DES Ganciclovir Niosomal Nanoformulation Chapter 5...DES Ganciclovir Niosomal Nanoformulation DESIGN AND DEVELOPMENT OF NANO SIZED OCULAR DRUG DELIVERY SYSTEM 66 Chapter 5 ... improved

DES Ganciclovir Niosomal Nanoformulation

DESIGN AND DEVELOPMENT OF NANO SIZED OCULAR DRUG DELIVERY SYSTEM 65

Chapter 5

Jamia Hamdard

5.1. Introduction

Human eye is prone to common viral infections such as Herpes Zoster and Herpes simplex

Infections (herpetic keratitis), cytomegalovirus (CMV) infection in immuno-compromised

subjects and viral conjunctivitis which is a contagious infection and inflammation of the

conjunctiva (McGavin and Goa., 2001, Akhter et al., 2011). Ganciclovir (GCV), an acyclic

nucleoside analog of 2’-deoxyguanosine exhibits activity against herpes simplex virus and

cytomegalovirus at relatively low inhibitory concentrations (Colin, 2007; McGavin and Goa,

2001). Therefore, GCV plays an important role in the treatment of ocular viral infections.

Currently, Conventional oral and topical medications are available for the GCV application. The

recommended oral dose of GCV is 3.0 g/day that high dose results in dose-related toxicity

including bone marrow suppression and neutropenia (McGavin and Goa., 2001). For the topical

application, ophthalmic (eye) gel dosage form is available that recommended to be applied seven

to eight time in a day till the condition is improved (Colin., 2007). Such frequent and repetitive

application is totally non patient compliancable, rising nuisance and untoward effects caused by

systemic drug absorption. Gel form also leads to the visual and accommodation disturbance in

the initial of application. Moreover, it is also not a cost effective mean. To treat and manage the

local ophthalmic viral disorders, topical ocular delivery of GCV in the form of liquid eye drop is

valuable, which is the most common route and desirable dosage form respectively when

considering convenience of administration, the rapid local effect, accessibility of the ocular

tissue, relative safety and clinical compliance of the patients. However, due to the high

hydrophilic character of GCV (McGavin and Goa., 2001) and unique physiological structure of

eye and the rapid elimination, the conventional topical applications usually have quite limited

therapeutic benefits due to the poor bioavailability over and into the ocular tissues (Kaur et al.,



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2002; Vansantvliet and Ludwig., 1998). Development of GCV nano- sized novel formulations

is worthwhile since they are expected to prolong the pre-ocular retention and increase the ocular

bioavailability. In recent year, Vesicular drug delivery systems such as liposome and niosomes

were successfully studied in ophthalmic drug delivery to provide, control drug delivery,

prolonged drug precorneal residence time and enhance ocular bioavailability (Gregoriadis et al.,

1993; Le Bourlias et al., 1995; El-Gazayerly et al., 1997; Uchegbu and Vyas, 1998).Vesicular

drug delivery systems expected to provide prolonged and controlled action at the corneal surface

and preventing the metabolism of the drug by enzymes present at the tear/corneal surface (Kaur

et al., 2004). Drug enclosed in the vesicles allows for an improved partitioning and transport

through the cornea. Moreover, vesicles offer a promising avenue to fulfill the need for an

ophthalmic drug delivery system that has the convenience of a drop, but will localize and

maintain drug activity at its site of action (Kaur et al., 2000).

niosomes in topical ocular delivery may prefer over liposomal vesicular system as they are

chemically stable than liposome (Akhter et al., 2011), incur lower production cost and they are

composed of biodegradable and nonimmunogenic materials. Unlike phospholipids, niosomes

do not require expensive handling (storage at freezers and preparation under nitrogen gas).

Moreover, they handle surfactants with no special precautions or conditions; they can improve

the performance of the drug via better availability and controlled delivery at a particular site;

they are biodegradable, biocompatible and non-immunogenic (Kaur et al., 2004). However, the

untailored formulated niosomes are normally negative and neutral charged that may only

improved the ocular bioavailability to some extent due to corneal permeation enhancement

owing to their rapid clearance like conventional eye drops. It is expected that positively charged

niosomes may enhanced the drug corneal retention, permeation and subsequently the ocular



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bioavailability than the neutral and negatively charged niosomes due the result of interaction

of positively charged vesicles with the poly-anionic corneal and conjunctival surfaces due to

presence of mucin.

Taking into the account of this theory, it was thought that the use of mucoadhesive cationic

chitosan (CH) polymer is potentially worthfull for tailoring the niosomes to positively charge

coated niosomes as using cationic lipid such as stearylamine as positive charge substance

which may lead to irritation and potential toxic effect to the eye (Taniguchi et al., 1988). It is

reported from many studies about the negatively charged mucin and CH interaction induced

enhanced concentration and residence time of the associated drug (Akhter et al., 2011).

CH has unique properties such as acceptable biocompatibility and biodegradability with low

toxicity and high charge density, (Xu and Du, 2003; Dornish et al., 1997). Moreover, CH

exhibits interesting physico-chemical characteristic with a good potential for ocular drug

delivery such as bioadhesion (Illum, 1998; Paul & Sharma, 2000), prolonging the corneal

residence time (De Campos et al., 2004; 2001; Felt et al., 1999) and penetration-enhancing

properties, which were initially attributed to the modulation of the tight junction barrier between

epithelial cells (Koch et al., 1998; Schipper et al., 1997). It was found that, CH increases cell

permeability by affecting both paracellular and intracellular pathways of epithelial cells in a

reversible manner without affecting cell viability or causing membrane wounds (Dodane et al.,

1999; Artursson et al., 1994). Moreover, chitosan may impart favorable rheological behavior

where, its solutions have shown pseudoplastic and viscoelastic properties. This behavior is

particularly important in ophthalmic formulations since it facilitates the retention while it permit

the easy spreading of the formulation due to the blinking of the eye (Mucha, 1997).



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Therefore, this study aimed to develop the Chitosan coated biocompatible nano-sized niosomal

Dispersion loaded with GCV and evaluation of the developed mucoadhesive nano-system for its

controlled release and corneal permeation (in-vitro) and in-vivo performance evaluated in rabbit

model for corneal retention and ocular pharmacokinetic of the developed mucoadhesive Chitosan

coated niosomes.

5.2. Materials and Methods

Ganciclovir (GCV) was obtained as a gift sample from Ranbaxy laboratories (Gurgaon, India).

Sorbitan monopalmitate (Span40), Sorbitan monostearate (Span60) and Cholesterol were

purchased from S.D. Fine chemicals (Delhi, India). Chitosan (CH, deacetylation degree >80%)

was received as a gift sample from India Sea Foods (India). All other chemicals were of

analytical reagent grade and were used without further purification.

5.2.1. Methods

5.2.1.1. UPLC Chromatographic conditions for Ganciclovir assay

The quantification of GCV was carried out by in house developed rapid and sensitive ultra

pressure liquid chromatography (UPLC) method. In the method, UPLC analysis was performed

on a Waters Acquity UPLC system (Milford, MA, USA) equipped with a binary solvent

manager, an autosampler, column manager composed of a column oven, a precolumn heater and

a photo diode array detector. Five microliters of the final analytical solution was injected into a

Waters Acquity BEH C18 (50 mm x 2.1 mm, 1.7 µm) UPLC column kept at 50°C. The mobile

phase consisting of a mixture of 0.1% TFA in water (adjusted to pH 3.5 using 5.0% dilute

ammonia) and acetonitrile (95:5, v/v) with the flow rate of 0.45 mL/min was employed. The

analysis was performed at a wavelength of 254 nm with total run time of 3 min. Method was

found to be selective, linear (r2 = 0.999), accurate (recovery, 97.0–100.2%) and precise (CV,≤



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3.1%) in the concentration range of 0.1–1.0 µg/mL. Limit of detection and quantitation of GCV

in aqueous humor were 3.0 and 10.0 ng/mL, respectively.

For the GCV-pharmacokinetics study in aqueous humour, A 50 µL aliquot of rabbit aqueous

humor was pipetted into a 1.0 mL eppendorf tube, followed by the addition of 100 µL of

acetonitrile. The samples were vortexed for 5 min followed by 5 min of centrifugation at 10,000

rpm. The samples were filtered through a 0.22 µm nylon filter and 5 µL of the filtrate was

directly injected into the UPLC system.

5.2.1.2. Preparation and optimization of GCV loaded niosomes

15 batches of niosomal formulation as per table 5.3 and table 5.4 were prepared using ethanol

injection method as mentioned by Sheikh et al (2010) with slight modification. Appropriate ratio

of surfactant (span 60 and span 40) and cholesterol was dissolved in absolute ethanol (total

concentration of mixture was kept constant to 50mg/mL in every batch) to form organic phase

(Solution A). Aqueous phase (containing 50mg of GCV) was prepared by dissolving GCV in

phosphate buffer (pH 7). The aqueous phase was kept on a magnetic stirrer at 1000rpm (Solution

B). For the each batch, constant volume (0.75mL) of solution A was rapidly injected into

measured amount of solution B on the same temperature and rotating speed. The obtained

mixture was further kept on the same condition for 1h to remove the ethanol. Furthermore, to

maintain the uniformity of size of the vesicle, all the obtained niosomal formulations were

subjected to sonication using probe sonicator [Vibra-Cell™ VC 750; Sonics, USA] for desired

time at amplitude 35% and pulse 2sec: 5sec (on: off). Appearance of the characteristic

opalescence in the solution state was considered as the formation of niosomal colloidal

dispersion.To make the vesicles Mucoadhesive the formulations thus formed were incubated

with 1.0% w/w chitosan solution for 2 hrs.



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5.2.1.3. Formulation optimization

Box-Behnken statistical design with 3 factors, 3 levels and 15 runs was selected for the

optimization study. Theoretically, experimental design consists of a set of points lying at the

midpoint of each edge and the replicated center point of the multidimensional cube. The

independent and dependent variables for niosomal are listed in Table 5.1 and table 5.2 for

niosome prepared by span60 and span40 respectively. Using the software Design Expert®8.0, a

polynomial equation (quadratic model) was generated for experimental design the formulation.

Yi = b0 + b1X1 + b2X2 + b3X3 + b12X1X2 + b13X1X3 + b23X2X3 + b11X12 + b22X22 + b33X32

Where, Yi is the dependent variable; b0 is the intercept; b1 to b333 are the regression coefficients;

X1, X2 and X3 are the independent variables that were selected on the basis of pilot experiments.

Table 5.1:-Independent and dependent variables for niosome using span 60

Levels Independent Variables Transformed

Variables

X1

(S60:Ch Molar

ratio)

X2

(Vol. of Aqueous

phase)

X3

(Sonication time)min

[Amp 35%, pulse 2:5]

Low 1:1

(52%:48%w/w)

10 1 -1

Medium 2:1(69%:31%w/w) 15 3 0

High 4:1

(82%:18%w/w)

20 5 1

Dependent variables, Y1= niosome size (nm); Y2=%drug entrapment

S60=Span60; Ch=Cholesterol



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Table 5.2:-Independent and dependent variables for niosome using span 40

Levels Independent variables Transformed

variables

X1

(S40:Ch Molar

ratio)

X2

(Vol. of Aqueous

phase)

X3

(Sonication time)min

[Amp 35%, pulse 2:5]

Low 1:1 (51%:49%) 10 1 -1

Medium 2:1(67%:33%) 15 3 0

High 4:1 (80%:20%) 20 5 1

Dependent variables, Y1= niosome size (nm); Y2=%drug entrapment

S60=Span40; Ch=Cholesterol

5.2.1.4. Assessment of mucoadhesive strength of chitosan solutions

Chitosan in different concentration were prepared by dissolving the CH in 0.1% -1.0% (w/v)

acetic acid solutions and the final pH was adjusted to 5.5 with 0.5% (w/v) NaOH solution. The

prepared solutions were evaluated for the mucoadhesive strength using TA.XTPlus Texture

analyzer (Stable Micro Systems, Surrey, UK). The double-sided tape was placed on the tip of

load cell and CH solutions were placed on freshly excised goat cornea. Cornea with the CH

solutions was then placed beneath the load cell and force (0.08 N) was applied by the load cell

for 200 s. After this the load cell was pulled back and force required to detach the particles from

the cornea (by double sided tape) was determined as the mucoadhesive strength.

5.2.1.5. Preparation of mucoadhesive niosomal formulation

For CH-coated niosomal dispersion, 1% (w/v) of chitosan (optimized mucoadhesive chitosan

concentration) was prepared in the way presented above, then mixed with the GCV loaded

niosomal dispersion prepared. In each case of mucoadhesive NSDs, an aliquot of NSDs was

mingled with an equal volume of CH liquor by adding it drop-wise to the polymer solution under

continuous agitation at room temperature (20°C) for 2hrs incubation. The formulations



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developed mucoadhesive NSDs were further characterized for Dispersion morphology, Particle

size distribution and zeta potential in the similar fashion as written in the characterization

heading.

5.2.2. Characterization of niosomes

5.2.2.1. Vesicle morphology

Morphology of niosomes was determined by transmission electron microscopy (TEM). TEM

(Morgagni 268D SEI, USA) was set at 200 KV and of a 0.18nm capable of point to point

resolution. The diluted niosomal formulation was deposited on the holey film grid and observed

after drying.

5.2.2.2. Vesicle size, size distribution and zeta potential

Vesicle size was determined by differential light scattering that analyzes the variations in light

scattering due to Brownian motion of the particles using a Zetasizer (Nano-ZS, Malvern

Instruments, UK). 0.1 mL of formulations were dispersed in 50 mL of water in a volumetric

flask, mixed thoroughly with vigorous shaking and light scattering was monitored at 25°C at a

90° angle. For Zeta potential measurement same instrument i.e. Zetasizer (Nano-ZS, Malvern

Instruments, UK) was used.

5.2.2.3. Entrapment efficiency

The niosomal dispersions were separated from the nonentrapped drug using ultracentrifugation at

60,000 × g (Tomi MX-305, Tokyo, Japan) for 30 min at 4◦C. The pellets were resuspended in 2

ml of PBS. Triton X-100 was added in 1:1 (v/v) to disrupt the vesicles. The dispersion was

recentrifuged and the supernatant was quantitatively assayed for GCV content using developed

ultra-performance liquid chromatography (UPLC).



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Entrapment efficiency was calculated as reported by Akhter et al (2011):

Entrapment efficiency (EF) = (Amount entrapped / total amount) x 100

5.2.2.4. In-vitro drug release performance

In vitro release studies were performed using standard Franz diffusion cells (FDC-6, LOGAN

Instrument Corp., Somerset, NJ, USA). The diffusion area was 0.75 cm2 and receptor volume

was 5.0 mL. Receptor chambers were filled with 5 ml of PBS (pH 7.4; osmolality 297

mOSm/kg) and constantly stirred by small magnetic bars (figure 5.1 A and figure 5.1B).

Figure 5.1. A: Photograph of SFDC-6 Diffusion cell drive console



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Figure 5.1. B: LOGAN Assembly for in-vitro release and ex-vivo permeation study for drug permeation

through goat cornea.

The receptor fluid was stirred with a magnetic rotor at a speed of 600 rpm and the temperature

was maintained at 35 ±0.5°C in order to mimic the ocular surface temperature. Donor and

receptor chambers were separated by means of activated dialysis membrane bag (molecular

weight cut off 12,000 Da). One milliliters of each formulation were loaded into the donor

compartment before occluding the chamber with Para-film. Samples were withdrawn at regular

intervals (1, 2, 3, 4, 5, 6, 7, 8, 10, 12, and 24 h), filtered through 0.45-µm membrane filter and

analyzed for drug content by UPLC method. In the similar fashion, release study of

mucoadhesive niosomal dispersions was also performed. The experiments were performed in

triplicate.

Moreover, Kinetic analysis of in vitro release data was done according to zero-order, first-order,

and Higuchi model by fitting into the following equation.:-



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Where, Q = amount of drug released at time t, k = dissolution rate constant (with unit of μg/mL/h

for zero order model, 1/h for first-order model, and μg/mL/h for Higuchi model.

5.2.2.5. In vitro transcorneal permeation study

Goat corneas were used to study the transcorneal permeability of developed GCV formulations.

Fresh whole eyeballs of goats were obtained from a local slaughter house and transported to the

laboratory in cold condition in normal saline. The transcorneal permeation study was carried out

on standard Franz diffusion cells that were used in the In vitro release studies. The excised goat

cornea was fixed between clamped donor and receptor compartments of the Franz diffusion cell

in such a way that its epithelial surface faced the donor compartment. The lower chamber served

as a receiver compartment that was infused with freshly prepared simulated tear fluid. The

receptor fluid was stirred with a magnetic rotor at a speed of 600 rpm and the temperature was

maintained at 35±0.5°C. The perfusate was collected at periodic time intervals for up to 24

hours, filtered through 0.45-µm membrane and analyzed for drug content by UPLC method.

The cumulative amount of GCV permeated per unit of goat cornea surface area, Qt/S (S=0.75

cm2) was plotted as a function of time (t, h). The permeation rate of GCV at steady-state (Jss,

µg/cm2/h) through goat corneas was calculated by linear regression interpolation of the

cumulative amount permeated through goat corneas per unit area vs time:

Jss = ∆Qt / S.∆t (Eq. 4)

The permeability coefficient (Kp, cm/h) was calculated according to the equation:

Kp = Jss / Cd (Eq. 5)

Where Cd = concentration of drug in donor compartment and is assumed that under sink

conditions the drug concentration in the receiver compartment is negligible compared to that in

the donor compartment. The enhancement ratio (ER) was calculated according to the equation:



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ER = Flux from formulation / flux from formulation E. (Eq.6)

All skin permeation experiments were repeated three times and data were expressed as mean of

three experiments ± standard deviation (S.D)

5.2.2.6. In-vivo study

Ocular retention and aqueous humor pharmacokinetics study were carried out on New Zealand

Albino rabbits (2.25±0.25 kg). The study was carried out under the guidelines of CPCSEA

(Committee for the Purpose of Control and Supervision of Experiments on Animals, Ministry of

Culture, and Government of India). The protocol was approved by Institutional Animal Ethics

Committee, Jamia Hamdard, New Delhi (approval no. 822) and the ARVO guidelines for animal

usage were followed. Utmost care was taken to ensure that animals were treated in the most

humane and ethically acceptable manner.

5.2.2.7. Ocular retention study by Gamma scientigraphy

The precorneal retention of GCV niosomal dispersion was assessed by γ- scientigraphy study in

rabbits. The GCV niosomal dispersion was labelled by adding a specified amount of radioactive

substance 99mTc in the water phase and then processed via the same method as for preparation

of GCV niosomal dispersion and chitosan coated niosomal dispersion as per the protocol

developed by INMAS, New Delhi. Labeling efficiency was determined using instantaneous thin

layer chromatography (ITLC) and was found to be greater than 97.9% for more than 6 h for all

the tagged formulations. 99mTc-labelled chitosan coated niosomal dispersion was compared

with 99mTc-labelled GCV solution and GCV niosomal dispersion. A total of 20 μL of the

labeled formulations were instilled into the cul-de-sac of the left eye, and the eye was manually

blinked three times to distribute the formulation over the cornea. The right eye of each rabbit

served as a negative control. A gamma camera (Millenium VG, Milwaukee, Wisconsin),



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autotuned to detect the 140 KeV radiation of Tc-99m, was used for scintigraphy study. Rabbits

were anesthetized using ketamine HCl injection given intramuscularly in a dose of 15 mg/ kg

body weight. The rabbits were positioned 5 cm in front of the probe, and 50 μL of the radio

labeled formulation was instilled onto the left corneal surface of each rabbit. Recording was

started 5 seconds after instillation and continued for 30 minutes using 128 × 128 pixel matrix.

Sixty individual frames (60 × 30 seconds) were captured by dynamic imaging process. Region of

interest (ROI) was selected on one frame of the image, and time activity curve was plotted to

calculate the rate of drainage from the eye. Two minute static images were taken at 0.5, 1, 2, 4

and 6 h post-instillation. All the images were recorded on a computer system assisted with the

software Entegra Version-2.

5.2.2.8. Ocular pharmacokinetic study

Three groups, each having seven New Zealand Albino rabbits (2.25±0.25 kg), were used for the

ocular study. Each group received, in the eyes, a single topical instillation (50 µL) of GCV-

solution, GCV-NDs and CH-coated GCV-MNDs dose equivalent to 0.5% w/v of GCV. Eyes

were anesthetized using topical application of 4% Xylocaine- MPF sterile solution (AstraZeneca

LP) and 50 µL of the aqueous humor was collected using 30 gauze needles before instillation of

formulations and post treatment at 0.5, 1, 2, 4, 6, 8, 10, 12 and 24hr. All aqueous humor samples

were collected in pre-labeled eppendorf tubes, sealed and stored at -20ºC until UPLC analysis.

The aqueous humor samples were prepared as mentioned above. Pharmacokinetic parameters

(PK) were calculated by noncompartmental analysis also called as model independent analysis

using WinNonLin version 4.0 (Pharsight Corp., Mountain View, CA).



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5.3. Result and Discussion

5.3.1. Preparation and assessment of mucoadhesive strength of chitosan solutions

Forces of detachment of the chitosan solutions were measured using a texture analyzer apparatus

for evaluation of mucoadhesive strength, as shown in Fig. 5.2.

Figure 5.2: Texture analysis graphs showing the forces of detachment of the chitosan solutions The force of detachment of CH was significantly increased with the increased concentration of

CH (varied from 0.1% w/v to 1% w/v). Chitosan, being a cationic mucoadhesive biopolymer

interacts with the negative charged mucin present over the cornea is responsible for the

mucoadhesion of chitosan solution.

Usually, the force needed to move the eyelids during a normal blink is about 0.2N, and is 0.8 N

for a forceful blink (Yamaguchi et al., 2009). It is desirable to evaluate forces of detachment



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under experimental conditions close to ocular physiological conditions. 1% w/v of chitosan

solution showing the strongest and desirable mucoadhesion (0.153N) among the test

concentration which is significantly less than the normal blinking force required for the eyes.

Therefore, 1% w/v of chitosan solution was further used to produce the mucoadhesive

characteristic in the developed formulations for the further study.

5.3.2. Vesicle morphology

Electron microphotograph of the niosomal formulations showed the clear outline and the

core of the well identified vesicles displaying the vesicular structure (Figure 5.3). Similar

structural feature were reported earlier (Morilla et al., 2002). Niosomal vesicles were seen

in fairly dispersed and unaggregated form.

Figure 5.3: Transmission electron photomicrograph of (A) uncoated and (B) coated niosomes.



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5.3.3. Vesicle size, size distribution and zeta potential

15 batches of niosomes prepared by both the surfactants i.e. Span60 (S60-01 to S-60-15)

and Span40 (S40-01 to S40-15) were analyzed for particles size and put in the response

column in the experimental design and presented tables 5.3 and table 5.4 respectively. A

correlation between the different factors and formulation size is established using the

quadratic polynomial generated using the Box-Behnken design with the help of Design

Expert®8.0 software. The particles size ranged from 139.1nm to 301.8nm with mean of

206.06nm for S60 while smaller particle size were formed for S40 (100.8nm to 257.3nm;

mean=170.39nm). The smaller mean particles size observed for S40 is due to the lower

chain length of Span40. Overall effects of different independent variables on the size of

the niosome are similar in both cases. Effect of various factors is discussed separately.

Regarding polydispersity index (PDI), a value of zero indicates an entirely mono-disperse

populationand a value of 1 indicates a completely poly-disperse population (Zeisig et al.,

1996). PDI of the all the batches of both formulations were less than 0.3, thus there was no

need to optimize its dependency on the factors. The possible reason for low PDI is rapidity

of injection through a needle, co-solvency by alcohol and steady stirring rate. The size

distribution of Span 40 and Span 60 based optimized niosomal formulation and their

complimentary chitosan coated formulation is illustrated in figure 5.4. The obtained zeta

potential (ranging from -18 to -28mV) revealed that the developed formulations were

stable and having uniformly dispersed particles. After coating with chitosan there was an

increase in particle size of the niosomes. Zeta potential figure 5.5 of chitosan coated

niosomal formulation was changed from negative to positive zeta potential (-23.4 to

+47.8).



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Figure 5.4: Particle size distribution of niosomes prepared by span 40 (S40-3), Span60 (S60-3) and of same formulations after coating (CHS40-3 and CHS60-3) showing increased size of

coated noisome.

Figure 5.5: Zeta potential of the chitosan coated noisome and uncoated noisome. chitosan coated noisome has high positive zeta potential.



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Due to the highly positive charge that chitosan carried, the adsorption of chitosan

increased the density of positive electron cloud that resulted in the positive electricity of

integral particle as well. This increment in the surface charge was ascribed to the

formation of complexes with the coating mechanism involving hydrogen bonding between

the polysaccharide and the polar head groups of surfactants and cholesterol (Grant and

Allen, 2006).

5.3.4. Entrapment efficiency

The entrapment efficiency of S60 were in the range of 36.7% to 73.6% with an overall average

of 59.03% while for S40 it was between 19.5% to 56.1% and mean of 40.22%. It is evident that

the entrapment efficiency is clearly dependent of the type of surfactant used. Here, S60 is

showing more entrapment than S40 that may be attribute to the fact that Span 60 has high phase

transition temperature (50°C) than Span40 and thus at the preparation temperature more rigid

vesicle are obtained as compared to Span 40 and leads to high entrapment efficiency.

Furthermore, Span 60 has the longer saturated alkyl chain. The length of alkyl chain of surfactant

has a prominent effect on permeability of prepared niosomes as length of surfactant increases

entrapment efficiency also increases. Hence long chain surfactant results in high entrapment.

Thus span 60 having a longer saturated alkyl chain (C16) compared to span 40(C14) produces

niosomes with higher entrapment efficiency. Additionally, longer alkyl chain influences the HLB

value of the surfactant mixture which in turn directly influences the drug entrapment efficiency.

The lower the HLB of the surfactant the higher will be the drug entrapment efficiency and

stability as in the case of niosomes prepared using span 60. The entrapment efficiency of all

the batches of niosome prepared by using Span60 and Span40 are given tables 5.3 and

table 5.4 respectively. A correlation between the different independent factors and



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entrapment efficiency of formulations for the GCV is established using the quadratic

polynomial generated using the Box-Behnken design with the help of Design Expert 8

software. The possible reasons of the variability in the response due t the factors are

discussed ahead.

5.3.5. Formulation optimization using Box-Behnken Design

Different mathematical models like linear, 2FI, Quadratic and cubic for both Span60 and

Span40 based niosomal particle size outcome were analyzed for test of fit using the Design

Expert 8 software. The model fit summary is presented in the table 5.5 and table 5.6 for

Span60 based niosomes (S60) and span40 based niosomes (S40). For particle size of S60,

sequential p-value of quadratic model was found to be 0.0203 (significant), lack of fit p-

value was maximum (0.0549) and the difference between Adjusted and predicted R-squared was

less than 0.2 suggesting a good fit. S40 particle size response showed sequential p-value of

quadratic model was found to be 0.0026 (significant), lack of fit p-value was maximum

(0.1273) and the difference between Adjusted and predicted R-squared was less than 0.2

suggesting a good fit. These values suggests quadratic model for both S60 and S40 particle size

for further analyses.

Table 5.3: Different runs with varying levels of independent variables as generated by Design Expert® 8.0 software and obtained dependent variable (size and entrapment efficiency) of span60 based

niosomal formulations

Run Formulation

code

surfactant:

Cholesterol

(mol ratio)

Aqueous

phase

(mL)

Sonication

time (min)

Size

(nm)

Entrapment

(%)

1 S60-01 1 -1 0 207.7 59.3

2 S60-02 -1 0 1 162.1 51.9

3 S60-03 0 -1 1 191.9 54.3

4 S60-04 0 0 0 174.3 64.1



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5 S60-05 1 1 0 141.3 45.5

6 S60-06 1 0 -1 213.1 62.4

7 S60-07 -1 0 -1 301.8 73.6

8 S60-08 0 -1 -1 273.6 68.8

9 S60-09 0 1 1 143.8 36.7

10 S60-10 -1 -1 0 269.3 68.9

11 S60-11 -1 1 0 255.8 60.8

12 S60-12 0 0 0 177.6 65.6

13 S60-13 0 0 0 181.9 62.2

14 S60-14 0 1 -1 264.4 61.6

15 S60-15 1 0 1 139.1 49.8

Table 5.4: Different runs with varying levels of independent variables as generated by Design Expert® 8.0 software and obtained dependent variable (size and entrapment efficiency) of

span40 based niosomal formulations

Run Formulation

code

surfactant:

Cholesterol

(mol ratio)

Aqueous

phase

(mL)

Sonication

time (min)

Size

(nm)

Entrapment

(%)

1 S40-01 1 -1 0 181.8 40.3

2 S40-02 -1 0 1 137.8 33.5

3 S40-03 0 -1 1 161.3 39.6

4 S40-04 0 0 0 144.1 38.3

5 S40-05 1 1 0 109.7 34.6

6 S40-06 1 0 -1 168.8 46.2

7 S40-07 -1 0 -1 257.3 52.7

8 S40-08 0 -1 -1 242.5 56.1

9 S40-09 0 1 1 121.3 24.3

10 S40-10 -1 -1 0 234.9 48.7

11 S40-11 -1 1 0 212.9 45.2

12 S40-12 0 0 0 143.7 36.6



Chapter 5

Jamia Hamdard

13 S40-13 0 0 0 149.7 38.4

14 S40-14 0 1 -1 203.5 49.3

15 S40-15 1 0 1 100.8 19.5

5.3.5.1. Model fit report for Niosomal size

Table 5.5: Fit Summary of different mathematical models for span60 niosome size

Source Sequential

p-value

Lack of Fit

p-value

Adjusted

R-Squared

Predicted

R-Squared

Rejected/Suggested

Linear < 0.0001 0.0180 0.806067 0.732206

2FI 0.3361 0.0179 0.821266 0.702174

Quadratic 0.0203 0.0549 0.953628 0.743437 Suggested

Cubic 0.0549 0.995714 Aliased

Table 5.6: Fit Summary of different mathematical models for span40 niosome size

Source Sequential

p-value

Lack of Fit

p-value

Adjusted

R-Squared

Predicted

R-Squared

Rejected/Suggested

Linear < 0.0001 0.0201 0.840229 0.78428

2FI 0.4505 0.0183 0.839188 0.733973



5.3.5.2. Analysis of variance (ANOVA) report for niosome size

ANOVA study on the particles size of S60 (table 5.7) showed a Model F-value of 32.99 implies

the model is significant. There is only a 0.06% chance that a "Model F-Value" this large could

occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant.

In this case X1, X2, X3, X1X3 and X22 are significant model terms. The "Lack of Fit F-value" of



Chapter 5

Jamia Hamdard

17.37 implies there is a 5.49% chance that a "Lack of Fit F-chance that a "Lack of Fit F-value"

this large could occur due to noise. This model can be used to navigate the design space. For

particle size of S40 (table 5.8), Model F-value was found to be 84.87 suggesting that the model

is significant and there is only a 0.01% chance that a "Model F-Value" this large could occur due

to noise.

Values of "Prob > F" less than 0.0500 indicate model terms are significant.

X1, X2, X3, X1X2, X1X3, X12 and X2 are significant model terms. The "Lack of Fit F-value" of

7.01 implies the Lack of Fit is not significant relative to the pure error. There is a 12.73%

chance that a "Lack of Fit F-value" this large could occur due to noise.

Table 5.7:Analysis of variance table for S60 size

Source Sum of

Squares

df Mean

Square

F

Value

p-value

Prob> F

Model 40038.46 9 4448.718 32.98979 0.0006 significant

X1 10672.61 1 10672.61 79.14349 0.0003 significant

X2 2204.48 1 2204.48 16.34748 0.0099 significant

X3 21632 1 21632 160.4137 < 0.0001 significant

X1X2 588.0625 1 588.0625 4.360821 0.0911 not significant

X1X3 1079.123 1 1079.123 8.002312 0.0367 significant


X12 571.9339 1 571.9339 4.241218 0.0945 not significant

X22 2661.042 1 2661.042 19.73315 0.0068 significant


Lack of Fit 649.33 3 216.4433 17.36641 0.0549 not significant



Chapter 5

Jamia Hamdard

Table 5.8: Analysis of variance table for S40 size

Source Sum of

Squares

df Mean

Square

F

Value

p-value

Prob> F

Model 34355.74 9 3817.305 84.87143 < 0.0001 significant

X1 10368 1 10368 230.5153 < 0.0001 significant

X2 3806.281 1 3806.281 84.62634 0.0003 significant

X3 16065.28 1 16065.28 357.1848 < 0.0001 significant

X1X2 481.8025 1 481.8025 10.71208 0.0221 significant

X1X3 663.0625 1 663.0625 14.74209 0.0121 significant


X12 509.7692 1 509.7692 11.33387 0.0200 significant

X22 2462.513 1 2462.513 54.74989 0.0007 significant



5.3.5.3. Diagnostics for the selected model for S60 particle size

5.3.5.3.1. Normal Plot of Residuals

The normal probability plot indicates whether the residuals follow a normal distribution, in

which case the points will follow a straight line. Only moderate scatter is seen which indicate

normal data. No definite patterns like an "S-shaped" curve, is seen which would have indicated

that a transformation is required (Figure 5.6).



Chapter 5

Jamia Hamdard

Design-Expert® Softwaresize 60

Color points by value ofsize 60:

301.8

139.1

Internally Studentized Residuals

Norm

al % Pr

obabili

ty

Normal Plot of Residuals

-2.00 -1.00 0.00 1.00 2.00

1

5

10

2030

50

7080

90

95

99

Design-Expert® SoftwareSize 40

Color points by value ofSize 40:

257.3

100.8


Norm

al % Pr

obab

ility


-2.00 -1.00 0.00 1.00 2.00

1

5

10

2030

50

7080

90

95

99

Figure 5.6: Normal plot of residual for niosomal size prepared by span60 and span40.

5.3.5.3.2. Residuals vs Run

This is a plot of the residuals versus the experimental run order. It allows you to check for

lurking variables that may have influenced the response during the experiment. Trends indicate a

time-related variable lurking in the background. The plot here is showing a random scatter

indicating that there is no lurking variable affecting the experiment. (Figure 5.7)



Chapter 5

Jamia Hamdard



301.8

139.1

Run Number

Intern

ally St

uden

tized R

esidu

als

Residuals vs. Run

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

1 3 5 7 9 11 13 15



257.3

100.8

Run Number

Intern

ally St

uden

tized R

esidu

als

Residuals vs. Run

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

1 3 5 7 9 11 13 15

Figure 5.7: Residual vs run for size of both type of formulations showing a random scatter indicating that there is no lurking variable affecting the experiment.

5.3.5.3.3. Residuals vs Predicted Plot

This is a plot of the residuals versus the ascending predicted response values. It tests the

assumption of constant variance. The plot here is random scatter (constant range of residuals



Chapter 5

Jamia Hamdard

across the graph) indicating constant variance. Expanding variance ("megaphone pattern



Chapter 5

Jamia Hamdard

5.3.5.3.4. Predicted vs Actual

A graph of the actual response values versus the predicted response values. It helps to detect a

value, or group of values, that are not easily predicted by the model. The data points should be

split evenly by the 45 degree line. If they are not, a transformation (check the Box-Cox plot) may

improve the fit. In this experiment the data points are perfectly lying on the line indicating that

no transformation is needed (Figure 5.9).



301.8

139.1

Actual

Predict

ed

Predicted vs. Actual

100.00

150.00

200.00

250.00

300.00

350.00

100.00 150.00 200.00 250.00 300.00 350.00



257.3

100.8

Actual

Predic

ted


50.00

100.00

150.00

200.00

250.00

300.00

100.00 150.00 200.00 250.00 300.00

Figure 5.9: Predicted vs Actual graph formulation size showing a good distribution between the line indicating no transformation of data is needed.



Chapter 5

Jamia Hamdard

Design-Expert® SoftwareFactor Coding: Actualsize 60

Design Points301.8

139.1

X1 = A: surfactant: CholX2 = B: Aqueos phase

Actual FactorC: Sonic time = 0.00

-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00size 60

A: surfactant: Chol

B: Aq

ueos

phas

e 160

180

200220

240

240

3

Figure 5.10: Contour plot showing the effect on size of S60 on the variation of volume of Aqueous phase and Surfactant: cholesterol ratio.

Design-Expert® SoftwareFactor Coding: ActualSize 40

Design Points257.3

100.8


Actual FactorC: Sonic time = -1.00

-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00Size 40

A: surfactant: Chol

B: Aq

ueos

phas

e

200250

Figure 5.11: Contour plot showing the effect on size of S40 on the variation of volume of Aqueous phase and Surfactant: cholesterol ratio.



Chapter 5

Jamia Hamdard


Design Points301.8

139.1

X1 = A: surfactant: CholX2 = C: Sonic time

Actual FactorB: Aqueos phase = 0.00

-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00size 60

A: surfactant: Chol

C: So

nic tim

e

150

200

250

300

3

Figure 5.12: Contour plot showing the effect on size of S60 on the variation of volume of sonication time and Surfactant: cholesterol ratio.


Design Points257.3

100.8



-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00Size 40

A: surfactant: Chol

C: So

nic tim

e

100

150

200

250

3

Figure 5.13: Contour plot showing the effect on size of S40 on the variation of volume of sonication time and Surfactant: cholesterol ratio.



Chapter 5

Jamia Hamdard


Design Points301.8

139.1

X1 = B: Aqueos phaseX2 = C: Sonic time

Actual FactorA: surfactant: Chol = 0.00

-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00size 60

B: Aqueos phase

C: So

nic tim

e

150

200

250250

3

Figure 5.14: Contour plot showing the effect on size of S60 on the variation of volume of sonication time and Aqueous phase volume.


Design Points257.3

100.8



-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00Size 40

B: Aqueos phase

C: So

nic tim

e

120

140

160

180200

200

220

240

3

Figure 5.15: Contour plot showing the effect on size of S40 on the variation of volume of sonication time and aqueous phase volume.



Chapter 5

Jamia Hamdard


Design points above predicted valueDesign points below predicted value301.8

139.1



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

140 160 180 200 220 240 260 280

size

60

A: surfactant: Chol B: Aqueos phase

Figure 5.16: 3D-response surface for the effect of variation of volume of Aqueous phase volume and Surfactant: cholesterol ratio on S60 size.



100.8


Actual FactorC: Sonic time = -1.00

-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

150

200

250

300

Size

40


Figure 5.17: 3D-response surface for the effect of variation of volume of Aqueous phase volume and Surfactant: cholesterol ratio on S40 size.



Chapter 5

Jamia Hamdard



139.1



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

100

150

200

250

300

size

60

B: Aqueos phase C: Sonic time

Figure 5.18: 3D-response surface for the effect of variation of sonication time and aqueous phase volumeS60 size.



100.8



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

100 120 140 160 180 200 220 240 260

Size

40


Figure 5.19: 3D-response surface for the effect of variation of sonication time and volume of aqueous phase volume on S40 size.



Chapter 5

Jamia Hamdard



139.1



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

100

150

200

250

300

350

size

60

A: surfactant: Chol C: Sonic time

Figure 5.20: 3D-response surface for the effect of variation of sonication time and surfactant: cholesterol ratio on S60 size.



100.8



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

50

100

150

200

250

300

Size

40


Figure 5.21: 3D-response surface for the effect of variation of sonication time and surfactant: cholesterol ratio on S40 size.

5.3.5.4. Effect of different factors of the size of niosome

5.3.5.4.1. Effect of Factor X1: Surfactant: cholesterol ratio

Model graphs revealed that Surfactant: cholesterol ratio has profound effect on the size of

the niosome. As the ratio increased the size of the niosomal size decreased. This can be



Chapter 5

Jamia Hamdard

attributed to the fact that Cholesterol orients in a surfactant bilayer with its polar hydroxyl

group encountering the aqueous phase and the hydrophobic steroid ring oriented parallel

to, and buried in the hydrocarbon chains of the surfactants. Furthermore, incorporation of

increasing levels of cholesterol broadens and eventually eliminates altogether the

cooperative gel/liquid-crystalline phase transition of the host lipid bilayer thus increases

the rigidity of the bilayer due to which the size reducing effect of sonication also get

minimized.

5.3.5.4.2. Effect of Factor X2: Volume of Aqueous phase volume

Effect of volume of Aqueous phase volume also has effect on the size of the niosome but

not so much as compared to the effect of sonication time (X3) and Surfactant: cholesterol

ratio (X1). The increased aqueous phase led to lowering of niosomal size which may be

due to rapid solubilization of ethanol in higher volume of aqueous phase and thus rate of

orientation of surfactant to form vesicle become higher leading to lower size of the

niosomes.

5.3.5.4.3. Effect of Factor X3: Sonication time

It is very evident from the model graphs (figure 20) that there is an overall movement of

response surface from higher size to lower size with the increase in sonication time.

5.3.6. Model fit report for Niosomal Entrapment efficiency

To derive a relation between the different factors and niosomal encapsulation efficiency,

different mathematical models like linear, 2FI, Quadratic and cubic were analyzed for test

of fit using the Design Expert 8 software. The fit summary of the entrapment efficiency

(%EE) for S60 is presented in the table 5.9 and table 5.10. Sequential p-value of quadratic

model was found to be 0.0238 and 0.0650 for S60 and S40 respectively that implies a



Chapter 5

Jamia Hamdard

significant model, for both S60 and S40 lack of fit p-value was maximum and not significant

(S60, p=0.3623; S40, p=0.1177) and the difference between Adjusted and predicted R-squared

was less than 0.2. These values suggests quadratic model for further analyses.

Table 5.9: Fit Summary of different mathematical models for S60 entrapment efficiency

Source Sequential

p-value

Lack of Fit

p-value

Adjusted

R-

Squared

Predicted

R-Squared

Rejected/Suggested

Linear < 0.0001 0.1178 0.800051 0.721765

2FI 0.4579 0.1079 0.797739 0.629246



Table 5.10: Fit Summary of different mathematical models for S40 entrapment efficiency

Source

Sequential

p-value

Lack of Fit

p-value

Adjusted

R-

Squared

Predicted

R-Squared

Rejected/Suggested

Linear < 0.0001 0.0687 0.8816 0.8150

2FI 0.4760 0.0619 0.8788 0.6910



5.3.6.1. Analysis of variance report for niosome entrapment efficiency

ANOVA study on the %EE of S60 (table 5.11) showed a Model F-value of27.20 implies the

model is significant. There is only a 0.10% chance that a "Model F-Value" this large could occur

due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this

case X1, X2, X3,X22 andX32 are significant model terms. The "Lack of Fit F-value" of 7.83

implies there is a 30.53% chance that a ""Lack of Fit F-value" this large could occur due to



Chapter 5

Jamia Hamdard

noise. This model can be used to navigate the design space. For %EE of S40 (table 5.12), Model

F-value was found to be 29.95 suggesting that the model is significant and there is only a 0.08%

chance that a "Model F-Value" this large could occur due to noise.

Values of "Prob > F" less than 0.0500 indicate model terms are significant.

X1, X2, X3, and X22are significant model terms. The "Lack of Fit F-value" of 7.66 implies the

Lack of Fit is not significant relative to the pure error. There is a 11.77% chance that a "Lack of

Fit F-value" this large could occur due to noise.

Table 5.11: Analysis of variance table for S60 %EE

Source Sum of

Squares

df Mean

Square

F

Value

p-value

Prob> F


X1 182.405 1 182.405 33.86755 0.0021 significant

X2 272.6113 1 272.6113 50.61635 0.0009 significant

X3 678.9613 1 678.9613 126.0643 < 0.0001 significant

X1X2

8.1225 1 8.1225 1.508123 0.2741

not

significant

X1X3

20.7025 1 20.7025 3.843881 0.1072

not

significant

X2X3

27.04 1 27.04 5.020579 0.0752

not

significant

X12

1.481026 1 1.481026 0.274985 0.6224

not

significant

X22 81.85256 1 81.85256 15.19775 0.0114 significant

X32 56.40026 1 56.40026 10.47196 0.0231 significant

Lack of Fit

21.1225 3 7.040833 2.425086 0.3053

not

significant



Chapter 5

Jamia Hamdard

Table 5.12: Analysis of variance table for S40 %EE

Source Sum of

Squares

df Mean

Square

F

Value

p-value

Prob> F


X1 195.0313 1 195.0313 38.16783 0.0016 significant

X2 122.4613 1 122.4613 23.9658 0.0045 significant

X3 954.845 1 954.845 186.8642 < 0.0001 significant

X1X2 1.21 1 1.21 0.236798 0.6471 Not significant



X12 0.00641 1 0.00641 0.001254 0.9731 Not significant

X22 71.21256 1 71.21256 13.93638 0.0135 significant



5.3.6.2. Diagnostics for the selected model

5.3.6.2.1. Normal Plot of Residuals

Only moderate scatter is seen which indicate normal data. No definite patterns like an "S-shaped"

curve, is seen which would have indicated that a transformation is required (Figure 5.22).

Design-Expert® Softwareentrapment 60

Color points by value ofentrapment 60:

73.6

36.7


Norm

al % Pr

obabili

ty


-2.00 -1.00 0.00 1.00 2.00

1

5

10

2030

50

7080

90

95

99



Chapter 5

Jamia Hamdard



56.1

19.5


Norm

al %

Proba

bility


-2.00 -1.00 0.00 1.00 2.00

1

5

10

2030

50

7080

90

95

99

Figure 5.22: Normal plot of residual for niosomal size prepared by span60 and span40.

5.3.6.2.2. Residuals vs Run

The plot here is showing a random scatter indicating that there is no lurking variable affecting

the experiment. (Figure 5.23)



73.6

36.7

Run Number

Intern

ally St

uden

tized R

esidu

als

Residuals vs. Run

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

1 3 5 7 9 11 13 15



Chapter 5

Jamia Hamdard



56.1

19.5

Run Number

Intern

ally St

uden

tized R

esidu

als

Residuals vs. Run

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

1 3 5 7 9 11 13 15

Figure 5.23: A Random scatter of Residual vs run graph of both S60 and S40 %EE indicating that there is no lurking variable affecting the experiment.

5.3.6.2.3. Residuals vs Predicted Plot

The plot here is random scatter (constant range of residuals across the graph) indicating constant

variance. Expanding variance ("megaphone pattern



Chapter 5

Jamia Hamdard



56.1

19.5

Predicted

Intern

ally St

uden

tized R

esidu

als

Residuals vs. Predicted

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

20.00 30.00 40.00 50.00 60.00

Figure 5.24: Residual vs predicted graph of S60 and S60 %EE showing random scatter (constant range of residuals across the graph) indicating constant variance.

5.3.6.2.4. Predicted vs Actual

The data points are perfectly lying on the line indicating that no transformation is needed (Figure

25).



73.6

36.7

Actual

Predic

ted


30.00

40.00

50.00

60.00

70.00

80.00

30.00 40.00 50.00 60.00 70.00 80.00



Chapter 5

Jamia Hamdard



56.1

19.5

Actual

Predic

ted


10.00

20.00

30.00

40.00

50.00

60.00

10.00 20.00 30.00 40.00 50.00 60.00

Figure 5.25: Predicted vs actual graph of %EE of both type of formulations.

Design-Expert® SoftwareFactor Coding: Actualentrapment 60

Design Points73.6

36.7



-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00entrapment 60

A: surfactant: Chol

B: A

queo

s pha

se

50

55

60

65 3

Figure 5.26: Contour plot showing the effect of variation of volume of Aqueous phase volume and Surfactant: cholesterol ratio for S60 entrapment efficiency.



Chapter 5

Jamia Hamdard


Design Points56.1

19.5



-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00entrapment 40

A: surfactant: Chol

C: S

onic

time

30

40

50

3

Figure 5.27: Contour plot showing the effect of variation of volume of Aqueous phase volume and Surfactant: cholesterol ratio for S40 entrapment efficiency.


Design Points73.6

36.7



-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00entrapment 60

A: surfactant: Chol

C: S

onic

time

50

60

70

3

Figure 5.28: Contour plot showing the effect of variation of volume of sonication time and Surfactant: cholesterol ratio for S60 entrapment efficiency.



Chapter 5

Jamia Hamdard


Design Points56.1

19.5



-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00entrapment 40

A: surfactant: Chol

C: S

onic

time

30

40

50

3

Figure 5.29: Contour plot showing the effect of variation of volume of sonication time and Surfactant: cholesterol ratio for S40 entrapment efficiency.


Design Points73.6

36.7



-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00entrapment 60

B: Aqueos phase

C: So

nic tim

e

50

60

3

Figure 5.30: Contour plot showing the effect of variation of volume of sonication time

and aqueous phase ratio for S60 entrapment efficiency.



Chapter 5

Jamia Hamdard


Design Points56.1

19.5



-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00entrapment 40

B: Aqueos phase

C: So

nic tim

e

30

40

50

50

3

Figure 5.31: Contour plot showing the effect of variation of volume of sonication time and aqueous phase volume ratio for S40 entrapment efficiency.



36.7



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

45

50

55

60

65

70

entr

apme

nt 60


Figure 5.32: 3D-response surface showing the effect of variation of volume of Aqueous phase volume and Surfactant: cholesterol ratio on S60 entrapment efficiency.



Chapter 5

Jamia Hamdard



19.5



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

30

35

40

45

50

55

entr

apme

nt 40

A: surfactant: Chol

B: Aqueos phase

Figure 5.33: 3D-response surface showing the effect of variation of volume of Aqueous phase volume and Surfactant: cholesterol ratio on S40 entrapment efficiency.



36.7



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

40

50

60

70

80

entr

apme

nt 60


Figure 5.34: 3D-response surface showing the effect of variation of sonication time and Surfactant: cholesterol ratio on S60 entrapment efficiency.



Chapter 5

Jamia Hamdard



19.5



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

10

20

30

40

50

60

entr

apme

nt 40

A: surfactant: Chol

C: Sonic time

Figure 5.35: 3D-response surface showing the effect of variation of sonication time and Surfactant: cholesterol ratio on S40 entrapment efficiency.



36.7



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

30

40

50

60

70

entr

apme

nt 60


Figure 5.36: 3D-response surface showing the effect of variation of Sonication time and volume of aqueous phase volume on S60 entrapment efficiency.



Chapter 5

Jamia Hamdard



19.5



-1.00

-0.50

0.00

0.50

1.00

-1.00

-0.50

0.00

0.50

1.00

20

30

40

50

60

entr

apme

nt 40

B: Aqueos phase

C: Sonic time

Figure 5.37: 3D-response surface showing the effect of variation of Sonication time and volume of aqueous phase volume on S40 entrapment efficiency.

5.3.6.3. Effect of different factors of the entrapment efficiency of niosome

5.3.6.3.1. Effect of Factor X1: Surfactant: cholesterol ratio

According to model graphs Surfactant: cholesterol ratio has promising effect on the

entrapment efficiency of the niosomes. As the ratio increased the size of the niosomal

size decreased which also decreases the entrapment efficiency. This is because of lower

entrapment volume. Further, increased cholesterol level increases the rigidity of vesicle

which prevents the leaching of GCV.

5.3.6.3.2. Effect of Factor X1: Volume of Aqueous phase volume

Effect of volume of aqueous phase volume also found to have some effect on the

entrapment efficiency of the niosomes. The increased aqueous phase led to lowering of

niosomal size which eventually led to lowering of entrapment efficiency. Furthermore,

increase in aqueous phase also decreases the total percentage of lipid content leading to

further decrease in entrapment efficiency.



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Jamia Hamdard

5.3.6.3.3. Effect of Factor X3: Sonication time

It is very evident from the model graphs (figure 5.34-5.37) that there is an overall

movement of response surface from higher entrapment efficiency to lower entrapment

efficiency with the increase in sonication time due to the overall reduction of vesicle size.

Table 5.13: Final equation in terms of coded factors

Response Intercept X1 X2 X3 X1X2 X1X3 X2X3 X12 X22 X32

Size (S60) 178.13 -36.525 -16.6 -52 -12.12 16.425 -9.725 12.44583 26.84583 13.44583

p-value 0.0003 0.0099 < 0.0001 0.0911 0.0367 0.1548 0.0945 0.0068 0.0767

Size (S40) 145.7 -36 -21.81 -44.812 -10.97 12.875 -2.15 11.75 25.825 8.725

p-value < 0.0001 0.0003 < 0.0001 0.0221 0.0121 0.5496 0.0200 0.0007 0.0545

%EE 63.96 -4.775 -5.837 -9.2125 -1.425 2.275 -2.6 -0.63333 -4.70833 -3.90833

p-value 0.002 0.0009 < 0.0001 0.2741 0.1072 0.0752 0.6224 0.0114 0.0231

%EE 37.76 -4.937 -3.912 -10.925 -0.55 -1.875 -2.125 0.041667 4.391667 0.166667

p-value 0.0016 0.0045 < 0.0001 0.6471 0.1580 0.1189 0.9731 0.0135 0.8929

5.3.7. Formulation selection

On the basis of Box-behnken design relationship has been established between independent and

dependent variables. This helped to understand the exact mechanism of formation of noisome

and the factors affecting the response of the prepared niosomes. From the prepared runs we

selected three formulations from both S40 (SN40-1, SN40-2SN40-3) and S60 (SN60-1, SN60-2

and SN60-3) were selected and coating was done with chitosan. Table 5.14 shows final selected

formulations (uncoated and coated with chitosan) based on span 40 and span 60 and their

different molar ratio of Cholesterol that used for the further studies.



Chapter 5

Jamia Hamdard

Table 5.14: Selected uncoated and chitosan coated niosomal formulations and their

characterization parameters

Formulation Surfacta

nt

Formulation

code

Particle*

size(nm)

PDI* *Zeta

potential

(mV)

*Maximum

drug

release

Uncoated

niosomes

Span 40 SN40-1 181.8 0.122 -28.1 67.12%

SN40-13(SN40-

2)**

149.7 0.105 -25.3 79.35%

SN40-09(SN40-

3)**

121.3 0.114 -21.9 93.78%

Span 60 SN60-01 207.7 0.213 -29.5 53.11%

SN60-04(SN60-

2)**

174.3 0.149 -24.1 69.48%

SN60-15(SN60-

3)**

139.1 0.167 -23.4 85.69%

Chitosan

coated

niosomes

Span 40 CHSN40-1 211.3 0.219 +37.9 72.15%

CHSN40-2 171.8 0.188 +41.9 83.53%

CHSN40-3 147.9 0.165 +49.3 99.09%

Span 60 CHSN60-1 237.9 0.311 +36.9 82.78%

CHSN60-2 194.4 0.251 +38.7 85.18%

CHSN60-3 166.7 0.132 +47.8 90.59% *mean of three results; **code given to formulation

Initially fast releases followed by slow release pattern were seen in case of all the formulation

irrespective to the surfactants. CH-coated niosomal formulation showed relatively fast release as

compared to their uncoated form. In addition, maximum amount of GCV from the CH-coated

niosomal formulation were also relatively higher than the normal niosomes. Release data of

optimized niosomal formulation were analyzed according to zero-order model, first order model

and Higuchi model. Release pattern was found to follow zero-order kinetics as average values of



Chapter 5

Jamia Hamdard

correlation coefficient were 0.9864 for zero-order model (approaching to one), 0.945 for first-

order model and 0.883 for Higuchi model.

Overall the factors affecting the formulation development and characterization parameters of

Span40 and Span60 based selected formulation are summarized as follow:

The particle size distribution of selected Span40, Span60-based niosomes and their chitosan

coated form were found to be varied from 121.3nm- 181.8nm (Span40- uncoated), 139.1nm-

207.7nm (Span 60-uncoated) and 147.9nm-211.3nm (Span40- CH coated) and 166.7nm-

237.9nm (Span 60- CH coated). Average particle size was found to be 121.3nm nm with PDI

0.114 for optimized formulation SN40-3. Regarding PDI, a value of zero indicates an entirely

mono-disperse population and a value of 1 indicates a completely poly-disperse population

(Zeisig et al., 1996).

Results of entrapment efficiency showed that the incorporation of Cholesterol into niosomes

significantly increased the drug entrapment efficiency up to an optimum ratio of Span60:

Cholesterol and Span40: Cholesterol. These results were supported by the fact that Cholesterol

alters the fluidity of chains by reducing the transition of gel to liquid phase of surfactant bilayer

and hence providing the transition state leading to the high drug entrapment (Uchegbu et al.,

1995; Devaraj et al., 2002; Hao et al., 2002). Moreover, it also increased the microviscosity of

niosomal membrane conferring more rigidity, resulting in a higher stability which leads to the

greater drug retention 25. In addition, the length of the alkyl chain influences the HLB value of

the surfactant mixture that directly affects the drug entrapment efficiency. The HLB values for

Span40 and Span60 are 6.7 and 5, respectively, the lower value of HLB of both the surfactant are

comparable enough that leads to the high drug entrapment (Guinedi et al., 2005).



Chapter 5

Jamia Hamdard

5.3.8. In vitro drug release performance

A comparative in vitro release profile of all the formulations is shown in Figure 5.38. The

release profile of GCV chitosan coated and uncoated niosomes showed that span 40 based

niosomes have maximum GCV release in 24 h. As compared to span 60 based niosomal

dispersion. Span 40 surfactant based niosomes have 67.12% (SN40-1), 79.35% (SN40-2) and

93.78% (SN40-3) of GCV release over the period of 24 h in sustained manner. In case of span 60

based formulation, maximum release was seen with SN60-3(85.69%). Chitosan coating over the

same formulation increased the release of GCV as compared to the uncoated niosomes. The

maximum release in case of SN40-3 after coated with chitosan was found to be 99.09%

(CHSN40-3). Similarly in case of span 60 based niosomes, after coating it was found to be

90.59%

Figure 5.38: Percentage drug release profile of GCV from normal () and chitosan coated niosomal

dispersion.



Chapter 5

Jamia Hamdard

Result shows that the increase of Cholesterol molar ratio significantly reduced the efflux of

Ganciclovir, showing Cholesterol membrane stabilizing ability and space filling action (Raja

Naresh et al., 1994, Namdeo and Jain , 1999). Furthermore, Cholesterol is known to increase

the rigidity of niosomes and abolish the gelto- liquid phase transition of niosomal systems

resulting in niosome formulations that are less leaky (Cable, 1989; Alsarra et al., 2005) thus

decreasing the drug release from niosomes. We tried to establish the correlation between the

vesicular size, maximum percentage of drug release and nature of surfactant, its molar ratio with

cholesterol (figure 5.39).

Figure 5.39: Effect of nature of surfactant and the cholesterol on drug release and vesicular size

Here it was found that niosomes prepared using Span60 were slightly larger in size (139.1 to

207.7) than those prepared using Span40. The result was in accordance with the previous finding

(Manconi et al., 2005; Guinedi et al., 2005). The increased size of the developed niosomes may

be due to the presence of Span60 which has a longer saturated alkyl chain as compared with

Span40 (Manosroi et al.,2003). Furthermore, the larger vesicles are formed when the



Chapter 5

Jamia Hamdard

hydrophilic portion of the molecule is decreased relative to the hydrophobic portion (Uchegbu et

al., 1997), it may also be attributed to the fact that the increase in alkyl chain length as the series

is ascended from the C12 to the C18 ester would result in an increase in the value of the critical

packing parameter (Uchegbu et al., 1995). Moreover, figure 5.37 showed that that the increase

of Cholesterol ratio into niosomes significantly decreases the drug release. Cholesterol decreased

the fluidity and diffusion of chains by reducing the transition of gel to liquid phase of surfactant

bilayer so, this feature may consider as the factor responsible for release effect.

5.3.9. In vitro transcorneal permeation study

Figure 5.40 shows transcorneal permeation profiles of GCV from the investigated formulations

using goat eyes corneas. The following permeation parameters were calculated: steady-state flux

and apparent permeability coefficient (Table 5.15). The steady-state flux calculated for

optimized niosomal formulation [SN40-09 (SN40-3)] was 14.7-fold higher than those for GCV

solution (control). Moreover, transcorneal flux were improved after the chitosan (table 5.15).

The enhanced hydrophilic characteristic over the surface of lipophilic surfactant bilayer can be

considered as reason for this effect.



Chapter 5

Jamia Hamdard

Figure 5.40: Transcorneal permeation profiles of GCV from the investigated formulations (normal and

chitosan coated niosomes) using goat eyes corneas

Table 5.15: Steady state flux (Jss), Permeation coefficient (Kp) and enhancement ratio (ER) of GCV

from niosomes and control (GCV solution) (mean ± SD, n = 3)

Formulation Jss (μg h-1 cm-2) Kp ( x 103 cm h-1)

SN40-1 159.48±08.33 0.02658±0.0090

CHSN40-1 164.27±8.59 0.02667± 0.0091

SN40-2 179.52±09.83 0.02992±0.0053

CHSN40-2 183.61±09.73 0.03201±0.0061

SN40-3 217.32±10.12 0.03622±0.0062

CHSN40-3 225.39±10.73 0.03923±0.0057

SN60-1 74.88±06.90 0.01248±0.0058

CHSN60-1 79.71±06.99 0.01318±0.0072

SN60-2 102.59±08.02 0.01709±0.0067

CHSN60-2 107.43±8.09 0.01908±0.0069

SN60-3 138.59±07.98 0.02309±0.0072

CHSN60-3 139.92±07.99 0.02503±0.0072

GCV solution 15.75±05.90 0.00262±0.0004



Chapter 5

Jamia Hamdard

These results correlated well with the in vitro release data suggesting that the corneal permeation

process of GCV was dependent on its release characteristics. Similar results were obtained from

literature using lipid based delivery systems such as liposomes and solid lipid nanoparticles

(SLN) using excised rabbit cornea and bioengineered human cornea, respectively (Law et al.,

2000; Attama et al., 2009)

The permeation rate and apparent permeability constant of acyclovir-encapsulated liposomes and

timolol maleate-loaded SLNs were significantly lower than the corresponding drug solutions.

Moreover, this suggests that the permeation across the cornea rather than the partitioning of the

drug through the bilayer membranes to the in vitro release media is rate limiting. Similar findings

were reported with timolol-loaded SLN using bioengineered human cornea (Attama et al.,

2009).

5.3.10. In-vivo study

5.3.10.1. Ocular retention study by Gamma scientigraphy

Gamma scintigraphy is a well-established technique for in vivo evaluation of the ocular retention

study of ophthalmic drug delivery. The precorneal clearance of the optimized GCV non

mucoadhesive (SN40-3) and CH-coated mucoadhesive niosomal formulation (CHSN40-3) and

control (GCV- sol) were monitored using γ-scientigraphy. GCV non mucoadhesive (SN40-3)

and CH-coated mucoadhesive niosomal formulation (CHSN40-3) were abbreviated here GCV-

NDs and GCV-MNDs respectively. In present ocular retention studies, GCV niosomal dispersion

and GCV solution was radiolabeled with radionuclide Tc-99m. GCV niosomal dispersion and

GCV solution was instantaneously labeled with Tecnetium-99m with good labeling efficiency (≥

95%) and less number of colloids (≤ 5%). After administration of the radio labeled ophthalmic



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Jamia Hamdard

formulation, a good spreading was observed over the entire precorneal area. Identifying ROIs

and defini

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