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Synthesis of fish gelatin nanoparticles and their application for the drug delivery based on response surface methodology

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Fish gelatin nanoparticle is produced using Tilapia fish skin for the first time by the two-step desolvation method. Fish gelatin is chosen for producing gelatin nanoparticles because no experiment have been done in using fish gelatin and to counter the problem associated with the use of mammalian gelatin, such as bovine spongiform encephalopathy disease. The effects of several factors on the particle size such as pH, acetone concentration, glutaraldehyde volume, stirring speed and stirring time are evaluated. Optimum conditions for the formation of gelatin nanoparticles are obtained using response surface method. Fish gelatin nanoparticles with optimum size of 198.46 ± 6.1 nm can be produced using pH of 2.45, acetone percentage of 16% (vol%), glutaraldehyde 400 μℓ, stirring speed of 600 rpm, and stirring time for 6 h. The thermogram and molecular interaction of fish gelatin and fish gelatin nanoparticles are characterized using DSC and FTIR. In vitro drug release kinetic is examined using 5-fluorouracil as the model drug. The entrapment efficiency of 5-fluouracil as model drug is determined to be 40%. Fish gelatin could be used as a good alternative drug carrier for mammalian gelatin.
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Synthesis of fish gelatin nanoparticles and their application for the drug
delivery based on response surface methodology
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1. Introduction
Gelatin nanoparticles are an excellent material for drug
delivery applications due to their biodegradability [1], bio-
compatibility [2], ability for surface modication [3], ligand
attachment for target delivery and the availability of raw mat-
erials [4, 5]. Gelatin is produced from hydrolyzed collagen
extracted from bovine bone, pig skin, and sh skin. Bovine
and porcine gelatins have been extensively used to fabricate
nanoparticles and to encapsulate the drug. For examples,
bovine gelatin has been used to encapsulate cisplatin for
cancer cell treatment [6], and porcine gelatin has been studied
for encapsulating amphotericin [7] and ibuprofen [8]. The
encapsulation efciency of anticancer drugs have also been
investigated using mammalian gelatin for doxorubicin [9],
paclitaxel [10], cisplatin [11] and uorouracil [12].
However, the use of mammalian-origin materials in drugs
and food products are limited due to religion and ethical rea-
sons. Not only Jew and Muslim groups are forbidden from
consuming porcine gelatin [13], but also not so long ago
bovine spongiform encephalopathy (BSE) in Europe has
raised several questions concerning the use of pig gelatin
Advances in Natural Sciences: Nanoscience and Nanotechnology
Synthesis of sh gelatin nanoparticles and
their application for the drug delivery based
on response surface methodology
DeniSubara1, IrwandiJaswir1,2, MaanFahmiRashidAlkhatib1 and
IbrahimAliNoorbatcha
1 Faculty of Engineering, Department of Biotechnology Engineering, International Islamic University
Malaysia, 53100, Malaysia
2 International Institute for Halal Research and Training (INHART), International Islamic University
Malaysia, 53100, Malaysia
E-mail: irwandi@iium.edu.my
Received 31 May 2018
Accepted for publication 20 September 2018
Published 13 December 2018
Abstract
Fish gelatin nanoparticle is produced using Tilapia sh skin for the rst time by the two-step
desolvation method. Fish gelatin is chosen for producing gelatin nanoparticles because no
experiment have been done in using sh gelatin and to counter the problem associated with
the use of mammalian gelatin, such as bovine spongiform encephalopathy disease. The effects
of several factors on the particle size such as pH, acetone concentration, glutaraldehyde
volume, stirring speed and stirring time are evaluated. Optimum conditions for the formation
of gelatin nanoparticles are obtained using response surface method. Fish gelatin nanoparticles
with optimum size of
198.46 ±6.1 nm
can be produced using pH of 2.45, acetone percentage
of 16% (vol%), glutaraldehyde
400 µ
, stirring speed of 600 rpm, and stirring time for 6 h.
The thermogram and molecular interaction of sh gelatin and sh gelatin nanoparticles
are characterized using DSC and FTIR. In vitro drug release kinetic is examined using
5-uorouracil as the model drug. The entrapment efciency of 5-uouracil as model drug
is determined to be 40%. Fish gelatin could be used as a good alternative drug carrier for
mammalian gelatin.
Keywords: sh gelatin, nanotechnology, desolvation, macromolecular drug, cross-lingking
Classication numbers: 4.02, 5.08, 5.09
D Subara etal
045014
ANSNCK
© 2018 Vietnam Academy of Science & Technology
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10.1088/2043-6254/aae988
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Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014 (11pp)
D Subara etal
2
for gelatin application [14]. Under these circumstances, sh-
origin materials provided an alternative for producing gelatin
nanoparticles. Furthermore, sh gelatins are being rarely
used to produce gelatin nanoparticles. Until now, sh gelatin
are used for producing gelatin lm [15], and nanobre [16].
Because of that, the experiment should be done to optimize
the production of gelatin nanoparticles from sh gelatin.
Several methods have been used for producing gelatin nan-
oparticles such as the emulsion [17], coacervation [18], self-
assembly [19], and desolvation method [20]. Among those
approaches the desolvation method was found to provide
small particles and narrow size distribution [21]. The effects
of several factors on gelatin nanoparticles size have been
investigated, such as gelatin concentration, pH, and acetone
volume [2225]. However, those reports have just focused
on single factor exploration, which precluded the interaction
between various factors. Systematic investigations have not
been carried out yet in optimizing sh gelatin nanoparticles
(FGNPs) production incorporating the simultaneous effects of
several signicant factors. Thus, the optimization of produc-
tion sh gelatin nanoparticles using response surface method
should be employed.
The chosen factors for producing gelatin nanoparticles
depend on the gelatin characteristic, whereas the gelatin prop-
erties also depend on their resources [26]. Thus there are dif-
ferences between mammalian gelatin and sh gelatin. The
specic characteristic of gelatin is the triple helical structure.
The proline and hydroxyproline contents are approximately
30% for mammalian, and 22-25% for warm-water shes such
as tilapia and nile perch [27]. These differences on sh gelatin
lead to lower gel modulus and lower melting temperatures
compared with mammalian gelatin. Hence the conditions
required for the production of sh gelatin might vary with that
for the production of mammalian gelatin.
In this study focus was to explore the potential of sh gel-
atin to produce FGNPs. Tilapia sh skin was used as a source
of gelatin. Tilapia sh is commonly breaded in sh farms in
Malaysia and hence tilapia sh skin is in abundance such as in
lled production company. In this context, we aim to improve
the desolvation method and to obtain optimum conditions for
the production of FGNPs. The signicant factors were chosen
based on the one-factor-at-time method, and response surface
method then was used to optimize these signicant factors.
The differences in thermal behavior and molecular interac-
tion between sh gelatin and sh gelatin nanoparticles were
studied using differential scanning calorimetry (DSC) and
Fourier-transform infrared (FTIR) spectroscopy. The nano-
particles shape was also quantied using scanning electron
microscope (SEM). These experiments allowed us to deter-
mine a reproducible formulation of small sized FGNPs with
narrow size distribution.
2. Experimental
2.1. Materials
Tilapia sh gelatin (gelatin type A) with gel strength of
128.11 g bloom. Acetone, hydrochloric acid and sodium
hydroxide were of analytical grade and were ordered from
Sigma. Glutaraldehyde (25 vol%, grade I aqueous solu-
tion), and 5-uorouracil (5-FU) were purchased from Sigma.
Double distilled water was used for all the experiments. All
chemicals were of analytical grade and used as received.
2.2. Production of gelatin nanoparticles
FGNPs were produced using the two-step desolvation method
with slight modications [28]. The rst step is the fraction-
ation of low molecular weight (LMW) and high molecular
weight (HMW) of gelatin. The second step is the precipita-
tion step to produce nanoparticles. As the second step or pre-
cipitation step is crucial for the formation of nanoparticles, the
focus was on the optimization of this step.
The fractionation step started by dissolving 0.9 g sh gela-
tins in 10 ml distilled water under constant heating
(45C)
and
stirring (600 rpm) until a clear solution was achieved. Acetone
was used as co-solvent to precipitate the high molecular
weight. The percentage composition of acetone was calculated
with respect to the total volume of the mixture (100 ml). About
10 ml acetone was added to the gelatin solution and was cen-
trifuged at 12000 g for 5 min. The HMW fraction was obtained
in the precipitate, and LMW fraction was in the supernatant
solution. The supernatants containing LMW fractions were
discarded. The HMW gelatin was dissolved again with 10 ml
distilled water. The precipitation step begins with adjustment
of pH of gelatin solution to the desired value by adding 0.1 M
HCl and NaOH. FGNP were produced by adding 16 ml ace-
tone to HMW gelatin solution and
400 µ
of glutaraldehyde
solution (25 vol%). The nanoparticles were centrifuged and
washed three times. The acetones were removed by evapora-
tion in a water bath at
45C
temperature. The nanoparticles
were stored in temperatures of
3C5C
for further research.
2.3. Design of experiment
The precipitation step in the production of FGNPs was optim-
ized sequentially. The screening for signicant factors was
done using the one-factor-at-a-time (OFAT) method. On the
basis of previous work, the effects of parameters like pH, ace-
tone concentration, glutaraldehyde volume, stirring speed, and
stirring time on the size of FGNPs were studied. The levels
of factors are summarized in table1. The experiments were
done in triplicates. The results were given as mean ± standard
Table 1. Factors and levels for screening signicant factors Total
volume of the mixture is kept at 100 ml.
No Factor
Level
1234
1 pH 1.5 2.5 3.5 4.5
2 Concentration of
acetone (%, v/v)
5 15 25 35
3 Volume of
glutaraldehyde
(µ)
300 475 650 825
4 Stirring speed (rpm) 150 300 450 600
5 Stirring time (h) 3 6 12 18
Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
D Subara etal
3
deviation (SD) of three independent experiments and statis-
tically analyzed by ANOVA, followed by the Tukey test to
compare the different nanoparticle batches.
The signicant factors were optimized using response sur-
face methodology (RSM) based on three levels of face cen-
tered central composite design (FCCD) (table 2). The levels
of parameters were selected based on the OFAT results. Mean
particle sizes of FGNPs were used as the response variable.
A total of 18 experimental runs including six center points
were generated by design-expert version 7.0 software (State-
Ease Inc., Minneapolis, MN). The runs were carried out in
triplicates.
2.4. Determination of isoelectric point of sh gelatin
Isoelectric point of sh gelatin was identied according to
preferred method [29]. In brief, about 10 ml of 1% (w/v) sh
gelatin was prepared in distilled water and pH was adjusted
with 0.1 M HCl or 0.1 M NaOH. The isoelectric point of sh
gelatin was determined by the measuring the turbidity as iden-
tied through the measured maximum intensity at 360 nm in
UV-vis spectrum [29, 30].
2.5. Loading of sh gelatin nanoparticles with drug
5-uorouracil (5-FU) was chosen as a model drug because
it has been used as the major chemotherapeutic agent. 5-FU
loaded FGNPs were produced by adding 5-FU directly to sh
gelatin solution at the precipitation step.
2.6. Determination of particle size and zeta potential
Mean particle size and zeta potential of FGNPs were deter-
mined using Zeta Sizer Nano Malvern (Zen 3600, UK). The
mean diameters and polydispersity index (PI) values were
Table 2. Independent variables and their corresponding levels of
second step FGNPs preparation for FCCD.
Factor Variable
Level
1 0 +1
X1pH 1.5 2.5 3.5
X2Concentration of acetone (%, v/v) 5 15 25
X3Volume of glutaraldehyde
(µ)
300 475 650
spectrum FTIR over a diamond crystal. Small amount of sam-
ples (±1 mg) was placed in the diamond crystal and the FTIR
spectra were recorded in the range of
4000400 cm1
. The
results were plot between transmittance (%) and wavenumber
(cm1)
.
The thermal behaviour of sh gelatin and sh gelatin nano-
particles was obtained using a differential scanning calorim-
eter (DSC-60, Shimadzu, Japan). The samples were prepared
using aluminium pans and empty aluminium pan was used as
a reference. About 5 mg of samples were sealed and heated
from 20 °C to
300C
at a rate of heat ow of
10 C min1
.
2.8. Morphologies characterization by scanning electron
microscopy
Field emission scanning electron microscopy (FESEM JEOL,
JSM 6700F Model) was used to observe the size and shape of
FGNPs. Briey, a small amount of dry FGNPs was mounted
on aluminum plates and, pasted with double sided copper
tapes. Then the samples were sputtered with a thin layer of
gold and placed on the packet chamber at an accelerating
voltage of 10 kV.
2.9. Transmission electron microscopy
Transmission electron microscopy (TEM) of sh gelatin
nanoparticles was performed using a Philips Tecnai F
20.2 µ
of FGNP sample was dropped on the copper TEM grid and air
dried for 2 h. The morphology data was carried at an acceler-
ating voltage of 200 kV.
2.10.Drug content and in vitro drug release
UV-vis spectrophotometer was used to determine the concentra-
tion of 5-FU in the FGNPs. About 5 mg of dried FGNP loaded
with 5-FU were dispersed in 5 ml of phosphate-buffered saline
(PBS) (pH 7.4) at room temperature
(23 C±2C)
, containing
2.5 mg trypsin. After 6 h of digestion, the samples were diluted
to 25 ml and ltered using
0.22 µm
lters. The absorbance was
measured at
λmax =265 nm
using Sartorius-Stedim VivaSpec
UV-vis spectrophotometer. A calibration curve was prepared
with different 5-FU concentrations in PBS (the presence of
gelatin and trypsin had no effect on the absorbance intensity).
Unloaded FGNP were used as a blank. The entrapment ef-
ciency (EE) was calculated using following equation
obtained at 90° angle in 10 mm diameter cells. Each measure-
ment was conducted in triplicates.
2.7. Physiochemical characterization
The experiment of FTIR was made on lyophilized samples of
sh gelatin and sh gelatin nanoparticles using Perkin Elmer
EE(%) =
total amount of drug added-amount of free drug present in supernatant
total amount of drug added in formulation
×
100.
(1)
In vitro release of 5-FU was determined using the method
previously described [25]. A weight of 5 mg of FGNPs was
dispersed in 25 ml of PBS (pH 7.4). About 2 ml sample was
withdrawn at dened time intervals and was centrifuged for
20 min at 10000 g. Then 1 ml aliquots were withdrawn from
the supernatant and added back to the original solution. The
pellets were redispersed in 1 ml of PBS and added to the
Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
D Subara etal
4
original solution to keep the particle concentration constant.
The amount of 5-FU in the supernatant was quantied using a
UV-vis spectrophotometer. All the experiments were repeated
three times and the average values along with the errors were
calculated.
2.11. Calculation of release kinetics
In vitro 5-FU release behavior from FGNPs was studied using
DDSolver utilizing data up to 60% cumulative release [31].
DDsolver is the supplement program in microsoft excel which
used nonlinear least-squares curve tting by minimizing the
sum of square differences between the observed and the pre-
dicted drug dissolution values at time intervals
t
, with the
best model parameters [32]. Five models were used in this
experiment namely zero order, rst order, Higuchi, Weibull,
and Korsmeyer-Peppas. The DDsolver also calculates several
parameters allowing the statistical tness evaluation of the
model. The most appropriate model to t the dissolution data
should give the highest value of adjusted coefcient of deter-
mination (
R2
adjusted), smallest value of akaike information
criterion (AIC), and the largest value of model selection cri-
terion (MSC).
Exponent
n
of the Korsmeyer-Peppas model gives infor-
mation about the release mechanism of the drug according to
following equation
Qt=ktn,
(2)
where
Qt
is a fraction of drug released at the time
t
,
k
is the
release rate constant and
n
is the release exponent. The
n
value
is used to characterize different release for cylindrical shaped
matrices. If
n0.45
it is a Fickian diffusion, if
n=0.85
it is
a case II transport, which is related to polymer matrix relax-
ation and swelling. If
0.45 <n<0.85
it corresponds to an
anomalous transport, resultant from the combination of both
mechanisms and if
n>0.85
it is a super case II transport
[33].
3. Results and discussion
3.1. Selection of signicant variables
3.1.1. Effects of pH. Figure 1 shows the effects of pH on the
size of FGNP. The smallest nanoparticles were found to form
at pH 2.5. Decreasing the pH produced small nanoparticles
until a pH of 2.5
(p0.05)
. No signicant increase in the
particle size was observed if the pH was below 2.5.
The pH value of gelatin solution before the precipitation
step is shown at pH of 4.6. In the precipitation step, pH was
adjusted to be below the isoelectric point because the isoelec-
tric point of sh gelatin was determined at pH 6 (gure 2). As
shown in gure1, the nanoparticle size increased as the pH
is near to the isoelectric point. The lowering of pH leads to
protonation of the sh gelatin molecules.
The lowest pH value to produce the smallest FGNPs was at
2.5. This result is slightly different compared to most mamma-
lian gelatin. Previous studies on porcine gelatin have indicated
that the pH value to produce nanoparticles in small sizes was
3.25 [34]. This might be due to the difference on negatively
charged amino acid contents in the gelatin molecule. The
sh gelatins have more negatively charged amino acids than
mammalian gelatin; hence sh gelatin needs a more acidic pH
for smaller nanoparticle formation compared to mammalian
gelatin. Gelatin is sequences of amino acids. Fish gelatin mol-
ecule contains ~14% of negatively charged glutamic acid and
aspartic acid, ~7% of positively charged lysine and arginine,
~8% of the chain hydrophobic amino acids (leucine, isoleu-
cine, methionine, and valine) and ~40% of glycine [21] while
mammalian gelatin contains ~13% of positively charged,
~12% of negatively charged and ~11% of hydrophobic chain.
This difference in the amino acid composition affects the solu-
bility characteristic of sh gelatin in water molecules and the
processing parameters in the production of FGNPs [27].
3.1.2. Effects of acetone concentration. Acetone is used as
a co-solvent in the production of FGNPs. Figure3 shows the
Figure 1. Effect of different pH levels to nanoparticles size, while
acetone concentrations at 40% (vol%), glutaraldehyde volume at
350 µ
, stirring speed at 600 rpm and stirring time at 6 h. Signicant
differences by students t-test are marked by asterisk,
p<0.05
.
Figure 2. UV-vis absorption (at 360 nm) of sh gelatin aqueous
(1% w/v) at different pH.
Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
D Subara etal
5
effects of acetone concentration on the FGNPs size. It can be
seen that increasing acetone concentration lead to increase
in nanoparticle size whereas 15% (vol%) acetone produced
smallest nanoparticle sh gelatin
(p0.05)
. Similar trends
were observed in the previous studies [35, 36].This has been
attributed to the role of acetone molecules in disturbing the
interaction between water and gelatin molecules resulting in
gelatin molecule aggregation.
Interestingly, tilapia sh gelatin requires lesser acetone
to produce nanoparticles compared to mammalian gelatin.
Tilapia sh gelatin used less than 15% acetone whereas mam-
malian gelatin needs around 70% of acetone [37, 38]. This is
also due to the difference on amino acid content.
Acetone have been chosen as a desolvating agent because
acetone is highly miscible in water and prevents denaturation
of gelatin compared to ethanol [39]. In addition, acetone pro-
duced small sized gelatin nanoparticles with lower polydis-
persity index compared to ethanol [40].
The addition of acetone to the gelatin solution is to change
the solubility character of the gelatin molecule. In the water
environment, the hydrophilic amino acids existing on the sur-
face interact with the polar water molecules forming hydrogen
bonds, while the hydrophobic amino acids remain inside
that gelatin in the core. The water molecules surrounded the
hydrophilic sectionof gelatin, and stabilized the gelatin mol-
ecule structures by hydrogen bonds.
When acetones are added to the gelatin solution, the solu-
bility of gelatin will begin to decrease and become insoluble
until a certain acetone concentration is reached. This is because
of the decrease in the amount of hydrogen bond that available
to interact with the hydrophilic amino acids of gelatin [40].
Decreasing the hydrogen bonds destabilized the individual
gelatin molecules leading to their aggregation. Hence, we can
say that the aggregation is the rearrangement of the interaction
between non-polar sectionsof gelatin to stabilize the gelatin
molecule.
3.1.3. Effects of glutaraldehyde as crosslinking agent. The
effect of glutaraldehyde volume on the size of the FGNPs was
shown in gure4. It can be seen that increasing the volume
of glutaraldehyde during production of FGNPs decreased
the size of the FGNPs initially
(p0.05)
. However, further
addition of glutaraldehyde volume increased the FGNPs size.
Glutaraldehyde is soluble in water and categorized as a
hydrophilic cross-linking agent [21]. Glutaraldehyde works
by hardening the particle through the crosslinking of amino
Figure 3. Effect of different acetone concentration levels to
nanoparticles size, while pH at 3.5, glutaraldehyde volume at
350 µ
, stirring speed at 600 rpm and stirring time at 6 h. Signicant
differences by students t-test are marked by asterisk,
p<0.05
.
Figure 4. Effect of different glutaraldehyde volume levels to
nanoparticles size, while pH at 3.5, acetone concentration 40%
(vol%), stirring speed at 600 rpm and stirring time at 6 h. Signicant
differences by students t-test are marked by asterisk,
p<0.05
.
Figure 5. Effect of different stirring speed levels to nanoparticles
size, while pH at 3.5, acetone concentration of 40% (vol%),
glutaraldehyde volume of
350 µ
, and stirring time at 6 h.
Figure 6. Effect of different stirring time levels to nanoparticles
size, while pH at 3.5, acetone concentration 40% (vol%),
glutaraldehyde volume at 350 µl, and stirring speed at 600 rpm.
Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
D Subara etal
6
acid chains [41]. Initial addition of glutaraldehyde volume
enhances the formation of smaller size nanoparticles by
crosslinking the intra amino acid bonds. However, fur-
ther increase in glutaraldehyde facilitates the formation of
inter-molecular linkages among the nanoparticles, thereby
increasing the nanoparticle size at high concentrations. Thus,
the nanoparticle sizes are controlled by the delicate balance
between inter and intra-molecular bonds among the gelatin
molecules [40].
3.1.4. Effects of stirring speed. As shown in gure 5, the
nanoparticle size is found to decrease as the stirring speed
increased from 150 rpm to 600 rpm which is the maximum
allowable stirring speed in our experimental setting. However,
no signicant effect
(p0.05)
of the stirring speed on the
nanoparticle size was observed. The increase in stirring speed
lead to increase in shearing energy to break the large particles
into smaller particles, which could prevent huge agglomera-
tion, but further increase of the stirring rate, does not have
a signicant impact in decreasing the particle size [42, 43].
Thus, the stirring speed should be maintained at 600 to pro-
duce smaller nanoparticle sizes.
3.1.5. Effects of stirring time. Figure 6 depicts the effects of
stirring time on nanoparticles size. It shows that 6 h of stirring
time produced smaller nanoparticles compared to 12 h of stir-
ring time. This demonstrates that further increase in stirring
time led to increase in particle size, which might be attributed
to agglomeration due to enhancement in kinetic energy with
an additional input of energy [44]. However, the variation in
stirring time shows insignicant differences to the response
(p0.05)
. Hence, 6 h was chosen and kept constant in the
optimization design.
From the screening results, factors with p-value of less than
0.05 were signicant on the response, and were selected for
further optimization. Results showed that pH, acetone con-
centration and glutaraldehyde volume have signicant effects
on producing smaller nanoparticle sizes, while stirring speed
and stirring time gave insignicant effects. The optimum
levels of these signicant factors (pH, acetone concentration,
Table 3. Experimental design and results of the face central composite design.
Treatment No.apH Acetone (%, v/v) Glutaraldehyde
(µ)
Experimental
mean size (nm)
Predicted mean
size (nm)
1 1.5 5 300 219.26 ± 1.84 217.32
2 3.5 5 300 245.43 ± 1.48 248.78
3 1.5 25 300 234.25 ± 1.19 236.20
4 3.5 25 300 232.73 ± 2.88 231.86
5 1.5 5 650 276.09 ± 3.24 274.14
6 3.5 5 650 215.59 ± 4.84 218.92
7 1.5 25 650 233.39 ± 0.91 235.34
8 3.5 25 650 216.79 ± 1.09 215.92
9 1.5 15 475 219.23 ± 1.55 220.11
10 3.5 15 475 212.24 ± 2.22 208.23
11 2.5 5 475 221.01 ± 2.19 218.93
12 2.5 25 475 210.38 ± 0.76 208.97
13 2.5 15 300 207.38 ± 1.33 205.63
14 2.5 15 650 225.38 ± 2.31 223.63
15 2.5 15 475 187.03 ± 3.30 203.97
16 2.5 15 475 197.68 ± 9.60 203.97
17 2.5 15 475 189.79 ± 5.13 203.97
18 2.5 15 475 195.88 ± 9.35 203.97
19 2.5 15 475 227.16 ± 5.79 203.97
20 2.5 15 475 205.33 ± 9.48 203.97
The result are mean
±SD(n=3)
.
a The treatment were run as a random order.
Table 4. Analysis of variance (ANOVA) for the parameters of
response surface methodology tted to second-order polynomial
equation.
Source of variation
Sum of
squares
Degree of
freedom
F-
value p-value
Model 15668.51 10 39.22 <0.0001
X1
1060.09 1 26.54 <0.0001
X2
745.24 1 18.65 0.0001
X3
486.18 1 12.17 0.0013
X1X3
3882.82 1 97.19 <0.0001
X2X3
718.90 1 18.00 0.0001
X2
1
793.50 1 19.86 <0.0001
X2
2
787.61 1 19.71 <0.0001
X2
3
898.60 1 22.49 <0.0001
X1X2X3
1921.74 1 48.10 <0.0001
X2
1X3
286.67 1 7.18 0.0110
Residual 1478.14 37
Lack of t 276.70 4 1.90 0.1338
Pure error 1201.44 33
Cor total 17146.65 47
Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
D Subara etal
7
Figure 7. Response surface plots for mean nanoparticles size of FGNPs showing interaction between (a): acetone concentration (v/v) and
pH, (b): glutaraldehyde volume
(µ)
and pH, and (c): glutaraldehyde volume and acetone concentration.
Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
D Subara etal
8
and glutaraldehyde volume) were further determined by a
response surface method.
3.2. Optimization by response surface methodology
The optimization process was conducted using response sur-
face method (RSM). Three factors namely pH solution
(X1)
,
acetone concentration
(X2)
and glutaraldehyde volume
(X3)
were chosen as independent factors, whereas mean particles
size was a dependent factor. The other two factors namely the
stirring speed and stirring time were kept constant at 600 rpm
and 6 h. All experimental designs were conducted in trip-
licates. The result of the response surface method is shown
in table3. It can be seen that the mean particles size varied
from 190 nm to 276 nm. The small particle was produced at
the center point of the design.
A suitable polynomial equation involving the main indi-
vidual effects and interaction factors were selected based on
estimation of several statistical parameters provided by the
Design of Expert® software. Equation(3) depicts a multiple
regression analysis of the experimental data for mean parti-
cles size, where
X1
is pH,
X2
is acetone concentration, and
X3
is volume of glutaraldehyde. All parameters used in the
polynomial equationswere found to be statistically signicant
(p<0.05)
.
Mean particle size
(
nm
)=
203.97
5.94X
1
4.98X
2
+9.00X312.72X1X25.47X2X3+10.02X2
1+9.98X
2
2
+10.66X2
3
+8.95X
1
X
2
X
3
7.734X2
1
X
2
.
(3)
The statistical signicance of the model equationwas eval-
uated by the F-test for analysis of variance (ANOVA), which
showed that regression is statistically signicant at 95%
(p<0.05)
condence level. The value of p less than 0.05
indicates that the model terms are also signicant. The lack of
t p-value larger than 0.05 implies that the lack of t is non-
signicant, which means the model is strong enough with less
noise. In the response surface design, ANOVA was also used
to determine the signicant contribution of main variables and
their interactions on the response variables.
It can be seen from table4, pH
(X1)
, concentration of ace-
tone
(X2)
, glutaraldehyde volume
(X3)
, cross product contrib-
ution
(X1X3,X2X3)
, quadratic contribution
X1X2X3
, and
X2
1X3
were signicant. The regression equation obtained from
ANOVA depicted that the
R2
(multiple correlation coefcient)
was at 0.9195 (a value > 0.75 indicates tness of the model).
This results estimates that if the fraction of overall variation in
the data is accounted by the model, thus the model is capable
of explaining 91% of the variation in response. The adjusted
R2
is 0.8949 and the predicted
R2
is 0.8959; these values near
to 1.0 indicate that the model is good. The adequate precision
value at 23.266 also explains that the model is good.
The response surface plots were constructed by plotting the
mean particle size as a response on the z-axis against any two
independent variables, while other variables were kept at their
optimal levels. This plot is to determine the optimal levels of
each variable for production FGNPs (gure 7).
The formulations of variables were then optimized for
response of mean particle size. The optimum values of the
variables were obtained by numerical analysis using design
expert software and based on the criterion of desirability.
Afterwards, a new run of production FGNPs with the pre-
dicted levels were conducted to conrm the validity of the
Figure 8. DSC thermograms of (a) sh gelatin and (b) sh gelatin
nanoparticles in the optimum formulation.
Figure 9. FTIR spectra of (a) sh gelatin and (b) sh gelatin
nanoparticles in the optimum formulation.
Figure 10. TEM image of sh gelatin nanoparticles.
Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
D Subara etal
9
optimization procedure. The optimized variables were found
at pH 2.45, acetone percentage at 16 %(v/v) and glutaralde-
hyde volume at
400 µ
.
Figure 7(a) represents multiple interactions between pH
and acetone to FGNP size. It can be seen that around 15%
concentration of acetone was required to produce small nano-
particles at pH 2.5, while a slightly higher concentration of
20% acetone was required to produce small nanoparticles at
pH 1.5. A decrease in pH led to the increase of interactions
between the positive charges in the gelatin molecule with
water ions [34]. This condition requires high acetone concen-
tration to interrupt the water ion interaction. Thus increasing
acetone concentration means increasing the effects of inter-
ruption between the hydrogen bond in the gelatin molecule.
Because in low pH the hydration of network in the gelatin
molecules were strong and more intensive [45].
Figure 7(b) shows multiple interactions between the pH and
the volume of glutaraldehyde. Around
400 µ
of volume of gluta-
raldehyde was needed to produce small sizes of FGNP at pH 2.5.
As we can see at pH 2.5, the particle size decreased when glutar-
aldehydes were added from
300 µ
to
400 µ
. Then, the particle
size becomes huge by increasing glutaraldehyde volume. It can
be concluded that low glutaraldehyde volume was required to
produce small nanoparticle sizes at low pH, moreover slightly
higher volumes of glutaraldehyde were used to produce small
particles at high pH. This is because at high pH, a huge volume
of glutaraldehyde would harden the gelatin nanoparticles from
inside the gelatin molecule instead of making new interactions
outside the molecule. However, in low pH, just a little volume of
glutaraldehyde was interacted to produce small size nanoparti-
cles, and further increments of glutaraldehyde will make a new
interaction to intra-molecular produce large nanoparticle.
Multiple interactions between glutaraldehyde volume and
acetone concentration are shown in gure7(c). This picture
depicted that around
440 µ
of glutaraldehyde was required
to produce small particles when 20% of acetone concentration
is used as the co-solvent, and its volume becomes less when
the acetone concentrations are decreased. This phenomenon
was created because as more acetone is added to gelatin solu-
tion, more gelatin molecules are destabilized and aggregated
by intermolecular interaction.
3.3. Validation of models
The results of the statistical model and regression equa-
tion were validated by running ve experiments under
optim ized variables. The variables were pH at 2.45, acetone
percentage at 16% (vol%) and glutaraldehyde volume at
400 µ
. Under these optimal variables, the predicted
mean particles size calculated by software was 201.8 nm,
and the observed experimental value after average was
198.46 ±6.1 nm
. The results conrmed that the model is
valid by the error of experiment which is quite close to about
1% error and indicated that the results are in good agreement
with the predictive value.
The particle sizes of sh gelatin nanoparticles prepared
in the present work are different from those of mammalian
origin gelatin [46, 47]. The difference in particle size can be
explained by the bloom number of its source. Previous studies
using mammalian gelatin showed that gelatin nanoparticles
have been produced with size at 160 nm using type A gelatin
bloom number 300 [46]. Different bloom numbers give dif-
ferent particle sizes, with larger bloom number yielding
smaller particle size [47].
From the results, the small nanoparticles were produced in
the interactions between pH, acetone concentration and gluta-
raldehyde volume. At a high pH, the intermolecular bonding
is more extensive than intramolecular, while in low pH intra-
molecular bonding has the highest effect. The col lision of
molecules increases the effects of molecular bonding between
each other. In addition, in high pH conditions, high volumes of
glutaraldehyde were used to produce small particles, because
the high volume glutaraldehyde will enforce the bonding in
the molecule (intramolecule).
The DSC thermographs of sh gelatin and sh gelatin
nanoparticles are presented in gure8. The thermograph of
sh gelatin (curve a in gure8) shows the presence of two
Figure 11. SEM images of (a) unloaded FGNPs and (b) 5-FU loaded FGNPs.
Figure 12. In vitro release prole of 5-FU in PBS (pH 7.4).
Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
D Subara etal
10
endothermic events which are glass transition temperature
(Tg)
and melting temperature
(Tm)
at
98 C
and
220 C
,
consistent with the literature value for gelatin [48]. For sh
gelatin nanoparticles
Tg
and
Tm
are observed as endothermic
peak around
81C
and
220 C
, respectively. The rst of endo-
thermic peak represents the energy consumed during vapor-
izing the water present in the matric and association of the
glass transition of
α-
amino acid blocks in the polypeptide
chain [49, 50]. It was found that the
Tg
of sh gelatin is higher
than its sh gelatin nanoparticles, these results suggested that
thermal stability of sh gelatin is stronger than sh gelatin
nanoparticles. The lower temperature of glass transition in the
sh gelatin nanoparticles indicated that higher water bound in
the structure because the increasing of polymer free due to the
transformation of nanoparticles [51].
Figure 9 shows the FTIR spectra of sh gelatin and sh gel-
atin nanoparticles. The FTIR analysis was used to evaluate and
to compare the chemical structural between sh gelatin and
sh gelatin nanoparticles. The spectrum showed characteristic
bands at approximately
3260 cm1
(amide A, NH stretching
vibrations of
NH2
),
2920 cm1
(amide B, CH stretching),
1640 cm1
(amide I, C=O stretching),
1540 cm1
(amide II,
NH bending),
1440 cm1
(
CH2
bending), and
1180 cm1
(amide III, CN and NH Stretching) [52, 53]. The similar
results on mammalian gelatins were also found by Hoseini
etal [15], Dixit etal [6], and Sarmah et al [54]. In general,
the result shows similar FTIR spectra for sh gelatin and sh
gelatin nanoparticles but different intensity. Furthermore, the
ratio of intensity of Amide I could be used to observe the loss
of secondary structure and formation of random coil [55].
The expense of secondary structure and formation of random
coil was related to the increasing of amide I intensity [55]. It
is because of the preparation step for producing sh gelatin
nanoparticles which used temperature of
45 C
to dissolve
the gelatin. The protein start to lost its triple structure as the
temper ature rich to
30 C
[55]. TEM image (gure 10) shows
that the sh gelatin nanoparticles are spherical and have a
homogeneous size distribution in the range 24 to 80 nm.
3.4. Drug loading
The entrapment efciency, mean diameter, polydispersity,
and zeta potential of prepared 5-FU under optimum condi-
tions were 40% (wt%),
238.02 ±7.4 nm
, 0.3, and 12 mV,
respectively. The SEM characterization revealed that FGNPs
are spherical in shape (gure 11).
The size between FGNPs and 5-FU loaded FGNPs also
varied. The size of FGNP after validation showed to be
around
198.46 ±6.1 nm
, while 5-FU loaded FGNP was
at
238.02 ±7.4 nm
. It can be concluded that the loading
increases nanoparticle size. The size of drug-loaded FGNPs
was 20% larger than unloaded FGNPs.
3.5. In vitro drug release kinetics
Figure 12 shows the release behavior of 5-FU from FGNPs in
PBS at pH 7.4. It can be seen at the 5 h incubation, almost 50%
of 5-FU was released to medium and after 12 h of incubation,
80% release was found. From the results, two stages of 5-FU
release from FGNPs were observed. The rst stage is rapid
release where around 50% of the drug was released in the ini-
tial 5 h. The second stage is followed by the sustained release.
The rapid release is due to the nature of the sh gelatin. As
a carrier, gelatin is highly hydrophilic thus it facilitates water
uptake from release medium to matrix and results in a higher
initial burst release. This condition have been described by
Nahar et al [47] that the release behavior of drugs from par-
ticles depend on several factors, such as the size of particles,
type of the polymer carrier, swelling characteristic of the par-
ticles, nature of crosslinking agent, and nature of the drug.
Another cause of initial burst release is also because of the
presence of the drug attached on the surface of particles [56,
57]. In vitro drug release from FGNP is expected to be sim-
ilar to type A mammalian gelatin that has similar isoelectric
points. According to previous papers, this nding is similar to
mammalian gelatin nanoparticles releasing amphotericin and
resveratrol through initial burst in PBS at pH 7.4 [57, 58].
This experiment also revealed that FGNP has similar release
kinetics to mammalian gelatin. Mammalian gelatin nanopar-
ticles follow the Fickian mechanism in releasing ibuprofen
and 5-FU to medium release [8, 12]. As shown in table5, the
goodness of t for the various models ranked in the order:
Korsmeyer-Peppas Higuchi >First-order >Zero-order
.
The value of exponent
n
from the Korsmeyer-Peppas model
is around 0.5 indicating that the 5-FU release from FGNP fol-
lows the Fickian diffusion.
4. Conclusions
FGNPs are produced with particle sizes of 191245 nm by
the two-step desolvation method using acetone as a non-
solvent and glutaraldehyde as a cross linker. Three factors
Table 5. Fitting of release kinetic model to 5-FU release data for FGNP (particle size 196 nm).
Model Parameters
R2
adjusted AIC MSC
Zero
k=12.47 ±0.35
0.84 ± 0.01 32.13 ± 0.50 0.81 ± 0.03
First
k=0.19 ±0.01
0.96 ± 0.00 24.96 ± 0.43 2.24 ± 0.08
Higuchi
k=26.67 ±0.76
0.98 ± 0.00 18.55 ± 0.80 3.53 ± 0.13
Korsmeyer-Peppas
k=23.53 ±0.97
0.98 ± 0.00 20.98 ± 0.75 3.04 ± 1.09
n=0.45 ±0.01
k
is constant;
n
is release exponent Korsmeyer-Peppas model,
R2
is correlation coefcient, AIC is akaike information criterion, MSC is model selection
criterion.
Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
D Subara etal
11
on production of FGNPs such as concentration of acetone,
volume of glutaraldehyde and pH are found to have signicant
effects on the size of the gelatin nanoparticles. The effects of
the signicant factors have been evaluated. Increasing pH and
acetone concentration leads to increase in the particles size,
while increasing volume of glutaraldehyde decreases the size
of FGNP. The optimum conditions of those factors are pH
2.45, acetone percentage 16% (vol%) and glutaraldehyde at
400 µ
. FGNP then was produced using those optimum con-
ditions and was loaded with the model drug. 5-FU was used
as a model hydrophilic drug loaded into FGNP. The in vitro
release kinetics of 5-FU was investigated. The release of 5-FU
from FGNP followed the Korsmeyer-Peppas model kinetics
with Fickian mechanism. Thus it can be concluded that FGNP
presented a good alternative for the delivery of hydrophilic
drugs such as 5-FU.
Acknowledgments
This work was nancially supported by Science Fund,
MOSTE, No. SF14-006-0056.
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Adv. Nat. Sci.: Nanosci. Nan otechnol. 9 (2018) 045014
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Aims: Many methods can improve the cytotoxic activity of a plant extract. A previous study reported that a plant extract having antibacterial activity and loaded into gelatin nanoparticles (GNPs) was able to be improved the antibacterial activity. The purpose of this study was to evaluate whether Cantigi (Vaccinium varingiaefolium Blume Miq.) leaf extract loaded into GNPs was able to provide cytotoxic activity (CA) improvement on T47D cells compared to the crude extract. Methodology: The extraction of Cantigi leaf powder used hexane (HX) and then ethyl acetate (EA). The EA extract was then prepared into GNPs using the desolvation method and optimized at two process parameters (mixing speed and temperature) and three material concentrations (poloxamer 188, gelatin, and glutaraldehyde). The EA extract-loaded GNPs formed were characterized for particle size (PS), polydispersity index (PI), zeta potential (ZP), entrapment efficiency (EE), CA (IC50, using the MTT method on T47D cells), FTIR profile, and morphology. Results: Compared to the EA extract (IC50 = 75.16 μg/mL), the EA extract-loaded GNPs manufactured using the formulation processed at 500 rpm and 40 o C provided the highest cytotoxic activity (IC50 = 16.88 μg/mL). Conclusion: The results showed that EA extract-loaded GNPs improved the CA of the EA extract on T47D cells significantly.
... In brief, a small amount of the nanoparticles was dried, mounted on aluminum plates, and pasted with double sided copper tapes. The samples were sputtered with a thin layer of gold and placed on the packet chamber at an accelerating voltage of 10 kV (Subara, 2018) ...
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Cantigi is an endemic plant of sub-alpine area of Mount Tangkuban Parahu in Bandung, Indonesia. Previous study showed ethanol extract of young red leaves had antioxidant activity, however no information on this activity if changed into nanoparticles. The purpose of this study was to determine the effects of gelatin and glutaraldehyde concentrations on the characteristics of Cantigi extract loaded gelatin nanoparticles and to evaluate the antioxidant activity of nanoparticles. Cantigi leaves were extracted by maceration using n-hexane, ethyl acetate, and ethanol 96%. The ethanol extract was dried, made into nanoparticles by varying gelatin (0.1; 0.2; and 0.3 g) and glutaraldehyde (0.1; 0.2; and 0.3 mL) amounts, and conducted at 500 rpm and 40 °C for 3 hours. Nanoparticles were evaluated for particle size, zeta potential, morphology, and antioxidant activity. Nanoparticles with glutaraldehyde amount variation had particle sizes (PS) of 105.9±26.2; 37.1±8.7; and 32.5±7.4 nm; polydispersity indeces (PI) of 0.508; 0.717; and 0.563; zeta potential values (ZPV) of 0.55; 0.89; and 0.78 mV; and antioxidant activities (IC50) of 56.15±0.16; 53.67±0.10; and 51.57±0.39 ppm, respectively. Then, nanoparticles with gelatin amounts variation had PS of 22.5±5.1; 37.1±8.7; and 83.3±21 nm; PI of 0.604; 0.717; 0.326; ZPV of 1.27; 0.89; 0.18 mV; and antioxidant activities of 51.58±0.19; 53.67±0.12; and 55.46±0.04 ppm, respectively. Nanoparticle morphology was spherical. Cantigi leaf extract can be made into gelatin nanoparticles; the smaller the concentration of the polymer used and higher the concentration of the glutaraldehyde, the smaller the resulted particle size and increased antioxidant activity. Antioxidant activities of nanoparticles was lower than those of the extract (IC50 16.84±0.30 ppm).
... There are many studies reporting a decrease in size and/or polydispersity of albumin [38], silk fibroin [39], αlactalbumin [40] nanoparticles at higher stirring speeds. The same findings were provided for gelatin nanoparticles by different research groups [41,42]. Conversely, Pei et al. [43] showed that an increase of gelatin concentration in water-ethanol mixture under agitation leads to growth of gelatin nanoparticle size and even to gelation. ...
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Gelatin nanoparticles found numerous applications in drug delivery, bioimaging, immunotherapy, and vaccine development as well as in biotechnology and food science. Synthesis of gelatin nanoparticles is usually made by a two-step desolvation method, which, despite providing stable and homogeneous nanoparticles, has many limitations, namely complex procedure, low yields, and poor reproducibility of the first desolvation step. Herein, we present a modified one-step desolvation method, which enables the quick, simple, and reproducible synthesis of gelatin nanoparticles. Using the proposed method one can prepare gelatin nanoparticles from any type of gelatin with any bloom number, even with the lowest ones, which remains unattainable for the traditional two-step technique. The method relies on quick one-time addition of poor solvent (preferably isopropyl alcohol) to gelatin solution in the absence of stirring. We applied the modified desolvation method to synthesize nanoparticles from porcine, bovine, and fish gelatin with bloom values from 62 to 225 on the hundreds-of-milligram scale. Synthesized nanoparticles had average diameters between 130 and 190 nm and narrow size distribution. Yields of synthesis were 62–82% and can be further increased. Gelatin nanoparticles have good colloidal stability and withstand autoclaving. Moreover, they were non-toxic to human immune cells.
... In addition, 37°C is the physiological human temperature, hence, the possible association of therapeutic biomolecules or drugs to GNPs could be done without risk of damage or degradation. Most references uses temperatures of 40°C or higher for preparing gelatin nanoparticles [34][35][36][37]52], among the few works that evaluate the effect of water phase temperature on nanoparticle size, there is no consensus; the work of Shirzad et al [33] shows a size increment with the temperature, on the contrary a doctoral dissertation [53] finds a minimum size when working at 50°C. Probably, the heterogeneous nature of gelatin is the responsible of the diversity in the experimental conditions employed for nanoparticle preparation. ...
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... The behaviour of this result was characterized by the increased of PDI and lowered the zeta potential. Results from this study was in agreement with previous experiment [15], [27], despite the raw material is different. Based on this result, 30% acetone concentration was chosen for the production of gelatin hydrolysate nanoparticle. ...
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Fish gelatin hydrolysate is a well-known fish by-product that is high in protein content. It is produced from by-product waste from the fish processing industry, which includes fish skin, head and bones. Gelatin hydrolysates have recently received much attention due to its high protein content and bioactivity, which includes antioxidant, antimicrobial and antihypertensive activities. The transformation of gelatin hydrolysate into nanoparticles is believed to increase its economic value. Furthermore, reduction into nano-size increases the absorption characteristic of this material. Here, fish gelatin hydrolysate nanoparticles are prepared for the first time using desolvation method. The effects of concentration of gelatin hydrolysate, pH of solution, and acetone concentration on nanoparticle size are determined. The prepared gelatin hydrolysate nanoparticles were found to have spherical shape with sizes varying from 300-400 nm with a mean size of 408 ± 11.4 nm, zeta potential of-16.4 ± 1.2 mV and Polydispersity Index (PDI) 0.203 ± 0.07. This study showed that concentration of gelatin hydrolysate, pH and concentration of solvent have significant effects on nanoparticle size. The gelatin hydrolysate nanoparticles can be applied in the pharmaceutical industry for the encapsulation of drugs to facilitate delivery to target sites.
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Gelatin is obtained via partial denaturation of collagen and is extensively used in various industries. The majority of gelatin utilized globally is derived from a mammalian source. Several health and religious concerns associated with porcine/bovine gelatin were reported. Therefore, gelatin from a marine source is widely being investigated for its efficiency and utilization in a variety of applications as a potential substitute for porcine/bovine gelatin. Although fish gelatin is less durable and possesses lower melting and gelling temperatures compared to mammal-derived gelatin, various modifications are being reported to promote its rheological and functional properties to be efficiently employed. The present review describes in detail the current innovative applications of fish gelatin involving the food industry, drug delivery and possible therapeutic applications. Gelatin bioactive molecules may be utilized as carriers for drug delivery. Due to its versatility, gelatin can be used in different carrier systems, such as microparticles, nanoparticles, fibers and hydrogels. The present review also provides a perspective on the other potential pharmaceutical applications of fish gelatin, such as tissue regeneration, antioxidant supplementation, antihypertensive and anticancer treatments.
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Gelatin nanoparticles found numerous applications in drug delivery, bioimaging, immunotherapy, and vaccine development as well as in biotechnology and food science. Synthesis of gelatin nanoparticles is usually made by a two-step desolvation method, which, despite providing stable and homogeneous nanoparticles, has many limitations, namely complex procedure, low yields, and poor reproducibility of the first desolvation step. Herein, we present a modified one-step desolvation method, which enables the quick, simple, and reproducible synthesis of gelatin nanoparticles. Using the proposed method one can prepare gelatin nanoparticles from any type of gelatin with any bloom number, even with the lowest ones, which remains unattainable for the traditional two-step technique. The method relies on quick one-time addition of poor solvent (preferably isopropyl alcohol) to gelatin solution in the absence of stirring. We applied the modified desolvation method to synthesize nanoparticles from porcine, bovine, and fish with bloom values from 62 to 225 on the hundreds-of-milligram scale. Synthesized nanoparticles had average diameters between 130 and 190 nm and narrow size distribution. Yields of synthesis were 62-82% and can be further increased. Gelatin nanoparticles have good colloidal stability and withstand autoclaving. Moreover, they were non-toxic to human immune cells.
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Full-text available
Gelatin nanoparticles found numerous applications in drug delivery, bioimaging, immunotherapy, and vaccine development as well as in biotechnology and food science. Synthesis of gelatin nanoparticles is usually made by a two-step desolvation method, which, despite providing stable and homogeneous nanoparticles, has many limitations, namely complex procedure, low yields, and poor reproducibility of the first desolvation step. Herein, we present a modified one-step desolvation method, which enables the quick, simple, and reproducible synthesis of gelatin nanoparticles. Using the proposed method one can prepare gelatin nanoparticles from any type of gelatin with any bloom number, even with the lowest ones, which remains unattainable for the traditional two-step technique. The method relies on quick one-time addition of poor solvent (preferably isopropyl alcohol) to gelatin solution in the absence of stirring. We applied the modified desolvation method to synthesize nanoparticles from porcine, bovine, and fish with bloom values from 62 to 225 on the hundreds-of-milligram scale. Synthesized nanoparticles had average diameters between 130 and 190 nm and narrow size distribution. Yields of synthesis were 62-82% and can be further increased. Gelatin nanoparticles have good colloidal stability and withstand autoclaving. Moreover, they were non-toxic to human immune cells. (PDF) Modified desolvation method enables simple one-step synthesis of gelatin nanoparticles from different gelatin types with any bloom values. Available from: https://www.researchgate.net/publication/353346658_Modified_desolvation_method_enables_simple_one-step_synthesis_of_gelatin_nanoparticles_from_different_gelatin_types_with_any_bloom_values [accessed Jul 21 2021].
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