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Drug Development and Industrial Pharmacy

ISSN: 0363-9045 (Print) 1520-5762 (Online) Journal homepage: http://www.tandfonline.com/loi/iddi20

Nanoparticles as tool for enhanced ophthalmicdelivery of vancomycin: a multidistrict-basedmicrobiological study, solid lipid nanoparticlesformulation and evaluation.

Carol Yousry, Rania Hassan Fahmy, Tamer Essam, Hanan M. El-laithy &Seham A. Elkheshen

To cite this article: Carol Yousry, Rania Hassan Fahmy, Tamer Essam, Hanan M. El-laithy& Seham A. Elkheshen (2016): Nanoparticles as tool for enhanced ophthalmic deliveryof vancomycin: a multidistrict-based microbiological study, solid lipid nanoparticlesformulation and evaluation., Drug Development and Industrial Pharmacy, DOI:10.3109/03639045.2016.1171335

To link to this article: http://dx.doi.org/10.3109/03639045.2016.1171335

Accepted author version posted online: 27Mar 2016.

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Nanoparticles as tool for enhanced ophthalmic delivery of vancomycin: a multidistrict-

based microbiological study, solid lipid nanoparticles formulation and evaluation.

Carol Yousrya, Rania Hassan Fahmy

a*, Tamer Essam

b, Hanan M. El-laithy

a,c, Seham A. Elkheshen

a,d

aDepartment of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., Cairo,

Egypt

bDepartment of Microbiology and Immunology, and Biotechnology Center, Faculty of Pharmacy, Cairo University, Kasr

El-Aini St., Cairo, Egypt

cDepartment of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, October University for Modern Sciences

and Arts, Cairo, Egypt

dDepartment of Pharmaceutical Technology, Faculty of Pharmaceutical Sciences and Pharmaceutical Industries, Future

University in Egypt, Cairo, Egypt

* Corresponding author:

Associate Prof. Dr. Rania H. Fahmy

Tel: +201005840256

E-mail address: raniafahmy@gmail.com

Address: Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Cairo University, Kasr El-

Aini, Cairo 11562, Egypt.

Keywords:

Multi-district microbiological survey; vancomycin; ocular infection; Egypt; solid lipid nanoparticles.

Abbreviations: Solid lipid nanoparticles, SLNs; Vancomycin hydrochloride, VCM; Fourier transform infrared,

FTIR; Glyceryl tripalmitate, GTP; Polyvinyl alcohol, PVA; Glyceryl monopalmitate, GMP; Glyceryl dipalmitate,

GDP; analysis of variance, ANOVA; polydispersity index, PDI; encapsulation efficiency, EE; scanning electron

microscopy, SEM; transmission electron microscopy, TEM; hydrophilic lipophilic balance, HLB; particle size, PS.

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Abstract:

Context: A microbiological multidistrict-based survey from different Egyptian governorates was conducted

to determine the most prevalent causative agents of ocular infections in the Egyptian population. Antibiotic

sensitivity testing was then performed to identify the most potent antimicrobial agent. Vancomycin (VCM) proved

the highest activity against gram-positive Staphylococcus bacteria, which are the most commonly isolated causative

agents of ocular infection. However, topically applied VCM suffers from poor ocular bioavailability because of its

high molecular weight and hydrophilicity. Objective: the aim of the present study was to develop VCM-loaded solid

lipid nanoparticles (SLNs) using water-in-oil-in-water (W/O/W) double emulsion, solvent evaporation technique to

enhance ocular penetration and prolong ophthalmic residence of VCM. Method: Two consecutive full factorial

designs (24 followed by 3

2) were adopted to study the effect of different formulation and process parameters on SLN

formulation. The lipid type and structure, polyvinyl alcohol (PVA) molecular weight and concentration, sonication

time, as well as lipid:drug ratio were studied as independent variables. The formulated SLN formulae were

evaluated for encapsulation efficiency, particle size, and zeta potential as dependent variables. Results: The

statistically-optimized SLN formula (1:1 ratio of glyceryltripalmitate:vancomycin with 1% low molecular weight

PVA and 1 min sonication time) had average particle size of 277.25 nm, zeta potential of -20.45, and 19.99% drug

encapsulation. Scanning and transmission electron micrographs showed well-defined, spherical, homogenously

distributed particles. Conclusion: The present study suggests that VCM incorporation into SLNs is successfully

achievable; however, further studies with different nanoencapsulation materials and techniques would be valuable

for improving VCM encapsulation.

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1. Introduction:

The eyes are among the most readily accessible organs in terms of their location in the body. However, drug

delivery to ocular tissue is particularly challenging. Ocular tissue is highly susceptible to different types of infection;

many of the viruses, bacteria, and fungi that invade the human body can attack the surface and/or the interior of the

eye, causing ocular infection (conjunctivitis, blepharitis, keratitis, and endophthalmitis).1,2

When treating ocular

infections, the selection of the appropriate antimicrobial agent should be based on the causative agent. Various

ocular infections represent serious threats to the Egyptian population owing to the high pollution rate and lack of

hygienic precautions in some rural areas of Egypt. Generally, Staphylococcus species, a gram-positive bacteria, is of

the most commonly isolated causative agents of ocular infection, as reported internationally.3 Vancomycin (VCM), a

glycopeptide antibiotic, is the antimicrobial agent of choice for the treatment of such ocular infections. Clinically,

VCM is known to be an excellent antibiotic with high antibacterial activity against gram-positive bacteria including

methicillin-resistant Staphylococcus aureus (MRSA), resistant enterococci4, and ß-lactam-resistant bacteria

5.

However, obstacles such as its strong hydrophilicity and high molecular weight (1485.7 g/mol) hinder the use of

VCM6. When used orally, such properties hinder VCM absorption from the gastrointestinal tract, leading to limited

bioavailability Intramuscular administration can cause pain, hypersensitivity, and muscular tissue necrosis.

Intravenous administration presented an alternative route, but its side effects limited its clinical application.

Ototoxicity, and nephrotoxicity were some of the reported side effects in patients receiving VCM intravenously, as

well as “Red-man syndrome (which occurs in around 47% of patients receiving intravenous VCM).7

VCM has excellent bactericidal activity particularly in cases of superficial bacterial ocular infections.

Nevertheless, it is not currently used in topical ocular therapy because topically applied VCM has several associated

difficulties, which hinder the development of formulations appropriate for topical ocular use and lead to poor ocular

bioavailability. Alternatively, intravenous administration of VCM is used as the route of choice although it is

associated with inadequate therapeutic levels of VCM in the aqueous humor. Recently, intraocular injections

(intravitreal and subconjunctival) have been used8. However, in addition to the repeated inconvenience, they fail to

maintain appropriate ocular VCM levels over a sufficient period of time.9

The cornea consists of three consecutive membranes: the epithelium, stroma, and endothelium. Two of these

(the corneal epithelium and endothelium) are lipophilic in nature and act as barriers against the absorption of

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hydrophilic drug molecules.10

Therefore, after administration of eye drops, typically less than 5% of the applied dose

penetrates the cornea, because of the relatively impermeable corneal barrier structure.10

VCM is a highly hydrophilic

molecule with a high molecular weight, which hinders its prolonged retention in the external eye structures, leading

to lack of satisfactory corneal penetration and low absorption into the ocular tissue.8 For all these reasons,

encapsulation of VCM into nano-sized lipophilic carriers (SLNs) could overcome such barriers, prolonging its

residence time in the cornea and conjunctival sac, maximizing corneal drug absorption, and improving ocular

bioavailability.

Particulate delivery systems have been proven to improve the residence time of topically applied ocular drugs

at the site of administration, where the drug is gradually released from the particles.11

Thus, the use of various

particulate delivery systems (e.g. microparticles 8,9

and liposomes 12

) to enhance the performance of topical ocular

VCM has been investigated. Topically applied particulate systems with appropriate particle sizes and narrow size

ranges ensure low irritation, adequate bioavailability, compatibility with ocular tissue, and better patient

compliance.10

Many studies successfully encapsulated hydrophilic drugs into SLNs to overcome certain drug-related or

targeting-related difficulties. For ocular administration, Attama et al.13

succeeded in achieving high diclofenac

sodium encapsulation into SLNs, leading to prolonged ocular residence time and enhanced its permeation through

the cornea construct. Also, in order to enhance the penetration of a highly hydrophilic compound, paromomycin,

through the stratum corneum of the skin, Ghadiri et al.14

encapsulated the drug into lipidic nanoparticles. For brain

targeting, Hansraj et al.15

developed sumatriptan succinate-loaded SLNs to optimize the brain uptake potential of the

orally administered drug, and Shah et al.16

encapsulated rivastigmine hydrogen tartrate into SLNs to bypass the

reticuloendothelial system (RES), thereby improving the brain uptake of the drug through improved nasal absorption

and prolongation of its residence time.

In the present study, addressing ocular infections in the Egyptian population was based on a national goal,

because of the lack of information in this area. This stepwise approach was achieved through a multidistrict-based

survey to identify the most prevalent causative agent of ocular infection in the Egyptian population of different

geographic areas. This was followed by antibiotic-sensitivity testing of the isolated microorganisms using various

antimicrobial agents; VCM was found to be among the most potent agents. In order to enhance its ocular

bioavailability by enhancing its ocular penetration and prolonging its pre-ocular residence time, VCM was

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incorporated into solid lipid nanoparticles (SLNs). In preliminary studies, the regular melting-based formulation

techniques for encapsulation of VCM into SLNs proved difficult, because of both miscibility and stability concerns.

W/O/W double emulsion is a well-known technique for the formulation of polymeric nanoparticles17

and has proven

successful in the field of SLN formulation.18,19

Therefore, this promising technique was used for the formulation of

VCM-loaded SLNs. Furthermore, the effects of different processes and formulation variables on the SLNs were

thoroughly examined through statistical approaches.

2. Materials and methods

2.1. Materials:

Vancomycin hydrochloride (VCM) was purchased from Acros Organics (New Jersey, USA). DL–α-Palmitin

(99%), glyceryl 1,3-dipalmitate (≥ 99%), and glyceryl tripalmitate (minimum 85%) were purchased from Sigma

Aldrich Inc. (St. Louis, USA). Gelucire 44/14 (lauroylmacrgol- 32 glycerides) was kindly provided by Gattefossè

(Lyon, France). Polyvinyl alcohol (PVA with molecular weights of 22000 and 72000) was purchased from MP

Biomedicals, LLC (California, USA). Methylene dichloride was purchased from the El-Nasr Pharmaceutical

Company (Cairo, Egypt). Antimicrobial discs were purchased from Himedia Laboratories, India. All

microbiological agents were purchased from Oxoid Limited (Basingstoke, U.K.).

2.2. Methods

2.2.1. Multidistrict survey and screening of the ocular infections’ causative

microorganisms:

2.2.1.1. Sample collection and cultivation: A total of 165 swab (sterile swabs, Copan innovation,

Italy) samples were collected from various hospitals in four different Egyptian governorates (Cairo,

Giza, Ismalia, and Fayoum). Diversity of the population was ensured through the choice of

governorates and the sample gathering. Governorates with high populations, varying social levels,

and diverse occupations (e.g. agricultural, industrial, and coastal) were selected.

Initially, the conventional colony-count cultivation technique for examining the collected swab

samples was applied. Each of the collected swabs was streaked in duplicate onto two appropriately

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marked plates supplemented with 15 mL nutrient agar. Solidified plates were promptly inverted and

incubated in a Heraeus function line incubator (Heraeus, USA) for 48 h at 35 ± 2°C. Plates with

clear visible colonies were selected for a further screening regimen.

2.2.1.2. Isolation and characterization of selected tested microbial markers: Different

shapes of colonies on the nutrient agar plates were chosen and subjected to conventional

identification, first microscopically (Gram stain), and then macroscopically, using standard

selective and/or differential media.

2.2.2. Antibiotic susceptibility test and determination of minimum inhibitory

concentration (MIC):

Antibiotic susceptibility testing was conducted by applying the disc diffusion method (Kirby-Bauer method)

using Mueller-Hinton agar, according to the guidelines of the Clinical and Laboratory Standards Institute.20

All

isolates showing typical positive characteristics of S. aureus were further characterized through determination of

their antibiogram against 22 different antibiotics, representing all relevant classes and modes of action (Table 1).

The tested antibiotics were in the form of commercially available antimicrobial discs. The susceptibility pattern of

the tested isolates was determined according to the zone of inhibition based on the standard database.20

The preliminary MICs were firstly determined by the micro-broth dilution method 21

. Briefly, 100 µL of

double-strength trypticase soya broth were placed in each well of a 96-well micro titer plate. Aliquots of 100 µL of

the solutions to be tested were added to the first column. Then, two-fold dilutions were carried out from one column

to the next, up to column 10. All these columns were inoculated with 20 µL of bacterial suspension (108 CFU mL

-1).

The 11th column was used as a sterility control (neither product nor bacteria was added) and the 12

th column was

used as a growth control (inoculated without adding the test antimicrobial). Plates were then incubated at 37°C for

24 h under aerobic conditions. MIC was determined as the lowest concentration with no visible growth.

MICs were then determined using the agar diffusion technique according to CLSI standards22

, as follows: 60-

70 µL of each bacterial suspension (108 CFU mL

-1) were placed into sterilized petri dishes. A total of 15 mL molten

(45°C) trypticase soya agar was added, properly mixed, and allowed to solidify. Finally, 10 µL of each dilution of

the tested solution was placed into sterilized filter paper discs (wattman 1), and the plates were allowed to stand for

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10 min. Then, all plates were incubated at 37°C for 24 h under aerobic conditions. The zones of inhibition were then

measured, and a correlation was constructed between log concentration and the zone of inhibition in mm.

2.2.3. Application of Factorial designs for the optimization of SLN formulation:

2.2.3.1. Influence of process and formulation variables on VCM-loaded SLNs:

A 24 full factorial design was used to evaluate the influence of four formulation and process variables on the

SLN characteristics. In this design, four factors were evaluated as independent variables, each at two levels, and

experimental trials were performed with all 16 possible combinations in duplicate. The type of lipid (X1, Gelucire

44/14 and Glyceryl tripalmitate (GTP)), molecular weight of PVA (X2, 22000 and 72000), concentration of PVA in

the secondary emulsion (X3, 0.5% and 1% (w/w), and sonication time of the secondary emulsion (X4, 1 and 2 min)

were selected as independent variables, while the particle size (PS) of the SLNs, their zeta potential (Z), and VCM

encapsulation efficiency (EE%) were selected as dependent variables (Table 2 and Table 3). The selected levels of

the independent variables were chosen based on preliminary experiments and run order was randomized to avoid the

effects of time-related variables and to satisfy the statistical requirement of independence of observations. The

lipid:drug ratio was kept constant throughout the design, at a ratio of 3:1. A significance level of 5% was used as the

criterion to reject the null hypothesis.

Statistical analyses to evaluate the influence of these four formulations and process variables on the

dependent variables were performed using the Design-Expert® Software (Version 7.0.0, Stat-Ease Inc., Minneapolis,

USA). Furthermore, the values of the dependent variables were statistically optimized with the optimization criteria

set to highest EE%, smallest PS, lowest PDI, and highest Z to yield a formula with the highest desirability factor for

use in further investigations.

2.2.3.2. Influence of lipids on VCM-loaded SLNs:

Based on the statistical analysis and optimization results of the first experimental design, and in order to

achieve better compromise between the particle size and the encapsulation efficiency, another 32 full factorial design

was implemented to evaluate the effects of lipid structure and lipid:drug ratio on the formulated SLNs. Each factor

was evaluated at three levels, and the experimental trials were performed in duplicate for all 9 possible combinations.

Lipid structure (X1, Glyceryl monopalmitate or GMP, Glyceryl dipalmitate or GDP, and Glyceryl tripalmitate or

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GTP) and lipid:drug ratios (X2, 1:1, 3:1, and 5:1) were studied as independent variables, and particle size, zeta

potential, and VCM encapsulation efficiency were statistically analyzed as dependent variables (Table 4 and Table

5). Furthermore, statistical analysis of the effect of the lipid structure and the lipid:drug ratio results were optimized

(highest EE%, smallest PS, lowest PDI, and highest Z) to yield an optimized formula with the highest desirability

factor.

2.2.4. Formulation of VCM-loaded nanoparticles:

Double emulsion, solvent evaporation technique, was (with slight modifications) to prepare VCM-loaded

SLNs.17,19

Briefly, 50 mg of VCM was dissolved in 0.5 mL of distilled water while lipids were dissolved in 5 mL

dichloromethane. To produce the primary emulsion, the aqueous phase was added to the organic phase drop-wise

while homogenizing at 5000 rpm for 5 min using a high shear homogenizer (HG-15D Wise mix homogenizer;

DAIHAN Scientific Co. Ltd., South Korea). This primary emulsion was then added portion-wise to 20 mL PVA

solution while homogenizing (5000 rpm, 5 min), followed by sonication using probe sonicator (Hielscher, Germany)

for the specified amount of time. The final emulsion was then added to 40 mL of 0.3% (w/w) PVA aqueous solution

for stabilization and magnetically stirred with SB162 magnetic stirrer (Stuart, UK) at room temperature for nearly 2

h, to ensure evaporation of the organic solvent.

2.2.5. Evaluation of the prepared VCM-loaded SLNs:

2.2.5.1. Particle size and zeta potential:

The SLN size, polydispersity index (PDI, which indicate the particle size distribution), and zeta potential of

all the formulations were determined via Photon Correlation Spectroscopy using Malvern Zetasizer Nanoseries

(nanoZS; Malvern Inst. Limited, UK). All measurements were done at a temperature of 25°C and an angle of 173°

using samples appropriately diluted with distilled water. The individual values for two runs were determined (each

of the duplicates with three determinations), and their mean values were reported.

2.2.5.2. Drug encapsulation efficiency (EE%):

The amount of free (un-entrapped) VCM in the supernatant was measured after SLN centrifugation

(Megafuge 1.0 R; Heraeus, Germany) at 4°C for 2 h at 15000 rpm followed by double washing. Free VCM was

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measured using a UV Spectrophotometer (UV-1601 PC; Shimadzu, Kyoto, Japan) at λmax281 nm, and the

percentage of the drug encapsulated within the SLNs was calculated from the following equation:23

𝐸𝐸 % = [𝐷𝑖 − 𝐷𝑓

𝐷𝑖] 𝑋 100

Where “Di” is the initial weight of the drug incorporated in SLN formulations and “Df” is the amount of the

free drug in the supernatant. The individual values were determined in duplicate, and their mean values were

reported.

For the spectroscopic determination of VCM, a calibration graph constructed in the 40-200 µg/mL range

(nine points, each determined in triplicate) showed linearity with an R2 coefficient value of 0.999 for the regression

equation Y = 0.004406X + 0.011145. The LOQ (limit of quantitation) was found to be 40 µg/mL. The spectroscopic

method was validated with respect to accuracy and inter- and intra-day precision. The intra-day precision and

accuracy of the analytical procedure were evaluated after replicate analysis (n = 3) of the samples at three

concentration levels (40, 100, and 200 μg/mL) on the same day. The inter-day precision was evaluated for the same

concentrations (in triplicate) on three consecutive days. The percent coefficient of variation (CV%) was lower than

0.3, proving that the adopted analysis method was reproducible and reliable.

2.2.5.3. Morphological analysis and surface topography:

Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to elucidate

the shape and the surface topography, respectively, of the optimized VCM-loaded SLN formulations. For SEM, one

drop of the SLN dispersion was placed on a holder, dried, and coated with gold palladium for 1 min using a sputter

coater. Then, scanning was performed using a scanning electron microscope (Quanta 250 FEG; FEI Company,

Netherlands) fitted with a field emission gun electron source to increase its nanometer resolution.

For TEM (JEM-1400 TEM; JEOL, Tokyo, Japan), 1-2 drops of the diluted formula were placed on EM grids

(400-mesh carbon coated grids), then immersed in 1% phosphotungstic acid for 60 s to negatively stain the sample.

Negative staining was used to enhance the contrast and improve the images.

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2.2.5.4. Fourier Transform Infrared (FT-IR) spectroscopy:

FT-IR scanning was performed for VCM, GTP, PVA, 1:1 physical mixtures of the drug with each of the

components, and freeze-dried VCM-loaded SLNs (the optimized formula). FT-IR spectra of the samples were

scanned with an FTIR spectrometer (IR Affinity- 1; Shimadzu, Kyoto, Japan) using the potassium bromide (KBr)

disc technique. Briefly, 2-3 mg of the powdered samples was mixed with 100 mg of dry KBr powder and

compressed into a thin disc using a hydraulic pressing machine. The FT-IR spectrum of each KBr disk was then

measured in the spectral region 4000–400 cm-1

.

3. Results and discussion

3.1. Isolation and characterization of the most prevalent ocular infections’ causative

microorganisms:

Preliminary screening and isolation of the bacterial strains from ocular swabs collected from different

Egyptian governorates resulted in isolation of 166 bacterial strains. Although 32 samples (19.4%) were free from

any aerobic bacterial contamination, the other 133 samples (80.6%) showed positive bacterial growth. Of these, 78

swabs (47.3%) had gram positive bacteria, 22 swabs (13.3%) had gram negative bacteria, and 33 swabs (20%) had

mixed gram positive and gram negative bacteria, as presented in Figure 1a. Further investigation using microscopic

examination revealed that the isolated gram-negative bacteria were mainly rod-shaped, while most of the isolated

gram-positive bacteria were cocci-shaped and arranged in bunches. Based on the recorded high prevalence of the

gram-positive bacteria, further identification and characterization were attempted. The conducted identification

scheme revealed that among the isolated 111 gram-positive bacterial isolates, 81 belong to the Staphylococci species,

while 30 were non-Staphylococci. Among the isolated Staphylococci, 46 isolates showed typical positive

characteristics of S. aureus, while the rest showed atypical characteristics and were considered non-S. aureus

(Figure 1b).

3.2. Antibiotic susceptibility test and determination of minimum inhibitory

concentrations (MIC):

The disc diffusion method (Kirby-Bauer method) was used for antibiotic susceptibility testing, in which all S.

aureus isolates were characterized against 22 different antibiotics representing all relevant classes and modes of

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action (Table 1). Five antibiotics showed the highest activity against the tested S. aureus isolates: VCM, linezolid,

levofloxacin, ampicillin/sulbactam, and amikacin. VCM and linezolid proved antibacterial activity against all the

tested S. aureus isolates, as shown in Figure 2, whereas 80% of the tested isolates were susceptible to levofloxacin

and ampicillin/sulbactam, followed by Amikacin which was efficient against 70% of the tested isolates. Furthermore,

while the rest of the tested antibiotics showed mild activity ranging from 50–60%, all isolated strains of S. aureus

were resistant to the antibiotics cefoperazone and cefotaxime at the tested concentrations.

Because both VCM and linezolid showed 100% activity against all the tested S. aureus isolates, based on the

antibiotic policy, VCM was chosen as the model antimicrobial agent, in order to avoid the development of multidrug

resistant strains. Linezolid was used only as an alternative agent in VCM-resistant cases.24

For further characterization, MIC ranges of all S. aureus isolates were determined by the micro broth dilution

method; results are shown in Figure 3. Most of the tested isolates (29) were highly susceptible to VCM, with MIC

ranges of 2-8 μg/mL. On the other hand, 11 and 6 isolates had higher MIC ranges of 8-16 and 16-32 μg/mL,

respectively. Interestingly, none of the tested S. aureus isolates had MIC higher than 32 μg/mL.

3.3. Statistical analyses of the experimental designs for optimization of SLN

formulation:

3.3.1. Influence of process and formulation variables on the characteristics of VCM-

loaded SLNs:

3.3.1.1. Particle size and zeta potential:

Because the VCM-loaded SLNs were intended for ocular administration, particle size and particle size

distribution are essential parameters for patient convenience and safe administration. According to Aksungur et al.25

,

particles intended for ocular applications should not exceed 10 µm in size.

Most of the formulated SLNs were in the nano-range, with mean particle sizes ranging from 135.75 to 1303

nm (Table 3). The influence of the selected process and formulation variables on particle size was statistically

analyzed via analysis of variance (ANOVA); the results are shown in Table 6. The results revealed that the type of

lipid, the molecular weight of PVA, and the sonication time significantly affected the NP size, with P-values of

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<0.0001, 0.0006, and 0.0269, respectively. On the other hand, PVA concentration was shown to have an

insignificant effect on particle size, with at P-value <0.05.

Regarding the significant effect of the lipid type on the particle size of SLNs (Figure 4-a), Gelucire® 44/14

(lauroylmacrogol-32 glycerides), known for its surface active properties, significantly reduced the particle size when

used as SLN-forming lipid. As Gelucire® 44/14 possesses significant surface active properties; this may lead to

reduction in the interfacial tension between the aqueous and oily phases during SLN formulation, resulting in the

formation of smaller nanoparticles. In addition, this significant effect may be related to lipids’ structures; Gelucire

(44/14) is composed of a mixture of short, medium and only low proportion of long chain glycerides (around 30%

C16 and C18); while GTP consists mainly of long chain tri-glycerides (C16). Having lower chains’ length than GTP,

Gelucire® 44/14 might tend to form smaller particle size SLN

26,27. Also, the increase in the surfactant effect by using

Gelucire (44/14) can increase the stability of the particles by forming a steric barrier on the surface, which protects

the particles from coagulation.28

Furthermore, the larger size observed for the GTP-based nanoparticles can possibly

be a result of the significantly higher encapsulation efficiency of these nanoparticles.

Table 6 and Figure 4b show that high molecular weight PVA produced significantly larger SLNs than low

molecular weight PVA. This could be attributed to the fact that surfactants with longer alkyl chains give larger

vesicles29

, as molecules with larger chain lengths might occupy larger volumes when deposit on the surface of the

formed particles.30

Additionally, it was suggested that polymers with high molecular weights may flocculate the

dispersed particles by increasing the viscosity of the suspension. This increased viscosity of the suspension may

hinder the equal distribution of the applied ultrasonic energy, which in turn delays the subdivision of the large

particles into smaller ones.

Additionally, increasing the sonication time significantly reduced SLN size (Figure 4c). The use of

ultrasound waves in a liquid macroscopic dispersion generates cavitation bubbles, which implode, providing

sufficient local energy to generate nanometric-scaled droplets. Thus, increasing the time of exposure to such

ultrasound waves results in higher energy release, significantly reducing the particle size.31

The polydispersity index (PDI) gives an indication of the homogeneity and the quality of the prepared NPs,

where a lower PDI indicates SLNs with a narrow-range particle size. For most formulations, PDI ranged from 0.16

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to 0.43, which fell in the acceptable range. However, only two formulations with sizes larger than 800 nm showed

much higher PDI, indicating a wider particle size distribution range that could be a result of particle agglomerations.

The mean zeta potential of the prepared SLN formulations (F1- F16) ranged from – 12.2 to – 35.95 mV

(Table 3). All formulations showed negative particle charge, which is mainly related to the negatively charged

carboxylate group in the lipid matrix. Particle charge is one of the most important criteria in evaluating the physical

stability of nanoparticles. In low molecular weight surfactants, absolute zeta potential values above │30│mV

provide good physical stability, values around │20│mV are considered deflocculated stable systems, and

surfactants with values ranging between – 5mV and +5 mV undergo fast aggregation. For high molecular weight

stabilizers, which act mainly by steric stabilization, zeta potential values of around │20│mV provide good physical

stability.32

The lipid type, PVA molecular weight, and sonication time significantly affected NP charge, with P-

values of <0.0001, <0.0001, and 0.0167, respectively (Table 6). In all three factors, moving from the lower level of

the factor to the higher level negatively affected Z values, which may result in compromised stability of the

produced SLN.

3.3.1.2. Drug encapsulation efficiency (EE%):

The mean EE% of the SLN ranged from 3% to 25% (w/w) (Table 3). Such overall low encapsulation

efficiency can be attributed to the low solubility of the highly hydrophilic VCM in the lipid matrix.33

EE% was

statistically analyzed and only the lipid type was found to significantly affect VCM encapsulation into the SLNs,

with P-value < 0.0001 (Table 6). The effects of the PVA molecular weight, PVA concentration, and sonication time

on the VCM encapsulation were insignificant, at P < 0.05.

Although it was expected that Gelucire 44/14 (mixture of different fatty acids and different glycerides with

less ordered crystal lattice) would cause higher VCM loading into the SLNs, it was noticed that changing the lipid

type from Gelucire (44/14) to glyceryl tripalmitate significantly enhanced the EE% (Figure 5). This may be

explained by the high hydrophilic-lipophilic balance (HLB) value of Gelucire 44/14; the higher the HLB of the

surfactant, the lower the drug entrapment efficiency.34

The presence of Gelucire 44/14 augments the surfactant

function of PVA, leading to increased VCM escape and solubilization in the external phase, and hence, inhibiting

VCM encapsulation in Gelucire to form the SLNs.

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3.3.1.3. Statistical optimization of the results:

Statistical analysis of the effects of process and formulation variables was optimized using Design Expert

7.0.0 software. VCM-loaded SLNs were optimized to the highest EE%, lowest particle size (PS), lowest

polydispersity index (PDI), and highest zeta potential. Such optimization revealed that formula F6 was the optimum

formula, with a desirability factor of 0.640.

The selected optimized formula (F6) prepared using Glyceryl tripalmitate, 1% (w/w) low molecular weight

PVA and sonicated for 1 min. It showed 25.86% (w/w) VCM encapsulation, PS of 336.5 nm, PDI of 0.2185, and Z-

potential of around - 20.4 mV. This formula was integrated into the next design for further optimization, aiming to

enhance VCM encapsulation into SLN.

3.3.2. Influence of lipid structure on the characteristics of the VCM- loaded SLNs:

Because entrapment of the highly hydrophilic VCM into SLNs presented the main challenge, and since the

lipid type was the only factor that significantly influenced VCM entrapment into SLNs (EE%), it was necessary to

further investigate the influence of lipids by studying the effects of their structure and their ratio to the drug on the

characteristics of the produced VCM-loaded SLNs. Each of the independent factors was evaluated at three levels,

and experimental trials were performed in duplicate for all nine possible combinations as presented in Tables 4 and

5.

3.3.2.1. Particle size and zeta potential:

The mean particle size of the SLN formulations (F6.1- F6.9) ranged from 277.25 nm to 4414 nm, and the

mean PDI ranged from 0.205 to 0.682, except for one formula that reached 0.962 and was excluded from further

investigations (Table 5). ANOVA statistical analysis of these results revealed that only the lipid structure

significantly affected PS of SLNs, with a calculated P- value of 0.0058. The lipid:drug ratio did not show a

significant effect on SLNs at P<0.05 (Table 6 and Figure 6).

GMP-based SLNs showed significantly larger particle sizes compared to GDP and GTP, which were

insignificantly different from each other (Figure 6). Such large particle sizes of GMP-based SLNs may be due to the

physical instability of the monoglyceride-based SLNs, which tend to form aggregates, as previously reported by

Jenning and Gohla 35

. Figure 7 shows an agglomerated structure of GMP-based SLNs, where they formed large non-

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spherical (irregular shape) aggregates. Another reason for the large particle sizes of GMP-based SLNs could be the

perfect crystalline lattice of the monoacid triglycerides 36

, which may hinder their conversion to the less ordered

structure, especially because of the absence of any heat energy during SLN formation. On the other hand, GDP and

GTP are known to be bulky glycerides; this could sterically hinder the agglomeration of the formulated SLNs and

lead to discrete NP formation with smaller particle sizes.

In addition, a correlation was observed between PS and PDI of the formulated SLNs. The larger PS of the

SLNs produced using GMP as lipid was associated with broader particle size distribution (higher PDI values). On

the contrary, smaller particle size SLNs produced using either GDP or GTP were associated with smaller and more

acceptable particle size distributions (lower PDI values) (Table 5).

All nine formulations showed negative particle charge and mean zeta potentials between -16 and -25 mV,

which assures charge repulsion and stability of the formed SLNs. Statistical analysis via ANOVA revealed that only

the lipid structure significantly affected Z-values of the prepared SLNs, with a calculated P- value of 0.0147. The

lipid:drug ratio did not show significant effect at P<0.05.

3.3.2.2. Drug encapsulation efficiency (EE%):

The EE% of the SLN formulations F 6.1 through F 6.9, presented in Table 5, ranged from 10.69% to 23.42%

(w/w). Statistical analysis via ANOVA revealed that neither changing the lipid structure (GMP, GDP, or GTP), nor

the lipid:drug ratio (1:1, 3:1, or 5:1) significantly affected the EE% at P <0.05.

3.3.2.3. Statistical optimization of the results:

Statistical analysis of the lipids’ effects was optimized using Design Expert 7.0.0 software with optimization

set to the highest EE%, the lowest particle size (PS), the lowest polydispersity index (PDI), and the highest zeta

potential. Optimization results revealed that formula F 6.3 (prepared using glyceryl tripalmitate with a lipid:drug

ratio of 1:1) is the optimum formula, with a desirability factor of 0.815. F 6.3 showed 19.99% E.E., PS of 277.25 nm,

PDI of 0.2055, and Z-potential of around - 20.45 mV. This optimized formula was subjected to further investigation,

including morphological analysis, surface topography, and FTIR analysis.

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3.4. Morphological analysis and surface topography:

SEM images of the optimized formula (F 6.3) showed a relatively homogenous mixture, which is consistent

with the observed low PDI value. In addition, surface topography of the particles showed spherical appearance and

uniform size (Figure 8A).

TEM was carried out for the same formula (F 6.3) to obtain more information on the morphology of the

prepared SLNs. It revealed that the formulated SLNs have solid dense structures, and round homogenous shapes

(Figure 8B and C).

3.5. Fourier Transform Infrared Spectroscopy (FTIR):

FTIR spectroscopy was employed for the detection of any chemical interaction between the formulation

components and the drug by identifying the characteristic molecular groups.37

VCM spectrum (Figure 9A) showed a

phenolic O-H broad band at 3290 cm-1

and 3385 cm-1

, aromatic C=C at 1506 cm-1

, and C=O stretching at 1654 cm-

1.38

Also, the IR spectrum of GTP presented in Figure 9B showed the characteristic C-H stretching at 2848 cm-1

and

2916 cm-1

, and C=O stretching at 1735 cm-1

. The FTIR spectrum of PVA, presented in Figure 9C, displayed a broad

peak around 3414 cm-1

corresponding to the O-H groups, and C-H stretching at 2920 cm-1

and 2850 cm-1

.39

It was

observed that the IR spectra of the physical mixtures of the drug with different excipients proved that all

components maintained their characteristic bands (Figure 9D and Figure 9E), a fact that indicates the absence of any

possible chemical incompatibility or interaction between VCM and any of the formulation constituents in the solid

state. Furthermore, Figure 9F displays the IR spectrum for the final optimized formula (F 6.3), which demonstrated

attenuated peaks of the characteristic drug bands that may be due to drug dilution in the formula. However, there

was no shift in the position of the VCM characteristic peaks, which indicates the lack of significant interactions 40

between the drug and any of the constituents in the SLN formulation process. JUST A

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4. Conclusion:

In the current study, VCM-loaded SLNs were prepared by modified double emulsification (W/O/W), a

solvent evaporation technique. Statistical analyses via ANOVA of two full factorial designs were done to study the

effects of different process and formulation variables, as well as the effects of lipids’ structure and ratio on the

VCM-loaded SLNs.

All selected formulation and process variables proved successful in developing VCM-loaded SLNs of

appropriate particle sizes for ocular application and sufficiently high zeta potential that ensures good physical

stability of the suspension. However, low encapsulation efficiency restricted further clinical application. Such

compromised encapsulation may be due to very high water solubility of the drug, leading to its escape to the

external phase during SLN formulation. More trials were performed, aiming to achieve higher VCM encapsulation

by changing either the lipid structure or the lipid:drug ratio. However, only 25% VCM encapsulation was achieved.

Therefore, the present study proved that VCM incorporation into SLNs is successfully achievable. However,

further studies should be conducted to enhance its encapsulation into SLNs, by applying more restricting conditions

that prevent drug leakage to the external phase during formulation. Additionally, using different nanoencapsulation

materials and techniques, such as formulation of polymeric nanoparticles or nanomicellar carriers, could be valuable

for enhancing VCM encapsulation into the formulated NPs.

Acknowledgements:

The authors would like to thank the Ophthalmology teams at the Ophthalmology Hospital, the Research

Institute of Ophthalmology, and Kasr el-Aini Teaching Hospital (Cairo and Giza governorates, Egypt). Our gratitude

extends to Ophthalmology teams in Ismailia General Hospital (Ismailia governorate, Egypt) and the Ophthalmology

Hospital in Fayoum ) Fayoum governorate, Egypt) for their help and support in the multi-district microbiological

study.

Declaration of interest:

The authors report no conflict of interest, including financial, personal or any other relationships with other

people or organizations that could inappropriately influence or be perceived to influence the work in this paper.

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Figure Legends:

Figure 1. a) The observed bacterial class distribution (%) among the collected bacterial isolates. b) Distribution of

the identified bacterial species among the collected gram-positive isolates

Figure 2. Antibiotic susceptibility pattern of the five antibiotics proven active against the most prevalent S. aureus

isolates causing ocular infection in the Egyptian population

Figure 3. Susceptibility of the collected S. aureus isolates to VCM and MIC range

Figure 4. Line plots presenting the significant effects of a) type of lipid, b) molecular weight of PVA and c)

sonication time on the particle size of SLNs

Figure 5. Line plot presenting the significant effect of lipid type on the encapsulation efficiency

Figure 6. Line plot presenting the significant effect of lipid structure on the particle size

Figure 7. Agglomerated structure of VCM-loaded glyceryl monopalmitate-based SLNs

Figure 8. Particle morphology of glyceryl tripalmitate-based VCM-loaded SLNs (F 6.3) using SEM (A) and TEM

(B and C)

Figure 9. FTIR spectrum of VCM (A), glyceryl tripalmitate (B), Physical mixture of VCM:GTP (1:1) (C), polyvinyl

alcohol (PVA) (D), physical mixture of VCM:PVA (1:1) (E), the optimized SLNs (F 6.3) (F)

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Table 1. List of the standard antibiotic discs used in antibiotic susceptibility test and

their corresponding concentrations

Disc Content Conc.

(µg/ µl)

Amikacin (AK) 30

Ampicillin (AMP) 10

Ampicillin/Sulbactam (SAM) 10/10

Amoxycillin (AML) 10

Amoxycillin/Clavulinic acid (AMC) 20/10

Azithromycin (AZM) 15

Aztreonam (ATM) 30

Cefoperazone (CFP) 30

Cefoperazone/sulbactam (SCF) 105

Cefotaxime (CTX) 30

Ciprofloxacin (CIP) 5

Clarithromycin (CLR) 15

Gentamycin (CN) 10

Imipenem (IPM) 10

Levofloxacin (LEV) 5

Methicillin (MET) 5

Mezlocillin (MEZ) 75

Novobiocin 5

Piperacillin (PRL) 100

Trimethoprim/Sulphamethoxazole (SXT) 1.25/23.75

VCM (VA) 30

Linezolid 10

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Table 2. Levels of the studied variables in a 24 full factorial design for the process and

formulation variables affecting the characteristics of VCM- loaded SLNs

Factor

Investigated Level

Low level

(-1)

High level

(+1)

X1: Lipid type Gelucire 44/14 GTPa

X2:PVAbM.Wt. Low M.Wt. (22000) High M.Wt.(72000)

X3:PVA (%) 0.5 1

X4:Sonication time (min.) 1 2

Abbreviations: aGTP: Glyceryl tripalmitate,

bPVA: Polyvinyl alcohol.

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Table 3. Composition of SLNs formulation corresponding to the 24 full factorial design with

their resultant dependent variables (Characteristics of SLNs)

Formulae and their composition at various

independent

variables levels

Dependent variables

Formula

number

Lipid

type

PVA

M.Wt.

PVA

Conc.

(%

w/w)

Sonicat

ion

time

(min)

Mean

PSd ±

SD

(nm)

Mean

PDIe ±

SD

Mean

Zf ±

SD

Mean

EE%g

± SDh

(%

w/w)

F1 Gelucire

44/14 Low

b 0.5 1

280.10±

152.31

0.377±

0.098

-

35.95±

2.62

10.77±

0.56

F2 GTPa

Low 0.5 1 347.60±

21.49

0.270±

0.070

-

22.30±

0.42

16.56±

6.12

F3 Gelucire

44/14 High

c 0.5 1

286.95±

7.57

0.430±

0.052

-

25.45±

1.63

3.02±0

.45

F4 GTP High 0.5 1 762.35±

6.43

0.375±

0.001

-

17.15±

1.77

18.00±

3.99

F5 Gelucire

44/14 Low 1 1

162.25±

5.30

0.218±

0.004

-

29.60±

0.71

8.77±0

.11

F6 GTP Low 1 1 336.55±

17.75

0.219±

0.004

-

20.40±

0.85

25.86±

7.93

F7 Gelucire

44/14 High 1 1

214.05±

41.51

0.414±

0.031

-

25.15±

0.07

6.25±1

.82

F8 GTP High 1 1 1303.00

±96.17

0.827±

0.074

-

15.45±

0.64

19.11±

7.93

F9 Gelucire

44/14 Low 0.5 2

141.75±

0.21

0.237±

0.027

-

30.90±

3.54

6.52±5

.39

F10 GTP Low 0.5 2 268.65±

3.61

0.164±

0.042

-

23.65±

2.05

13.87±

1.82

F11 Gelucire

44/14 High 0.5 2

136.70±

1.13

0.358±

0.001

-

20.65±

2.76

7.56±0

.03

F12 GTP High 0.5 2 432.70±

40.16

0.356±

0.023

-

12.20±

3.89±1

.65

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1.56

F13 Gelucire

44/14 Low 1 2

135.75±

0.21

0.184±

0.001

-

25.55±

0.35

7.75±6

.11

F14 GTP Low 1 2 269.00±

5.52

0.162±

0.002

-

21.35±

0.64

11.85±

6.02

F15 Gelucire

44/14 High 1 2

192.75±

11.95

0.321±

0.046

-

24.20±

5.23

7.48±1

.27

F16 GTP High 1 2 799.40±

20.93

0.539±

0.062

-

14.10±

0.99

19.58±

2.48

Abbreviations: aGTP: Glyceryl tripalmitate,

b Low PVA M. Wt.: 22000,

c high PVA M. wt.: 72000,

d PS: particle size,

e PDI: Polydispersity index,

fZ: zeta potential,

g EE%: percentage encapsulation efficiency of VCM- loaded SLNs

and h SD: standard deviation from the mean.

Table 4. levels of the studied variables in a 32 full Factorial design addressing the influence of

lipid structure and lipid: drug ratio on the characteristics of VCM- loaded SLNs

Independent Variable

Level investigated

Low level

(-1)

Medium level

(0)

High level

(+1)

X1: Lipid structure GMPa

GDPb

GTPc

X2:Lipid: Drug ratio 1:1 3:1 5:1

Abbreviations: aGMP: Glyceryl monopalmitate,

b GDP: Glyceryl dipalmitate and

c GTP: Glyceryl

tripalmitate

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Table 5. Composition of SLNs formulation corresponding to the 32 full factorial design with

their resultant dependent variables (Characteristics of SLNs)

Formulae and their composition at various

independent variables levels

Dependent variables

Mean PSd

± SD

(nm)

Mean

PDIe ± SD

Mean

Zf± SD

Mean

EE %

g ±

SDh

(%

w/w)

Formula Lipid

Structure

Lipid : Drug

Ratio

F6.1 GMPa

1:1 741.25±55

.37

0.611±0.0

53

-

25.50±

0.99

17.15±

5.49

F6.2 GDPb 1:1

295.90±3.

96

0.266±0.0

07

-

16.35±

0.21

20.28±

0.20

F6.3 GTPc 1:1

277.25±5.

87

0.206±0.0

01

-

20.450

±0.92

19.99±

0.39

F6.4 GMP 3:1 2772.45±2

536.46

0.682±0.0

48

-

21.65±

4.03

23.42±

4.35

F6.5 GDP 3:1 344.60±2.

12

0.240±0.0

16

-

19.90±

0.14

15.72±

0.40

F6.6 i GTP 3:1

339.60±6.

22

0.281±0.0

04

-

20.60±

2.55

21.49±

4.04

F6.7 GMP 5:1 4414.00±2

135.46

0.962±0.0

25

-

20.40±

2.26

13.52±

3.59

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F6.8 GDP 5:1 372.80±18

.67

0.248±0.0

26

-

17.95±

0.64

15.13±

7.40

F6.9 GTP 5:1 393.20±28

.85

0.279±0.0

43

-

20.40±

2.26

10.69±

7.03

Abbreviations: aGMP: Glyceryl monopalmitate,

b GDP: Glyceryl dipalmitate,

c GTP: Glyceryl tripalmitate,

d PS: particle size,

e PDI: Polydispersity index,

f Z: zeta potential,

g EE%: percentage encapsulation

efficiency of VCM- loaded SLNs, and h

SD: standard deviation from the mean,

i: formula F6.6 is the same as formula F6 obtained from the first factorial design.

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Table 6. Results for statistical analysis via Analysis of variance (ANOVA) for the characteristics

of VCM- loaded SLNs

Source of Variation

P- value

PSa

PDIb Z

c EE%

d

Effect of

Process and

formulation

variables

X1: Lipid type < 0.0001* 0.2716 < 0.0001* <0.0001*

X2:PVA M.Wt. 0.0006* < 0.0001* < 0.0001* 0.2526

X3:PVA 0.1901 0.3502 0.1036 0.0809

X4:Sonication time 0.0269* 0.0220* 0.0167* 0.0506

Effect of lipid

X1: Lipid structure 0.0058* < 0.0001* 0.0147* 0.9411

X2:Lipid: Drug ratio 0.0800 < 0.0001* 0.3814 0.0534

Abbreviations: *: Significant at p<0.05, aPS: particle size,

bPDI: Polydispersity index,

cZ: zeta potential,

dEE%: percentage encapsulation efficiency of VCM- loaded SLNs.

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