Formulation, development and optimization of raloxifene-loaded chitosan nanoparticles for treatment of osteoporosis.

http://informahealthcare.com/drd ISSN: 1071-7544 (print), 1521-0464 (electronic) Drug Delivery, Early Online: 1–14 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/10717544.2014.900153

RESEARCH ARTICLE

Formulation, development and optimization of raloxifene-loaded chitosan nanoparticles for treatment of osteoporosis Deepa Saini, Mohammad Fazil, Mushir M. Ali, Sanjula Baboota, Ameeduzzafar, and Javed Ali

Drug Delivery Downloaded from informahealthcare.com by Rutgers University on 06/23/14 For personal use only.

Department of Pharmaceutics, Faculty of Pharmacy, Jamia Hamdard, New Delhi, India

Abstract

Keywords

Context: Osteoporosis (OP) is a disease of skeletal system and is associated with fragility fracture at the hip, spine and wrist. Various drugs have been used to treat OP. One of them is raloxifene hydrochloride (RLX), a second-generation selective estrogen receptor modulator (SERM) approved by the USFDA. RLX possesses only 2% absolute bioavailability (BA) by oral route due to its extensive first-pass metabolism. Objective: The purpose of the current research work was to develop and evaluate RLX-loaded chitosan nanoparticles (CS-NPs) for treatment of OP with enhanced BA. Materials and methods: The RLX-loaded CS-NPs were prepared by gelation of CS with tripolyphosphate (TPP) by ionic cross-linking. Formulation was optimized and in vitro drug release and in vivo study were performed. Results and discussions: CS-NPs were formed by the ionic gelation method. The particle size, entrapment efficiency and loading efficiency varied from 216.65 to 1890 nm, 32.84 to 97.78% and 23.89 to 62.46%, respectively. Release kinetics showed diffusion-controlled and Fickian release pattern. In vivo study indicated higher plasma drug concentration with NPs administered intranasally as compared to drug suspension administered through oral route (p50.05). A significantly higher drug concentration in plasma was achieved in 10 min after nasal administration with respect to oral administration. Conclusion: The results suggest that RLX-loaded CS-NPs have better BA and would be a promising approach for intranasal (i.n.) delivery of RLX for the treatment of OP.

Bioavailability enhancement, nanoparticles, optimization osteoporosis, raloxifene

Introduction Osteoporosis (OP) is a disease of skeletal system and is associated with fragility fracture at the hip, spine and wrist. Various drugs have been used to treat OP. One of them is raloxifene hydrochloride (RLX). RLX is a second-generation non-steroidal benzothiophene, selective estrogen receptor modulator (SERM) approved by the FDA in 1997 for the prevention and treatment of post-menopausal bone loss at a dose of 60 mg/day. It acts as estrogen agonist in bone. RLX inhibits vertebral bone loss by inhibiting the activity of cytokines, which stimulate bone resorption (Viereck et al., 2003). RLX possesses absolute bioavailability to 2% orally. Although RLX is characterized by highly permeable, low soluble and low bioavailable (Elsheikh et al., 2012). RLX is rapidly absorbed from the gastrointestinal tract and undergoes extensive first-pass glucuronidation. Because of extensive pre-systemic glucuronide conjugation, absolute

Address for correspondence: Dr. Javed Ali, Department of Pharmaceutics, Faculty of Pharmacy, Jamia Hamdard (Hamdard University), New Delhi – 110062, India. Tel: +00-91-9811312247. Fax: +00-91-11-26059663. Email: [email protected]; jali@ jamiahamdard.ac.in

History Received 21 January 2014 Revised 25 February 2014 Accepted 27 February 2014

bioavailability (BA) achieved is very low. The significant inter-patient differences in BA may result from alterations in the rate of glucuronide formation and enterohepatic recycling (Garg et al., 2009). RLX pertains to class II of the Biopharmaceutics Drug Disposition Classification System (BDDCS), where the drug is characterized by high permeability, poor solubility and high metabolism (Elsheikh et al., 2012). As a result, it is very important to develop a non-gastrointestinal delivery system of RLX for the treatment of osteoporosis which provides economic benefit to drug manufacturer and consumers. Oral dose of RLX (60 mg) has been approved for the prevention and treatment of post-menopausal osteoporosis once a day regimen. Oral drug delivery remains the most convenient route of administration. But by this route a little fractions of drug reaches into the systemic circulation and major parts of drugs become available to the non-target site, which are responsible for the peripheral side effects. The BA enhancement of RLX was achieved using a nanocarrier as polymeric nanoparticle through intranasal (i.n.) route. However, the BA enhancement of RLX has been achieved using solid lipid nanoparticles, microspheres and co-grinding mixture as in reports published (Jha et al., 2011; Kushwaha et al., 2013 and Jagdish et al., 2010). But in the present the authors have developed polymeric NPs for RLX through nasal route for BA


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enhancement due to its better biocompatibility and acceptability to the biological systems. Both the approaches have significant role for delivering the RLX for OP. Intranasal delivery is non-invasive, essentially painless, does not require sterile preparation, and can be easily and readily administered by the patients themselves or by a physician, e.g. in an emergency setting (Fazil et al., 2012). Drug delivery systems are designed with rationale of promoting the therapeutic effect of a drug and minimizing its 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. The present study deals with the development of CS-NPs of RLX. CS biomaterials are one of the most versatile used polymers in drug delivery systems due to their unique qualities. CS, obtained by deacetylation of chitin, is a natural, hydrophilic, non-toxic, biocompatible, bioadhesive and biodegradable polysaccharide suitable for applications in drug delivery. CS is a linear copolymer of b-(1–4) linked 2-acetamido-2-deoxy-b-D-glucopyranose and 2-amino-2-deoxy-b-D-glycopyranose. It is obtained by deacetylation of its parent polymer chitin, a polysaccharide widely distributed in nature (e.g. crustaceans, insects and certain fungi) (Dash et al., 2011). CS, which is an artificial variant of chitin, is more suitable for bio-applications (Mima et al., 1983). CS is readily soluble in dilute acidic solutions. The presence of the amino groups indicates that pH substantially alters the charged state and properties of CS (Yi et al., 2005). At low pH, these amines get protonated and become positively charged and that makes CS a water-soluble cationic polyelectrolyte. On the other hand, as the pH increases above 6, chitosan’s amines become deprotonated and the polymer loses its charge and becomes insoluble. The positive facets of excellent biocompatibility and admirable biodegradability with ecological safety and low toxicity with versatile biological activities such as antimicrobial activity and low immunogenicity have provided ample opportunities for its further development (Jayakumar et al., 2007).

Methods The drug raloxifene[6- hydroxy-2-(4-hydroxyphenyl) benzo[b]thien-3-yl] [4-[2-(1-piperidinyl) ethoxy]-phenyl] ethanone hydrochloride with a molecular weight of 510.05 g/moL was received as a gift sample from Ranbaxy Research Laboratories (Gurgaon, India). CS with medium molecular weight (Mw ¼ 750 000 Da) and degree of deacetylation about 85% and sodium tripolyphosphates (TPP) were purchased from Sigma Aldrich (Bangalore, India). HPLC grade acetonitrile (ACN), methanol were purchased from Merck Limited (Mumbai, India). High purity water was prepared by using Milli Q Plus water purification system (Millipore, Milford, MA). All reagents were of analytical grade. The in vivo study was performed in accordance with the OECD Principles of Good Laboratory Practice ENV/MC/CHEM (98) 17, Environment Directorate, and Organization for Economic Co-operation and Development, Paris, 1998 in DMPK Department of Zydus Research Center, Ahmadabad (India). Protocol for general procedures and use of animals for conducting pharmacokinetic study has been reviewed and

Drug Delivery, Early Online: 1–14

approved by the Institutional Animal Ethics Committee (Jamia Hamdard). The study was performed as per the recommendations of the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA) guidelines for Laboratory Animal Facility, The Gazette of India, 1998. Preparation of nanoparticles The CS-NPs were prepared by the ionic gelation process (Calvo et al., 1997; Vila et al., 2002; Aktas et al., 2005) of CS with tripolyphosphate (TPP). CS-NPs were obtained upon the addition of a TPP aqueous solution to a CS solution stirred at room temperature. The formation of NPs was a result of the ionic interaction between the positively charged amino groups of CS and negatively charged TPP. For this purpose, CS was dissolved in (2% w/v) acetic acid solution. The CS-NPs were prepared by the drop wise addition of TPP solution 10 mL chitosan solution at room temperature on magnetic stirring for 30 min. The variables for nanoparticulate suspension such as CS concentration (A), TPP concentration (B), stirring speed (C) and TPP pH (D) were optimized by a central composite design (Design expert software: Stat-Ease, Inc. Minneapolis, MN, USA) as shown in Table 1. The prepared nanoparticulate suspension was analyzed for particle size by transmission electron microscopy (TEM). 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 entrapment efficiency (%) and drug loading (%). Entrapment efficiency and loading efficiency The entrapment efficiency of RLX within the RLX-CS NPs was determined by pelletizing the sample at 20 000 rpm for 60 min. The resulting pellet was redispersed and further lyophilized. A known quantity (2 mg) of lyophilized sample was taken in 10 mL of ethanol; the solution was sonicated thoroughly using a probe sonicator (Sonics Vibra-cell: Sonics & Materials, Inc. 53 Church Hill Road, Newtown, CT,USA, Modal-VCX 130, O/P ¼ 130W) at 45% of amplitude for 5 min. The resulting solution was centrifuged at 20 000 rpm for 15 min and the supernatant was collected. Amount of drug within the supernatant was quantified by RP-HPLC at an absorption maximum of 287 nm. Entrapment efficiency (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 loading efficiency (LE) of RLX-loaded CS NPs were calculated as per the equations given below, while all the measurements were performed in triplicate and averaged. EE ð%Þ

total drug free drug 100 total drug

LE of the drug-loaded system was also calculated with respect to the yield of the nanoparticles obtained after centrifugation (Anitha, et al., 2011). LE ð%Þ

total drug free drug 100 nanoparticles weight

Raloxifene-loaded chitosan nanoparticles

DOI: 10.3109/10717544.2014.900153

Table 1. Levels of process parameters used in the experiment.

Characterization of optimized nanoparticulate suspension

Levels Code A B C D R1 R2 R3

Independent variables Chitosan concentration (%) TPP concentration (%) Stirring speed (rpm) TPP pH Dependent Particle size Entrapment efficiency (EE) Drug loading

2

1

0

0.05 0.10 0.05 0.10 400 600 4.0 4.5 variable

0.15 0.15 800 5.0

3

+1

+2

0.20 0.20 1000 5.5

0.25 0.25 1200 6.0

Minimized Maximized Maximized

Scanning electron microscopy Nanoparticles morphology such as shape and occurrence of aggregation phenomena was studied by SEM. For this, samples of nanoparticles were mounted on metal stubs, plating coated under vacuum and then examined on a Leo 435 VP: LEO, Cambridge, UK (10 kV Cambridge) scanning electron microscopy. FTIR analysis


Particle characterization The particle size, particle size distribution, polydispersity index (PDI) and zeta potential 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 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 the analysis of particle size and PDI. The surface morphology of the prepared NPs was determined for by using scanning electron microscopy (SEM), size of the NPs were also confirmed using 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 carboncoated grid was placed on sample and left for 1 min to allow CS-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). Experimental design Central composite statistical design was used to optimize the formulation parameters and systemically investigate the effect of wide range of independent and dependent variables. CS concentration (A), TPP concentration (B), stirring speed (C) and TPP pH (D) were independent variables (factors) considered in the preparation of RLX-loaded CS-NPs, while the average particle size (R1), entrapment efficiency (R2) and loading efficiency (R3) were the dependent variables (response). The details of design are shown in Table 1. For each factor, the experimental range based on the result of preliminary experiment was selected and process parameters were studied by conducting the runs at different levels of all factors. Data collected for responses in each run were analyzed using the software DESIGN EXPERT 8.1 (Statease, Minneapolis, MN) and fitted into a multiple linear regression model.

Fourier transform infrared (FTIR) spectra were obtained using Nicolet 60-SXB spectrometer: Thermo Electron Scienti?c Instruments Corporation, Madison, WI, USA in the range 450–4500 cm1 to evaluate the molecular states of RLX and the optimized nanoparticulate formulation after freeze drying. Samples were mixed with micronized KBr powder and compressed into discs using a manual tablet press. Differential scanning calorimetry analysis Differential scanning calorimetry (DSC) of RLX, physical mixture of drug and polymer and nanoparticulate formulation of RLX was done by Perkin-Elmer 6: Perkin Elmer Pyris 6 DSC, Massachusetts, USA Series differential scanning calorimeter thermal analysis system. Samples were weighed approximately to 5 mg, crimped into aluminum pans and heated at 10 C/min over a heating range of 100 C–350 C under a nitrogen purge. In vitro release studies The in vitro dissolution studies were carried out to compare the release of drug from the optimized nanoparticulate formulation and comparing it with the pure drug to observe the release pattern. The nanoparticulate suspension and drug suspension each having same quantity (4 mg) of RLX was taken. The in vitro drug release study was performed using dialysis sac (Mol. wt. cut-off: 12 000 Da, flat with 25 mm, diameter of 16 mm, capacity 60 mL ft) (Zheng et al., 2007) and to this 6 mL of dissolution media (phosphate buffer saline pH 7.4, i.e. physiological pH) was added, which was then sealed at both ends. The dialysis sac was dipped into the receptor compartment containing the dissolution medium, which was stirred continuously at 100 rpm maintained at 37 C. The receptor compartment was closed to prevent evaporation of the dissolution medium. Samples were withdrawn at regular time intervals and the same volume was replaced with fresh dissolution medium. The samples were measured by the RPHPLC method. Release kinetics The in-vitro release study of RLX-loaded CS-NPs was performed using the dialysis sac. Data obtained from the in vitro release studies of RLX loaded CS-NPs were fitted into various kinetic equations such as zero order, first order and the Higuchi model (Costa & Lobo, 2001).

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In vivo study for intranasal drug delivery In vivo study was done using female Wistar rat from Animal Breeding Facility, ARF, Zydus Research Centre, Ahmedabad, India. Age of the animals at experiment started was 6–8 weeks old with an average weight 200 g with variation of ± 20% mean weight. The formulations were instilled in the respiratory region of the rat nostrils with blunt edge cannula ethically. The blood samples were collected at predetermined time and finally vital organs have been removed the analyzed them. Using software the authors have calculated all the pharmacokinetic parameters. Pharmacokinetics was carried out on the day of dosing to evaluate plasma concentration of RLX. Administration route is nasal. The nanoparticles were administered as a nanosuspension and the drug was used as solution. About 400 mL of blood was collected through a retro orbital plexus at different time points, such as pre-dose, 0.166, 0.666, 1, 2, 3, 4 and 6 h in the pre-labeled micro centrifuge tubes containing 20 mL of diluted heparin. Blood samples were centrifuged at 6000 rpm for 10 min to separate the plasma. The samples were stored at 20 C for the analysis. The plasma concentration of RLX in each sample was analyzed by a validated LC-MS/MS method. The pharmacokinetic parameters such as Cmax, Tmax, T1/2;, AUC(0-t) and AUC(0-1) were calculated by using WinNonlin software version 5.2.1: Pharsight Corporation, E. Evelyn Ave, Mountain View, CA, USA. Graphical presentation of data was performed using Graphpad prism software (Version 4.00). The pharmacokinetic data among different formulations were compared for statistical significance by a one-way analysis of variance (ANOVA) followed by the Tukey– Kramer multiple comparison tests using GraphPad Instat software (GraphPad Software Inc., CA, USA).

Stability studies The nanoparticulate formulation is intended to be stored under refrigerated (5 C) condition (Agnihotri & Vavia, 2009). As per the ICH guidelines, for the products intended to be stored in a refrigerator, the storage conditions of temperature and relative humidity for accelerated stability studies are 25 ± 2 C and 60 ± 5% RH. Three batches of RLX-loaded CS nanoparticulate suspension were prepared. These batches were kept at a temperature of 25 ± 2 C/ 60 ± 5% RH, for six months. Samples were withdrawn after specified time intervals (0, 1, 2, 4 and 6 months) and physical appearance, particle size, PDI and zeta potential of the samples were determined. These parameters were analyzed for statistical significance by the one-way ANOVA followed by the Tukey–Kramer multiple comparison test using GraphPad Instat software (GraphPad Software Inc., CA). The remaining drug content was also determined using developed stability-indicating ultra performance liquid chromatography (UPLC). UPLC method was used to separate RLX and its degradation product formed during forced degradation studies and to elute RLX as a symmetrical peak. The UPLC separation of RLX was achieved on a Waters Acquity BEH C18, 50 2.1 mm, 1.7 mm column with mobile phase containing a gradient mixture of solvent


A (0.1% of Formic acid in water) and solvent B (Acetonitrile). The method was linear in the range of 0.1–50 mg/mL (r2 ¼ 0.9998) and having a run time of 6 min, which allows for a large number of samples to be analyzed in less time. Injection volume was 5 mL at a flow rate of 0.35 mL/min and at 30 C column temperature at detection wavelength 287 nm. Statistical analysis Statistical analysis was carried out using GraphPad prism 3.0 (GraphPad software, San Diego, CA). All results were expressed as mean ± SD. Groups of data were compared with the ANOVA followed by Dunnett’s t-test. The values were considered statistically significant, the p value was 50.05.

Results and discussion Nanoparticle size NPs were prepared by the ionic gelation method (Calvo et al., 1997; Vila et al., 2002; Aktas et al., 2005). The ionic gelation technique is mostly used for the preparation of CS-NPs and considered to be adequate nanocarriers for the encapsulation of drug. The –NH2 moiety of CS is protonized to NHþ 3 in acidic medium, which interacted with an anion moiety, PO2 4 group of tripolyphosphate (TPP), by electrostatic interaction and formed NPs (Bernkop-Schnurch & Dunnhaupt, 2012). The prepared RLX-loaded CS-NPs were found in nanorange and there spherical shapes were confirmed using TEM (Figure 1a) and SEM (Figure 1b). The particle size was analyzed by Malvern Nano Zetasizer as shown in Figure 1(c). The placebo CS-NPs were prepared in different batches with different ratio of CS and TPP and visual observations were recorded. All the formulations were visually analyzed and three different systems were identified namely: clear solution, opalescent suspension and aggregates. The particle size of 157 ± 6.86 nm and PDI of 0.347 ± 0.01 was recorded. On the basis of the ratio the formulation was further optimized by Design expert software (Central composite statistical design). Different concentrations of CS and TPP were used to optimize the best CS/TPP ratio on the basis of particle size, PDI and process yield. Particle size, EE and LE varied from 216.65 to 1890 nm, 32.84 to 97.78% and 23.89 to 62.46%, respectively, depending upon the CS and TPP ratio used as shown in Table 2. Out of all the CS-NP formulations shown in Table 5, OF2 having CS/TPP ratio 1.25/1 was selected as best formulation due to its optimum particle size, EE and loading capacity. The particle size of optimized CS-NPs was also characterized with TEM and the result was found to be in the range 191.97–211.48 nm as shown in Figure 1(a). On the basis of experimental data analysis, it was observed that the ratio between CS and TPP 1.25.0/1 to 1.47/1 was found optimum in which the particle size was found nearer to 200 nm. Above or below this range a large variation in size was observed. This fact was also observed in our study that when concentration of CS and TPP was below 2.0/1 micro-particles were formed. It means that the concentration of CS and TPP may not be in stoichiometric ratio for proper ionic gelation (Papadimitriou et al., 2008) (Figure 2).



DOI: 10.3109/10717544.2014.900153

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Figure 1. (a) Particle size distribution, (b) TEM image RLX-loaded CS-NPs, (c) SEM image of RLX-loaded CS-NPs.

Table 2. Results of central composite design for chitosan nanoparticulate formulation.

S.No.

Chitosan conc % w/v

TPP conc % w/v

RPM

F1 F2 F3 F4 F5 F6 F7 F8 F9 F 10 F 11 F 12 F 13 F 14 F 15 F 16 F 17 F18 F 19 F 20 F 21 F 22 F 23 F 24 F 25 F 26 F 27 F 28 F 29 F 30

0.05 0.20 0.15 0.20 0.15 0.10 0.20 0.15 0.10 0.20 0.10 0.15 0.25 0.15 0.15 0.15 0.10 0.10 0.15 0.15 0.20 0.15 0.20 0.20 0.15 0.20 0.10 0.10 0.10 0.15

0.15 0.20 0.15 0.10 0.15 0.10 0.10 0.25 0.20 0.20 0.10 0.15 0.15 0.15 0.15 0.15 0.10 0.10 0.15 0.15 0.20 0.15 0.20 0.10 0.15 0.10 0.20 0.20 0.20 0.05

800 600 800 1000 800 600 600 800 600 600 1000 400 800 1200 800 800 1000 600 800 800 1000 800 1000 600 800 1000 600 1000 1000 800

TPP pH

Particle size ± SD (n ¼ 3)

Entrapment efficiency ± SD (n ¼ 3)

Loading efficiency ± SD (n ¼ 3)

5.00 5.50 5.00 4.50 5.00 5.50 4.50 5.00 4.50 4.50 5.50 5.00 5.00 5.00 4.00 5.00 4.50 4.50 5.00 5.00 5.50 5.00 4.50 5.50 6.00 5.50 5.50 5.50 4.50 5.00

395.90 ± 15.15 1552.56 ± 12.78 590.42 ± 20.60 840.73 ± 17.54 534.53 ± 14.58 600.45 ± 11.83 658.34 ± 18.96 1029.65 ± 12.85 256.58 ± 11.54 1567.56 ± 17.69 650.29 ± 19.38 517.90 ± 16.75 1890 ± 11.83 870.53 ± 12.64 758.98 ± 14.75 517.83 ± 12.54 770.50 ± 16.81 440.89 ± 16.48 517.83 ± 12.54 550.59 ± 14.58 1550.78 ± 17.53 590.42 ± 20.60 1795.78 ± 17.53 680.85 ± 20.75 678.95 ± 17.27 545.52 ± 18.42 216.39 ± 14.63 335.4 ± 11.65 430.6 ± 13.86 400.34 ± 10.67

90.9 ± 2.56 95.78 ± 6.78 60.31 ± 2.75 33.71 ± 9.65 62.74 ± 10.65 60.13 ± 4.08 47.56 ± 1.30 80.9 ± 2.75 82.99 ± 9.64 97.78 ± 7.50 65.78 ± 1.67 75.56 ± 6.96 83.56 ± 8.38 53.76 ± 5.74 55.67 ± 6.97 63.87 ± 10.53 63.23 ± 5.40 66.89 ± 4.78 63.87 ± 10.53 60.75 ± 10.65 69.78 ± 5.89 60.31 ± 2.75 70.56 ± 5.89 48.9 ± 10.56 53.15 ± 4.87 48.2 ± 12.74 77.28 ± 1.74 70.65 ± 11.85 65.78 ± 8.63 32.84 ± 8.95

59.89 ± 4.62 65.89 ± 6.78 40.77 ± 5.82 23.75 ± 2.47 42.84 ± 2.61 44.26 ± 6.50 30.45 ± 4.61 55.56 ± 3.71 54.9 ± 3.64 62.46 ± 6.42 42.31 ± 6.40 53.90 ± 5.73 53.89 ± 6.37 33.54 ± 4.31 36.89 ± 7.31 42.89 ± 7.12 43.68 ± 5.83 44.76 ± 3.83 42.89 ± 7.12 41.65 ± 2.76 49.90 ± 5.53 40.77 ± 5.82 47.34 ± 5.53 31.75 ± 5.32 31.75 ± 2.50 29.89 ± 3.74 56.78 ± 4.81 44.89 ± 5.41 42.53 ± 5.34 23.89 ± 2.45

Fitting the model to the data In the present study, the NPs were formed by the ionic interaction between cationic moiety of chitosan and anionic moiety of TPP. A four-factor, three-level Central composite statistical design (Design Expert software 7.1.1) was employed to optimize the formulation (Table 1). Total 30 runs with six center points were generated and their responses

are shown in Table 3. The ranges of R1, R2 and R3 for all formulations were 216.39 ± 14.63 to 1890 ± 11.83 nm, 32.84 ± 8.95 to 97.78 ± 7.50 and 23.89 ± 2.45 to 65.89 ± 6.78%, respectively. All the values for 30 formulations of all responses were fitted to first order, second order and quadratic models using Central composite statistical design. It was observed that the best-fitted model was quadratic and the comparative values of R2, SD, and


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Figure 2. Regression line of response surface plot of actual versus predicted value for (A) particle size, (B) EE, (C) LC.

Table 3. Summary of results of regression analysis for responses R1, R2 and R3. Model Response (Y1) Linear 2FI Quadratic Response (Y2) Linear 2FI Quadratic Response (Y3) Linear 2FI Quadratic

R2

Adjusted R2

Predicted R2

SD

%CV

Remark

0.6229 0.8932 0.9943

0.5626 0.8370 0.9890

0.4325 0.8166 0.9713

301.02 183.78 47.71

– 6.29

– – Suggested

0.6071 0.7930 0.9869

0.5443 0.6841 0.9746

0.3921 0.6327 0.9331

10.78 8.98 2.54

– – 3.88

– – Suggested

0.6521 0.8413 0.9906

0.5965 0.7578 0.9818

0.4612 0.7257 0.9524

6.95 5.39 1.48

– – 3.35

Suggested

Regression equations of the fitted model. Particle size ðR1 Þ ¼ þ550:27 þ 353:93 A þ 157:99 B þ 68:18 C 32:24 D þ 311:21 A B 25:87 A C 26:35 A D þ 4:87 B C 9:19 B D 56:09 C D þ 146:33 A2 þ 39:34 B2 þ 34:15 C 2 þ 40:34 D2 Entrapment efficiency ðR2 Þ ¼ þ61:98 2:21 A þ 12:26 B 5:63 C þ 0:21 D þ 7:30 A B 2:99 A C þ 1:26 A D 4:16 B C 0:83 B D þ 2:02 C D þ 6:46 A2 1:13 B2 þ 0:82 C 2 1:74 D2 : Drug loading ðR3 Þ ¼ þ41:97 1:72 þ8:36 B 4:63 C þ 0:44 D þ 5:57 A B 0:99 A C þ 0:48 A D 2:95 B C þ 0:077 B D þ 0:44 C D þ 3:85 A2 0:44 B2 þ 0:56 C 2 1:26 D2 :

% CV are given in Tables 4 and 5 along with the regression equation generated for each response. All statistically significant (p50.05) coefficients are included in the equations. A positive value represents an effect that favors the optimization, while a negative value indicates an inverse relationship between the factor and the response. It was observed that all the four independent variables, i.e. the concentration of chitosan (A), TPP (B), stirring speed (C) and pH (D) have individual and interactive effects on the three responses, i.e. particle size (R1) and EE (R2) and LE (R3).

Response analysis through polynomial equation The polynomial equation for average particle size is given as: Particle size ðR1 Þ ¼ þ550:27 þ 353:93 A þ 157:99 B þ 68:18 C 32:24 D þ 311:21 A B 25:87 A C 26:35 A D þ 4:87 B C 9:19 B D 56:09 C D þ 146:33 A2 þ 39:34 B2 þ 34:15 C 2 þ 40:34 D2

ð1Þ



DOI: 10.3109/10717544.2014.900153

where, R1 is the particle size of NPs, A is the CS concentration, B is the TPP concentration, C is the stirring speed and D is the TPP pH. The model F value of 187.45 implied that the model was significant (p50.0001). The ‘‘Lack of Fit F value’’ of 2.560 implied that the Lack of Fit is not significant (p ¼ 0.1555). Non-significant lack of fit is good so that the model fits. In this case A, B, C, D, AB, BD, CD, A2, B2, C2 and D2 are significant model terms. The ‘‘Pred R-Squared’’ of 0.9713 was in reasonable agreement with the ‘‘Adj R-Squared’’ of 0.9890. The ‘‘Adeq Precision’’ of 49.803 indicated an adequate signal. A positive value represents an effect that favors the optimization, while a negative value indicates an inverse relationship between the factor and the response (Honary et al., 2013). In this study, the effect of CS concentration was found having more pronounced effect than TPP concentration on particle size. An increase in the concentration of CS increased the particle size of the NPs. This can be explained on the basis of solution viscosity. Increment in the concentration of

polymer increases the viscosity of the solution, resulting inadequate interaction between polymer and TPP. Also, due to super saturation of the solution, the interparticulate contact decreases which enhances the susceptibility of particles aggregation (Dudhani & Kosaraju, 2010). The polynomial equation showed TPP has positive effect on the particle size that means when the concentration of TPP was increased the particles size also increased due to an adequate interaction between positive charges of CS with negative charge of TPP. On increasing the pH of TPP, the particles size decreases this is because up to pH 6 chitosan and TPP showed compact interaction (Li et al., 2011). Stirring speed is usually an important parameter that influences particle size and polynomial equation also showed positive effect on particle size (Table 3 and Figure 3a). The polynomial equation for average entrapment efficiency is given as: drug entrapment ðR2 Þ ¼ þ61:98 2:21 A þ 12:26 B 5:63 C þ 0:21 D þ 7:30 A B 2:99 A C þ 1:26 A D 4:16 B C 0:83 B D

Table 4. Analysis of variance of calculated model for response. Result of the analysis of variance

Particle size (nm)

Regression Sum of squares Degrees of freedom (df) Mean squares F value p Lack of Fit Tests Sum of squares degrees of freedom (df) Mean squares F value p R2 Correlation of variation (% CV) Residual Sum of squares degrees of freedom (df) Mean squares SD

Entrapment efficiency (%) 7298.13 14 521.30 123.18 50.0001

3443.24 14 245.95 187.73 50.0001

28564.66 10 2856.47 2.560 0.1555 0.9943 6.29

82.25 10 8.23 2.78 0.135 0.9869 3.88

27.37 10 2.74 2.52 0.1598 0.9906 3.35

34 141.26 15 2276.08 47.71

97.06 15 6.47 2.54

32.80 15 2.19 1.48

ð2Þ

þ 2:02 C D þ 6:46 A2 1:13 B2

Drug loading

5.973E + 006 14 4.266E + 005 187.45 50.0001

7

þ 0:82 C 2 1:74 D2 : where, R2 is the drug entrapment efficiency of NPs, A is the CS concentration, B is the TPP concentration, C is the stirring speed and D is the TPP pH. The model F value of 123.18 implied that the model was significant (p50.0001). The ‘‘Lack of Fit F value’’ of 2.78 implied that the Lack of Fit is not significant (p ¼ 0.135). In this case A, B, C, AB, AC, BC, A2, B2, D2 were the significant model terms. The ‘‘Predicted R-Squared’’ of 0.9331 was in reasonable agreement with the ‘‘Adjusted R-Squared’’ of 0.9746. The ‘‘Adequate Precision’’ of 43.707 indicated an adequate signal. Therefore, this model was used to navigate the design. The polynomial equation showed CS has negative effect on EE, whereas TPP having positive effect on EE but TPP has significant effect on EE as compared to chitosan. The EE depends upon ratio of concentration of chitosan and TPP. When concentration of chitosan increased as compared to TPP

Table 5. Point prediction check point for optimization, actual value, experimental value and % error. Formulation code

Optimized composition

Responses

Predicted value

Actual value (experimental value)

% Error

OF1

0.15:0.15:800: 5.00

OF2

0.10:0.20:600: 5.10

OF3

0.10:0.17:600: 5.10

OF4

0.15:0.20:600: 5.10

OF5

0.13:0.20:600: 5.10

Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3 Y1 Y2 Y3

558. 38 66. 29 41.97 180.64 81.49 56.90 220.95 77.02 56.76 671.42 82.27 57.46 476.40 80.52 56.44

550.27 ± 6.97 61.97 ± 2.542 44.76 ± 5.54 174.79 ± 10.25 78.54 ± 6.12 59.43 ± 3.46 216.65 ± 4.54 81.43 ± 2.92 54.99 ± 4.76 681.87 ± 3.56 87.28 ± 2.14 56.86 ± 5.63 473.76 ± 14.76 85.91 ± 4.24 59.54 ± 3.65

1.52 6.52 +6.23 3.23 +3.76 4.25 5.43 +5.41 3.21 3.22 5.74 4.53 +0.55 6.27 +5.21

Bold values indicate the optimized parameters for the formulation.

D. Saini et al.



8

Figure 3. 3-D plots showing relative effects of different process parameters on (a) particle size (b) entrapment efficiency; (c) particle size.



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Figure 3. Continued.

the EE decreased due to increased particle size or inadequate cross-linking between positive charges of chitosan with negative charge of TPP. The stirring has negative effect on EE (Equation 2). On increasing the stirring speed, there is decrease in the EE because of inadequate encapsulation of drug in NPs or improper contact of drug with chitosan and TPP which was showed in 3-D plot (Figure 3b) (Ahad et al., 2012; Fan et al., 2012; Honary et al., 2014). The pH has nonsignificant effect on EE. The polynomial equation of third response, i.e. loading capacity was given as: Drug loading ðR3 Þ ¼ þ41:97 1:72 þ8:36 B 4:63 C þ 0:44 D þ 5:57 A B 0:99 A C þ 0:48 A D 2:95 B C þ 0:077 B D þ 0:44 C D

of 0.9818. The ‘‘Adequate Precision’’ of 52.714 indicated an adequate signal. Therefore, this model was used to navigate the design space. The polynomial equation showed CS has negative effect on LC, whereas TPP having positive effect on LC but TPP has significant effect on LC as compared to chitosan. LC depends upon ratio of concentration of chitosan and TPP. When concentration of chitosan is increased as compared to TPP, the LC decreased due to increase in the particle size or inadequate cross-linking between positive charges of CS with negative charges of TPP. The stirring has negative effect on LC (Equation 3). On increasing the stirring speed, the LC decreases because of inadequate encapsulation of drug in NPs or improper contact of drug with CS and TPP which is represented in 3-D plot (Figure 3c) (Elmizadeh et al., 2013; Fan et al., 2012). The pH has non-significant effect on LC. Optimization

þ 3:85 A2 0:44 B2 þ 0:56 C 2 1:26 D2 : ð3Þ where, R3 is the drug loading of nanoparticles, A is CS concentration, B is TPP concentration, C is the stirring speed and D is the TPP pH. The model F value of 187.73 implied that the model was significant (p50.0001). The ‘‘Lack of Fit F-value’’ of 2.52 implied that the Lack of Fit was non-significant (p ¼ 0.1598). In this case A, B, C, AB, AC, BC, BD, A2, B2 and D2 are significant model term. The ‘‘Predicted R-Squared’’ of 0.9524 was in reasonable agreement with the ‘‘Adjusted R-Squared’’

Point prediction of the design expert software was used to determine the optimum values of the factors for maximum entrapment efficiency and smaller particle size of the NPs. Five formulations (OF1–OF5) were selected from point prediction software of design expert and their responses, i.e. particle size and EE were evaluated. The composition of all optimum check point formulations, their predicted and experimental values for the responses and the % prediction error are shown in Table 5. The low value of % prediction error assures the validity of generated equations and thus depicts the domain of applicability of RSM model.

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D. Saini et al.

Finally, the optimum values CS (0.10 % w/v), TPP (0.20% w/v) RPM (600) and TPP pH (5.1) were selected. These values predicted 180.64 nm size of the NPs, the EE of 81.49 to 56.90%. These predicted values of responses were validated by preparing the NPs using previously optimized process parameters and gave average particle size of 174.79 ± 10.25 nm, EE 78.54 ± 6.12 and drug loading 59.43 ± 3.46% (Table 5). This shows 96.76, 96.38 and 104.45% validity of the predicted model for particle size and EE, respectively. This optimized formulation (OF2) was used for further studies. The RSM results showed experimental values of responses and were compared with predicted value and their prediction error was laid between 4.25 to + 3.76 from calculation.


FTIR evaluation FTIR spectrum of RLX showed the characteristic peaks of the drug structure. These characteristic peaks of the drug at 3506, 3378, 3251, 2952, 2669, 1572, 1490, 1438, 1366, 1143, 966, 816, 572, 526 and 507 cm1 were also observed in the FTIR spectrum of RLX-loaded CS-NPs without any distinct shift as in Figure 4. This fact verified that no chemical interaction between the drug and the polymer occurred and RLX retained its chemical nature even after formulation into the NPs (Papadimitriou et al., 2009). DSC analysis The thermo grams of CS, placebo CS-NPs, CS-NPs loaded with RLX formulation and RLX are presented in Figure 5. A sharp endothermic peak (Tpeak ¼ 268.7 C) was observed for RLX at the temperature corresponding to its melting point. In the case of CS polymer and CS-NPs, the first endotherm at 100 C corresponds to evaporation of bound water as polysaccharides have strong affinity for water. There was no drug peak at 268.7 C in DSC thermograms of CS and placebo CS-NPs as there was no drug present in the samples. In the case of drug loaded CS-NPs, the peak at 268.7 C was reduced and shifted to lower temperature, implying that drug was encapsulated by polymer in the nanoparticulate system and also because of decreased crystallinity indicating change in solid state structure of CS due to cross-linking Figure 4. FTIR spectra of raloxifene HCl and raloxifene HCl nanoparticulate formulation.


(Joshi et al., 2010). The first stage begins at 80 C with weight loss of 6%. The second stage starts at 260 C as seen in Figure 5. The first stage appears due to loss of water from CS polymers as it is already reported by Dhawade & Jagpat (2012). In vitro release and kinetics studies RLX-loaded CS-NPs showed much faster in vitro dissolution rate than drug suspension. It was apparent that RLX released from the formulation in vitro showed a rapid initial release followed by slow drug release (Zhou et al., 2001). Within 1 h, approximately 75% of drug was released into the dissolution medium from nanoparticulate system and more than 90% of drug was released within 2 h, while drug suspension showed less than 60% release even after 2 h. The rapid release of RLX from drug solution may be due to rapid dissolution of the RLX upon dilution under sink conditions. The initial rapid release of drug may be due to release of RLX from the surface of NPs, while at a later stage RLX may be constantly released from the NPs core as a consequence of CS hydration and swelling that is responsible for the prolonged release, which is desired for sustained action (Sadeghi et al., 2008). The graph of the release and subsequent diffusion of the drug from the NPs becomes similar to the diffusion of drug solution from the dialysis sacs as shown in Figure 6. This improvement in dissolution rate of drug from nanoparticulate system was because of dissolution rate enhancer property of CS which was earlier reported by Sawanagi et al. (1983) and Genta et al. (1995), and also can be due to their small size and large surface area available to the dissolution media. The two-tailed t-test showed statistically significant differences (p50.0001) between the release behavior of nanoparticulate system and drug suspension. The kinetic analysis of the in vitro release profile of the optimized CS-NPs was done to ascertain release order as shown in Table 6. Since the coefficient of correlation (R2) for Higuchi’s model was nearer to unity, i.e. (0.9764) for RLX-loaded CS-NPs, therefore the best fit model for CS-NPs was Higuchi’s model (Ritger & Peppas, 1987). The Fickian diffusional release occurs by the usual molecular diffusion of the drug due to a chemical potential gradient (Cox et al., 1999).


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Figure 5. DSC thermograms of chitosan (1), placebo chitosan nanoparticles (2), raloxifene HCl loaded with chitosan nanoparticulate formulation (3), raloxifene HCl (4).

Figure 6. In vitro release profile of raloxifene HCl suspension and raloxifene HCl nanoparticulate formulation (n ¼ 3).

Table 6. Release Kinetics of CS-NPs using various release models. Formulation CS-NPs

Model

Formula

R2

Rate constant (K)

Zero order First order Matrix (Higuchi) Korsmeyer–Peppas Hixson–Crowell

m0 m ¼ kt ln m ¼ kt m0 m ¼ kt1/2 log (m0 m) ¼ log K + nlog t M01/3 m1/3 ¼ kt

0.9058 0.9712 0.9784 0.9645 0.8058

10.7698 0.7285 0.4533 0.6432 n ¼ 0.322 3.5412

In vivo study The pharmacokinetic parameters of RLX-loaded CS-NPs through, i.n. administration were compared with the pharmacokinetic parameters of RLX suspension administered through oral route (Table 7). The different pharmacokinetic parameters of RLX-loaded CS-NPs suspension when administered intranasally were determined and concentrations of drug in blood plasma were calculated using validated LC-MS/ MS method. The results are shown in Figure 7. Results of the study showed that i.n. administration of RLX-loaded CS-NPs remarkably increased the RLX concentration in plasma as compared to concentration found with oral formulation, the similar pattern was achieved previously (Lobenberg et al., 1998). The two-tailed unpaired t-test showed statistically

Significant (T-test) 3.642 5.254 9.437 4.142 2.863

(Pass) (Pass) (Pass) (Pass) (Pass)

significant differences in the drug plasma concentration (p50.05) between the intranasal nanoparticulate delivery system and oral administration at Tmax. A considerably high drug concentration in plasma was reached in 10 min after nasal administration (Cmax 124.4 ng/mL: Tmax 10 min) with respect to oral administration (Cmax 30.3 ng/mL: Tmax 4 h). Tmax at 10 min after i.n. administration of NPs indicated a very rapid absorption from nose which did not occur in case of oral route at that time period. Peak concentration after oral ranged from 4 to 6 h or longer. This finding is in concurrent to the results of Koester and associates where peak plasma was achieved after 300 min (Koester et al., 2004). Both encapsulation of RLX in nanoparticulate system and the nasal route increased the BA of RLX. High RLX absorption through nasal mucosa occured because the drug

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D. Saini et al.


Table 7. Mean pharmacokinetic parameters ( ± S.D., n ¼ 6) of raloxifene chitosan nanoparticulate formulation via intranasal and oral administration for blood. Route of administration Intranasal Oral

Tmax (h)

Cmax (ng/mL)

T1/2 (h)

AUC(0-t) (h.ng/mL)

AUC(0-inf) (h.ng/mL)

0.17 4.00

124.4 10.45 30.3 ± 5.32

8.892 ± 0.87 15.691 ± 1.56

407.1795 ± 15.43* 239.2203 ± 11.22

529.0589 ± 19.83 388.6288 ± 22.65


*AUC is more in case intranasal administration, highly significant (p50.05).

of the polymer has also played a prominent role in drug delivery via nasal route, which helps the NPs in adhering to the nasal mucosa thereby enhancing the delivery of the drug to the systemic circulation. This is also established by the Tobıó and associates where they observed that higher level of radioactivity is found in blood and lymph node following nasal administration of PLGA, which indicated that drug reaches into systemic circulation via nasal route with carrier as NPs (Tobıó et al., 1998). Figure 7. Mean plasma concentration–time curve of drug after intranasal nanoparticles and oral suspension formulation administration.

is loaded into NPs using CS as carrier. The CS has mucoadhesive property which leads to adherence on the mucosal surface thus increasing the nasal retention time of formulation in nasal cavity and also by passing the hepatic first-pass metabolism, it is also concurrent to the results obtained by Khan and associates when mucoadhesion minimizes the nasal mucocilliary clearance and enhances the absorption from the site of administration (Khan et al., 2010). Small size and large surface area of NPs favor the dissolution of NPs in little amount of mucous present in nasal mucosa. This nasal mucocilliary clearance is overcome by CS-NPs due to strong mucoadhesiveness, which prevents the loss of drug from nasal environment. In addition, chitosan is characterized by absorption enhancing effects, and it improves the paracellular transport by opening the tight junctions, as reported in previous studies (Illum, 2003; Gavini et al., 2005). This could also be important for lipophilic drugs, for which the transcellular transport might dominate or if drug showed high dissolution rate, even if poorly soluble, due to the presence of the CS. Therefore, both the higher local drug concentration and increased paracellular transport are likely to play an important role in absorption. The biodistribution studies were performed in Wistar rats to analyze the amount of drug reaching in various vital organs. The biodistribution of RLX-loaded CS-NPs (i.n.) and drug solution oral are shown in Table 8.The biodistribution study of RLX-loaded CS-NPs via intranasal route resulted in a higher plasma drug concentration as compared to drug suspension administered through oral route (p50.05). The RLX concentrations in plasma following the i.n. of drug-loaded CS-NPs were found to be significantly higher at all the time points compared to the drug solution orally. When the nanoparticulate suspension was given intranasally, there was an increased concentration of drug in blood as compared to the oral route; this was because of the higher blood vasculature in nasal cavity and avoidance of first pass metabolism which is the most important factor for low drug concentration in plasma via oral route. Mucoadhesive nature

Stability studies on CS nanoparticulate formulation of raloxifene One of the major criteria for any rational design of a dosage form is its stability. Drug instability in pharmaceutical formulations may be detected in some instances by the changes in the physical appearance, color, odor, taste or texture of the formulation. Whereas, in other cases, chemical changes may occur which are not self-evident and may only be ascertained through chemical analysis. There were no changes in their physical appearance. It was observed that the initial drug content and the drug contents of the samples analyzed after 1, 2, 4 and 6 months of storage at various conditions were not significant changes, indicating that there were no significant changes in the physical as well as chemical characteristics of the formulations. No statistically significant (p50.05) changes were observed in the particle size and PDI of the formulations after 6 months of storage. The statistically significant changes in the zeta potential during 6 months of storage (p40.05) were found as shown in Table 9. It was concluded that the developed CS-NPs of drug RLX were physically and chemically stable and retained their pharmaceutical properties at various temperatures and humidity conditions over a period of 6 months.

Conclusion The present research work proposed a novel nanoparticulate formulation for the intranasal delivery of RLX. The effect of different variables on NPs preparation was investigated. The EE, typically varies from 30 ± 4.76 to 76 ± 6.65 depending on the amount of drug added and the reproducibility of the preparations were found to be satisfactory. The in vitro release was found to be 98.15±0.55 over 24 h indicated a controlled and sustained release profile of RLX-loaded CS-NPs. The release kinetics had the maximum R2 value of 0.9764 for the Higuchi model. Thus, it followed the Higuchi model which is an indicative of diffusion mechanism of drug release. The biodistribution studies RLX-loaded CS-NPs system via intranasal route resulted in a higher plasma drug concentration as compared to drug suspension administered through oral route (p50.05). Mucoadhesive nature of the polymer has


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Table 8. Biodistribution of drug intranasal nanoparticulate formulation and oral suspension in different organs at Tmax. Drug concentration in various tissues (ng/g of organ weight) ( ± S.D., n ¼ 3)

Route of administration organs Intranasal Oral

Brain

Lungs

Liver

Stomach

Kidney

Uterus

Ovary

Blood

9.61 ± 0.12 18.23 ± 1.82 47.32 ± 5.37 44.05 ± 9.22 26.56 ± 4.02 24.46 ± 2.92 30.64 ± 08.25 122.67 ± 8.22 6.66 ± 0.026 20.43 ± 2.51 209.72 ± 18.95 164.78 ± 10.63 78.40 ± 8.74 35.77 ± 09.72 79.55 ± 13.26 27.53 ± 4.75

Table 9. Mean particle size, PDI, zeta potential drug content and percentage of drug remaining of RLX-loaded chitosan nanoparticulate suspension stored at 25 ± 2 C and 60 ± 5% RH. Time (Months)

Mean particle size ± S.D. (n ¼ 3)

Mean PDI ± S.D. (n ¼ 3)

Mean zeta potential (mV) ± S.D. (n ¼ 3)

Drug content (mg)

% Drug remained

226.5 ± 10.02 232.6 ± 09.63 235.5 ± 11.04 230.5 ± 05.67 225.5 ± 12.86

0.395 ± 0.078 0.299 ± 0.085 0.230 ± 0.091 0.305 ± 0.071 0.221 ± 0.051

28.5 ± 3.89 29.3 ± 5.73 30.4 ± 5.31 29.7 ± 4.97 27.2 ± 5.68

4.00 3.96 3.95 3.92 3.87

100.00 99.06 98.64 97.92 96.82


0 1 2 4 6

also played a prominent role in drug delivery through nasal route, which helps the NPs in adhering to the nasal mucosa thereby enhancing the direct delivery of the drug to the systemic circulation. The shelf life of the nanoparticulate suspension at 5 C (refrigerator temperature) was found to be 15.45 months against 18.07 months calculated using software according to the Arrhenius plot. On the basis of these research findings, it was concluded that RLX-loaded CS-NPs could be a novel nanoparticulate drug delivery system for the treatment of OP. However, clinical study is warranted to evaluate the risk/benefit of colloidal systems and toxicity of polymers to the living cells.

Acknowledgements Authors thank Mr. Harilal Patel, Head DMPK division, for his support and encouragement to carry out this work in Research and Development Unit.

Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article. The authors are thankful to the management of Zydus Research Centre, Ahmedabad, India, for supporting this work. Authors are grateful to Indian Council of Medical Research (ICMR), New Delhi, for providing SRF to Mohammad Fazil (Project Ref. no. 35/18/2011-BMS).

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