/l)	Serum calcium (mg/dl)	Serum creatinine (mg/dl)	SGOT (μ/l)	SGPT (μ/l)
Normal control	390.55 ± 29.95	10.09 ± 0.13	1.16 ± 0.25	20.58 ± 1.25	9.92 ± 1.22
Toxic	506.11 ± 14.72##	7.63 ± 0.36##	3.66 ± 1.26##	37.74 ± 1.18##	14.53 ± 1.62#
IV- sol	462.5 ± 13.41	8.36 ± 0.40	1.99 ± 0.44	31.94 ± 2.25*	14.53 ± 2.21
IN-NPs	440.27 ± 11.55**	10.61 ± 0.78**	0.95 ± 0.09**	22.06 ± 1.96**	10.63 ± 1.06*
IN-sol	467.22 ± 4.11	8.44 ± 0.44	1.72 ± 0.52	32.19 ± 4.04	13.82 ± 1.06

Each value is mean ± S.D. ##p < 0.01 versus normal control, **p < 0.01 versus the toxic group, *p < 0.05 versus the toxic group, #p < 0.05 versus normal control.

Histology of bone internal structure

After 28 d, rats were sacrificed and femur of the rats was separated from flesh. The bones were removed by dissection and placed 10% formalin, prior to assessment of histology of femur. The internal structure of bone was examined using microscope (Motic, Nagoya, Japan). The section of group-I showed discreet micro array in image but in case of control as well as IN-NPs group it looks like dense and continuous structure (Figure 9).

Figure 9. Internal structure of rat bone of treated groups T – toxic, C – control, INP – intranasal NPs, INS – intranasal solution (magnification: A – 40× and B – 10×).

Discussion

Particle size and size distribution

The particle size is a very important parameter for the nanoparticulate delivery of drug. The particle sizes ranged from 184.87 ± 4.33 to 77.86 ± 8.67 nm. Equation showed that the positive value of the coefficient is an indicative of the favorable effect whereas a negative value for the coefficient indicates an unfavorable effect of that particular factor on the response. On increasing the polymer (A) concentration, particles size of NPs increased due to increase in the viscosity of polymeric solution which led to inefficient stabilization of stabilizer to the particles. The stabilizer has an overall negative effect on particle size as shown in Equation (1(1) $\begin{array}{l}\text{Particle}\text{size}\left(R_1\right):+119.43+24.14\times A-12.34\times B\\ -12.52\times C+21.40\times AB-15.66\times AC-15.21\times BC\\ -9.72\times A^2+42.59\times B^2+0.061\times C^2\end{array}$(1) ). Initially on increasing the concentration of stabilizer, the size of NPs decreased up to a certain level due to high stabilization capacity of stabilizer whereas above that level the particle size of NPs increased because of the high viscosity of stabilizer solution. As expected, the particle size decreased with increased number of HPH cycle due to increasing stress on particles which led to reduce the particle aggregation. In the study, the effect of PLGA concentration was found having more pronounced effect than stabilizer concentration on particle size. An increase in the concentration of PLGA increased the particle size of the NPs. At a high PLGA concentration, the viscosity of the diffusing phase increased which led to decrease the polymer and stabilizer contact which enhanced the susceptibility of particle aggregation (Seju et al., 201). The particle size of the NPs decreased with increasing the number of cycles of HPH. It is due to the better homogenization of mixture which also led to more contact of polymer with stabilizer and more reaction between them. The organic phase to aqueous phase ratio was optimized by preparing NPs into the ratio of 1:1, 1:2, 1:4, 1:5, 1:8, and 1:10. Stirring speed was varied from 400 to 800 rpm. The optimum speed of stirrer was found 600 rpm. Above this level, the size particle size distribution (i.e., PDI) observed was higher and non-uniform distribution and, below this level, the particle size was larger due to aggregation of the formed NPs. Rate of organic phase addition to aqueous phase was taken 1 ml/min as previously studied by Seju and associates. Drug concentration was kept constant in each run as 15 mg. After optimization, it was found that 1:4 drug polymer ratio gave good particle size as well as EE, which is also studied by another group of researchers (Budhian et al., 2007; Shah et al., 2009; Seju et al., 2011).

EE and LC

The prepared PLGA NPs showed (%) EE 41.66 ± 3.26 to 52.65 ± 5.21 whereas (%) LC 10.57 ± 1.48 to 13.84 ± 1.68 depending upon the experimental runs and observed values. PLGA NPs have low EE and LC due to the hydrophilicity of the drug. Because the polymer is water insoluble and drug is water soluble so the interaction between two is too low. To increase the EE and LC, the higher pH of aqueous phase in which the drug dissolved was tried and mixed with the organic phase with PLGA, with which slight increase in encapsulation was reported (Govender et al., 1999). As in case of EE, RIS decreased with the increase in drug concentration and reverse was observed with LC. This can be attributed to the experimentally determined fact that the higher the drug concentration, less is the EE and more is the LC (Mohanraj & Chen., 2006). As equation revealed that a polymer concentration has positive effect on EE. At a higher PLGA concentration, the viscosity of the diffusing phase increased which resulted in improved entrapment of RIS by reduction of RIS nanoparticles leaking into the dispersing phase and also more polymers were available for drug entrapment. The similar results were also reported as a higher polymer concentration led to higher encapsulation efficiency and larger size of the NPs (Blanco & Alonso., 1997; Song et al., 1997; Fazil et al., 2012).

DSC analysis

DSC thermograms of polymer (PLGA), RIS, physical mixture, and NPs clearly showed endothermic peaks at 48.56 °C, drug showed exothermic peak at 275.533 °C, physical mixture showed all peaks of drug as well as polymers and RIS-loaded NPs did not show any prominent peak of drug. A small diminished peak was observed near the glass transition temperature (T_g) of RIS, which may be due to the drug adsorbed onto the surface of NPs. The RIS and polymer mixture did not show significant shift in T_g which reflected that there is no polymer and drug interaction. The straight line of PLGA-loaded NPs indicated that crystalline nature of drug is transformed into the amorphous nature due to the encapsulation of drug into formed NPs. Absence of RIS peak in polymeric NPs showed that the drug has been encapsulated into the NPs core as it supported the previous studies as well (Joshi et al., 2010; Mirza et al., 2011; Fazil et al., 2012).

In vitro release study

Drug release studies of RIS-loaded NPs showed biphasic release profile for PLGA NPs whereas RIS drug solution releases quickly and not maintained the tailored released as desired. In vitro release study of PLGA-NPs showed initial fast release followed by sustained release. It may be due to smaller particle size of NPs which is associated with smaller diffusion path, so drug accessible to the solid/dissolution medium interface can diffuse easily to the surface (Dunne et al., 2000). It may be due to release of RIS from the NPs surface. Thereafter, the release rate decreased that revealed the release of drug from the core of NPs as a consequence of polymer hydration and swelling. The release rate in the second phase is assumed to be controlled by diffusion rate of drug across the polymer matrix (Corrigan & Li., 2009). The results obtained from in vitro drug release studies was fitted to various release models like the zero-order first-order, Higuchi, and Korsmeyer–Peppas model to understand the mechanism of drug release from the NPs (Ge et al., 2002). Release of RIS from PLGA was achieved by a degradation process of the polymer. Degradation is occurred mainly through uniform bulk degradation of the matrix where as the water penetration into the matrix is higher than the rate of polymer degradation. The degradation of PLGA copolymer is the collective process of bulk diffusion, surface diffusion, bulk erosion, and surface erosion (Makadia & Siegel, 2011). These are the reason by which PLGA NPs showed sustained release of drug for a longer period of time. Initial burst release is related to drug type, drug concentration, and polymer nature. Drug on the surface, in contact with the medium, is released as a function of solubility as well as penetration of water into polymer matrix. In the second phase, the drug is released progressively through the thicker drug-depleted layer. The water inside the matrix hydrolyses the polymer into soluble oligomeric and monomeric products reported by Amann et al. (2010). This created a passage for drug to be released by diffusion and erosion until complete polymer solubilization. Drug type also plays an important role here in attracting the aqueous phase into the matrix. The release study data were also applied to ascertain that the release pattern by PLGA-NPs and the co-efficient of correlation (R²) were calculated. This value is corresponding to the Higuchi model from PLGA-NPs indicating a good model fit. The ‘n’ values (0.519 for PLGA) were found from the Korsmeyer–Peppas model indicated that both diffusion-controlled and swelling-controlled drug release as for spheres values of n between 0.43 and 0.85 are an indication of both diffusion-controlled and swelling-controlled drug release (anomalous transport). Values above 0.85 indicated case II transport which is related to polymer relaxation during hydrogel swelling and values below 0.43 indicate that drug release from polymer is due to Fickian diffusion (Ritger & Peppas, 1987).

Ex vivo permeation of RIS through porcine nasal mucosa

Ex vivo permeation study was performed and permeation pattern is given as Figure 8. Permeation from RIS-loaded PLGA-NPs was found to be significantly higher as compared with pure RIS drug solution, due to the nanosizing of the formulations. PLGA NPs showed a higher permeation as compared with drug solution. It is due to the property of nanosizing which led to enhanced permeation (Vinogradov et al., 2002; Richter & Keipert, 2004). The saturation was achieved in case of drug solution within 4 h within 20% but the permeation of RIS from NPs increased even after 48 h. This revealed that RIS permeated and maintained the drug concentration in sustained manner for a longer time. Another reason for lesser permeation seen through pure drug solution is the hydrophilic nature of drug. PLGA takes more time for degradation in biological system which showed permeation over 48 h. The results indicated that for sustained delivery of the developed PLGA, NPs are appropriate for longer duration. Fluxes (µg/cm²/h) and permeability coefficients (P_b) were found to be 13.27 and 10.28 and 6.674 × 10⁻² and 2.056 × 10⁻², respectively. The differences between fluxes and permeability coefficients indicated that PLGA-NPs is better for nasal delivery as compared with drug solution (p < 0.01).

In vivo study

Biochemical analysis

The animals were divided into five groups and drug and formulation were administered to the animals as given in Table 6. After 28 d, the blood was collected and analyzed for biochemical examination. The serum level of alkaline phosphatase (ALP) was highest in DEXA (s.c.)-treated group alone as 506.11 ± 14.72 (μ/l) whereas in case of group-I normal control showed 390.55 ± 29.95 (μ/l), but in case group-IV as DEXA treated with IN-NPs have 440.27 ± 11.55 (μ/l). Toxic dose of glucocorticoids alters the skeletal integrity by affecting bone metabolism, reducing the life span of osteoblast, and inhibiting osteoblastogenesis (Hurson et al., 2007). It creates an imbalance between bone formation and bone resorption which led to increased risk of fractures (Struijs et al., 2000). It also hinders the synthesis of collagen and affects differentiation of osteoblast causing rapid bone loss (mcDonough et al., 2008; Yao et al., 2008). Thus the DEXA is an ideal candidate to induce experimental osteoporosis in rats. Serum alkaline phosphatase is a biochemical marker of bone turnover. It is a good indicator of internal bone activity and used to monitor metabolic bone disease (Regidor et al., 2008). Calcium is abundant in bone, essential to maintain bone mineral density (BMD). Serum calcium level was estimated using the o-cresolphthalein complexone (OCPC) method. So serum calcium levels are also a good indicative of osteoporotic bone. Bone resorption marker creatinine was estimated by the Jaff’e kinetic method. Besides this, estimation of SGOT and SGPT was also estimated to assess other metabolic abnormalities due to the administration of toxic dose of DEXA and other drug effects. ALP is used to evaluate bone disease as well as liver disease also. The increased level of ALP in toxic group indicated that osteoporosis has been induced, but in case of group-IV, a significant (p < 0.01) recovery has been seen as the level of ALP is reduced from toxic to normal. Whereas in case of groups-III and V both showed a significant rise in the serum ALP level from normal but below the toxic due to the inhibitory effect of RIS. Calcium is categorized as a bone mineral content marker which was estimated by the OCPC method. Serum calcium level is lower in the toxic group where as highest in case of IN-NPs. As in osteoporotic condition, bone becomes fragile and breakdown of bone is easy, therefore, bone content, i.e., calcium reaches into blood and excreted out through urine which leads to a significant reduction of calcium level as glucocorticoids enhances its urinary excretion and reduces intestinal absorption. In case of IN-NPs, the calcium level is highest as it prevents the bone breakdown, RIS slows bone loss, and increases bone mass, which leads to the prevention of bone fractures. It indicated that the RIS increases the calcium loss from blood and enhances its absorption and deposition to the bone. Creatinine was estimated by the Jaffe’s kinetic method and considered as a bone resorption marker. Biochemical estimations study indicated that the intranasal NPs delivered risedronate effectively and maintained the drug concentration in the blood which prevents the bone-breaking tendency in a condition of OP. It was further assessed by histological evaluation of bone microstructure. Various biochemical parameter results are shown in Table 7.

Histology of bone internal structure

The internal structure of bone revealed that group-I showed discrete micro-array in image but in case of control as well as IN-NPs group, it looks like dense and continuous structure. Thus IN-NPs showed significant recovery after experimental osteoporosis induction. Whereas in case of IN-drug solution, the recovery was not achieved to a great extent due to the nasal mucociliary clearance, which led to the wash out of drug and decreased the availability of drug in the nasal cavity.

Conclusion

The present study revealed that polymeric nanoparticulate formulation of RIS for the prevention and treatment of OP is of great interest. As per literature search, a little work has been done on RIS drug delivery system (DDS) and there is no intranasal nanoparticulate approach that has been investigated for treating OP. Thus PLGA-NPs for RIS could be a DDS using nanoparticulate approach for the prevention and treatment of OP via nasal route. The current intranasal investigational study may contribute to the development of novel DDS for OP. However, clinical study is needed to evaluate the benefit/risk ratio before its clinical application in near future.

Acknowledgements

The authors are thankful to Jubilant Life Sciences (Noida, Uttar Pradesh, India) for providing gift sample of risedronate sodium and Evonik Research (Bangalore, India) for providing gift sample of PLGA (50:50, Resomer 502 H).

Declaration of interest

The authors report no conflicts of interest. The authors are grateful to Indian Council of Medical Research (ICMR), New Delhi, for providing financial support to carry out this work (Ref. no. 35/18/2011-BMS).