Optimization of artemether-loaded NLC for intranasal delivery using central composite design.

http://informahealthcare.com/drd ISSN: 1071-7544 (print), 1521-0464 (electronic) Drug Deliv, Early Online: 1–15 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/10717544.2014.885999

ORIGINAL ARTICLE

Optimization of artemether-loaded NLC for intranasal delivery using central composite design Kunal Jain, Sumeet Sood, and Kuppusamy Gowthamarajan

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J.S.S. College of Pharmacy, Department of Pharmaceutics, Rocklands, Udhagamandalam, Tamil Nadu, India

Abstract

Keywords

The objective of the study was to optimize artemether-loaded nanostructured lipid carriers (ARM-NLC) for intranasal delivery using central composite design. ARM-NLC was prepared by microemulsion method with optimized formulation having particle size of 123.4 nm and zeta potential of 34.4 mV. Differential scanning calorimetry and powder X-ray diffraction studies confirmed that drug existed in amorphous form in NLC formulation. In vitro cytotoxicity assay using SVG p12 cell line and nasal histopathological studies on sheep nasal mucosa indicated the developed formulations were non-toxic and safe for intranasal administration. In vitro release studies revealed that NLC showed sustained release up to 96 h. Ex vivo diffusion studies using sheep nasal mucosa revealed that ARM-NLC had significantly lower flux compared to drug solution (ARM-SOL). Pharmacokinetic and brain uptake studies in Wistar rats showed significantly higher drug concentration in brain in animals treated intranasally (i.n.) with ARM-NLC. Brain to blood ratios for ARM-NLC (i.n.), ARM-SOL (i.n.) and ARM-SOL (i.v.) were 2.619, 1.642 and 0.260, respectively, at 0.5 h indicating direct nose to brain transport of ARM. ARM-NLC showed highest drug targeting efficiency and drug transport percentage of 278.16 and 64.02, respectively, which indicates NLC had better brain targeting efficiency compared to drug solution.

Artemether, central composite design, intranasal, lipid, nanoparticle

Introduction Cerebral malaria (CM) is the most severe and rapidly fatal neurological complication of Plasmodium falciparum infection and responsible for more than 2 million deaths annually in nonimmune individual. This represents an enormous burden of disease, due to the high prevalence of infection (Jain et al., 2013a). It is characterized by impaired consciousness, seizures, hallucinations, severe metabolic acidosis, jaundice, renal failure and respiratory distress (Beales et al., 2000; Maitland & Newton, 2005). The underlying factors that are hallmark of cerebral malaria are sequestration and cytoadherence of infected RBC, platelets, leukocytes; rosetting, auto-agglutination, release of inflammatory cytokines, hypoxia and cerebral oedema. As a result of these central nervous system (CNS) complications, the disease may progress to unarousable coma and death (Newton et al., 2000). Artemether (ARM) is oil soluble methyl ether of artemisinin effective against both chloroquine-resistant and chloroquine-sensitive strains of P. falciparum, as well as against Plasmodium vivax. It is also used in the management of CM (Medana & Turner, 2006). It contains sesquiterpene lactone rings with an endoperoxide bridge that is cleaved by an iron-dependent mechanism. It is a potent inhibitor of cysteine Address for correspondence: Kunal Jain and Kuppusamy Gowthamarajan, J.S.S. College of Pharmacy, Department of Pharmaceutics, Rocklands, Udhagamandalam 643001, Tamil Nadu, India. E-mail: kunaljain_15@ yahoo.co.in

History Received 8 January 2014 Accepted 17 January 2014

protease by virtue of its inhibition of hemozoin formation as well as hemoglobin degradation (Klayman, 1985). It suffers from poor aqueous solubility and short half life usually between 3 and 5 h. Furthermore, oily intramuscular (i.m) injection of ARM for the treatment of CM is associated with pain on injection, erratic absorbtion and thus poor patient compliance. In addition, i.m. administration is not suited to deliver the drug to treat CM or when quick eradication of the malarial parasite is required (Aditya et al., 2010). Treatment of CM requires hospital admission, since it requires parenteral administration. This is a major limitation as hospitals are not accessible in all the endemic areas (Touitou et al., 2006). Hence to overcome these inherent drawbacks associated with the parenteral delivery of ARM, efforts are being undertaken to investigate alternative modes of antimalarial drug delivery to the brain. The conventional drug delivery system that releases the drug into systemic circulation fails to deliver drugs effectively to brain and is therefore not very useful in treating CM. Therefore, there is need for a patient compliant method to deliver ARM to the brain in a better and effective way. In the recent years, intranasal (i.n.) administration has received a great deal of attention as a convenient, reliable and an acceptable alternative to parenteral administration of various drugs to target brain directly via the olfactory neurons. The nasal route of drug administration provides a route of entry to the brain that circumvents the blood-brain barrier (BBB) and this neuronal connection constitutes a


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direct pathway to the brain (Illum, 2003). It is a non-invasive technique with ease of administration even by less skilled health personnel. Touitou et al. investigated i.n. dihydroartemisinin (DHA) delivery as non-invasive treatment of malaria. Treatment and prophylaxis with DHA was effective in ameliorating Plasmodium infection in a rodent model of severe malaria and it was found that i.n. DHA was at least as effective when compared to intraperitoneal administration of same drug dose (Touitou et al., 2006). With aid of nanocarriers, drugs can be targeted to site of action avoiding distribution to nontarget sites thereby overcoming side effects observed with conventional dosage forms. It is of utmost importance to achieve higher drug concentration in the microenvironment of parasite than in systemic circulation. Various colloidal drug carriers such as polymeric nanoparticles, liposomes, nanoemulsions, solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been investigated for i.n. delivery (Kumar et al., 2008; Phachonpai et al., 2010; Kaur & Verma, 2011; Seju et al., 2011; Haque et al., 2012; Alam et al., 2013). Among these, NLCs have distinct advantage of being composed of biodegradable and biocompatible lipids and overcome the limitations of SLNs such as drug leakage during storage, drug expulsion and low drug loading capacity. The incorporation of liquid lipid to solid lipid leads to crystal order disturbance resulting in greater imperfections in the crystal lattice leading to improved drug loading and long-term stability (Souto et al., 2004). Additionally, by controlling the amount of liquid lipid, NLC can be maintained in solid state at body temperature and controlled release can be achieved (Mu¨ller et al., 2002). Researchers have explored the potential of NLC in improving the antimalarial efficacy of ARM on parenteral administration. In a study by Joshi et al. (2008), ARM-NLC (Nanoject) was investigated for parenteral delivery in Plasmodium berghei ANKA mice model of malaria. Intraperitoneal administration of Nanoject showed significant improvement in the antimalarial activity and duration of action of ARM as compared to the currently marketed oily intramuscular injectable formulation (LaritherÕ , IPCA Laboratories, Ltd, Mumbai, India). The antimalarial activity of Nanoject lasted for a longer duration (more than 20 days) and showed significantly higher survival rate (60%) even after 31 days as compared to marketed formulation, which showed 0% survival (100% mortality). In another study by Aditya et al. (2010), intraperitoneal administration of ARM-NLC (5 mg/kg) in P. berghei infected mice resulted in a survival of 468% infected mice for more than 30 days in comparison to control (5–7 days) and plain ARM solution (12–16 days) treated groups. In terms of parasite progression, ARM-NLC, marketed and plain ARM solution showed decrease of parasitemia initially; however, parasitemia reappeared in groups treated with marketed and plain ARM solution. These results were attributed to enhanced bioavailability of the drug when formulated as NLC. The concept of ‘‘quality by design’’ was introduced by international conference on harmonization Q8 guideline on pharmaceutical development, which states that quality should not be tested into products, but should be built in (Yu, 2008). Design of experiments (DoE) is a systematic and scientific approach to study the interaction between independent and

Drug Deliv, Early Online: 1–15

dependent variables. This approach allows the effect of several factors to be assessed by a reduced amount of trials and to make experiments maximally informative (Bergtho´ra et al., 2011). Response surface methodology (RSM) is one of the techniques used to estimate the main effects, their interaction, quadratic effects and shape of response surface. It provides estimates of the relative significance of different variables. Thus, it overcomes the limitations of conventional formulation development methodologies that are quite time consuming, expensive and laborious. Central composite rotatable design (CCRD) is one of the techniques of RSM for optimization of pharmaceutical dosage forms and its rotatable characteristic enables it to identify optimum responses around its center point without changing the predicting variance (Zhang et al., 2010). In this investigation, a microemulsion method was applied to prepare ARM-loaded NLC. Trimyristin (TM) and medium chain triglycerides (MCT) were chosen as the solid and liquid lipid materials of lipid nanoparticles, respectively. To design an optimized pharmaceutical formulation of ARM-NLC with maximum drug encapsulation efficiency (EE) and drug loading (DL) and appropriate mean particle size through minimum trials, a computer optimization technique based on RSM was used. Hence, in this study, we propose to develop ARM-loaded NLC for the nasal delivery by employing the CCRD.

Materials and methods Materials ARM was obtained as a gift sample from Ipca Laboratories (Mumbai, India). Medium chain triglyceride was obtained as gift samples from Lipoid GmbH, Germany. Trimyristin and Pluronic F 68 were purchased from Sigma Aldrich, Bangalore, India. Sucrose was purchased from S.D Fine-chem Ltd, Mumbai, India. All other reagents used were of analytical grade. Double distilled water was used. Preparation of NLCs ARM-loaded NLC was prepared by microemulsion technique as reported earlier (Sood et al., 2013). TM and MCT were used as the solid and liquid lipid, respectively. Pluronic F68 (hydrophilic surfactant) solution was used as a continuous phase. First, oil in water (o/w) microemulsion was prepared. The chosen solid lipid and liquid lipid were melted at 70 C, to which drug was added under continuous stirring for 5 min. Ten milliliter of hydrophilic surfactant solution heated at same temperature was added to the melted lipid with mechanical stirring for 15 min. A clear microemulsion was formed under stirring at a temperature close to the melting point of the lipid used. NLCs were obtained by dispersing the warm o/w microemulsion dropwise into ice cold water (2–3 C) in a beaker under continuous stirring. The liquid nano droplets of melted lipid transformed into solid nanoparticles at low temperature and produced NLC dispersion, which was further stirred for 3 h after complete addition of microemulsion. The basic rule for the formulation of NLC is to maintain process temperature at least 5 C above the melting point of the solid lipid (Wissing et al., 2004).

Optimization of artemether-loaded NLC

DOI: 10.3109/10717544.2014.885999

As the melting point of the solid lipid was around 60 C, the processing temperature was selected 65–70 C. Lyophilization of the resultant NLC dispersion was carried out by using sucrose (5% w/v) as a cryoprotectant. The NLC dispersion was frozen at 20 C for about 24 h and lyophilization was carried out for 72 h to get the NLC powders. The freeze-dryer (Christ, Alpha 2-4 LD plus, Germany) was operated at temperature of 40 C and pressure of 0.001 bar.


Design of experiment Based on number of factors and their level, CCRD-RSM was used to systemically evaluate the effect of formulation parameter affecting the physico-chemical properties of ARMNLC. The effect of four independent variables (lipid concentration, liquid lipid to total lipid ratio, drug to lipid ratio and surfactant concentration) on dependent variable (particle size, drug loading (DL) and entrapment efficiency (EE)) was studied using Design expertÕ software (Version 8.0.7.1; M/s Stat-Ease, Minneapolis, USA). A total of 30 experiments with 6 centre points (in order to allow the estimation of pure error) were designed by the software. Table 1 shows the coded and uncoded independent variables.

3

of ARM in the supernatant after centrifugation (10 000 rpm for 30 min) was determined by HPLC method as described in Bioanalytical HPLC Method section. Drug entrapment efficiency ð%Þ ¼ Wdrug =Wtotal 100 ð1Þ Drug loading ð%Þ ¼ Wdrug =Wlipid 100

ð2Þ

Wdrug, analyzed amount of ARM in the supernatant; Wtotal, total amount of ARM used in formulation; Wlipid, weight of lyophilized NLC formulation. Transmission electron microscopy The shape and morphology of the optimized ARM-NLC dispersion was characterized by transmission electron microscopy (TEM, TOPCON 002B) with an accelerating voltage of 200 kV. A drop of the NLC dispersion was placed onto a carbon-coated 200-mesh copper grid to create a thin film. Before the film dried on the grid, it was negatively stained with 2% (w/v) phosphotungstic acid by adding a drop of the staining solution to the film for 30 s; any excess droplets were drained off with a filter paper. The grid was allowed to air-dry under room temperature and samples were observed by TEM.

Particle size and zeta potential determination

Differential scanning calorimetry

The mean particle size and zeta potential of NLC were determined using a zetasizer ZS 90 (Malvern Instruments, UK). The mean particle size was measured based on photon correlation spectroscopy technique that analyzes the fluctuations in dynamic light scattering due to Brownian motion of the particles. The mean diameter was obtained at an angle of 90 in 10 mm diameter cells at 25 C. The zeta potential, reflecting the electric charge on the particle surface, is a very useful way of evaluating the physical stability of any colloidal system. It was determined based on an electrophoretic light scattering technique (Jain et al, 2013b). All size and zeta potential measurements were carried out at 25 C using disposable polystyrene cells and disposable plain folded capillary zeta cells, respectively, after appropriate dilution with original dispersion preparation medium (Jain et al., 2013c). Three replicate analyses were carried out for each formulation, and data presented as mean ± S.D.

The differential scanning calorimetry (DSC) analysis was performed using DSC Q200 (TA Instruments, USA). A heating rate of 10 C/min was employed at a range of 20–100 C. Analysis was performed under nitrogen purge at a flow rate of 50 ml/min. A standard aluminium sample pans were used. About 5 mg of sample was taken for analysis. An empty pan was used as a reference.

Determination of EE and DL percentage EE and DL percentage of lyophilized NLC were determined according to the procedure described earlier (Sood et al., 2013). Ten milligram of ARM-NLC was dissolved in hydroalcoholic solution of ethanol and water in 50:50 ratio under water bath at 70 C for 30 min and then cooled to room temperature to preferentially precipitate the lipid. The amount Table 1. Variables for study for ARM-NLC. Levels Factor code A B C D

Independent factors

2

1

0

1

2

Lipid concentration (%) Liquid lipid to total lipid ratio Drug to lipid ratio Surfactant concentration (%)

0.3 0.05 0.05 1.0

0.6 0.13 0.08 1.5

0.9 0.20 0.10 2.0

1.2 0.28 0.13 2.5

1.5 0.35 0.15 3.0

Powder X-ray diffractometry X-ray diffraction patterns were obtained using a Bruker AXS D8 Advance powder diffractometer by exposing the samples to Cu Ka radiation (40 kV, 35 mA). The measurements were performed at room temperature, scanning at 2 from 3 to 80 , with a 0.020 step size and 31.2 s step time. Cytotoxicity studies The toxicity studies of the blank and ARM-loaded NLC formulations were carried out in SVG p12 cells, a human brain cell line. The cells were maintained in minimum essential medium (MEM), supplemented with 10% (v/v) fetal bovine saline, penicillin (100 IU/ml), streptomycin (100 mg/ ml) and amphotericin B (5 mg/ml) in a humidified atmosphere of 5% CO2 at 37 C until confluent. The cells were then seeded in multiwall culture plates for experimental procedure. The cytotoxicity assay was carried out using cell suspension, containing 5000 cells seeded in each well of a 96-well microtiter plate (Nunc and Tarsons, Kolkata, India) and incubated for 24 h at 37 C. Cells were treated with 250– 2000 mg/ml. Control cells were incubated without the test compound and with MEM. The microtiter plates were incubated at 37 C in a humidified incubator with 5% CO2 for a period of 72 h. Morphological changes in the cells were inspected daily and observed for microscopically detectable alterations, i.e. loss of monolayer, granulation and vaculation

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in the cytoplasm. The cytopathic effect was observed. IC50 (Concentration of the drug that produces 50% inhibition of the cells) was determined by sulphorhodamine B assay (Skehan et al., 1990).


Nasal histopathological studies Nasal histopathological studies were carried out by using the freshly isolated sheep nasal mucosa collected from a slaughter house in a phosphate buffered saline (PBS, pH 6.4) (Seju et al., 2011). Each piece was treated with drug solution in PBS pH 6.4 (27.6 mg/ml), blank NLC, ARM-loaded NLC (equivalent to 27.6 mg), PBS pH 6.4 (as negative control) and isopropyl alcohol (nasal mucociliary toxicity agent used as a positive control), respectively. After treatment for 2 h, all the samples were washed properly with distilled water, sectioned and stained with hematoxylin and eosin. The mucosa was then dissected out, and the mucocillia was examined on an optical microscope by a pathologist. The drug solution was prepared by dissolving 27.6 mg in mixture of 1 ml ethanol and 2 ml propylene glycol and final volume was made to 10 ml with distilled water. In vitro release studies The release of ARM from NLC and solution was performed in simulated nasal fluid (SNF) pH 6.4 containing 1% sodium lauryl sulphate (SLS) using the dialysis bag method. Dialysis membrane having pore size of 2.4 nm and molecular weight cut off 12 000–14 000 (Dialysis membrane-150; HiMedia, Mumbai, India) was used. The dialysis bag retains nanoparticles and allows the free drug into the dissolution media (Luo et al., 2006). The bags were soaked in distilled water for 24 h before use. Drug solution (27.6 mg/ml) and NLC (equivalent to 27.6 mg suspended in 1 ml of SNF) were placed in dialysis bag separately. Dialysis bags were immersed in 200 ml of SNF maintained at 37 ± 0.5 C and stirred at 100 rpm. Aliquots of the dissolution medium were withdrawn at each time interval and the same volume of fresh dissolution medium was added to maintain a constant volume. Samples withdrawn from the dissolution medium were analyzed for drug content by HPLC. All measurements were carried out in triplicate. The kinetic analysis of the release data were fitted to various kinetic models such as zero order, first order, Higuchi’s equation and Korsmeyer’s-Peppas model (Peppas, 1985; Costa & Lobo, 2001). Ex vivo permeation studies on nasal mucosa In order to investigate the permeation efficacy of ARMloaded NLC across nasal mucosa, ex vivo permeation studies were performed by using the Franz diffusion cell (Seju et al., 2011). Diffusion cells were purchased from Kovai Glass Works, Coimbatore, India, with surface area of 1.79 cm2 and volume of 25 ml. The freshly excised sheep nasal mucosa was collected from the slaughter house in PBS, pH 6.4. Excised superior nasal membrane was cut to a appropriate size and mounted between the donor and receptor compartment of the Franz diffusion cell, with mucosal side facing the donor compartment. The tissue was allowed to stabilize and stirred under SNF pH 6.4 containing 1% SLS for 15 min on a magnetic stirrer. The diffusion cell was thermostated


at 37 ± 0.5 C. Solution from both the compartments was removed after 15 min, and the receptor compartment was freshly filled with SNF. The mounting of nasal membrane was done on the rim of the receptor compartment; the donor compartment of diffusion cell was placed over it and secured with a clamp to avoid the leakage of diffusion media. Permeation studies of pure drug solution (27.6 mg/ml) and lyophilized ARM-loaded NLC (NLC equivalent to 27.6 mg of ARM) reconstituted with SNF were carried out by placing 1 ml onto stabilized sheep nasal membrane in the donor compartment of Franz diffusion cell and continuously magnetic stirred at 600 rpm. Aliquot (0.5 ml) from the receptor compartment were withdrawn at predetermined time intervals, filtered through 0.45 mm nylon filter paper and analyzed using HPLC. Each removed sample was replaced immediately by an equal volume of fresh diffusion media maintained at 37 ± 0.5 C. Each study was carried out for a period of 6 h, during which the amount of drug permeated across the sheep nasal mucosal membrane was determined at each sampling point. The permeation profile was constructed by plotting the amount of drug permeated per unit skin surface area (mg/cm2) versus time (h). The steady state flux (Jss, mg/cm2 h) was calculated from slope of the plot using linear regression analysis. All measurements were carried out in triplicate. The kinetic analysis of the release data were fitted to various kinetic models such as zero order, first order and Higuchi’s equation (Costa & Lobo, 2001). Pharmacokinetic and brain uptake Pharmacokinetic and brain uptake studies were carried out using male Wistar rats as reported earlier (Kumar et al., 2008; Haque et al., 2012). The animal experiments were carried out with approval from institutional animal ethical committee (IAEC) of J.S.S. College of Pharmacy, Udhagamandalam, India. All the rats had free access to standard laboratory diet (Lipton feed, Mumbai, India) and water ad libitum. The animals were divided into three groups – Groups I received ARM-NLC (i.n.) at dose of 5 mg/kg dispersed in isotonic saline solution, Group-II received ARM-SOL (i.v. into the tail vein) and Group-III received ARM-SOL (i.n.). Intranasal administration was carried out after light anaesthesia with diethyl ether with help of micropipette attached to low-density polyethylene tube having 0.1 mm internal diameter. Animals were sacrificed at different time intervals (0.25, 0.5, 0.75, 1, 2, 4 and 6 h) by cervical dislocation and blood was collected by cardiac puncture. Blood samples were placed into tubes containing 0.3 ml of anticoagulant solution and centrifuged immediately. After centrifugation, the plasma obtained was stored at 20 C until further analysis. The brain samples were collected by cutting open the skull, rinsing with saline solution and blotting dry. Brain samples were homogenized in PBS pH 7.4. The homogenate was centrifuged at 6000 rpm for 15 min at 4 C, supernatant was collected and stored at 20 C until further analysis. Pharmacokinetic parameters like Cmax (peak plasma concentration), Tmax (time of peak plasma concentration), elimination rate constant (Ke), half life (t1/2) and area under curve (AUC) were calculated from plasma concentration–time


DOI: 10.3109/10717544.2014.885999

profile and brain concentration–time profile. Furthermore, drug targeting efficiency (DTE%) that represents time average partitioning ratio was calculated as follows: Drug targeting efficiencyðDTE%Þ ¼ ðAUCbrain =AUCblood Þi:n: =ðAUCbrain =AUCblood Þi:v: ð3Þ 100

Direct transport percentageðDTP%Þ ¼ Bi:n: Bx =Bi:n: 100


and UV wavelength were 1.0 ml min1 and 209 nm, respectively with run time of 16 min. Retention time of curcumin (internal standard, IS) and ARM was 4.8 min and 14.4 min respectively.

Results and discussion Design of experiment

Nose to brain direct transport percentage (DTP%) will be calculated as follows: ð4Þ

where Bx ¼ (Bi.v./Pi.v.) Pi.n., Bx is the brain AUC fraction contributed by systemic circulation through the BBB following i.n. administration; Bi.v. is the AUC0–6 (brain) following i.v. administration; Pi.v. is the AUC0–6 (blood) following i.v. administration; Bi.n. is the AUC0–6 (brain) following i.n. administration; Pi.n. is the AUC0–6 (blood) following i.n. administration. Bioanalytical HPLC method The HPLC system consisted of a mobile phase delivery pump (LC-20 AD; Shimadzu, Japan), a photodiode array detector (SPDM20A; Shimadzu, Japan) and a 20 mL loop (Rheodyne, CA, USA). A C18 reverse-phase column (Phenomenex Gemini C18, 250 4.6 mm i.d., 5 m) was utilized for drug separation, using acetonitrile–25 mM ammonium acetate buffer pH 3.0 (70:30,v/v) as mobile phase. The flow rate

A total of 30 experiments were carried out to study the effect of formulation variables on particle size, DL and EE. Response data for all experiments are given in Table 2. The value of responses y1 (particle size), y2 (DL) and y3 (EE) ranges from 85.1 to 298.7 nm, 4.41 to 13.48% and 77.45 to 97.18%, respectively. The ratio of maximum to minimum for responses y1, y2 and y3 is 3.50, 3.05 and 1.25, respectively. Therefore, power transformation of values is not required. Transformation of response is an important component of data analysis. Transformation is required if the error (residuals) is a function of magnitude of response (predicted values). Box-Cox plot provides guideline for selecting the correct power transformation. A recommended transformation is listed based on best lambda value, which is found at the minimum point of the curve generated by the natural log of the sum of squares of residuals. In simpler terms, power transformation of responses is required when ratio of maximum to minimum response is greater than 10. For ratios less than 3, transformation has little effect (Sood et al., 2014). The selection of model for analyzing the responses was done based on sequential model sum of squares, lack

Table 2. Experimental design for ARM-NLC.

Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

5

Factor A

Factor B

Factor C

Factor D

Response 1, particle size (nm)

Response 2, DL (%)

Response 3, EE (%)

0.60 0.30 0.60 1.20 0.90 1.20 0.90 0.90 1.20 0.90 0.90 0.90 0.90 0.60 1.20 1.20 0.90 1.20 0.90 0.90 0.90 0.60 0.60 0.90 1.20 0.60 0.60 1.20 1.50 0.60

0.13 0.20 0.13 0.28 0.20 0.13 0.35 0.20 0.05 0.13 0.20 0.20 0.20 0.13 0.28 0.13 0.20 0.28 0.20 0.20 0.20 0.28 0.13 0.20 0.13 0.28 0.28 0.28 0.20 0.28

0.08 0.10 0.13 0.13 0.10 0.13 0.10 0.15 0.10 0.08 0.10 0.10 0.05 0.13 0.08 0.13 0.10 0.13 0.10 0.10 0.10 0.08 0.08 0.10 0.08 0.13 0.08 0.08 0.10 0.13

2.50 2.00 2.50 2.50 1.00 2.50 2.00 2.00 2.00 1.50 3.00 2.00 2.00 1.50 1.50 1.50 2.00 1.50 2.00 2.00 2.00 2.50 1.50 2.00 2.50 2.50 1.50 2.50 2.00 1.50

131.5 85.1 124.7 170.2 196.4 206.3 149.5 140.2 229.2 238.7 98.1 146.2 133.1 136.2 225.1 240.5 146.2 221.6 146.2 146.2 146.2 105.2 138.1 146.2 198.4 109.1 144.6 163.1 298.7 141.5

6.86 13.48 10.24 8.56 7.65 9.41 8.22 11.11 7.2 5.8 8.49 7.88 4.41 9.64 5.9 8.9 7.88 9.36 7.88 7.88 7.88 7.19 6.26 7.88 6.32 10.3 6.58 6.65 10.32 10.26

92.8 93.29 89.13 86.26 84.23 81.85 90.45 85.23 79.23 81.92 93.45 86.79 92.8 83.9 87.62 77.45 86.79 81.42 86.79 86.79 97.18 97.18 84.6 86.79 85.52 96.28 91.45 89.49 81.84 89.23


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of fit and model summary statistics. The Prob 4F value of p50.0001, low standard deviation, high R-squared and lower Predicted Residual Error Sum of Square (PRESS) value suggests to select the quadratic model for both responses. Analysis of variance (ANOVA) of the experimental data confirms that model was significant (Model Prob4F less than 0.05). The Model F value for response y1, y2 and y3 was 34.78, 19.54 and 90.80, respectively, which implies that the model is significant. ANOVA identifies the significant factors that affect the responses. For particle size, lipid concentration, liquid lipid to total lipid ratio and surfactant concentration were identified as significant model terms. For DL, lipid concentration and drug to lipid ratio were identified as significant model terms. For EE, lipid concentration, liquid lipid to total lipid ratio, drug to lipid ratio and surfactant concentration were identified as significant model terms. The multiple regression terms were also analyzed. The predicted R-squared values for response y1, y2 and y3 were 0.8278, 0.7006 and 0.9329, respectively. Adjusted R-squared values for response y1, y2 and y3 were 0.9422, 0.8995 and 0.9775, respectively. The predicted R-squared value was found to be in reasonable agreement with adjusted R-squared value, which indicates that the model has predicted the responses well. Adeq precision for response y1, y2 and y3 was 22.19, 20.04 and 38.99, respectively. Adeq Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. This model can be used to navigate the design space. Final equations in term of coded factors for responses y1, y2 and y3 is given in Equations (5), (6) and (7), respectively. y1ðparticle sizeÞ ¼ þ146:20 þ 44:17 A 12:23 B þ 0:82 C 19:77 D 4:61 A B þ 1:33 A C 6:13 A D þ 0:21 B C 5:79 B D þ 1:18 C D þ 11:78 A2 þ 11:14 B2 2:03 C2 þ 0:62D2 ð5Þ y2 ðDLÞ ¼ þ7:88 0:53 A þ 0:95 B þ 3:11 C þ 0:035 D 0:055 A B 0:031 A C þ 0:14 A D þ 0:29 B C 0:19 B D5 0:092 C D 0:056 A2 þ 0:10 B2 0:16 C2 0:37D2

ð6Þ

y3 ðEEÞ ¼ þ86:23 þ 0:86 A þ 0:14 B þ 1:61 C þ 0:19 D 0:082 A B 0:12 A C 0:056A D 0:047 B C 0:10 B D 0:13 C D þ 0:86 A2 0:18 B2 0:17 C2 0:094D2

ð7Þ

In case of response y1, positive coefficient of A and C indicates particle size increases with increase in lipid concentration and drug to lipid ratio. Negative coefficients of B and D indicate decrease in particle size with increase in liquid lipid to total lipid ratio and surfactant concentration. For response y2 and y3, positive coefficients of A, B, C and D

indicate that both DL and EE increases as concentration of lipid, liquid lipid to total lipid ratio, drug to lipid ratio and surfactant concentration increases. The relationship between variables was further studied using three-dimensional (3D) response surface graphs. Response surface graphs for most statistical significant variables are shown in Figure 1. It is evident from the contour plots that particle size significantly increased with increasing lipid concentration. This can be explained in terms of tendency of lipid to coalesce at high lipid concentration, also, in an NLC formulation, when increasing the solid lipid content, the dispersion viscosity also increases, leading to higher surface tension and thus higher particle size (Varshosaz et al., 2010). Huge augmentation of particle size was observed at 1.2% lipid concentration. These results are in agreement with our previously published reports (Sood et al., 2013). The possible reason might be that amount of lipid was high compared to concentration of surfactant used. In other words, surfactant concentration was not sufficient enough to effectively cover the lipid microemulsion droplets and thus reduce the surface tension. Hence, the droplet size of emulsion was higher and formed larger particles when poured into ice cold water. Particle size decreases with increase in liquid lipid to total lipid ratio and concentration of surfactant. The particle size was minimum when the content of liquid lipid was 30% with respect to solid lipid, perhaps because excess oil is excluded during lipid crystallization. The excess oil inhibits crystallization of the solid lipid leading to smaller particles (Lin et al., 2007). Smaller particle sizes were obtained at low concentration of lipid with higher surfactant concentration as depicted in contour plots. Particle size dramatically decreased with increasing surfactant concentration up to 2% w/v. Higher amount of surfactant reduced the surface tension of the melted lipid droplets, which helped further breakdown of the lipid droplets into smaller size. Additionally, the surfactant molecules stabilized and prevented coalescence of microemulsion droplets (Das et al., 2012). Further increase in surfactant concentration did not result in significant decrease in particle size. The concentration of surfactant is of paramount importance, since it influences stability of NLCs on storage and improper selection may lead to aggregation and increased particle size. Excess of surfactant can induce toxicity and faster drug release (Severino et al., 2011). Taking into account of the influence of aforementioned variables on particle size, it is well known that the smaller the particles the higher their mucoadhesive strength to the surfaces, such as tissues (Florence, 2004). A closer contact between the drugloaded particles and the biological membrane may enable a more efficient permeation of the drug into the tissue (Liu & Wu, 2010). Figure 1(C) showed the response surface model for DL in response to the investigated factors. It could be seen that DL of NLC was highly influenced by concentration of lipid and drug to lipid ratio indicating that lipid was important determining factor for loading of drug. Higher DL was achieved when more drug was added into the formulation. This can be explained by the fact that low drug concentration may not be sufficient to achieve saturation of the lipid matrix. Furthermore, the addition of liquid lipid (oil) to the solid

Figure 1. Three-dimensional (3D) response surface graphs of artemether nanostructured lipid carrier showing effect of independent variables on particle size (A, B), drug loading (C) and entrapment efficiency (D, E).


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Optimization of artemether-loaded NLC 7


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lipid in NLC causes distortion of crystalline structure of the lipid and is favourable for achieving higher DL. Hence, at higher drug concentration, DL was significantly increased. Liquid lipid to total lipid ratio had a positive influence on EE as shown in Figure 1(D). Higher amount of liquid lipid in NLC enhances the solubilization capacity of total lipid phase thus entrapping more drug during formulation. Higher EE was achieved at lower concentration of lipid and found to decrease with increase in lipid concentration. EE significantly increased with increasing surfactant concentration. Similar results have been reported by other researchers (Liu et al., 2007; Das et al., 2012). This has been attributed to efficient loading retention of drug molecules within the lipid matrix or at surface of NLC at higher surfactant concentration. Optimization of NLC was carried out to find the level of factors A, B, C and D which gives y1 in range of 90–150 nm, y2 in range of 10–11% and y3 in range of 90–98%. The model predicted y1, y2 and y3 in required range at A, B, C and D values of 0.61%, 0.26%, 0.12% and 1.65%, respectively. The predicted value of responses y1, y2 and y3 were 119.5 nm, 10.01% and 90.9%, respectively. To validate the experimental models, the optimized formulation was prepared in triplicate by using these values of factors. The actual experimental values of y1, y2 and y3 were found to be 123.4 ± 3.6 nm, 10.56 ± 0.59 and 91.2 ± 2.5%, respectively, which is in close agreement with predicted values. The zeta potential of the optimized formulation was found to be 34.4 ± 1.2 mV indicating good stability of NLC dispersion. The measured zeta potential originates from the height of Nernst potential (surface charge) and the additional charges created by adsorbed ions or surfactant or stabilizer molecules in the Stern layer (Kovacevic et al., 2011). From the literature, zeta potential value higher than +30 or less than 30 mV is essential for the stability of nanoparticle dispersion and also for preventing the aggregation between the particles (Singh et al., 2011). The negative zeta potential of NLC could be attributed to the presence of free anionic fatty acids present on the surface of NLC. TM is composed of myristic acid which is tetradecanoic acid. Similarly, MCT are composed of medium chain fatty acids like caprylic acid (C8) and capric acid (C10). In a study by Garcia-Fuentes et al. (2005), lipid nanoparticles prepared with tripalmitin showed a zeta potential of 50.3 ± 1.8 mV. The authors attributed negative zeta potential to the presence of anionic lipids in composition. An optimized batch of ARM-NLC was selected for further experiments. Transmission electron microscopy The external morphological study of the optimal ARM-NLC using TEM revealed that the NLC particles are nearly spherical in shape as shown in Figure 2. The particle size observed by TEM correlated well with the results obtained from particle size analysis using zetasizer. Differential scanning calorimetry DSC is a tool to investigate the melting temperature and recrystallization behaviour of crystalline material. DSC thermograms were recorded for pure drug, lipid, physical mixture (drug and lipid in 1:1 ratio) and lyophilized ARM-


Figure 2. Transmission electron micrograph (TEM) of artemetherloaded nanostructured lipid carriers.

NLC as shown in Figure 3. The DSC curve of ARM showed a single sharp endothermic peak at 86.48 C. The physical mixture exhibited the endothermic peak of drug and lipid indicating their crystalline nature and their compatibility with each other. The thermograms of ARM-loaded NLC did not show the melting peak of ARM crystals. This suggests that ARM was not in crystalline state but is in amorphous state. Since, NLC were prepared by rapid quenching of microemulsion, drug molecules dispersed in lipid phase are not able to crystallize (Cavalli et al., 1995). Furthermore, the presence of surfactants inhibits crystallization of the drug. Degree of crystallinity of lyophilized NLC was calculated by comparing the enthalpy of NLC with enthalpy of bulk lipid (Freitas & Mu¨ller, 1999).The melting enthalpy of bulk lipid was used as a reference (100%) to calculate the percentage of crystallinity of NLC. ARM-NLC showed percent crystallinity of 64.46%. The endothermic peak of bulk TM (59.4 C) shifted to 55.59 C in ARM-loaded NLC. This could be attributed to the NLC matrix, which is composed of the mixture of lipids (solid and liquid lipids) and presence of drug (drug-loaded NLCs). The peak height (also area under the curve) of lipid was further reduced in NLCs. Thus, reduction in crystallinity was observed for both drug and lipid matrix (less solid lipid crystals) after formulating them as NLCs. This supports that the oil is moleculary dispersed in the lipid blend, which creates distoration in the lipid matrix (Kovacevic et al., 2011). For large production of NLC, the control of polymorphism is a demand due to its influence on EE and drug expulsion during storage (Jores et al., 2004). The lipid polymorphism is also an important characteristic, since the crystalline structures of long-chain compounds such as triglycerides particularly TM can occur in different polymorphic forms (a, b, 0 ) (Kovacevic et al., 2011). In general, these lipids crystallize in two or three different phases, a and 0 , or a, b and 0 , respectively (Freitas & Mu¨ller, 1999). This phenomenon is due to the numerous possible lateral packing patterns of fatty

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Figure 3. Differential scanning thermogram of artemether (ARM), trimyristin (TM), physical mixture of artemether and trimyristin (PM) and artemether-loaded nanostructured lipid carrier (ARM NLC).

acid chains in a particular organization of hydrocarbon chains (Teeranachaideekul et al., 2007). The a-form (hexagonal) is the least stable with a lower melting point and latent heat, whereas the b-form (triclinic) is the most stable with higher melting point and higher latent heat. The transformation of a to 0 (orthorhombic) and b is irreversible and occurs toward a more hydrodynamic stable system (Severino et al., 2011). The onset of melting and melting points of lipid in NLC formulation were depressed when compared to melting points of corresponding bulk lipid. This indicates that lipid in the formulation might be in the b-polymorphic form (stable modification). This melting point depression and broadening peaks were observed when transforming a bulk lipid into NLC form due to Gibbs-Thompson effect, i.e. the larger ratio of specific surface area to volume of particle with a smaller size when compared to bulk material (Perez, 2005) and/or the presence of surfactants (Jenning et al., 2000). Depending on the lipophilicity, the surfactants partition between water phase, interface and the lipid phase. Surfactant in the lipid phase can distort crystallization and result in a lower melting enthalpy and lowering of the melting temperature. Furthermore, a shoulder peak of TM was observed at 49.24 C. This may be attributed to a-polymorphic form (thermodynamically unstable modification) of TM and also due to presence of liquid lipid (oil) in NLC, which suppresses the recrystallization of solid lipid during cooling phase of preparation (Kovacevic et al., 2011). Powder X-ray diffraction Powder x-ray diffraction pattern of ARM exhibits sharp crystalline peaks at 2 scattered angles 9.5, 10.8, 17.5 and 19.1, which indicates crystalline nature of ARM (Figure 4). In physical mixtures of ARM with TM, decreased peak intensities were observed at 2 scattered angles 9.5, 17.5 and 19.1 indicating reduction of degree of crystallinity of

Figure 4. Overlaid powder x-ray diffraction pattern of pure drug (ARM), bulk trimyristin (TM), physical mixture of drug and lipid (PM) and drug-loaded nanostructured lipid carrier (NLC).

ARM. However, there were no crystalline peaks of ARM in lyophilized ARM-NLC diffraction pattern. This suggests that either ARM was converted to amorphous form in the lipid matrix or ARM was completely solubilized in the lipid matrix. The bulk lipid exhibited the sharp peaks at 2 scattered angles of 22.87, 23.69 and 23.95, which correspond to short spacings of the chains at 0.38, 0.37 and 0.37 nm, respectively. These peaks were present in the diffraction pattern of physical mixture and lyophilized ARM-NLC with reduced intensity. The intensity and width of the peaks

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Figure 5. Percentage growth curve of SVG p12 cell line treated with blank and artemether-loaded nanostructured lipid carrier.

(resolution of the peaks) depend on various factors, such as the amount of sample used and the particle size (Souto et al., 2006). These results may be attributed to the incorporation of the drug molecule between parts of the crystal lattice of the lipid matrix, which creates a more massive crystal order disturbance (lattice defects) leading to more imperfections in the crystals. This allows enough space to accommodate drug molecule and is favorable for entrapping the drug in the lipid matrix during the shelf life (Mu¨ller et al., 2002). This reduction in crystallinity of both drug and lipid after ARM-NLC formulation is in conformity with DSC results. Therefore, it can be concluded that ARM was molecularly dispersed in lipid matrix which may lead to enhanced solubility. Cytotoxicity studies The objective of the cytotoxicity studies of NLC formulations was to assess their safety for brain delivery. The cytotoxicity studies of blank and drug-loaded NLC were carried out in SVG p12 cell lines. For blank and drug-loaded NLC, IC50 was found to be greater than 2000 mg/ml as illustrated in percentage growth curve (Figure 5). ARM has been found to exert neurotoxic effect when administered at the dose of 25 mg/kg parenterally (Akinlolu & Shokunbi, 2010). Moreover, nanoparticles themselves may exert cytotoxic effects on brain cells directly or through their degradation products. Nanoparticles have the capacity to bypass the BBB and cause neurotoxicity (Hu & Gao, 2010). This study confirmed that blank and ARM-loaded NLC formulations were nontoxic and hence safe for brain delivery. Nasal histopathological studies Nasal histopathological studies are useful to study the toxicity of excipients used in a formulation. As shown in Figure 6, nasal mucosa treated with PBS pH 6.4 showed no nasociliary damage and epithelial layer was intact. Treatment with IPA caused extensive damage to nasal mucosa with loss of epithelial cells, loss of cilia and shrinkage of mucosal layer. Some cilias were detached after treating with drug solution, which indicates drug toxicity on the microscopic structure of the nasal mucosa. The effect of blank and NLC formulations on mucosal histopathology was negligible with no structural changes in nasal membrane and no damage to cilia.

These observations indicated that the developed NLC formulations were safe for nasal administration, which was in accordance with results obtained by Seju et al. (2011). In vitro release studies The release profiles indicate that ARM-NLC formulation showed a retarded release of the drug from the lipid matrix when compared with plain ARM solution (ARM-SOL) as shown in Figure 7. ARM is a lipophilic drug with log p value of 3.48 having very limited solubility in aqueous phase. Hence, 1% SLS was used in the dissolution media to maintain the sink conditions. Solubility of ARM in dissolution media was determined and was found to be 6.96 ± 1.16 mg/ml. It was observed that ARM-SOL showed 68.4 ± 1.5% release in 12 h which was significantly higher (p50.001) than ARM-NLC which showed 22.7 ± 1.6% release at same time interval. This is due to fact that there is no barrier for diffusion at dialysis membrane interface for ARM molecules and sink conditions were maintained (Cs/Cd ¼ 50.4) for diffusion to take place from drug solution into dissolution medium through dialysis membrane. To maintain sink conditions, Cs/Cd value should be greater than or equal to 3, where Cs is the saturated solubility of the compound in the medium and Cd is the concentration of compound in the bulk medium (Gowthamarajan & Singh, 2010). Hence, higher release was observed in case of ARM-SOL. The in vitro release graph of ARM-NLC revealed a biphasic release pattern of ARM, i.e. faster release in the initial stage followed by a sustained release. In first 8 h, 20.4 ± 3.3% drug was released sustained up to 96 h with final release of 39.7 ± 1.2%. The initial occurrence of faster release clearly indicates the location of a certain amount of ARM adsorbed onto the surface of NLC or precipitated from the superficial lipid matrix. This has also been explained by inhomogeneity of oil in inner lipid matrix. The difference in melting behaviour of liquid and solid lipid leads to accumulation of oil in outer shell of NLC resulting in faster release of drug in initial stages (Hu et al., 2005; Teeranachaideekul et al., 2007). The liquid lipidenriched shell possessed higher solubility for lipophilic ARM and drug could be released by diffusion or erosion of matrix (Mu¨hlen et al., 1996). Subsequent sustained release of the drug suggests the diffusion of ARM from the core of the lipid


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Figure 6. Histological sections of sheep nasal mucosa treated with negative control phosphate buffered saline pH 6.4(PBS), positive control isopropyl alcohol (IPA), pure drug solution (ARM-SOL), blank nanostructured lipid carrier (Blank NLC) and drug-loaded NLC (ARM NLC) (magnification 400).

Figure 7. In vitro release studies of artemether (ARM) from solution (ARM-SOL) and nanostructured lipid carriers (NLC).

matrix to the release medium (Sood et al., 2013). Slow release of ARM from NLC suggests that ARM is homogenously dispersed in lipids matrix. Furthermore, solid lipid matrix has higher viscosity thus slowing down the release according to Stokes-Einstein’s law (Teeranachaideekul et al., 2007). The drug release data obtained were fitted into release kinetic model: zero order, first order and Higuchi’s equation. Release of drug from NLC followed Higuchi model better than other equations (r240.98) and was found to be diffusion controlled from homogenous and granular matrix systems. The drug release from a matrix system is said to follow Higuchi’s

Figure 8. Ex vivo diffusion studies of artemether (ARM) from solution (ARM-SOL) and nanostructured lipid carriers (NLC) through sheep nasal mucosa.

release kinetics if the amount of drug released is directly proportional to the square root of time (Luo et al., 2006). The value of diffusion exponent ‘‘n’’ obtained from the Korsmeyer’s Peppas was found to be 0.362 indicated that release was by Fickian diffusion (Costa & Lobo, 2001). Evaluation of ex vivo permeation Ex vivo permeation studies through sheep nasal mucosa were performed for both ARM-SOL and ARM-NLC as shown in Figure 8. The use of natural membrane is significant for


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predicting the permeation characteristic of a drug or its formulation. Since, the volume of receptor compartment is limited, it must be ensured that sink conditions are maintained. Cs/Cd value was determined and was found to be 6.3 which is greater than 3 as required to maintain the sink. NLC formulations also showed significantly lower (p50.001) flux values compared to drug solution. Flux for ARM-NLC and drug solution was found to be 119.3 ± 12.3 mg/cm2 h and 430.6 ± 32.6 mg/cm2 h respectively. It was observed that the permeation of ARM across the nasal mucosa was significantly higher (p50.001) from drug solution compared to NLC. Almost 1002.1 ± 262.8 mg/cm2 drug permeated within 1 h from drug solution, while in the case of ARMloaded NLC slower permeation was clearly observed (244.3 ± 108.8 mg/cm2) at the end of same time interval. However, unlike in vitro release, no biphasic release behaviour could be observed. It would be logical to attribute that lower flux and permeation of ARM is due to the barrier properties of both lipid matrix and the nasal mucosa, both of which act as rate limiting membrane for permeation of ARM from the NLC (Seju et al., 2011). It is evident that being a lipophilic drug, ARM had high affinity for the lipid matrix and amount of drug diffused from NLC was lower compared to drug solution. Furthermore, NLC can permeate through nasal mucosa as such into the receptor compartment retaining the drug within their matrix. Since, the nasal mucosa is devoid of lipid digesting enzymes like lipases, NLC can be expected to remain intact and also protect the drug from degradation. The amount of drug permeated per unit area from solution and NLC formulation was found to be 2806.8 ± 181.6 mg/cm2 and 740.4 ± 144.9 mg/cm2, respectively. The kinetic pattern of drug diffusion was studied by fitting the amount of drug permeated in various kinetic models like zero order, first order and Higuchi’s equation. The regression coefficient of different orders for formulations was compared and found that the release pattern of ARM followed Higuchi’s order as determined by higher r2 value (40.99). Bioanalytical HPLC method for estimation of drug in rat plasma Seven-point calibration curve (CC) was prepared by serial dilution of ARM stock solution (100 mg/ml) in the range of 1–15 mg/ml. Calibration standards were prepared by spiking 0.2 ml of blank plasma with working solution of ARM resulting in concentration of 1, 2, 4, 5, 10, 12 and 15 mg/ml in plasma. The IS was added so as to obtain concentration of 1 mg/ml. Best-fit calibration lines of the ratio of ARM to IS peak area (response factor) versus the concentration of calibration standards were determined by least-square regression analysis. The linearity (r2 value) of the CC was found to be 0.999. The average extraction recoveries of ARM and IS from plasma were found to be 86.92% and 92.13%, respectively. The limit of quantization and limit of detection for ARM by the developed method was found to be 900 ng/ml and 300 ng/ml, respectively. The extraction of ARM from plasma and brain samples was carried out using protein precipitation method (Gao et al., 2007). Plasma sample and homogenized brain tissue (0.2 ml)


Figure 9. Artemether (ARM) concentration in rat brain following administration of solution (ARM-SOL, i.v. and i.n.) and nanostructured lipid carriers (ARM-NLC, i.n.).

Figure 10. Artemether (ARM) concentration in plasma following administration of solution (ARM-SOL, i.v. and i.n.) and nanostructured lipid carriers (ARM-NLC, i.n.).

were mixed with curcumin solution (1 mg/ml) for 2 min on vortex mixer and precipitated with 0.2 ml of 10% v/v perchloric acid. The mixture was centrifuged at 6000 rpm for 15 min and supernatant was collected. The amount of drug in supernatant obtained from plasma and brain samples was analyzed by HPLC. Pharmacokinetic studies and brain uptake The pharmacokinetic and brain distribution of ARM formulations following i.v. (ARM-SOL) and i.n. (ARM-SOL, ARM-NLC) administration were carried out in male Wistar rats. The concentration of ARM in brain and blood was estimated up to 6 h using HPLC method (Figures 9 and 10). The pharmacokinetic parameters are shown in Table 3. ARM had high plasma concentration (6.82 ± 0.66 mg/ml) upon i.v. administration within 15 min and declined rapidly reaching 0.57 ± 0.07 mg/ml in 2 h. This shows that the high initial plasma concentration after i.v. administration may be due to lower transport of ARM across the BBB by passive diffusion (Mahajan et al., 2013). The peak plasma concentration of ARM after administration of ARM-SOL (i.n.) and ARM-NLC


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Table 3. Pharmacokinetic parameters of ARM-SOL (i.v.), ARM-SOL (i.n.) and ARM-NLC (i.n.). Formulation ARM-SOL (i.v.) ARM-SOL (i.n.)


ARM-NLC (i.n.)

Organ/tissue

Cmax (mg/ml)

Tmax (h)

AUC0–t (mg/ml min)

Ke (h1)

t1/2 (h)

Brain Blood Brain Blood Brain Blood

1.163 ± 0.135 6.820 ± 0.664 1.333 ± 0.054 1.640 ± 0.264 1.510 ± 0.107 2.573 ± 0.330

0.5 0.25 0.75 0.75 2.0 2.0

1.659 ± 0.695 5.329 ± 0.945 2.900 ± 0.659 3.946 ± 0.773 7.380 ± 0.820 8.531 ± 1.944

0.764 ± 0.303 0.970 ± 0.076 0.439 ± 0.056 0.470 ± 0.090 0.067 ± 0.006 0.299 ± 0.012

1.242 ± 0.760 0.717 ± 0.054 1.596 ± 0.207 1.508 ± 0.270 10.305 ± 1.004 2.320 ± 0.095

(i.n.) was 1.64 ± 0.26 and 2.57 ± 0.33 mg/ml achieved at 0.75 h and 2 h, respectively. The presence of ARM in plasma is expected since i.n. route can also lead to systemic drug absorption (Alam et al., 2013). Another reason may be reduced mucociliary clearance in rats due to anaesthesia, thereby increasing the residence time and nasal absorption of ARM from the nasal mucosa (Al-Ghananeem et al., 2011). NLC formulation had significantly higher (p50.01) half life in brain and plasma compared to ARM-SOL (i.v. and i.n.) with slower elimination rate. The plasma concentration of ARM in brain was significantly higher (p50.001) for NLC compared to ARM-SOL (i.v. and i.n.) from 1 to 6 h. This could be related to the longer residence time of the NLC in the rat nasal cavity, resulting in enhanced nasal absorption which provides the opportunity for sustained drug delivery to brain. During first hour, the drug concentration was statistically nonsignificant. This may be attributed for time taken for NLC to be transported i.n. and slower release of drug from them due to the entrapment in lipid matrix. This is an advantage over conventional drug solutions as drug concentration could be maintained in brain for longer duration. The enhanced absorption of ARM from ARM-NLC may be due to the small size as well as lipidic nature of NLC and protection from metabolic enzymes localized in the nasal mucosal cavity and epithelial cells lining the cavity (Hussain & Aungst, 1994; Chung & Donovan, 1996). NLC can be transported transcellularly through olfactory neurons to the brain via various endocytic pathways of sustentacular or neuronal cells in the olfactory membrane. Mistry et al. reported that polystyrene nanoparticles coated with Tween 80 were taken up by cells of olfactory epithelia as visualized by fluorescence microscopy in a size-dependent manner with 100 nm particles having greater uptake than 200 nm nanoparticles. They also reported transport of nanoparticles into respiratory epithelium but it was not significantly dependent on particle diameter (Mistry et al., 2009a). In addition, the pluronic F68, a non-ionic surfactant used in NLC may also contribute to increased absorption of drug by virtue of its permeation enhancing effects (Aungst & Rogers, 1988). The coating of polystyrene nanoparticle with Tween 80 was found to increase the transport of nanoparticles across olfactory mucus compared to uncoated particles. This was as a result of increased hydrophilicity and reduced negative charge of coated nanoparticles (Mistry et al., 2009b). This in turn would improve the availability by both solubilization of drug and direct effect on the cell membrane integrity thereby creating pathways for drug penetration (Aungst & Rogers, 1988). The concentration of ARM at the end of 6 h was 38.33and 10.17-fold greater for NLC formulation compared to ARM-SOL (i.v.) and ARM-SOL (i.n.), respectively. These

findings are in agreement with that previously reported by Seju et al. (2011). Intranasal olanzapine PLGA nanoparticles showed 10.86 times greater accumulation in brain compared to drug solution. The brain to blood ratio was also calculated for all formulations and found to be higher for ARM-NLC at all time intervals compared to ARM-SOL (i.v. and i.n.). The brain to blood ratios for ARM-NLC (i.n.), ARM-SOL (i.n.) and ARM-SOL (i.v.) were 2.619, 1.642 and 0.260, respectively, at 0.5 h indicating direct nose to brain transport of ARM. The DTP% and DTE% are the parameters used to assess the direct transport of drug to brain via olfactory pathway. These were calculated using tissue/organ distribution data following i.v. and i.n. administration. ARM-NLC showed highest DTE% and DTP% of 278.16 and 64.02, respectively, which indicates NLC formulations improved brain targeting efficiency of ARM compared to solution. The higher DTE% and DTP% of NLC formulations may be due to reduced clearance compared to drug solution. NLC can permeate into nasal epithelial cells as suggested by Mistry et al. (2009a,b). These results are in agreement with previously reported studies where nano formulations were found to enhance direct transport to brain of drugs like risperidone, saquinavir and nimodipine upon i.n. administration (Zhang et al., 2004; Kumar et al., 2008; Mahajan et al., 2013). The drug can be released from NLC by diffusion and undergo further transport into olfactory bulb and brain stem finally reaching brain or cerebrospinal fluid. The ideal diameter of nanoparticles for intra-axonal or intraneuronal transport is less than 100 nm (Mistry et al, 2009a). In this study, optimized ARM-NLC had particle size of 123.4 ± 3.6 nm indicating that intraneuronal transport is not feasible. Furthermore, NLC can be transported through epithelial cells by transcellular route to reach lamina propria rather than paracellular route, since NLCs are too large to ˚ (Mistry pass through tight junctions having diameter of 4–8 A et al, 2009b). Once nanoparticles reach lamina propria they may undergo one or combination of these fates: absorption into systemic circulation, absorption into deep cervical lymph modes of the neck or extracellular diffusion through perineural and perivascular spaces with subsequent entry into cranial compartment (Lochhead & Thorne, 2012). The results of this investigation prove that drug could be transported directly to the CNS after i.n. delivery of NLC, thereby enhancing drug concentration in the brain and nasal bioavailability of ARM.

Conclusion ARM-loaded NLC were successfully prepared using DoE approach and validated. The ideal batch was selected based

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on desired particle size, DL and EE which are an important characteristic for NLC formulations. The developed formulations were nontoxic as determined by cell line and histopathological studies. In vitro release studies revealed NLC formulations exhibited sustained release compared to drug solution. NLC formulations had significantly lower flux compared to drug solution. However, pharmacokinetic and brain uptake studies in rats revealed significantly higher concentration of drug in brain upon administration of NLC by i.n. route and was maintained up to 6 h owing to slower release of drug. Thus, this study demonstrated the utility of ARM-NLC delivery to brain via i.n. route.

Acknowledgements


The authors are grateful to IPCA Labs, Mumbai, India, and Lipoid GmbH, Germany, for providing the gift samples.

Declaration of interest The authors report no conflicts of interest. Mr. Kunal Jain acknowledge Council of Scientific and Industrial Research (CSIR), New Delhi, Government of India for financial assistance in the form of Senior Research Fellowship (File No:8/484 (0006)/2012-EMR-I). Mr. Sumeet Sood expresses his gratitude to Department of Science and Technology (DST), New Delhi, Government of India for award of INSPIRE Fellowship (IF10316).

References Aditya NP, Patankar S, Madhusudhan B, et al. (2010). Arthemeterloaded lipid nanoparticles produced by modified thin-film hydration: pharmacokinetics, toxicological and in vivo anti-malarial activity. Eur J Pharm Sci 40:448–55. Akinlolu AA, Shokunbi MT. (2010). Neurotoxic effects of 25 mg/kg/ bodyweight of artemether on the histology of the trapezoid nuclei and behavioural functions in adult male Wistar rats. Acta Histochem 112: 193–8. Alam MI, Baboota S, Ahuja A, et al. (2013). Intranasal infusion of nanostructured lipid carriers (NLC) containing CNS acting drug and estimation in brain and blood. Drug Deliv 20:247–51. Al-Ghananeem AM, Malkawi AH, Crooks PA. (2011). Bioavailability of D9-tetrahydrocannabinol following intranasal administration of a mucoadhesive gel spray delivery system in conscious rabbits. Drug Dev Ind Pharm 37:329–34. Aungst BJ, Rogers NJ. (1988). Site dependence of absorption-promoting actions of laureth-9, Na salicylate, Na2EDTA, and aprotinin on rectal, nasal, and buccal insulin delivery. Pharm Res 5:305–8. Beales PE, Brabin B, Dorman E, et al. (2000). Severe falciparum malaria. Trans R Soc Trop Med Hyg 94:S1–90. Bergtho´ra S, Gudnason SPI, Thorsteinsson F, et al. (2011). Experimental design for optimizing drug release from silicon elastomer matrix and investigation of transdermal drug delivery. Eur J Pharm Sci 42: 559–67. Cavalli R, Aquilanov D, Carlotti ME, et al. (1995). Study by X-ray powder diffraction and differential scanning calorimetry of two model drugs, phenothiazine and nifedipine, incorporated into lipid nanoparticles. Eur J Pharm Biopharm 41:329–33. Chung FY, Donovan MD. (1996). Nasal pre-systemic metabolism of peptide drugs: substance P metabolism in the sheep nasal cavity. Int J Pharm 128:229–37. Costa P, Lobo JMS. (2001). Modeling and comparison of dissolution profiles. Eur J Pharm Sci 13:123–33. Das S, Ng WK, Tan RBH. (2012). Are nanostructured lipid carriers (NLCs) better than solid lipid nanoparticles (SLNs): development, characterizations and comparative evaluations of clotrimazole-loaded SLNs and NLCs? Eur J Pharm Sci 47:139–51.


Florence AT. (2004). Issues in oral nanoparticle drug carrier uptake and targeting. J Drug Target 12:65–70. Freitas C, Mu¨ller RH. (1999). Correlation between long-term stability of solid lipid nanoparticles (SLNTM) and crystallinity of the lipid phase. Eur J Pharm Biopharm 47:125–32. Gao X, Yao G, Guo Na, et al. (2007). A simple and rapid high performance liquid chromatography method to determine levofloxacin in human plasma and its use in a bioequivalence study. Drug Discov Ther 1:136–40. Garcia-Fuentes M, Prego C, Torres D, et al. (2005). A comparative study of the potential of solid triglyceride nanostructures coated with chitosan or poly(ethylene glycol) as carriers for oral calcitonin delivery. Eur J Pharm Sci 25:133–43. Gowthamarajan K, Singh SK. (2010). Dissolution testing for poorly soluble drugs: a continuing perspective. Dissolut Technol 17:24–32. Haque S, Md S, Fazil M, et al. (2012). Venlafaxine loaded chitosan NPs for brain targeting: pharmacokinetic and pharmacodynamic evaluation. Carbohydr Polym 89:72–9. Hu FQ, Jiang SP, Du YZ. (2005). Preparation and characterization of stearic acid nanostructured lipid carriers by solvent diffusion method in an aqueous system. Colloids Surf B Biointerfaces 45:167–73. Hu YL, Gao JQ. (2010). Potential neurotoxicity of nanoparticles. Int J Pharm 394:115–21. Hussain MA, Aungst BJ. (1994). Nasal mucosal metabolism of an LHRH fragment and inhibition with boroleucine. Int J Pharm 105:7–10. Illum L. (2003). Nasal drug delivery-possibilities, problems and solutions. J Control Rel 87:187–98. Jain K, Sood S, Gowthamarajan K. (2013a). Modulation of cerebral malaria using curcumin as an adjunctive therap. Braz J Infect Dis 17: 579–91. Jain K. Kumar RS, Sood S, et al. (2013b). Betaxolol hydrochloride loaded chitosan nanoparticles for ocular delivery and their antiglaucoma efficacy. Curr Drug Deliv 10:493–9. Jain K, Kumar RS, Sood S, et al. (2013c). Enhanced oral bioavailability of atorvastatin via oil-in-water nanoemulsion using aqueous titration method. J Pharm Sci Res 5:18–25. Jenning V, Thunemann AF, Gohla SH. (2000). Characterization of a novel solid lipid nanoparticle carrier system based on binary mixtures of liquid and solid lipids. Int J Pharm 199:167–77. Joshi M, Pathak S, Sharma S, et al. (2008). Design and in vivo pharmacodynamic evaluation of nanostructured lipid carriers for parenteral delivery of artemether: nanoject. Int J Pharm 364:119–26. Jores K, Mehnert J, Drechsler M, et al. (2004). Investigations on the structure of solid lipid nanoparticles (SLN) and oil-loaded solid lipid nanoparticles by photon correlation spectroscopy, field-flow fractionation and transmission electron microscopy. J Control Rel 95:217–27. Kaur IP, Verma MK. (2011). Exploring solid lipid nanoparticles for intranasal administration of streptomycin. Int J Pharma 1:110–17. Kovacevic A, Savic S, Vuleta G, et al. (2011). Polyhydroxy surfactants for the formulation of lipid nanoparticles (SLN and NLC): effects on size, physical stability and particle matrix structure. Int J Pharm 406: 163–72. Klayman DL. (1985). Qinghaosu (artemisinin): an antimalarial drug from China. Science 228:1049–55. Kumar M, Misra A, Babbar AK, et al. (2008). Intranasal nanoemulsion based brain targeting drug delivery system of risperidone. Int J Pharm 358:285–91. Lin X, Li X, Zheng LQ, et al. (2007). Preparation and characterization of monocaprate nanostructured lipid carriers. Colloids Surf Physicochem Eng Aspects 311:106–11. Liu J, Hu W, Chen H, et al. (2007). Isotretinoin-loaded solid lipid nanoparticles with skin targeting for topical delivery. Int J Pharm 328: 191–5. Liu CH, Wu CT. (2010). Optimization of nanostructured lipid carriers for lutein delivery. Colloids Surf Physicochem Eng Aspects 353: 149–56. Lochhead JJ, Thorne RG. (2012). Intranasal delivery of biologics to the central nervous system. Adv Drug Deliv Rev 64:614–28. Luo YF, Chen DW, Ren LX, et al. (2006). Solid lipid nanoparticles for enhancing vinpocetine’s oral bioavailability. J Control Rel 114:53–9. Mahajan HS, Mahajan MS, Nerkar PP et al. (2013). Nanoemulsion-based intranasal drug delivery system of saquinavir mesylate for brain targeting. Drug Deliv doi: 10.3109/10717544.2013.838014. Maitland K, Newton CR. (2005). Acidosis of severe falciparum malaria: heading for a shock? Trends Parasitol 21:11–16.


DOI: 10.3109/10717544.2014.885999

Medana IM, Turner GDH. (2006). Human cerebral malaria and the blood-brain barrier. Int J Parasitol 36:555–68. Mistry A, Glud SZ, Kjems J, et al. (2009a). Effect of physicochemical properties on intranasal nanoparticle transit into murine olfactory epithelium. J Drug Target 17:543–52. Mistry A, Stolni S, Illum L. (2009b). Nanoparticles for direct noseto-brain delivery of drugs. Int J Pharm 379:146–57. Mu¨hlen AZ, Mu¨hlen EZ, Niehus H, et al. (1996). Atomic force microscopy studies of solid lipid nanoparticles. Pharm Res 13:1411–16. Mu¨ller RH, Radtke M, Wissing SA. (2002). Nanostructured lipid matrices for improved microencapsulation of drugs. Int J Pharm 242:121–8. Newton CRJC, Hien TT and White N. (2000). Cerebral malaria. J Neurol Neurosurg Psychiatry 69:433–41. Peppas NA. (1985). Analysis of Fickian and non-Fickian drug release from polymers. Pharm Acta Helv 60:110–11. Perez M. (2005). Gibbs–Thomson effects in phase transformations. Scripta Mater 52:709–12. Phachonpai W, Wattanathorn J, Muchimapura S, et al. (2010). Neuroprotective effect of quercetin encapsulated liposomes: a novel therapeutic strategy against Alzheimer’s disease. Am J Appl Sci 7: 480–5. Seju U, Kumar A, Sawant KK. (2011). Development and evaluation of olanzapine-loaded PLGA nanoparticlesfor nose-to-brain delivery: in vitro and in vivo studies. Acta Biomater 7:4169–76. Severino P, Pinho SC, Souto EB, et al. (2011). Polymorphism, crystallinity and hydrophilic-lipophilic balance of stearic acid and stearic acid–capric/caprylic triglyceride matrices for production of stable nanoparticles. Colloids Surf B Biointerfaces 86:125–30. Singh SK, Srinivasan KK, Gowthamarajan K, et al. (2011). Investigation of preparation parameters of nanosuspension by top-down media milling to improve the dissolution of poorly watersoluble glyburide. Eur J Pharm Biopharm 78:441–6. Skehan P, Storeng R, Scndiero D, et al. (1990). New colorimetric cytotoxicity assay for anticancer-drug screening. J Natl Cancer I 82: 1107–12.


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Sood S, Jawahar N, Jain K, et al. (2013). Olanzapine loaded cationic solid lipid nanoparticles for improved oral bioavailability. Curr Nanosci 9:26–34. Sood S, Jain K, Gowthamarajan K. (2014). Optimization of curcumin nanoemulsion for intranasal delivery using design of experiment and its toxicity assessment. Colloids Surf B Biointerfaces 113:330–7. Souto EB, Wissing SA, Barbosa CM, et al. (2004). Development of a controlled release formulation based on SLN and NLC for topical clotrimazole delivery. Int J Pharm 278:71–7. Souto EB, Mehnert W, Mu¨ller RH. (2006). Polymorphic behaviour of CompritolÕ 888 ATO as bulk lipid and as SLN and NLC. J Microencapsul 23:417–33. Teeranachaideekul V, Souto EB, Junyaprasert VB, et al. (2007). Cetyl palmitate-based NLC for topical delivery of Coenzyme Q10 – Development, physicochemical characterization and in vitro release studies. Eur J Pharm Biopharm 67:141–8. Touitou E, Waknine JH, Godin B, et al. (2006). Treatment of malaria in a mouse model by intranasal drug administration. Int J Parasitol 36: 1493–8. Varshosaz J, Tabbakhian M, Mohammadi MY. (2010). Formulation and optimization of solid lipid nanoparticles of buspirone HCl for enhancement of its oral bioavailability. J Liposome Res 20: 286–96. Wissing SA, Kayser O, Mu¨ller RH. (2004). Solid lipid nanoparticles for parenteral drug delivery. Adv Drug Deliv Rev 56:1257–72. Yu LX. (2008). Pharmaceutical quality by design: product and process development, understanding, and control. Pharm Res 25: 781–91. Zhang Q, Jiang X, Jiang W, et al. (2004). Preparation of nimodipineloaded microemulsion for intranasal delivery and evaluation on the targeting efficiency to the brain. Int J Pharm 275:85–96. Zhang X, Liu J, Qiao H, et al. (2010). Formulation optimization of dihydroartemisinin nanostructured lipid carrier using response surface methodology. Powder Technol 197:120–8.

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