Ranjan AP, Mukerjee A, Helson L, Vishwanatha JK. Scale up, optimization and stability analysis of Curcumin C3 complex-loaded nanoparticles for cancer therapy. J Nanobiotechnol 10: 38

Journal of Nanobiotechnology (Impact Factor: 4.12). 08/2012; 10(1):38. DOI: 10.1186/1477-3155-10-38
Source: PubMed


Nanoparticle based delivery of anticancer drugs have been widely investigated. However, a very important process for Research & Development in any pharmaceutical industry is scaling nanoparticle formulation techniques so as to produce large batches for preclinical and clinical trials. This process is not only critical but also difficult as it involves various formulation parameters to be modulated all in the same process.

In our present study, we formulated curcumin loaded poly (lactic acid-co-glycolic acid) nanoparticles (PLGA-CURC). This improved the bioavailability of curcumin, a potent natural anticancer drug, making it suitable for cancer therapy. Post formulation, we optimized our process by Reponse Surface Methodology (RSM) using Central Composite Design (CCD) and scaled up the formulation process in four stages with final scale-up process yielding 5 g of curcumin loaded nanoparticles within the laboratory setup. The nanoparticles formed after scale-up process were characterized for particle size, drug loading and encapsulation efficiency, surface morphology, in vitro release kinetics and pharmacokinetics. Stability analysis and gamma sterilization were also carried out.

Results revealed that that process scale-up is being mastered for elaboration to 5 g level. The mean nanoparticle size of the scaled up batch was found to be 158.5 ± 9.8 nm and the drug loading was determined to be 10.32 ± 1.4%. The in vitro release study illustrated a slow sustained release corresponding to 75% drug over a period of 10 days. The pharmacokinetic profile of PLGA-CURC in rats following i.v. administration showed two compartmental model with the area under the curve (AUC0-∞) being 6.139 mg/L h. Gamma sterilization showed no significant change in the particle size or drug loading of the nanoparticles. Stability analysis revealed long term physiochemical stability of the PLGA-CURC formulation.

A successful effort towards formulating, optimizing and scaling up PLGA-CURC by using Solid-Oil/Water emulsion technique was demonstrated. The process used CCD-RSM for optimization and further scaled up to produce 5 g of PLGA-CURC with almost similar physicochemical characteristics as that of the primary formulated batch.


Available from: Amalendu P Ranjan
RES E A R C H Open Access
Scale up, optimization and stability analysis of
Curcumin C3 complex-loaded nanoparticles for
cancer therapy
Amalendu P Ranjan
, Anindita Mukerjee
, Lawrence Helson
and Jamboor K Vishwanatha
Background: Nanoparticle based delivery of anticancer drugs have been widely investigated. However, a very
important process for Research & Development in any pharmaceutical industry is scaling nanoparticle formulation
techniques so as to produce large batches for preclinical and clinical trials. This process is not only critical but also
difficult as it involves various formulation parameters to be modulated all in the same process.
Methods: In our present study, we formulated curcumin loaded poly (lactic acid-co-glycolic acid) nanoparticles
(PLGA-CURC). This improved the bioavailability of curcumin, a potent natural anticancer drug, making it suitable for
cancer therapy. Post formulation, we optimized our process by Reponse Surface Methodology (RSM) using Central
Composite Design (CCD) and scaled up the formulation process in four stages with final scale-up process yielding
5 g of curcumin loaded nanoparticles within the laboratory setup. The nanop articles formed after scale-up process
were characterized for particle size, drug loading and encapsulation efficiency, surface morphology, in vitro release
kinetics and pharmacokinetics. Stability analysis and gamma sterilization were also carried out.
Results: Results revealed that that process scale-up is being mastered for elaboration to 5 g level. The mean
nanoparticle size of the scaled up batch was found to be 158.5 ± 9.8 nm and the drug loading was determined to
be 10.32 ± 1.4%. The in vitro release study illustrated a slow sustained release corresponding to 75% drug over a
period of 10 days. The pharmacokinetic profile of PLGA-CURC in rats following i.v. administration showed two
compartmental model with the area under the curve (AUC
) being 6.139 mg/L h. Gamma sterilization showed no
significant change in the particle size or drug loading of the nanoparticles. Stability analysis revealed long term
physiochemical stability of the PLGA-CURC formulation.
Conclusions: A successful effort towards formulating, optimizing and scaling up PLGA-CURC by using Solid-Oil/
Water emulsion technique was demonstrated. The process used CCD-RSM for optimization and further scaled up to
produce 5 g of PLGA-CURC with almost similar physicochemical characteristics as that of the primary formulated
Keywords: Scale up, Optimization, PLGA nanoparticles, Cancer, Response surface methodology (RSM), Curcumin C3
complex, Central composite design (CCD)
* Correspondence:
Department of Molecular Biology & Immunology and Institute for Cancer
Research, Graduate School of Biomedical Sciences, University of North Texas
Health Science Center, Fort Worth, TX76107, USA
Full list of author information is available at the end of the article
© 2012 Ranjan et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38
Page 1
Curcumin is one of the most promising natural anti-
cancer agents and hence been much investigated for the
past few decades [1,2]. Several phase I and phase II clin-
ical trials indicate that curcumin is quite safe and may
exhibit therapeutic efficacy [3-5]. A purified form of cur-
cumin which consists of three main component s: curcu-
min (76.07%); bisdemethoxy curcumin (3.63%); and
demethoxy curcumin (20.28%) is defined as curcumin
C3 complex [6]. Henceforth, curcumin C3 complex will
be referred to as curcumin in this paper. Poor water
solubility, poor physiochemical properties and low bio-
availability continue to pose major challenges in devel-
oping a curcumin formulation for clinical efficacy.
Lower serum and tissue levels of curcumin are observed
irrespective of the route of administration due to exten-
sive intestinal and hepatic metabolism and rapid elimin-
ation, thus restraining bioavailability of curcumin [7-10].
To improve its potential application in the clinical arena,
several formulation strategies like nanoparticles, lipo-
somes, complex with phospholipids, cyclodextrins and
solid dispersions are being developed to improve bio-
availability of curcumin and increasing its therapeutic ef-
ficacy [10-17]. Among these approaches, biodegradable
polymeric nanoparticle based delivery systems offer sig-
nificant advantage over other nanocarrier platforms as
there is tremendous versatility in the choice of polymer
matrices that can be used for tailoring nanoparticle
properties to meet various drug delivery needs.
Although much research emphasis are presently being
dedicated to various nanoparticle formulations in the
pharmaceutical industry, especially towards particle de-
sign and targeting, very few results have ever been pub-
lished on process scale-up. Scaling up of the formulation
process is essential for clinical use. In this paper, we have
made an effort towards optimizing and scaling up PLGA
nanoparticles encapsulating curcumin (PLGA-CURC) by
using Solid-Oil/Water (S-O/W) an emulsification-solvent-
evaporation/diffusion technique. The major goals in
designing polymeric nanoparticles as a delivery system are
to control particle size and polydispersity, maximize drug
encapsulation efficiency and drug loading, optimize sur-
face properties and tailor release of pharmacologically ac-
tive agents to achieve a site specific action of the drug at
the therapeutically optimal desired rate and dose regimen
[18,19]. Optimization becomes especially important when
the formulation needs to be scaled up for industrial pro-
duction. The organic solvent used in the formulation
becomes critical for pilot and industrial scale production
and hence only class 3 solvents are preferred for formula-
tion while scaling up. In our formulation, we used ethyl
acetate as the organic solvent. Partially hydrolyzed PVA
was used as emulsion stabilizer as it prevents redispersibil-
ity problems [20].
For the optimization process, our aim was to use Re-
sponse Surface Methodology (RSM) in conjunc tion with
Central Composite Design (CCD) to establish the func-
tional relationships between three chosen operating vari-
ables: polymer (PLGA) concentration, stabilizer (PVA)
concentration and volume of organic phase (ethyl acet-
ate). Four responses were identified namely, mean par-
ticle size, po lydispersity, encapsulation efficiency (EE)
and drug loading (DL) of PLGA-CURC for this study.
The optimization procedure involved systematic formu-
lation designs to minimize the number of trials, and
analyze the response surfaces in order to rea lize the
effects of factors and to obtain the appropriate formula-
tions with target goals [21,22]. Further, for analyzing the
responses to the variables, mathematical model equa-
tions were derived by using Design-Expert
5.0 software.
For a better understanding of the three variables or the
optimal PLGA-CURC performance, the models were
presented as three-dimensional contour response surface
Once the optimized batch was determined, classical
scale up was followed to produce gram amounts of
nanoparticle formulation. The nanopa rticles obtained
from the scale up were then characterized for particle
size, polydispersity, drug loading and morpholo gy and
compared with non-scaled up optimized batch, thereby
establishing successful process scale-up. Nanoparticles
from the scaled up batch were fur ther evaluated for per-
centage cumulative release, functional assays , cellular
uptake in different cancer cell lines and storage stability.
Materials and methods
Poly (D,L-lactide-co-glycolide) 50:50; i.v. 0.77 dL/g
(~0.5% w/v in chloroform at 30
C); m.w. 124 kDa was
purchased from Lakeshore Biomaterials (Birmingham,
AL). Curcumin c3 complex was a kind gift from Sabinsa
Corporation (East Windsor,NJ), Polyvinyl alcohol (m.w.
9,000-10,000; 80% hydrolyzed), ethyl acetate, ethanol,
nile red, D(+) trehalose, sucrose, were purchased from
Sigma Aldrich (St. Louis, MO). The human prostate
cancer cell line - DU 145, breast cancer cell line - MDA-
MB-231 and pancreatic cancer cell line MiaPaCa were
obtained from ATCC (Manassas, VA). RPMI 1640,
DMEM and FBS was obtained from Gibco, Invitrogen
(Carlsbad, CA). Gold antifade mounting agent with 4-6-
diamidino-2-phenylindole (DAPI) was purchased from
Invitrogen (Carlsbad, CA). Double-distilled deionized
water was used for all the experiments.
PLGA-CURC preparation
PLGA-CURC was prepared by S-O/W, an emulsification-
solvent evaporation/diffusion method. The method is ideal
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 2 of 18
Page 2
for encapsulation of hydrophobic compounds like cur-
cumin. Briefly, the polymer PLGA (mg) was dissolved
in ethyl acetate. Curcumin (15% w/w) was added and
allowed to dissolve with intermittent vortexing using
Vortex Mixer (Fisher Scientific, Vortex Genie 2 G-560,
Scientific industries Inc, Bohemia, NY). Solid-in-oil mix-
ture was added to an aqueous phase of PVA (w/v) to
form S-O/W emulsion. Once all the drug/polymer mix-
ture was added to the PVA solution, the contents were
vortexed for 10 sec at a high setting. The resulting sus-
pension was sonicated for 60 sec at 45% amplitude with
a sonic disrupter (Hielscher UP200S ultrasonic proces-
sor, Ringwood, NJ). Immediately after sonication, the
emulsion was poured into to excess of aqueous phase
(0.1% PVA in water; 40 ml) for diffusion under rapid
stirring with a magnetic stirrer. This colloidal suspen-
sion was kept on a magnetic stirrer for complete solv-
ent evaporation for 56 h. The nanoparticles were then
collected by centrifugation, washed 3 times with distilled
Milli Q treated water. Finally, they were resuspended in
2 ml of cryoprotectent solution (sucrose (2 %w/w) and tre-
halose (5% w/w)), dried on a lyophilizer ATR FD 3.0 sys-
tem (ATR Inc., St Louis, MO) and stored at 4°C. Figure 1
shows the schematic representation of the experimental
procedure for the PLGA-CURC formulation.
Experimental design for optimization of formulation
The most efficient way to test different variables
simultaneously requires a systematic and detailed
experimental design. This eliminates the need for a
large number of independent runs when the classical
step-by-step method is used. Optimization procedures
like RSM run by selecting an objective function, finding
the contributing factors and investigating the relationship
between responses and factors [23]. Preliminary experi-
ments indicated that variables such as amount of polymer
PLGA, PVA concentration and volume of ethyl acetate
were the main factors that affected the particle size, size
distribution, percentage drug loading and encapsulation
efficiency of the PLGA-CURC. A CCD model was used to
statistically optimize the formulation parameters and
evaluate the main effects, interaction effects and quadratic
effects of the formulation factors on the particle size (Y
size distribution (Y
), percentage drug loading (Y
) and en-
capsulation efficiency (Y
) of PLGA-CURC. Details of the
design are listed in Table 1. For each factor, the experi-
mental range was selected on the basis of the results of
preliminary experiments and the feasibility of preparing
the PLGA-CURC at the extreme values. The value range
of the variables was: amount of PLGA (X
): 50120 mg,
PVA concentration (X
): 1.03.0%, and volume of ethyl
acetate (X
): 2.05.0 ml. The design consists of 15 runs (8
factorial points, 6 star points and 1 center point) and 5
replicated runs (center points) yielding 20 experiments in
total (Table 2). The purpose of replication was to estimate
experimental error and increase the precision. Each ex-
perimental run was repeated thrice. The star points repre-
sents extreme values (low and high) for each factor in the
Figure 1 Schematic representation of the experimental procedure for the formulation of PLGA-CURC.
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 3 of 18
Page 3
design and allow for estimation of second-order effects.
The star points are at some distance, alpha, from the cen-
ter based on the properties desired for the design and the
number of factors in the design. Alpha in coded units is
the axial distance from the center points and makes the
design rotatable. A rotatable design provides equally good
predictions at points equally distant from the center, a very
desirable property for Response Surface Methodology.
A design matrix comprising of 20 experimental runs
was constructed and the responses were modeled by the
following polynomial model:
y ¼ b
þ b
þ b
þ b
þ b
þ b
þ b
þ b
þ b
þ b ð1Þ
Where Y is the measured response associated with
each factor level combinations; b
is the Intercept; b
(for i = 1,2, and 3) are the linear effects, the b
are the
quadratic effects , the b
s (for i,j = 1,2,and 3, i < j) are the
interaction between the i
and j
Data were analyzed by using analysis of variance
(ANOVA), which helped determine if the factors and the
interactions between factors were significant. To test
whether the terms were statistically significant in the re-
gression model, t-tests were performed using a 95%
(α = 0.05) level of significance. An F-test was used to de-
termine whether there was an overall regression rela-
tionship between the response variable Y and the entire
set of X variables at a 95% (α=0.05) level of significance.
The coefficient of multiple determinations was denoted
by R
, which measured the proportionate reduction of
total variation in Y associated with the use of the set of
X variables. In addition, the validity of the regression
Table 2 Observed responses in central composite design for PLGA-CURC formulation
Wt. of
PLGA (mg)
PVA conc.
(% w/v)
Ethyl Acetate
Particle Size
Polydispersity Encapsulation
efficiency (%)
Drug loading
1 50 1 2 125.6 0.135 73.4 11.1
2 120 1 2 142.5 0.148 63.35 11.4
3 50 3 2 108.5 0.125 73.84 11.3
4 120 3 2 144.5 0.143 63.86 13.5
5 50 1 5 81.5 0.097 83.25 14.9
6 120 1 5 133.8 0.116 85.5 13.2
7 50 3 5 103.7 0.115 83.54 10.5
8 120 3 5 136.7 0.135 84.8 15.8
9 26.14 2 3.5 133.3 0.138 63.25 5.5
10 143.86 2 3.5 198.8 0.172 53.46 9.4
11 85 0.32 3.5 158.8 0.164 88.85 13.6
12 85 3.68 3.5 149.5 0.155 88.25 13.8
13 85 2 0.98 176.9 0.093 53.35 14.5
14 85 2 6.02 77.8 0.067 63.78 15.2
15 85 2 3.5 112.5 0.135 86.95 11.8
16 85 2 3.5 121.6 0.148 90.35 12.6
17 85 2 3.5 119.8 0.135 91.46 10.5
18 85 2 3.5 118.6 0.147 93.54 11.9
19 85 2 3.5 129.6 0.128 90.34 12.8
20 85 2 3.5 119.7 0.172 86.27 12.9
Table 1 Relationship between coded and actual values of
the variables used for PLGA-CURC formulation
Formulation factors Coded level of variables
-α 10 1 +α
= Amount of PLGA (mg) 26.14 50 85 120 143.86
= PVA (% w/v) 0.32 1.0 2.0 3.0 3.68
= Ethyl acetate (ml) 0.98 2 3.5 5.0 6.02
Dependent variables Constraints
= Particle Size (nm) Minimize
= Polydispersity Minimize
= Encapsulation efficiency (%) Maximize
= Drug loading (%) Maximize
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 4 of 18
Page 4
model was assessed according to statistical assumptions
and lack of fit test. The statistical analysis was performed
using the software Design Expert (Version 5) (Stat-Ease,
Inc, Minneapolis, MN).
Determination of desirability coefficient
All four responses studied are critical for the optimization
of PLGA-CURC formulation. However, it is difficult to
optimize all the objectives simultaneously because they do
not coincide with each other and conflict may occur be-
tween them. The optimum condition reached in one re-
sponse may have an opposite influence on another. For
finding the best optimized formulation for all responses,
the multi criteria problem can be treated as single criter-
ion by using the desirability function approach. This was
performed in two steps. A predictive model for the re-
sponse of performance parameters in the formulation of
nanoparticle was first obtained by an analysis of variance
and then a desirability index for each response was evalu-
ated using the software Design Expert. The individual de-
sirability indices were then used to construct the
combined objective function called the desirability coeffi-
cient, which is the geometric mean of all the transformed
responses and is given by the Eq. 2 [24].
δ ¼ d
where d
s are values obtained by transforming the mea-
sured response based on the desired goal. Hence, when
the goal was to maximize a certain response, the d
were defined as
where w
is the weight index and y
and y
are the
maximum and minimum values of the responses used for
calculating d. When the goal was to minimize a response,
the d
values were defined as
One may look upon the d
as the value of the response
on a new scale between zero and unity. The exponent
(weighting factor) defines curvature of the interpolation
equation. For example, when w
=1, the interpolation is
linear. Since the d
values are in the range 0 d
1, the de-
sirability coefficient is also in the range 0 δ 1. The
index n equals
. The contour plot of desirability coef-
ficients reported here is based on the d
values computed
for four variables, namely, for particle size (d
), polydisper-
sity (d
), encapsulation efficiency (d
) and drug loading
). The goals considered were minimizing particle size
and polydispersity, maximizing encapsulation efficiency
and drug loading. The desirability coefficient δ was com-
puted in this fashion and the contours of equal δ values
were plotted. To obtain the condition on the design vari-
ables that maximize δ, a three-dimensional graph of the
response against any two factors was plotted, from which
the region corresponding to optimum values for δ was
Characterization of PLGA-CURC
Particle size and polydispersity
Particle size measurement s and polydispersity of PLGA-
CURC were determined by laser diffraction using a
Nanotrac system (Mircotrac, Inc., Montgomeryville, PA).
Lyophilized PLGA-CURC were dispersed in double dis-
tilled water as described elsewhere [14,25] and analyzed
in triplicates with three readings per nanoparticle sam-
ple. The polydispersity was also calculated based on the
volumetric distribution of particles.
Determination of curcumin associated with PLGA-CURC
Lyophilized PLGA-CURC (5 mg) was dissolved in 2 ml
acetonitrile to extract curcumin into acetonitrile for deter-
mining the encapsulation efficiency. The samples in aceto-
nitrile were gently shaken on a shaker for 4 h at room
temperature to completely extract out curcumin from the
nanoparticles into acetonitrile. These solutions were cen-
trifuged at 14,000 rpm (Centrifuge 5415D, Eppendorf AG,
Hamburg, Germany) and supernatant was collected. Sus-
pension (20 μl) was dissolved in ethanol (1 ml) and was
used for the estimations. The curcumin concentrations
were measured spectrophotometrically at 450 nm. A
standard plot of curcumin (010 μg/ml) was prepared
under identical conditions.
The encapsulation efficiency (EE) of PLGA-CURC was
calculated using
Encapsulation efficiency %ðÞ
Total drug content in nanoparticles
Total drug amount
100 ð5Þ
Percentage drug loading
The percent drug loading was calculated by total
amount of drug extracted from the polymeric nanoparti-
cles to the known weight of nanoparticles
Drug loading %ðÞ¼
Drug content
Weight of nanoparticles
In vitro drug release study
The in vitro drug release profiles of optimized PLGA-
CURC formulations were determined by measuring the
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 5 of 18
Page 5
cumulative amount of drug released from the nanoparticle
over predetermined time intervals as described elsewhere
To study and better understand the release mechanism
of curcumin from nanopa rticle formulation, data
obtained from in vitro drug release studies were fitted in
different kinetic models [26,27]. For zero order, cumula-
tive amount of drug released was plotted versus time;
for first order, log cumulative percentage of drug
remaining was plotted versus time; for Higuchis model,
cumulative percentage of drug released was plotted ver-
sus the square root of time and for HixsonCrowell
cube root model, cumulative percentage of drug release
was plotted versus cube root of time. Plotted data were
fitted using a linear equation; the regression coefficient
) was calculated from the appropriate graphs. Selec-
tion of the best model was based on the comparisons of
the relevant correlation coefficients.
External morphology studies
Transmission electron microscopy (TEM)
The surface morphology of the nanoparticles was stud-
ied using transmission electron microscopy (TEM) as
described elsewhere [14,25].
Scanning electron microscopy (SEM)
The surface morphology of the formulated nanoparticle
was measured by scanning electron microscopy (SEM)
(EM- LEO 435VP, Carl Zeiss SMT Inc., NY) equipped
with 15 kV, SE detector with a collector bias of 300 V.
The lyophilized samples were carefully mounted on an
aluminum stub using a double stick carbon tape. Sam-
ples were then introduced into an automated sputter
coater an d coated with a very thin film of gold before
scanning the samples under SEM.
In vitro cellular uptake of PLGA -CURC in cancer cells
Curcumin is intrinsically fluorescent; this facilitated the
visualization of the PLGA-CURC uptake into cells under
confocal microscope. To observe the internalization of
nanoparticles under a confocal microscope, DU-145,
MDA-MB 231 and MiaPaCa were grown under standard
cell culture con ditions. Cellular uptake of Nile red-
labeled PLGA-CURC was determined using a confocal
microscope (Zeiss LSM 510 META attached to a Zeiss
Axiovert 200 inverted microscope) (Carl Zeiss MicroI-
maging, Inc., Thornwood, NY ). For these experiments,
cells were placed on a cover slip in a 6-well tissue culture
plate and incubated at 37°C until they reached sub-
confluent levels. The cells were then exposed to 1 mg/ml
concentrations of nile red labled PLGA-CURC. After 2 h,
the treated cells were fixed with standard paraformalde-
hyde (4%) an d fixed using Gold antifade mounting agent
with 4 -6-diamidino-2-phenylindole (DAPI). The slides
were viewed under the microscope to determine the
extent of intracellular nanoparticle uptake.
Western blot analysis to determine the functional integrity
For Western blot, 3050 μg of nuclear and cytoplasmic
and protein extracts, prepared by the nuclear extraction
kit (Pierce, USA) protocol, were resolved on 10% SDS-
PAGE gel. After electrophoresis, the proteins were elec-
trotransferred to a nitrocellulose membrane, blocked
with 5% non-fat milk, and probed with antibodies
against the p65 subunit of NFκβ. Thereafter, the blot
was washed, exposed to HRP-conjugated secondary anti-
bodies for 1 hour, and finally detected by ECL chemilu-
minescence reagents (Amersham Pharmacia Biotech,
Arlington Heights, IL).
Scale up for large batch of nanoparticles p roduction
The process scale up of PLGA-CURC formulation was a
multistage process wherein each stage was optimized. In
the first stage, we expected to scale up 5X to produce
500 mg of PLGA-CURC. In the second stage, 1 g pro-
duction was targeted. Subsequent stages would lead to
2 g and 5 g (50X) production of PLGA-CURC. For scal-
ing up, we reduced the ratio of organic phase and aque-
ous phase else the amount of organic solvent would be
very high and accordingly adjusted other parameters to
keep similar nanoparticle characteristics. All the para-
meters which were varied at each stage of scale up are
listed in Tables 3 and 4.
Table 3 Formulation factors and parameter variations for process scale up
Ethyl acetate
phase (ml)
Excess Water
Time (s)
Speed (rpm)
Time (h)
Primary optimized conditions 85 1 4.25 15 40 S2 60 2000 4
First stage 500 mg 500 1 5 20 50 S2 120 2000 4
Second stage 1g 1000 1 10 40 60 S14 120 3000 6
Third stage- 2 g 2000 1 20 60 80 S14 180 6000 6
Fourth stage 5g 5000 1 50 150 200 S14 300 8000 6
*S2 soncation tip dia 2 mm, S14 sonication tip dia 14 mm.
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 6 of 18
Page 6
Pharmacokinetic studies
Pharmacokinetic studies were carried out to analyze the
bioavailability of curcumin following intravenous (i.v.) ad-
ministration of PLGA-CURC. For this study, male Sprague
Dawley rats weighing 250300 g were used in a protocol
approved by the IACUC committee of UNTHSC, Fort
Worth, Texas, USA. Eight animals were administered
PLGA-CURC (7.5 mg curcumin equivalent/kg of body-
weight) by tail vein injection. Blood samples (200μL) were
collected into heparinized microcentrifuge tubes at prede-
termined time points. After each blood sampling, same
amount of normal saline was administered to compensate
for the blood loss. Plasma was separated by centrifuging
the blood samples at 3,500 rcf for 10 min at 4°C. To 100
μL aliquot of acetonitrile containing 0.15 μg/mL of in-
ternal standard was added to 100 μL of each plasma sam-
ples in order to precipitate the plasma protein. Samples
were vortexed for 5 min and then subjected to centrifuga-
tion at 2,500 rcf for 15 min to remove any precipitated
material. Finally, samples were injected into the HPLC sys-
tem through the autosampler. The concentration of cur-
cumin was determined by HPLC analysis and quantitated
with previous calibrations [28].
Stability studies
PLGA-CURC (20 mg) was kept in four sealed glass vials
and maintained at 4°C for a period of 6 months. Nanopar-
ticles were characterized for change in particle size, encap-
sulation efficiency and percent drug loading according to
the above mentioned protocols.
Gamma irradiation of nanoparticles
Gamma irradiation is recommended by European
Pharmacopoeia for the purpos e of sterilizing pharma-
ceutical product s. Such studies for our nanoparticles
were carried out by Steris Isomedix Services, IL, USA.
Drug loaded nanoparticles were γ-irradiated using
as irradiation source and received a dose of either
16.8 kGy for 241 minutes (Low), 25.3 kGy for 179 min-
utes (Medium) or 35.8 kGy for 241 minutes (High).
Non-irradiated samples were kept as reference for fur-
ther comparison.
Results and discussion
Optimization of PLGA-CURC using central composite
Response Surface Methodology (RSM) using the Central
Composite Design (CCD) model is a well-suited experi-
mental design strategy that offers the possibility of inves-
tigating a high number of variables at different levels
with only a limited number of experiments [29]. The
methodology was originally developed by Box and Wil-
son and improved by Box and Hunter. This is an ideal
tool for process optimization [23], and its rotatable char-
acteristic enables identification of optimum responses
around its center point without changing the predicting
variance. RSM is a collection of mathematical and statis-
tical techniques based on the fit of a polynomial equa-
tion to the experimental data, which must describe the
behavior of a data set with the objective of making stat-
istical provisions. CCD has been successfully used to
optimize the technology or production condition for
drug delivery systems such as sustained release tablets,
liposomes, microspheres , nanoparticles in recent years
The ranges for each of the variables in Table 1 were
chosen taking into account our preliminary experiments.
Table 2 shows the experimental results concerning the
tested variables on mean diameter of particle size, poly-
dispersity drug loading percentage and encapsulation ef-
ficiency. These four responses were individually fitted to
a second order polynomial model. For each response,
the model which generated a higher F value was identi-
fied as the best fitted model. Each obtained model was
validated by ANOVA. Three dimensional response sur-
face plots were drawn for the optimization of PLGA-
CURC formu lation. These types of plots are useful in
studying the effects of two factors on the response at
one time, when the third factor is kept constant.
Influence of formulation variables on particle size
Particle size is a critical factor for nanoparticle-based drug
delivery system. It is one of the factors that control the
kinetics of drug release. Generally, smaller particle size
permits a faster release rate. The following second order
reduced quadratic model equation was derived by the best
fit method to describe the relationship between the
Table 4 Scale up results for large batch production of PLGA-CURC
Particle size (nm) Polydispersity index Encapsulation efficiency (%) Drug loading (%)
Primary optimized conditions 129.5 ± 6.9 0.138 ± 0.023 91.4 ± 2.3 12.68 ± 3.5
First stage 500 mg 135.4 ± 9.4 0.139 ± 0.012 91.12 ± 1.5 11.98 ± 2.5
Second stage 1g 142.3 ± 8.9 0.142 ± 0.024 92.13 ± 3.5 11.12 ± 2.6
Third stage- 2 g 148.6 ± 7.7 0.137 ± 0.025 90.67 ± 2.8 10.92 ± 2.3
Fourth stage 5g 158.5 ± 9.8 0.141 ± 0.011 90.34 ± 3.2 10.32 ± 1.4
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 7 of 18
Page 7
particle size (Y
), the amount of PLGA, concentration of
PVA and volume of ethyl acetate.
Particle sizeðY
Þ¼180:43 1:35
Ethyl acetate
þ 11:85
Ethyl acetate
þ 0:077
Ethyl acetate
þ 3:35
Ethyl acetate
A positive value in regression equation for a response
represents an effect that favors the optimization (syner-
gestic effect), while a negative value indicates an inverse
relationship (antagonistic effect) between the factors and
the respo nse [30].
The reduced quadratic model was found to be signifi-
cant with an F value of 30.87 (p < 0.0001), which indicates
that response variable Y
and the set of formulation vari-
ables were significantly related. The high R
value indi-
cated that 96.53% of variation in particle size was
explained by the regression on formulation factors
(Additional file 1: Table S1).
The particle size values for the 20 batches show a wide
variation in response i.e., the response ranges from a
minimum of 77.8 nm to a maximum of 198.8 nm. The
data clearly indicate that the particle size value is
strongly dependent on the selected variables. The re-
sponse surface plots for particle size as a function of for-
mulation factors were constructed by holding one of the
factors at a constant level. Figure 2A shows the response
surface plot obtained for the interaction betw een PLGA
concentration and PVA at constant value of ethyl acet-
ate. An increase of the mean particle size was observed
(Figure 2A) when increasing con centration of PLGA for
all the amount of PVA used in the formulation (13%).
It was reported that an increase in the amount of PVA
in the formulation may lead to the smaller particle size
due to tight surface that was formed from PVA macro-
molecular chains of high concentration [36]. However,
too much PVA is not suggested as it will hinder in vivo
degradation. In addition, PVA has been found to have a
carcinogentic potential and removal of excess PVA from
the particle surface is difficult [37]. Our data support
that a lower concentration of PVA (1% w/v) was suitable
to obtain well controlled particle size formu lations. Ana-
lyzing the response surface of interactions of PLGA and
ethyl acetate at constant PVA, we found that initially,
with an increase of solvent volume, the part icle size does
not change much and then decreases with further in-
crease in the volume of ethyl acetate (Figure 2B). Forma-
tion of nanoparticles depends on the rate of diffusion of
the organic solvent into the aqueous phase, which in
turn influences the precipitation of polymer thereby in-
fluencing the particle size. The minimum particle size
and its corresponding experimental conditions were
derived from the regression model.
Influence of preparation factors on polydispersity index
After nanoparticle formation, the size population fre-
quently follows a multimodal distribution. The polydisper-
sity index is a very important parameter which is used to
describe variation of particle size in a sample of particles.
Figure 2 Three dimensional response surface plots showing the
effect of variables on response:- particle size; (A) effect of PLGA
and PVA concentration on particle size (Actual constant ethyl
acetate (ml) = 4.25); (B) effect of PLGA and ethyl acetate on
particle size (Actual constant PVA (%) = 1.0).
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 8 of 18
Page 8
When this index is close to 1, the size range becomes
wide. A desired optimal value is closer to 0. The response
surfaces of polydispersity index keeping ethyl acetate and
PVA constant are shown in Figure 3A and B respectively.
The polydispersity variations are found to be in the same
direction as the particle size in all the cases studied. In-
creasing the amount of polymer and decreasing the vol-
ume of organic phase leads to an increase of the
polydispersity index. The coefficient of correlation, R
0.8927 and the model gives a p-value <0.001 (ANOVA).
Polydispersity ¼ 0:091 0:00026
Ethyl acetate
þ 0:00458
Ethyl acetate
Ethyl acetate
þ 0:00433
Ethyl acetate
The minimum polydispersity index and its correspond-
ing experimental conditions were derived from this regres-
sion model. The values of polydispersity index predicted
from this regression model are shown in Table 5.
Influence of preparation factors on encapsulation
In our study, encapsulation efficiency of PLGA-CURC
reached up to 93.5% (Table 2). High encapsulation effi-
ciency is advantageous since it transports enough drug
at the target site and increase the residence time of the
drug. The high encapsulation efficiency in PLGA can be
attributed to several factors. First the hydrophobic na-
ture of PLGA molecules makes it relatively easy to en-
trap hydrophobic curcumin into PLGA-CURC. Second,
the hydrophobic nature of curcumin results in a mini-
mum loss of the drug to the external aqueous phase
during the formulation process. The response surface
diagrams reveal that the encapsulation efficiency first
increases with increasing PLGA concentration and then
decreases (Figure 4A and B) at constant PVA and ethyl
acetate concentration. Furthermore , there is no signifi-
cant change observed with variation of PVA con centra-
tion (13% w/v) (Figure 4C). The optimized variables
show a good fit to the quadratic model (Eq. 9) with an
F value of 14.15 (p =0.0001), which indicates that re-
sponse variable Y
and the set of formulation variables
were significantly related. The high R
value indicated
that 92.73% of variation in encapsulation efficiency was
explained by the regression on formulation factors
(Additional file 1: Table S1).
The statistical analysis of the results generated a quad-
ratic response for encapsulation efficiency is as follows
Encapsulation efficiency %
¼ 0:54 þ 1:07
PLGA 3:88
þ 24:17
Ethyl acetate þ 1:13
Ethyl acetate
Ethyl acetate
Ethyl acetate
Influence of preparation factors on percentage drug
The response surface graphs for the most statistically sig-
nificant variables on percentage drug loading are shown
in Figure 5(A-C). The response surface diagram depicting
Figure 3 Three dimensional response surface plots showing the
effect of variable on response:- polydispersity; (A) effect of
PLGA and PVA concentration on polydispersity (Actual
constant ethyl acetate (ml) = 4.25); (B) effect of PLGA and ethyl
acetate on polydispersity (Actual constant PVA (%) = 1.0).
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 9 of 18
Page 9
interactions of PLGA concentration and PVA showed
that increase in polymer concentration first increases the
percentage drug loading and then decreases implying an
optimum polymer concentration for maximum drug
loading. At higher PLGA concentration, initially there
was no significant change observed for drug load with re-
spect to PVA concentration, but drug loading increased
with increase in PVA concentration. The reverse was
observed at low PLGA concentration.
Drug loading %ðÞ¼14:34 þ 0:16
Ethyl acetate
þ 0:72
þ 0:50
Ethyl acetate
þ 0:032
Ethyl acetate ð10Þ
Optimization by desirability function
Optimization process w a s undertaken with desirability
function to optimize the four responses simultaneously.
Table 5 Comparison of the experimental and predicted
values of PLGA-CURC prepared under the predicted
optimum conditions
Response Predicted
, Particle size (nm) 120.73 129.5 6.77
, Polydispersity
0.13 0.138 6.15
, Encapsulation
efficiency (%)
92.45 91.4 1.13
, Drug loading (%) 13.56 12.68 6.49
Bias was calculated as (predicted value-experimental value)/predicted value
X 100.
Figure 4 Three dimensional response surface plots showing the effect of variable on the response:- encapsulation efficiency; (A) effect
of PLGA and PVA concentration on encapsulation efficiency (Actual constant ethyl acetate (ml) = 4.25); (B) effect of PLGA and ethyl
acetate on encapsulation efficiency (Actual constant PVA (%) = 1.0); (C) effect of PVA and ethyl acetate on encapsulation efficiency
(Actual constant PLGA (mg) = 4.5).
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 10 of 18
Page 10
A high value of desirability coefficient δ (0 δ 1) indi -
cates that the o perating point can produce acceptable
formulation result s. The responses: particle size ( Y
polydispersity index (Y
), enc apsulation efficiency (Y
and drug loading (Y
) were transformed into the desir-
ability scale d
, d
, d
and d
, respectively. Among them,
, Y
had to be minimized, while Y
, Y
had to be maxi-
mized. The overall object ive functio n (δ) was calculated
quadratic expression. The higher coefficient of deter-
mination and F value in t erms of the quadratic model
indicated the goodness of fit. Figure 6 shows the re-
sponse surface plot f or increa sing desirability coefficient
δ with respect to changes in variables: PLGA (X
) keeping the volume of ethyl acetate constant.
The maximum value of desirability coeff icient δ = 0.716
was obtained at the conditions, PLGA amount of 85 mg ,
PVA concentration of 1%(w/v) and 4.25 ml of ethyl
In order to evaluate the predictive power of this model
and desirability coefficient, PLGA-CURC was prep ared
under the optimal conditions. The results comparing the
experimentally obtained and model predicted values of
all four responses are presented in Table 5. The experi-
mental values of the multiple batches prepared under
the optimal conditions were very close to the predicted
values, with low percentage bias, suggesting that the
optimized formulation was reliable and reasonable. It
has been shown that the highest encapsulation efficiency
and drug loading with commensurate minimum mean
Figure 5 Three dimensional response surface plots showing the effect of variable on response:- drug loading; (A) effect of PLGA and
PVA concentration on the drug loading (Actual constant ethyl acetate (ml) = 4.25); (B) effect of PLGA and ethyl acetate on drug
loading (Actual constant PVA (%) = 1.0); (C) effect of PVA and ethyl acetate on drug loading (Actual constant PLGA (mg) = 4.5).
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 11 of 18
Page 11
particle size and size distribution was achieved by using
the optim al conditions of 85 mg PLGA, 1% (w/w) of
PVA and 4.25 ml volume of ethyl acetate.
Scale up for large batch production of PLGA-CU RC
Scaling up the nanoformulation process to produce large
batches of nanoparticles is the key to effective clinical use
of nanoparticle based drugs. To translate this formulation
into large scale production, we investigated seven critical
parameters and their correlation in four sequential stages.
Each stage was optimized to get the best parameter com-
bination in terms to target response of particle size, poly-
dispersity, encapsulation efficiency and drug loading. The
parameters chosen include polymer solvent ratio, aqueous
phase volume, organic and aqueous phase ratio, sonication
tip diameter, sonication time, stirrer speed and stirring
time. Our goal was to produce PLGA-CURC with similar
physicochemical characteristics in a scaled up batch pro-
duction. Results from all the different stages of scale up
production are compiled in Tables 3 and 4 which shows
the parameter variations and optimized scale up outcomes
of PLGA-CURC formulation.
In the first scale-up optimization stage, polymer
amount was increased from 85 mg to 500 mg but the
volume of ethyl acetate was increased to only 5 mL from
4.25 mL. This was a very critical step as we needed to
minimize the amount of organic solvent needed to pre-
vent problems of solvent evaporation. This resulted in an
increase in viscosity of the organic phase leading to larger
particle size. To overcome this, the sonication time was
increased from 60 sec to 120 sec keeping the sonication
tip diameter and stirring speed and stirring time same.
This resulted in average particle size of 135.4 nm, an in-
crease of about 6 nm from the primary optimized batch.
The drug loading in the first stage scale-up dropped by
0.7% while encapsulation efficiency remained almost the
same. Once we achieved the first stage, next we scaled up
~10 times for producing 1 g of PLGA -CURC in the sec-
ond stage. Doubling the amount of polymer and volume
of solv ents required inc reasing the sonication power to
get nanoparticles in the same particle size range. For that,
the sonication tip diameter was increased from 2 mm to
14 mm. Further, stirring speed was increased from
2,000 rpm to 3,000 rpm and stirring time was increased
by 2 h. The resulting optimized batch had an average
particle size of 142.3 nm and 11.12% of drug loading. In
the third stage (~20X) of scale-up, the aqueous phase
was optimized at 60 ml. To keep the nanoparticle size
comparable, the sonication time was increased to 180 sec
and the stirring speed was doubled to 6,000 rpm.
Accounting for more than double of total volume from
stage one to three, the exposed surface area for evapor-
ation of solvents was increased by using an open
mouthed vessel during stirring. With all these parameter
combinations, the final optimized batch for this stage
showed a 0.2% decrease in drug loading only and similar
~6 nm increase in particle size from previous stage. In
the final stage, we scaled up to ~50 times to produce 5 g
of PLGA -CURC. Here, the aqueous phase was increased
to 150 mL with the excess water being increased to
200 mL to account for 5 g of polymer being used. Such a
large volume of liquid phase needed high sonication
power which was brought about by increasing the sonic-
ation time from 180 sec to 300 sec, increasing the stirring
speed to 8,000 rpm and increasing the stirring time to
8 h. This resulted in nanoparticles having an average par-
ticle size of 158.5 nm, a total increase of only 29.4 nm
from the primary optimized batch. Also, the drug loading
decreased by 2.36% which is minimal considering ~50X
scale-up. The encapsulation efficiency and polydipersity
was found to be similar to the primary optimized batch.
We have successfully produced PLGA-CURC in 5 g
quantities through this route and identified critical para-
meters for scaling up the formulation process.
Characterization and evaluation of the optimized
scaled-up formulation
External morphology
The external morphology of lyophilized PLGA-CURC
prepared at optimal conditions are shown in Figure 7A
and B. PLGA-CURC were spherical, discrete without ag-
gregation, and smooth in surface morphology. The size
of the PLGA-CURC was found to be approximately
140 nm. The particle size determined by Diff erential
Light Scattering (DLS) for the same batch was found to
Figure 6 Response surface for overall desirability (δ as a
function of PLGA (mg) and PVA (%) at constant ethyl acetate at
4.25 ml.
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 12 of 18
Page 12
be 158.5 nm. This may be explained by the fact tha t par-
ticle size analyzer, based on DLS, measures the hydro-
dynamic diameter of the particle while the electron
microscope measures exact diameter of particles in solid
state. But at the same time, the amount of nanoparticles
seen under SEM or TEM is a very small random sample
from the bulk of nanoparticle batch produced.
In vitro release studies for PLGA-CURC prepared under
optimal conditions
The drug release profile is another important criterion
while formulating polymeric nanoparticles. The profile
of curcumin release in PBS (pH = 7.4) from the opti-
mized formulation is illustrated in Figure 8. It was
observed that the release consisted of an initial burst re-
lease phase corresponding to about 26% of drug release
in the first hour, followed by a slow sustain ed release
corresponding to 68% drug release in seven days and ~
75% in 10 days. Sustained release kinetics where 75%
curcumin was released from curcumin-PBCN nanoparti-
cles over 24 h has been reported by Sun et al. [38]. In
another study, Mohanty et al. (2010), showed 46% drug
release in 24 h and 66% drug release over a period of
10 days from nanoparticlulate curcumin [39 ]. Release of
curcumin from PLGA-CURC was more uniform and
sustained over the 10 day period of study. The burst re-
lease of curcumin may be due to the surface associated
curcumin bound weakly to the surface of the nanoparti-
cles which gets released first. The remaining amount of
curcumin which is encapsulated within the structure
was released in a controlled manner for the entire period
of study (10 days). Dissolution diffusion of the drug from
the matrices and the slow matrix erosion are the
mechanisms thought responsible for the slower drug re-
lease kinetics from the nanoparticles.
Further, the release profile of curcumin from PLGA
nanoparticles were investigated by using different release
kinetic models: zero order, first order, Higuchi and
Hixson-Crowell equations [26,27], and thei r regression
coefficient (r
) was calculated from appropriate plots.
The first order model describes the release to be con-
centration dependent while the Hixson Crowell cube
root model indicates a change in surface area or diam-
eter due to erosion with progressive release of drug as a
function of tim e. Release rate constants for burst release
and sustained release are illustrated in Table 6. Compar-
ing the amount of released curcumin with respect to
time; for the burst release phase (first 6 h), PLGA-CURC
followed the zero order model (R
= 0.997). Higuchi kin-
etics model which states that diffusion is one of the
major methods of drug release best described the con-
trolled release phase (R
= 0.996) during later part of the
release which may be controlled by a combination of
slow and gradual erosion and diffusion. Overall, the
in vitro release data indicates that PLGA-CURC is cap-
able of releasing curcumin in a controlled manner over a
period of 10 days.
Cellular uptake of nanoparticle prepared under optimal
In order to study the uptake of PLGA-CURC by differ-
ent cancer cell lines, we investigated the ability of nano-
particles to be endocytose d by the cells. Figure 9A and
Figure 7 (A) Transmission electron micrograph and (B) Scanning electron micrograph of PLGA-CURC formulated under optimum
Figure 8 In vitro release profiles of the optimized formulations
of PLGA-CURC (values reported are mean ± SD; n = 3).
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 13 of 18
Page 13
B illustrate panels of the confocal microscope images of
different cancer cell lines incubated with Nile red-
labeled PLGA-CURC for 2 h. Our results depict robust
uptake of the nanoparticles in all the cell lines. The
cells incubated with Nile red-labeled PLGA-CURC
exhibited either red (due to Nile red) or green (due to
curcumin) fluorescence, depending upon the excitation
Western blot analysis
A principal cellular target of curcumin in cancer cells is
activated nuclear factor kappa B (NFκB) which is a family
of five closely related proteins found in several dimeric
combinations and bind to the NFκB consensus sequence
on DNA [40]. NFκB is translocated to the nucleus from
cytosol, where it induces the expression of more than 200
genes that have been shown to suppress apoptosis and in-
duce cellular transformation, proliferation, invasion and
metastasis. Many of these activated target genes are crit-
ical for establishment of the early and late stages of
aggressive cancers. We studied the mechanism of action
of PLGA-CURC on breast cancer cell line, MDA MB 231,
and compared the functional pathways affected by PLGA-
CURC to what has been previously reported for free cur-
cumin [38]. The results of the Western blot analysis, as
seen in Figure 10, depicts that PLGA-CURC was able to
inhibit the translocation of NFκB from cytosol to nucleus
in MDA MB-231 cells. The degree of inhibition with
PLGA-CURC treatment was seen to be greater as com-
pared to untreated cells, as depicted with fainter bands of
NFκB corresponding to the nuclear extract from cells trea-
ted by PLGA-CURC. This result illustrates that the curcu-
min encapsulated within the PLGA-CURC retains its
functional activity on encapsulation and subsequent re-
lease from the nanoparticles.
Pharmacokinetic Studies
PLGA-CURC were formulated to improve the bioavail-
ability of curcumin. To evaluate this, male Sprague Daw-
ley rats were administered PLGA-CURC nanoparticl es
Table 6 Different kinetic models and regression coefficients of PLGA-CURC formulations
Model Equation R
value for burst release (%) R
value for sustained release (%)
Zero order m
m ¼ kt 0.997 0.955
First order lnm ¼ kt 0.977 0.976
Higuchis model m
m ¼ kt
0.967 0.996
Hixson crowell m
¼ kt 0.987 0.955
is the initial drug amount (100%, when represented as percentage); m the amount of drug remaining at a specific time (calculated as % of m
); k the rate
constant; t is the time.
Figure 9 Confocal images of different cancer cell lines incubated with PLGA-CURC - (A) Red :- nile red-labeled PLGA-CURC, Green:-
curcumin, Bright field merged with cell nuclei stained with DAPI; (B) Merged images of nile red-labeled PLGA-CURC with DAPI; Merged
images of curcumin with DAPI and DAPI.
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 14 of 18
Page 14
intravenously (7.5 mg/kg equivalent curcumin nanopar-
ticles). Blood samples were collected at predetermined
time intervals and the concentration of curcumin was
determined by HPLC analysis. Our res ults showed
(Figure 11) that the pharmacokinetic profile of PLGA -
CURC in rats by i.v. administration followed two com-
partmental model. The area under the curve (AUC
after i.v. injection of PLGA-CURC was found to be
6.139 mg/L h. This value is much higher compared to
3.16 mg/L h reported by Ma et al. (2007) for their micel-
lar formulation of curcumin at a dose of 5 mg/kg [41].
Duan et al. (2010) reported the area under the curve to
be 3.302 mg/L h for a 5 mg/kg dose of their curcumin-
PBCA nanoparticles. Both these groups reported
of free curcumin at 10 mg/kg dose to be only
1.67 mg/L h and 1.92 mg/L h respectively [42]. They
also reported a higher elimination half life of curcumin
when encapsulated within nanoparticles or micelles
alongwith a decrease in clearance rate. Our pharmacoki-
netic results for PLGA-CURC showed the same trend.
This is expected when the drug in circulation is
restricted to the blood compartment because of being
encapsulated with nanoparticles [42]. The higher level of
curcumin concentration observed in the ca se of PLGA-
CURC nanoparticles might be explained by increa sed
bioavailability as a function of increased aqueous disper-
sibility, smaller nanopartic le size and increased accumu-
lation of nanoparticles in different organs together with
sustained release of curcumin from them. Similar obser-
vations related to pharmacokinetic studies of curcumin
or nanoparticles have been reported by various other
groups [9,38,42].
Storage stability of PLGA-CURC nanoparticles
The long term storage stability of the PLGA-CURC is an
important parame ter when scaling up the formulation.
Nanoparticle formulations increase the surface area by
many folds and this may lead to very high aggregation
after long periods of storage. This poor long term stabil-
ity may be due to different physical and chemical factors
that may destabilize the system [43]. Lyophilization is a
promising approach for the stabilization of PLGA nano-
particles [44]. For lyophilized nanoparticles, cryoprotec-
tants serve as stabilizers during the freeze drying
process. For our study, sucrose (5% w/v) and trehalose
(2% w/v) were chosen as the cryoprotectants to prevent
the hydrolytic instability, aggregation between nanoparti-
cles, protection during processing and storage. After
6 months of storage with cryoprotectants at 4°C, the
nanoparticles appear to be stable without any collapse or
aggregation. Figure 12 shows effect of long term storage
on the particle size, encapsulation efficiency and drug
loading of nanoparticles. We saw no major changes be-
sides a slight increase in particle size and a slight de-
crease in encapsulation efficiency and drug loading.
Therefore, PLGA-CURC formulated by our s-o/w emul-
sion solvent evaporation and diffusion technique was
found to be stable for a long period of time.
Gamma irradiation PLGA-CURC nanoparticles
Gamma irradiation is critical as it renders sterility to the
nanoparticles before being injected into the body [45].
Figure 10 A) Western blot showing inhibition of translocation
of NFκB (p65) from cytosol to nucleus in MDA MB231 cells
treated with PLGA-CURC. B) Densiometric analysis of the western
blot. (CC-Control cytosol extract, CN- Control nuclear extract, TC
Treated cytosol extract and TN Treated nuclear extract).
Figure 11 In vivo bioavailability of curcumin using PLGA-CURC
nanoparticles. PLGA-CURC were administered intravenously to the
rats at a dose of 7.5 mg/kg (n = 6).
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 15 of 18
Page 15
There are many alternative techniques for sterilization
but we chose γ-irradiation as it is known for its high
penetration power and isothermal property of gamma
rays that permits sterilization of even sensitive materials
[46]. However, γ-irradiation may have some effects on
the nanoparticle size or drug loading. The changes in
particle size and drug loading for low, medium and high
exposures are graphed in Figure 13. Our results demon-
strate that there were no statistically different changes
observed betw een non-irradiated and different dose irra-
diated nanopa rticles. Further, γ-irradiation did not alter
the drug loading in the nanoparticles.
Scaling up of the nanoformulation process is essential for
the future development of nanoparticle based drug deliv-
ery technologies. In this paper, we have made a successful
effort towards formulating, optimizing and scaling up
PLGA-CURC by using Solid-Oil/Water emulsion tech-
nique. Once formulation was achieved, we optimized our
process by successful use of RSM using CCD model and
scaled up the formulational process in four stages with
final scale-up process yielding 5 g of PLGA-CURC. The
major goals while designing the scale up stages were to
control particle size and polydispersity while maximizing
drug encapsulation efficiency and drug loading which
were adequately achieved. PLGA-CURC, under the opti-
mized conditions were found to have a particle size of
158.5 ± 9.8 nm, polydispersity of 0.141 ± 0.011, encapsula-
tion efficiency of 90.34 ± 2.3% and drug loading of
10.32 ± 1.4%. Morphological studies of the final scaled up
batch showed that the PLGA-CURC were smooth, spher-
ical with a uniform surface. The release kinetics from
PLGA-CURC exhibited a biphasic pattern with an initial
burst release followed by a slower diffusion controlled
drug release for a period of 10 days. Intracellular uptake
studies revealed excellent uptake in prostate, breast and
pancreatic cell lines. Pharmacokinetic studies illustrated
of PLGA-CURC after i.v. injection to be
6.139 mg/L h which is higher than most reported in
literature for curcumin based formulation. Stability ana-
lysis showed long term physicochemical stability and
gamma irradiation studies showed no significant changes
after sterilization of the PLGA-CURC formulation. In con-
clusion, our nanoformulation, PLGA-CURC, significantly
overcame the limitation of the lack of aqueous solubility
of curcumin and thereby improved its bioavailability. The
formulation process was successfully optimized using
CCD-RSM and scaled up to produce 5 g of PLGA-CURC
with similar physicochemical characteristics as that of the
primary formulated batch. This scale-up process can be
further elaborated to produce higher quantities which
would prove beneficial for efficient manufacturing at an
industrial scale.
Additional file
Additional file 1: Table S1. The analysis of variance for the response
surfaces obtained for particle size, polydispe rsity, encapsulation efficiency
and drug loading.
Competing interests
This work was performed under a sponsored research agreement between
SignPath Pharma Inc. and UNTHSC. Conceptualization, design and
Figure 12 The particle size, encapsulation efficiency and drug
loading of PLGA-CURC against storage time at 4°C.
Figure 13 The particle size (A) and drug loading (B) of PLGA-
CURC after γ-irradiation at doses 16.8 kGy for 241 minutes
(Low), 25.3 kGy for 179 minutes (Medium) or 35.8 kGy for
241 minutes (High) (n = 3).
Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 16 of 18
Page 16
performance of experiments were by AR, AM and JKV. This product
technology has been licensed by SignPath Pharma Inc. from UNTHSC.
Authors contributions
APR and AM performed all optimization experiments, analyzed data and
wrote the manuscript. JKV supervised and mentored work and corrected the
manuscript. LH provided the funds in part for this research and reviewed the
manuscript. All authors read and approved the final manuscript.
This research was partially supported by SignPath Pharma Inc., Pennsylvania,
USA. Anindita Mukerjee was supported by Susan G. Komen postdoctoral
grant (KG101213). The authors would like to thank Sanjay Thamake for his
help in animal handling for the pharmacokinetics experiment. We would
also thank Laurie Mueller for processing the SEM samples at High Resolution
Scanning Electron Microscopy Facility at University of Texas Southwestern
Medical Center, Dallas, USA.
Author details
Department of Molecular Biology & Immunology and Institute for Cancer
Research, Graduate School of Biomedical Sciences, University of North Texas
Health Science Center, Fort Worth, TX76107, USA.
SignPath Pharmaceuticals
Inc., Quakertown, PA, USA.
Received: 10 May 2012 Accepted: 23 August 2012
Published: 31 August 2012
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Cite thi s article as: Ranjan et al.: Scale up, optimization and stability
analysis of Curcumin C3 complex-loaded nanoparticles for cancer
therapy. Journal of Nanobiotechnology 2012 10:38.
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Ranjan et al. Journal of Nanobiotechnology 2012, 10:38 Page 18 of 18
Page 18
  • Source
    • "matrix porosity, drug solubility and its interaction with the incorporated drug. More specifically, many drugs and biological materials have been incorporated into PLGA matrices, such as, procaine hydrochloride (Govender et al. 1999), doxorubicin (Amjadi et al. 2012), curcumin (Ranjan et al. 2012), alpha 1-antitrypsin (Pirooznia et al. 2012), and also mesenchymal cells from human umbilical cord (Wang et al. 2011). On the other hand, antimicrobial peptides (bacteriocins ) are small peptides produced by lactic acid bacteria that have been considered as an alternative to antibiotics in some applications (Rea et al. 2011). "
    [Show abstract] [Hide abstract] ABSTRACT: The use of poly(lactic-co-glycolic acid) (PLGA) matrix as a biomolecule carrier has been receiving great attention due to its potential therapeutic application. In this context, we investigated the PLGA matrix capacity to incorporate nisin, an antimicrobial peptide capable of inhibiting the growth of Gram-positive bacteria and bacterial spores germination. Nisin-incorporated PLGA matrices were evaluated based on the inhibitory effect against the nisin-bioindicator Lactobacillus sakei. Additionally, the PLGA-nisin matrix stability over an 8-months period was investigated, as well as the nisin release profile. For the incorporation conditions, we observed that a 5 h incorporation time, at 30 °C, with 250 μg/mL nisin solution in PBS buffer pH 4.5, resulted in the highest inhibitory activity of 2.70 logAU/mL. The PLGA-nisin matrix was found to be relatively stable and showed sustained drug delivery, with continuous release of nisin for 2 weeks. Therefore, PLGA-nisin matrix is could be used as a novel antimicrobial delivery system and an alternative to antibiotics incorporated into PLGA matrices.
    Full-text · Article · Feb 2015 · World Journal of Microbiology and Biotechnology
  • Source
    • "The Lzp-PLGA-NPs suspension was centrifuged (Remi, Mumbai, India) at 12,000 rpm at 4°C for 30 min, washed twice with HPLC water and supernatant was collected. The amount of unentrapped drug was determined by the developed RP-HPLC method and the percentage drug entrapment and drug loading [34] of nanoparticles was calculated by using the following equations: "
    [Show abstract] [Hide abstract] ABSTRACT: The aim of the present study was to optimize lorazepam loaded PLGA nanoparticles (Lzp-PLGA-NPs) by investigating the effect of process variables on the response using Box-Behnken design. Effect of four independent factors, that is, polymer, surfactant, drug, and aqueous/organic ratio, was studied on two dependent responses, that is, z-average and % drug entrapment. Lzp-PLGA-NPs were successfully developed by nanoprecipitation method using PLGA as polymer, poloxamer as surfactant and acetone as organic phase. NPs were characterized for particle size, zeta potential, % drug entrapment, drug release behavior, TEM, and cell viability. Lzp-PLGA-NPs were characterized for drug polymer interaction using FTIR. The developed NPs showed nearly spherical shape with z-average 167-318 d·nm, PDI below 0.441, and -18.4 mV zeta potential with maximum % drug entrapment of 90.1%. In vitro drug release behavior followed Korsmeyer-Peppas model and showed initial burst release of 21.7 ± 1.3% with prolonged drug release of 69.5 ± 0.8% from optimized NPs up to 24 h. In vitro drug release data was found in agreement with ex vivo permeation data through sheep nasal mucosa. In vitro cell viability study on Vero cell line confirmed the safety of optimized NPs. Optimized Lzp-PLGA-NPs were radiolabelled with Technitium-99m for scintigraphy imaging and biodistribution studies in Sprague-Dawley rats to establish nose-to-brain pathway.
    Full-text · Article · Jul 2014 · BioMed Research International
  • Source
    • "Lyophilized MA nanoparticles were dispersed in double distilled water and analyzed in three readings per nanoparticles sample. The poly dispersity was also calculated based on the volumetric distribution of particles [13] . "
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