Preparation and statistical optimization of LosartanPotassium loaded nanoparticles using BoxBehnken factorial design: Microreactorprecipitation

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chemical engineering researchand design 1 0 4 ( 2 0 1 5 ) 98–109

Contents lists available at ScienceDirect

Chemical Engineering Research and Design

journal homepage: www.elsevier .com/ locate /cherd

Preparation and statistical optimization of Losartan

Potassium loaded nanoparticles using Box

Behnken factorial design: Microreactor

precipitation

Pritam Patila, Gokul Khairnarb, Jitendra Naika,∗

a Department of Chemical Engineering, University Institute of Chemical Technology, NorthMaharashtra University,

Jalgaon, Maharashtra, Indiab Department of Pharmaceutical Technology, University Institute of Chemical Technology, North Maharashtra

University, Jalgaon, Maharashtra, India

a r t i c l e i n f o

Article history:

Received 27 January 2015

Received in revised form 4 June 2015

Accepted 21 July 2015

Available online 29 July 2015

Keywords:

Losartan Potassium

Microreactor

Box–Behnken design

Encapsulation efficiency

Drug release

a b s t r a c t

The current study deals with the development and optimization of Losartan Potassium (LP)

loaded nanoparticles by continuous flow microreactor precipitation methodology for sus-

tained release. Formulation of LP loaded nanoparticles was conducted by using box Behnken

(33) experimental design to evaluate the effect of three independent process variables viz.,

polymer concentration (Ethyl cellulose, X1), surfactant concentration (Tween 80,X2) and the

inner diameter (X3) of the microreactor on the preferred responses: encapsulation efficiency

(Y 1) and drug release (Y 2) over 12h. The average particle size and polydispersity index of LP

loaded nanoparticles were characterized by particle size analyzer. The interaction between

ethyl cellulose and the LP was investigated by FTIR spectroscopy. The surface morphology

and physical nature of LP loaded nanoparticle were characterized by FESEM and XRD anal-

ysis, respectively. The design formulations showed the encapsulation efficiency within the

range of 57.5 to 82.8% with sustained release profile of 77.2 to 100% over 12h. The actual

and predicted values of both the responses were close to each other. It is verified that the

Box Behnken factorial design provides a useful platform for the optimization of LP loaded

nanoparticle by microreactor precipitation methodology.

© 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

1. Introduction

Over the past two decades, development of nanoparticles has

been gaining more prominence from industries in the field

of chemistry, microelectronics, sensors, energy, optic, life sci-

ences and pharmaceutics (Capretto et al., 2012; Cheng et al.,

2012; Horn and Rieger, 2001; Zhao et al., 2010; Patil and Naik,

2014). In pharmaceutical industries, the size and size dis-

tribution of nanoparticles plays an important role in drug

delivery processes such as sustained release, targeted release

andthe kinetics of takingaway from thebody (Rapoport, 2007).

∗ Corresponding author. Tel.: +91 257 2258441; fax: +91 257 2258403.E-mail address: [email protected] (J. Naik).

Therefore, nanoparticles have prominent attribute because of

their physicochemical properties and better control of process

parameters, is vital in order to produce such nanomateri-

als with desired features (Carugo et al., 2011). In addition,

nanoparticles have a series of advantages, including control

drug delivery, a large amount of drug availability and increase

inthe amountof druguptakeby the targetcells (Capretto et al.,

2013).

Usually, nanoparticles or micro particles are prepared

by conventional methods such as solvent evaporation,

spray drying, ultrafine mechanical milling and high speed

http://dx.doi.org/10.1016/j.cherd.2015.07.0210263-8762/© 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

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chemicalengineeringresearch and design 1 0 4 ( 2 0 1 5 ) 98–109 99

homogenization, etc. However, this bottom up and top down

methods has some limitations, such as low yield, low ther-

mal efficiency and broad particle size distribution (Zhao et al.,

2007; Appalakutti et al., 2015). Owing to these disadvantages,

the progress in the field of microreactor technology offer a

series of advantages over conventional methods in terms of

smaller particle size, shorter residence time and controllabil-

ity of the process (Song et al., 2008; Dreher et al., 2008; Patilet al., 2012). Result reported by Zhang et al. and Chen et al.

showed that the micro channel reactor having an excellent

micromixing property with narrow size distribution and large

surface area throughout the reactor (Carugo et al., 2011; Zhao

et al., 2007). In addition, microreactor are characterized by

their high surface area to volume ratio, it enables to reduce

heat and mass transfer resistance for the development of uni-

form nanoparticle in their smallest dimension (Dreher et al.,

2008; Wang et al., 2014; Horikoshi et al., 2013; Fletcher et al.,

2002). However, the small dimension of microreactor restricts

fluids to low Reynolds number (


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100 chemicalengineeringresearch and design 1 0 4 ( 2 0 1 5 ) 98–109

Fig. 1 – Schematic diagram of the experimental set-up for preparation of LP loaded nanoparticle bymicroreactor

methodology.

diameter of the microreactor (X3, mm), which were varied at

three levels(−1,0,+1). The drugEE (Y 1%)andDRover12h(Y 2%)

were selected as dependent factors. The statistical experimen-

tal data were analyzed using the Design-Expert® Software

(Version-8.0.7.1, Stat-Ease Inc., Minneapolis). The matrix of design, including dependent and independent variables is

shown in Table 1. The interaction of independent variables

and measured responses were modeled using the following

quadratic mathematical model (Chaudhary et al., 2013),

Y = b0 + b1x1 + b2x2 + b3x3 + b12x1x2 + b13x1x3 + b23x2x3

+b11x21 + b22x

22 + b33x

23

Y is the response,b0 is theintercept andb1 to b33 are the regres-

sion coefficient. x1, x2, and x3 are individual effect, x1x2,x1x3,

and x2x3 are interaction effect, and x12,x22, and x32 are the

quadratic effect. ANOVA of the model was analyzed the sig-

nificance of the statistical parameters and model at the 95%

confidence level (P< 0.05).

2.4. Characterization of the nanoparticles

2.4.1. Evaluation of Encapsulation Efficiency (%)

The amount of LP drug encapsulated within the nanoparti-

cles was determined by UV–vis spectrophotometer (HITACHI

U-2900, Tokyo, Japan). An accurately weighed 10mg of pre-

pared LP loaded nanoparticles were stirred with enough

quantity of methanol to dissolve the polymeric coat and

extracted in distilled water.The mixture was stirred continued

until complete evaporation of methanol followed by filtration.The drug content within the filtrate was analyzed after suit-

able dilution using UV–vis spectrophotometer by measuring

absorbance at 210nm. The encapsulation efficiency (EE) of the

LP loaded nanoparticles was calculated using the following

equation (Deshmukh and Naik, 2014), % EE= actual quantity

of drug determined/theoretical quantity of drug ×100.

2.4.2. In vitro release study

In vitro dissolution studies of LP loaded nanoparticles were

performed using XXVIII apparatus, Type-I (rotating paddle

method) for 12h. 5 ml of nanosuspension was introduced into

the pretreated dialysis membrane, tied at both ends and then

membrane was placed in a dissolution test apparatus con-

taining distilled water. Stirring was performed at 100 RPM by

using a magnetic stirrer at 37±0.5 ◦C (Khairnar et al., 2014a;

Khairnar et al., 2014). Samples (2ml) were withdrawn at regu-

lar interval and replenished with contemporary dissolution

media to keep up sink condition. The LP content of each

sample was analyzed aftersuitabledilution usingUV–vis spec-

trophotometer by measuring absorbance at 210nm.

Table 1 – Variable and three levels.

Independent variable Low level (−1) Medium level (0) High level (+1)

X1 =Polymer conc. (mg) 100 300 500X2 = Surfactant conc (%). 0.5 0.75 1

X3 = Inner diameter of micro reactor (mm) 0.8 1 1.5

Dependent variables

Y 1 = %EE

Y 2 =%DR (at 12h)


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chemical engineering researchand design 1 0 4 ( 2 0 1 5 ) 98–109 101

2.4.3. Field emission scanning electron microscopy

(FE-SEM) analysis

The shape, size and surface characteristics of nanoparticles

were examined by Scanning electron microscopy (FESEM-S

4800, Hitachi, Japan) at a working distance of 8.6–8.8 mm and

accelerating voltage of 15.0kV. For the morphological study,

the nanoparticles were gold coated by mounted on a brass

stub using double-sided adhesive tape under vacuum (5pa)

in an ion sputter (Hitachi E1010) to make them electrically

conductive.

2.4.4. Fourier transform-infrared (FT-IR) spectroscopy

In order to examine the drug polymer interaction, the infrared

spectra of the LP, EC, and LP loaded nanoparticles were

obtained by using an FTIR spectrophotometer (FTIR-8400; Shi-

madzu, Asia Pacific Pvt. Ltd. Singapore) by the potassium

bromide pellet method. For that, sample (1mg) was mixed

with KBr (40mg) and compressed into a disc in a manual

press. Spectra were recorded in the wavelength region of

4000–400cm−1.

2.4.5. X-ray diffraction analysis (XRD)

The physical nature of the LP, EC and LP loaded nanoparti-

cles were examined by an X-ray diffractometer (Bruker, D8

Advanced, Germany) with Cu K radiation (= 1.5406 ´̊A). The

anode X-ray tube was operated at 40kV and 40mA. The sam-

ples were analyzed over the angle range of 0–80◦ on the 2

scale at a step size of 0.03/s.

2.4.6. Particle size distribution and polydispersity index(PDI)

The particle size and PDI of the LP loaded nanoparticles were

determined by laser scattering technique using nanozeta-

sizer (ZS 90, Malvern Instruments, UK) at 25◦C. The obtained

nanoparticles were suspended in distilled water as a dis-

persion medium and sonicated before analysis for 1min.

The sample SOP was generated at a refractive index of

1.52, 243.8 of count rates (kcps) and 0.8872(mps) of viscos-

ity.

2.5. Release kinetics and comparison of optimized

formulations with marketed formulation

There are various kinetic models viz., Zero order, first order,

Hixson Crowell, Higuchi and Korsmeyer-Peppas were selected

as a model dependent approach to review the mechanism of

in vitro release of the optimized nanoparticles (Armin et al.,

2014; Misra and Mohanty, 2014; Malakar and Nayak, 2012).

These model dependent approaches are used to describe the

in vitro release behavior of LP from the formulation system

when the release mechanism is not known or when more

than one type of release phenomenon is concerned. Corre-

lation coefficient (R2) was selected to define the accuracy and

prediction ability of these models. The model with a highest

correlation coefficient of determination (R2) was consideredto be the most relevant kinetic model for describing the

in vitro release of an LP from the formulation. The optimized

nanoparticle formulations were compared with the marketed

formulation Losakind (25mg).

3. Result and discussion

3.1. Mechanism of formation of drug loaded

nanoparticles in a microreactor

LP loaded nanoparticles are usually prepared by differ-

ent conventional methods by taking into consideration of

the physiochemical properties of LP and surfactants. Thus,

Microreactor precipitation technology opens a novel way of

preparation of drug loaded nanoparticles in a controlled

way. Two solutions streams of organic and non solvent were

injected with the constant flow rate and the solutions are

mixed with the central channel. The flow regime of the solu-

tions was usually laminar owing to the small dimension of

microreactor. At low Reynolds number solution streams were

alienated leading to diffusion controlled mixing throughout

the microreactor (Palanisamy and Paul, 2012). This may result

to the solution streams flows parallel to the side wall of the

microreactor and initiate the diffusion of drug molecule across

theinterface.The dissolved drug moleculesare comingin con-

tact with supersaturated non-solvent solution formed drug

nuclei within the interface; provides nucleation sites, increase

the nucleation rate and starts the growth of the particle. How-

ever, the growth of particle was controlled by the addition of

surface active agent (Aghajani et al., 2013). It was found that

with increase in volume ratio of non solvent to organic sol-

vent the nucleation rate will increase and reduces the particle

size,this may dueto thenucleation andgrowth kinetics (Sinha

et al., 2013).

From the preliminary studies, it was observed that LP, EC

and inner diameter of the microreactor were found to have

significant impact on the formulation of LP loaded nanoparti-

cles. The effects of these independent variables were studied

by Box Behnken factorial design and are described as follows.

3.2. Statistical analysis of EE and DR

A Box–Behnken statistical design with3 independentvariables

at 3 different levels was used to study the effects on selected

dependent variables (Khairnar et al., 2014b). The independent

variables were Polymer concentration (X1), Surfactant concen-

tration (X2) and the inner diameter of the microreactor (X3),

while the dependent variables were EE (Y 1) and DR over 12 h

(Y 2). The EE was found in the range of 57.50 (F10) to 82.8% (F4)

and minimum DR among the all batches of the nanoparticles

was 77.2% (F11) are listed in Table 2. Transformed values of

EE and DR are represented in Table 3. The quadratic equation

obtained for the EE (Y 1) was given by:

Y 1 = 84.52 − 2.76X1 + 0.45X2 + 0.97X3 + 6.68X1X2 − 3.12X1X3

−0.19X2X3 − 6.5X21 − 8.86X

22 − 13.30X

23

And for the DR (Y 2) was given by:

Y 2 = 93.84 − 3.44X1 + 0.72X2 + 4.77X3 − 7.08X1X2

−7.11X1X3 + 2.39X2X3 − 1.27X21 + 3.59X

22 − 7.02X

23

A positive value in the quadratic equation for a response

represents an effect that favors the optimization (synergistic

effect); whilea negativevalue indicates an inverserelationship

(antagonistic effect) between the factor and the response. In

regression equations of Y 1 and Y 2, the main effects of X1, X2,


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Table 2 – %EE and % DR over 12h.

Experimental run Polymer conc. (mg) Surfactant conc. (%). Inner diam. (mm) % EE % DR at t=12h

F1 100.00 0.75 1.50 70.56 99.40

F2 300.00 1.00 0.80 60.30 84.20

F3 300.00 0.50 1.50 64.72 92.77

F4 300.00 0.75 1.00 82.80 91.63

F5 500.00 1.00 1.00 73.28 83.48F6 100.00 0.50 1.00 72.56 88.20

F7 300.00 1.00 1.50 65.12 100.00

F8 300.00 0.75 1.00 81.00 90.40

F9 500.00 0.50 1.00 58.64 100.00

F10 500.00 0.75 1.50 57.50 79.20

F11 100.00 0.75 0.80 66.56 77.20

F12 300.00 0.75 1.00 82.00 88.50

F13 300.00 0.75 1.00 81.50 91.00

F14 500.00 0.75 0.80 64.00 86.77

F15 300.00 0.50 0.80 59.30 85.05

F16 300.00 0.75 1.00 81.00 91.50

F17 100.00 1.00 1.00 60.48 100.00

Table 3 – Formulation of LP nanoparticle using Box–Behnken design.Batches Factor Responses

X1 X2 X3 Y 1 Y 2

F1 −1 0 +1 70.56 99.40

F2 0 +1 −1 60.30 84.20

F3 0 −1 +1 64.72 92.77

F4 0 0 0 82.80 91.63

F5 +1 +1 0 73.28 83.48

F6 −1 −1 0 72.56 88.20

F7 0 +1 +1 65.12 100.00

F8 0 0 0 81.00 90.40

F9 +1 −1 0 58.64 100.00

F10 +1 0 +1 57.50 79.20

F11 −1 0 −1 66.56 77.20

F12 0 0 0 82.00 88.50

F13 0 0 0 81.50 91.00

F14 +1 0 −1 64.00 86.77

F15 0 −1 −1 59.30 85.05

F16 0 0 0 81.00 91.50

F17 −1 +1 0 60.48 100.00

and X3 represents the average results of changing 1 variable at

a time from its low-level to high level. The interaction terms

X1X2, X1X3, and X2X3 show how the EE and DR changes when

2 variables are simultaneously changed. The value of correla-

tion coefficient (R2) was found to be 0.9752 for the quadratic

model of response Y 1, indicating good fit, as shown in Table 4.

The results of ANOVA, as shown in Table 6, indicated that themodel F-Value was found to be significant at P


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Table 5 – Diagnostics case statistics for various response variables.

Batch no Response variables Actual value Predicted value Residual

F1 Y 1 70.56 71.50 −0.94

Y 2 99.40 100.96 −1.56F2 Y 1 60.30 62.04 −1.74

Y 2 84.20 84.06 0.14

F3 Y 1 64.72 63.07 1.65Y 2 92.77 92.16 0.61

F4 Y 1 82.80 81.66 1.14

Y 2 91.63 90.61 1.02F5 Y 1 73.28 72.03 1.25

Y 2 83.48 85.14 −1.66F6 Y 1 72.56 73.81 −1.25

Y 2 88.20 86.54 1.66F7 Y 1 65.12 63.59 1.53

Y 2 100.00 98.39 1.66F8 Y 1 81.00 81.66 −0.66

Y 2 90.40 90.61 −0.21F9 Y 1 58.64 57.60 1.04

Y 2 100.00 99.91 0.09F10 Y 1 57.50 59.75 −2.25

Y 2 79.20 79.86 −0.66F11 Y 1 66.56 63.33 3.23

Y 2 77.20 77.21 −0.01F12 Y 1 82.00 81.66 0.34

Y 2 88.50 90.61 −2.11F13 Y 1 81.50 81.66 −0.16

Y 2 91.00 90.61 0.39F14 Y 1 64.00 64.04 −0.043

Y 2 86.77 85.54 2.23F15 Y 1 59.30 60.74 −1.44

Y 2 85.05 87.41 −2.36F16 Y 1 81.00 81.66 −0.66

Y 2 91.50 90.61 0.89F17 Y 1 60.48 61.52 −1.04

Y 2 100.00 100.09 −0.090

design expert software and it was represented in the Table 5

with actual value, predicted value and residual value for each

response. Thus, the optimized difference between actual and

predicted values was very less; it concludes that the model

was best fitted. From the contour plot (Fig. 2A) itcouldbe pre-

dicted that when polymer concentration was maintained at

(−1) level the EE was between 60.48 to 72.56%. The EE was fur-

ther enhanced when polymer concentration was maintained

at (0) level. In this case the EE was between 59.30 to 82.80%.

But at (+1) level, there was a decrease in EE, it was found to

be 57.50 to 73.28%. The EE was in the order of 0> +1>−1. The

effect of polymer concentration on EE was found statistically

significant with P


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Table 6 – ANOVA ofmodels for Y 1 and Y 2.

Source DF Sum of squares Mean square F value P value

Model forY 1 9 1345.18 149.46 30.64


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Fig. 3 – Effect of individual variables (surfactant and Inner diameter) on responses EE% and DR% presented by contour plots

(A and B).

Fig. 4 – Effect of individual variables (Inner diameter and polymer) on responses EE% and DR% presented by contour plots (A

and B).

Fig. 5 – FE-SEM of optimized LP loaded nanoparticle formulation showing sphericity, smooth surface of nanoparticles.


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Fig. 6 – FTIR spectra of LP (A), Ethyl cellulose (B), and LP loaded nanoparticles (c).

3.3.4. Particle size distribution and polydispersity index

analysis

The particle size of LP loaded nanoparticles prepared by

microreactor precipitation method was found in a range of

141.8 to 342.09nm with the average particle size of 357.8 nmasshown in Fig. 8. The flow rate andvolumetric ratio of organic

and non solvent during the microreactor precipitation process

was 1:2. Owing to this higher volume flow of non solvent, the

particle size of nanoparticles decreases. In addition, due to

high surface area to volume ratio of microreactor, the pre-

liminary higher supersaturation level leading to increase the

nucleation rate followed by growth resulting uniform smaller

size of particles developed (Ali et al., 2009). The 0.469 PDI value

of LP loaded nanoparticle was close to zero indicates that the

monodispersed and narrow size distribution of particles.

3.4. Release kinetics and comparison of optimized

formulations with marketed formulation

The comparative in vitro drug release profile of optimized

formulations of nanoparticles with the marketed formula-

tion of the LP was shown in Fig. 9. A rapid burst release

was observed in case of Losakind (25 mg) tablet. Almost 100%

drug was released within the period of 2h while the formu-

lations 11 and 5 showed excellent sustained release behavior.

In case of Run 11(F11) the initial burst release was 23.95 and

Fig. 7 – XRD spectra of LP (A), Ethyl cellulose (B), and LP loaded nanoparticles (c)


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Fig. 8 – Particle size analysis of optimized LP loaded nanoparticle formulation.

Fig. 9 – In vitro drug released profile of formulations Run 11, Run 5 and losakind (25mg) tablet in distilled water.

for Run 5 (F 5) it was 27.35% in the first hour, and drug

release was sustained over the period of 19 and 16h, respec-tively. From this, the release pattern was biphasic consisting

initial burst release followed by slower sustained release of

drug. Due to such sustained release behavior of the nanopar-

ticles, it would definitely reduce the dosing frequency of the

medication which is the major drawback of the conventional

drug delivery system for the LP. The mechanism of in vitro

release of the nanoparticles was evaluated by employing a

different model dependent approach viz., zero order, first

order, Higuchi, Kors–Meyer–Peppas, Hixon–Crowell equation.

The kinetic model with higher values of coefficient correla-

tion (R2)was considered to be a best fit model for the drug

release. It was observed from the data that both the formula-

tion (Run 11 and 5) were best fitted to the Higuchi model withR2 = 0.9944 andR2 = 0.9817. This model is based on the hypoth-

esis that (i) initial drug concentration in the matrix is much

higher than drug solubility; (ii) drug diffusion takes place only

in one dimension (edge effect must be negligible); (iii) drug

particles are much smaller than system thickness; (iv) matrix

swelling and dissolution are negligible; (v) drug diffusivity isconstant; and (vi) perfect sink conditions are always attained

in the release environment (Dash et al., 2010).

Also, both the formulations showed slope (n) higher than

0.89 (n= 1.0562 and n= 1.1046 for Run 11 and 5, respectively),

this clearly indicated the super case–II transports, i.e. drug

release by both diffusion and relaxation of the polymer chain.

4. Conclusion

The Losartan Potassium loaded nanoparticles were success-

fully prepared by a microreactor precipitation method using

Box–Behnken design. The average particle size of nanoparti-

cle was 359.4 nm with narrow size distribution. The prepared

nanoparticles were found to be uniform surface, free flowing

in nature and without any channel and pores on the surface.

XRD and FTIR characteristics of optimized formulation indi-

cated that the LP drug was present in an amorphous state


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and there were no interaction between LP and EC polymer uti-

lized. The independent variables having a significant effect

on responses viz., encapsulation efficiency and drug release.

The high drug encapsulation efficiency(82.8%) of the formula-

tion was achieved by run 4 using 300 mg of EC, 0.75% of Tween

80 and 1mm inner diameter of the microreactor. The in vitro

drug release studies revealed that, the LP loaded nanoparticle

was found sustained as compare to themarketedformulation.Thus, LP loaded nanoparticles prepared by the microreac-

tor precipitation method represents a promising formulation

technique for encapsulation efficiency and sustained drug

release of the LP.

Conflict of interest statement

Authors do not have any conflict of interest.

Acknowledgements

The authors are grateful to Technical Education Quality

Improvement Program (TEQIP-II) for providing financial assis-

tance to carryout this research work.Authorsare also thankful

to Wockhardt research Centre (Aurangabad, India) for provid-

ing a gift sample of Losartan potassium.

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Preparation and statistical optimization of LosartanPotassium loaded nanoparticles using BoxBehnken factorial design: Microreactorprecipitation