Wound is one of the oldest suffering associated with the mankind and its history
is as old as humanity. Chronic wounds generate tremendous physical, psychological
and financial burdens for the patient, family and health care community (Martin,
1997). Wound healing is an intricate, biological progression involving contraction
and closure of wound and restoration of a functional barrier (Singer
and Clark, 1999). Wound healing becomes more difficult in case of delayed
wound as local or systemic antibiotics offer little therapeutic benefit due
to extravasation of fibrous material into the wound site (Guo
and Dipietro, 2010). Thus, the first goal of wound care is debridement as
necrotic tissue can be life threatening. It also helps in the cleaning of dead
and senescent cells that hinder the path of healing. Among various techniques,
enzymatic hydrolysis by proteases is the most efficient, selective and least
traumatic means of dissolving this coagulum (Yaakobi et
al., 2004). Being protein in nature, these bioactive agents exhibit
increased biochemical and structural complexity. Moreover, repeated and high
dose administration of these enzymes causes toxicity, limiting their therapeutic
potential. This necessitates effective formulation design for their controlled
delivery with effective tissue repair and treatment of pain and inflammation.
Wound healing process relies essentially on an inflammatory reaction involving
overlapping phases of inflammation, proliferation and remodeling (Martin,
1997). Among this category, Serratiopeptidase (STP) offers a powerful treatment
for pain and inflammation with widespread use in wound debridement, arthritis,
fibrocystic breast disease, chronic bronchitis, sinusitis, atherosclerosis and
carpal tunnel syndrome (Kee et al., 1989; Majima
et al., 1990). However, oral bioavailability of these peptide drugs
is generally very low, owing to the acidic conditions of the stomach and poor
permeability across intestinal mucosa (Rawat et al.,
2007). Alternative routes like topical, nasal and parenteral could be exploited
for maintaining its therapeutic effectiveness.
There is an increased surge of interest in polymeric microspheres and microcapsules
for the delivery of therapeutically useful proteins in a controlled way (Gombotz
and Pettit, 1995; Rudra et al., 2011). A
variety of microencapsulation techniques are used for effective encapsulation
of drugs (Tice and Gilley, 1985; Benita,
1996). Among the various microencapsulation techniques, the double emulsion
method (w/o/w) has been widely accepted as an alternative method for the encapsulation
of hydrophilic and labile drugs (Ogawa et al., 1988).
But, it limits encapsulation of water-soluble drugs due to solubility of the
drug in the two aqueous phases of the microparticles. However, the encapsulation
efficiency can be improved and the physicochemical properties of the microspheres
such as the particle size, surface texture, morphology and drug release profile
can be controlled by altering the preparative conditions (Brannon-Peppas
and Vert, 2000). Statistical models are extensively used nowadays in diversified
areas to strengthen the art of drug formulation. Box Behnken design is an established
method to study the effect of selected parameters. These use only three levels
for each factor and the domain is within the original factorial shape. The overall
structure of a three-factor Box-Behnken design is represented as a cube but
the experimental points are at the midpoints of the edges of the cube rather
than at the corners and centers of the faces, that is, v2 or 1.414 e.u. from
the center point. Each combination of the extreme values of two of the variables
is examined with the third variable having a value of zero (Gupta
et al., 2001; Ko et al., 2003). Therefore,
process optimization may be advantageous for the efficient entrapment of water-soluble
labile drugs like therapeutic enzymes (Rawat et al.,
In the present study, topical microparticulate system for controlled release of debriding agent in the alkaline media was developed as open wounds tend to have a neutral or alkaline pH, predominantly in the range of 6.5-8.5.
MATERIALS AND METHODS
Materials: Serratiopeptidase (MW 52 kDa; Advanced Enzyme Technologies Ltd. Nasik, India) and Eudragit RS100 (Rohm Pharma, Germany). PVA of molecular weight 30,000 was from Loba chemicals, Mumbai, India. All other chemicals used in the study were of analytical grade.
Preparation of STP loaded eudragit RS100 microspheres: STP loaded eudragit
RS100 microspheres were prepared by modified double emulsion solvent evaporation
technique (Blanco-Prieto et al., 1996). Briefly,
STP was dissolved in 1ml of a PVA 0.5% aqueous solution (W1) and
Eudragit RS100 (X1) with drug: Polymer ratio of 1:1; 1:3; 1:6 was
dissolved in 5 mL of dichloromethane (DCM) (O). Both phases were mixed by mechanical
stirring for 1 min (1000, 1500 or 2000 rpm) (X3) to form a primary
emulsion. This inner emulsion was then poured under vigorous stirring to External
Aqueous Phase (EAP) (X2) containing 1% w/v of PVA, 20% v/v Glycerol
and 6% w/v NaCl using a magnetic stirrer for 2 min (Singh
et al., 2008). The resulting double emulsion was stirred for at least
3 h under Room Temperature (RT) to allow solvent evaporation and microspheres
||Full factorial experimental design layout with coded levels
and actual values of variables for STP loaded eudragit RS100 MS (SE1-SE16)
|*Value in parenthesis indicate coded levels
After preparation, the microspheres were isolated by centrifugation 7000xg
for 10 min, washed with distilled water and freeze-dried. The trials were performed
in random order. The full factorial design layout is given in Table
Particle size: The particle size was measured directly by optical microscopy
using a compound microscope (Erma, Tokyo, Japan) on 300 microspheres (Qian
et al., 2004). A small amount of dry microspheres was suspended in
purified water (10 mL). The suspension was ultrasonicated for 5 sec. A small
drop of suspension was placed on a clean glass slide. The slide containing Eudragit
RS100 microspheres was mounted on the stage of the microscope and 300 particles
were measured using a calibrated ocular micrometer. The process was repeated
for each batch prepared.
Morphology: The morphology and surface appearance of microspheres were examined by Scanning Electron Microscopy (SEM) (Leo, VP-435, Cambridge, UK). Photomicrographs were observed at 300x magnification operated with an acceleration voltage of 15 kV and working distance of 19 mm was maintained. Microspheres were mounted on the standard specimen mounting stubs and were coated with a thin layer (20 nm) of gold by sputter coater unit (VG Microtech, UK).
Entrapment efficiency: Twenty milligrams of the dried microspheres were
accurately weighed and dissolved in DCM. After the microspheres dissolved completely,
5 mL of phosphate buffer (pH 7.4) was added to this solution and mixed thoroughly.
The resulting solution was filtered using whattman filter (0.45 μm pore
size) and analyzed for STP content by measuring absorbance in UV-spectrophotometer
(Shimadzu UV-1700, Pharmaspec, Tokyo, Japan) at 229.5 nm by first derivative
spectrophotometric method using phosphate buffer (pH 7.4) and DCM mixture (1:1)
as blank (Saudagar et al., 2007). Results were
expressed as (Mean±SD) of 3 experiments. Encapsulation efficiency (%)
was calculated using the following formula:
Box-Behnken design: A Box-Behnken experimental design was employed to statistically optimize the formulation parameters of Eudragit RS100 microsphere preparation for maximum entrapment and controlled drug release. The Box-Behnken design was specifically selected since it requires fewer treatment combinations than other design in cases involving three or four factors. The Box-Behnken design is also rotable and contains statistical missing corners which may be useful when the experimenter is trying to avoid combined factor extremes. This property prevents a potential loss of data in those cases. Generation and evaluation of the statistical experimental design was performed with the STAT-EASE, design expert, 7.0.3. A design matrix comprising of 16 experimental runs was constructed. An interactive second order polynomial model was utilized to evaluate both the response variables:
where, b0-b9 are the regression coefficients, X1 - X3 the factors studied and Yi is the measured response associated with each factor level combination.
In vitro drug release: Microsphere formulations exhibiting more
than 70% entrapment were subjected to in vitro release studies due to
the need of prolonged drug action. Weighed quantities of microspheres were suspended
in 50 mL of isotonic phosphate buffer (pH 7.4, 37±0.5°C). The dissolution
medium was agitated at 50 rpm and maintained at a constant temperature of 37±0.5°C
in a water bath. Samples were periodically removed at predetermined time intervals
and the volume was replaced immediately by fresh phosphate buffer. The samples
withdrawn were centrifuged (3000 rpm, 15 min, at room temperature). The supernatant
was analyzed for STP content using UV-Vis spectrophotometer (Shimadzu UV-1700,
Pharmaspec, Tokyo, Japan) at 229.5 nm by first derivative spectrophotometric
method using phosphate buffer (pH 7.4) as blank (Saudagar
et al., 2007). Results were expressed as (Mean±SD) of 3 experiments.
In vitro proteolytic activity: Prepared STP loaded microspheres were placed in 5 mL of phosphate buffer saline (PBS, pH 7.4) separately maintained at 37±0.5°C and stirred constantly at 100 rpm. After two hrs, samples were recovered by centrifugation at 3000 rpm for 15 min at room temperature (n = 3). The proteolytic activity was determined as per the method reported in Food and Chemical Codex (2003). The assay was based on a 30 min proteolytic hydrolysis of casein at 37°C and pH 7.0. Unhydrolyzed casein was removed by filtration and the solubilized casein was determined spectrophotometrically at wavelength of 275 nm. In this method, the protease activity is expressed as PC units of preparation derived from Bacillus subtilis var. and Bacillus licheniformis var. One bacterial protease unit (PC) is defined as quantity of enzyme that produces 1.5 μg mL-1 equivalent of L-tyrosine per minute under the condition of the assay. Activity of enzyme was calculated by equation:
where, Au is the value obtained by subtracting blank reading from test reading, As absorption of standard solution, 1.5 is the final volume in mL of reaction mixture, 30 is the time of the reaction in minutes and w is the weight of the original sample in g.
Preliminary studies: A total of 16 experiments (SE1-SE16) were performed for three factors at three levels each. Table 1 summarizes the experimental runs, their factor combinations and the levels of experimental units used in the study.
Effect of selected formulation variables: The effect of formulation
variables on yield, mean diameter and encapsulation efficiency of STP loaded
Eudragit RS100 microspheres (SE) are shown in Table 2. Microspheres
with smooth surface and spherical morphology were obtained for all batches of
formulation (SE1-SE16) (Fig. 1). Microsphere yield and entrapment
efficiency was relatively low with emulsifier alone in the EAP. This could be
due to rapid diffusion of hydrophilic drug into the continuous aqueous phase
leading to decreased entrapment and rapid loss of drug. So, 20% v/v Glycerol
concentration and NaCl concentration of 6% w/v were selected from our previous
studies (Singh et al., 2008).
Further entrapment and release was controlled by optimizing other parameters like polymer concentration (X1), external aqueous phase volume (X2) and stirring speed of primary emulsion (X3). All the trials of microspheres yielded smooth spherical microspheres with size in the range of 18.65±0.84 to 39.44±0.65 μm (Table 2). %Yield of microspheres obtained was more than 50% in almost all microspheres. Polymer concentration influenced the yield of microspheres.
In order to determine the levels of factors which yielded maximum entrapment,
mathematical relationships were generated between the dependent and independent
variables. For estimation of coefficients in the approximating polynomial function
(Eq. 2) applying uncoded values of factor levels, the least
square regression method was used. A suitable polynomial equation involving
the individual main effects and interaction factors was selected based on the
estimation of several statistical parameters such as the multiple correlation
coefficient (R2), adjusted multiple correlation coefficient (adjusted
R2) and the predicted residual sum of squares (PRESS) provided by
the design expert software 7.0.3.
|| Experimental responses obtained for the studied parameters
|*Values are shown as representative of Mean±SD for
three independent determinations (p<0.05)
|| Scanning electron micrograph of STP microspheres (SE13) with
|| Summary of results of (a) model analysis (b) lack of fit
(c) R-square analysis for measured responses
As presented in Table 3, the linear model was selected as
a suitable statistical model for optimized formulation with maximum entrapment
and optimum size because it had the smallest value of PRESS (250.42 for Y1
and 279.16 for Y2) signifying role of single factor. PRESS is a measure
of the fit of the model to the points in the design. The smaller the PRESS statistic
is, the better the model fits to the data points. From the p-values presented
in table 3, it can be concluded that for both responses the
cross product contribution (2FI) of the model was not significant indicating
the absence of interaction effects.
The Mean Diameter (MD) and percent drug entrapment of STP microspheres showed R2 values of 0.9876 and 0.9638 (Table 4) respectively; indicating good fit and it was concluded that the second order model adequately approximated the true surface. For estimation of significance of the model, the analysis of variance (ANOVA) was applied. Using 5% significance level, a model is considered significant if the p-value is less than 0.05. The results of multiple regression analysis and analysis of variance test (ANOVA) are also summarized in Table 4.
The resultant equations for both responses Y1 and Y2 (fitted model) are presented below:
A factor is considered to influence the response if the effects significantly
differ from zero and the p-value is less than 0.05. Coefficient signs also give
an indication of the effect produced (Table 5). A positive
sign indicates a synergistic effect, while a negative sign represents an antagonistic
effect of the factor on the selected response. Signs indicate the significant
positive effect of Eudragit RS100 (X1) on size of microspheres, EAP
exerted negative effect on entrapment efficiency. The large SME of Eudragit
RS100 (X1) for mean diameter indicated that the polymer concentration
was the main influential factor on the size of microspheres whereas EAP showed
large negative SME of entrapment efficiency indicating negative effect of EAP
on entrapment. This was further investigated by the study of ANOVA.
||Standardized main effects of the factors on the responses
and associated p-values
|aStandardized main effects (SME) were calculated
by dividing the main effect by the standard error of the main effect
The breakup of source sum of squares (Source SS) in ANOVA indicated that the
contribution of factor X1 (Eudragit RS100) (SSY1-558.95;
SSY2-0.19) is much higher than factor X2 (EAP) (SSY1-61.01;
SSY2-693.58) and X3 (stirring speed of primary emulsion)
(SSY1-10.64; SSY2-215.30) for optimizing the mean diameter
of microspheres whereas EAP and stirring speed contributed significantly for
Polymer at medium level (X1, 0), EAP at low level (X2, -1) and stirring speed of primary emulsion at high level (X3,+1) yielded microspheres with highest drug entrapment 80.12±1.96% with 26.05±0.42 μm mean diameter of microspheres. In Table 5, factor effects of the Box-Behnken model associated p-values and Standardized Main Effects (SME) values for both responses are presented.
The interaction terms X1X2, X2X3, X1X3 and the polynomial terms X1X1, X2X2 and X3X3 indicated insignificant values of individual source sum of squares. In addition, three dimensional response plots were presented to estimate the effects of the independent variables on entrapment efficiency by keeping one factor at constant level (Fig. 2-4).
Using the model generated with both responses (Eq. 4 and
5), the optimization tool in the experimental design software
was used to identify a formulation with a maximum entrapment. It predicted a
maximum entrapment of 80.12±1.96% and MD 26.05±0.42 μm with
a formulation comprising of 300 mg Eudragit RS100 concentration, 100 mL EAP
with 2000 rpm as stirring speed of primary emulsion (SE13).
To confirm the validity of the model, three batches of microspheres were prepared using this formulation and entrapment was determined. The actual experimental entrapment obtained was 80.12±1.96%. The predicted response and residual value performed at optical values investigated in this study was found to be 77.46% and 2.66 respectively, validating the model generated in this study.
In vitro release study: In vitro release behavior of microspheres exhibiting entrapment more than 70% (SE1, SE5, SE6, SE7, SE13, SE14 and SE15) was investigated in phosphate buffer (pH 7.4) for 5 days (Fig. 5). All formulations exhibited almost similar release pattern with initial burst followed by nearly sustained release for 5 days. Variation was in terms of initial burst and found to be dependent on polymer concentration. Polymer concentration exhibited negative effect whereas EAP showed positive effect on initial burst.
||3D surface curve for the effect of selected variables (X1,
X3) on the entrapment of Microspheres (X2, -1)
||3D surface curve for the effect of selected variables (X2,
X3) on the entrapment of Microspheres (X1, 0)
Formulation with maximum entrapment (SE13) showed an initial burst of 18.24±2.56%
observed in the first hour due to the drug located on or near the surface of
microspheres. At the end of the 5 th day test period the formulation (SE13)
showed 97.15±4.41% drug release. In order to investigate the release
mechanism of present drug delivery system, the release data of prepared STP
loaded Eudragit RS100 microspheres with entrapment more than 70% in phosphate
buffer (pH 7.4) were fitted to classic drug release kinetics models (Table
||3D surface curve for the effect of selected variables (X1,
X2) on the entrapment of Microspheres (X3 +1)
|| In vitro release profiles of STP from Eudragit RS100
The release rates were analyzed by least square linear regression method. Release
models such as first order model, Higuchi model and Ritger-Peppas empirical
model were applied to the release data (Table 6) (Dredan
et al., 1996; Peppas, 1985). The coefficient
of determination (R2) of equation for release of STP from all microspheres
in phosphate buffer was>0.9 for all models studied.
In vitro proteolytic activity: Proteolytic activity of free and
formulation with maximum entrapment (SE13) was evaluated separately before and
after treating them for 2 h in phosphate buffer saline (phosphate buffer, pH
|| Release behavior of STP in phosphate buffer (pH 7.4) from
(SE1, SE5, SE6, SE7, SE13, SE14 and SE15)
|*K: Release rate constant, R2: Coefficient of determination,
n: Release exponent
STP in Eudragit RS100 microspheres (SE13) showed about 4.21-1.34% loss of proteolytic
activity in basic medium whereas free STP showed around 18.65±0.89% loss
of activity. Microspheres exhibited much better retention of proteolytic activity
as compared to plain solution.
The objective of the present study was to optimize the formulation of Eudragit
RS100 microspheres loaded with STP in terms of uniform spherical shape, size,
maximum entrapment and controlled release of debriding agent with low initial
burst. Role of wound debridement using enzymes is well reported and established
for complete wound healing (Ajlia et al., 2010).
The potential of STP in wound healing has been supported in various reports
(Rath et al., 2011). But, being protein in nature
these bioactives exhibit high structural and biochemical instabilities. All
these factors demand effective formulation design for safe and effective delivery
of proteases. We selected Eudragit RS100 as the polymer to effectively release
the enzyme at alkaline wound site as natural polymer based systems have been
reported to show variation in predicted release (Owlia et
The variables selected were Eudragit RS100 concentration (X1) [drug
(STP): Polymer ratio as 1:1; 1:3; 1:6], external aqueous phase volume (X2)
(100, 200 or 300 mL) [internal aqueous phase to external aqueous phase volume
as 1:200; 1:400 or 1:600] and stirring speed of primary emulsion (X3)
(500, 1500 or 2000 rpm). The levels for these parameters were determined from
the preliminary trials. Eudragit RS100 was chosen for controlled release of
debriding agent in the alkaline media as open wounds tend to have a neutral
or alkaline pH. Double emulsification method is the commonly utilized method
for encapsulation of hydrophilic drugs particularly proteins and peptides. However,
hydrophilic drugs get partitioned in the aqueous phases leading to low entrapment.
External Aqueous Phase (EAP) volume with respect to internal phase volume (IAP)
was selected as another variable, which is reported to affect the encapsulation
efficiency and initial burst (Jain et al., 2005).
On the basis of above results, factor X1 (Eudragit RS100 concentration)
was found to be the main influential factor on the microsphere size. Although
it exerted positive effect on both mean diameter and entrapment of microspheres,
but effect on size was more as compared to entrapment, also supported by the
positive coefficients in the fitted model Eq. 4 and 5.
This significant increase in size may be because of the increase in the viscosity
of the droplets. Increase in entrapment with the increase in the polymer concentration
might be due to increase in the thickness of barrier separating two aqueous
phases (Ito et al., 2007). But it was not consistent
at higher concentration of polymer. External aqueous phase (X2) exerted
positive effect on mean diameter and negative on entrapment efficiency of microspheres.
Effect on entrapment efficiency is more significant as compared to size of microspheres.
It is also evident by factor effects and their signs. Low entrapment at higher
levels of EAP might be due to drug leakage in the large volume of continuous
aqueous phase and instability of droplet globules due to wide variation between
outer to inner aqueous phase volume leading to decreased entrapment.
Stirring speed (X3) exerted almost negligible effect on size of
microspheres whereas it exerted positive effect on entrapment efficiency of
microspheres. High loading efficiency have been reported by Shiomori
et al. (2000) from smaller primary emulsions (Shiomori
et al., 2000). This might be due to decrease in the ratio of droplet
diameter of primary emulsion against that of secondary emulsion. Increase in
stirring speed of primary emusion decreases the size of primary emulsion droplet
leading to increase in ratio of secondary emulsion droplet diameter to that
of primary emulsion. This signifies the thick oil layer with reduced leakage
of inner water phase to outer water phase.
STP entrapped in Eudragit RS100 microspheres exhibited much better retention of proteolytic activity as compared to plain STP solution. Possible explanation for the improved physical and chemical stability of proteolytic enzyme in microsphere may be due to reduced mobilization and effective protection of enzyme from acidic environment.
Box-Behnken design was used to investigate the effects of selected formulation variables and to optimize the formulation of Eudragit RS100 microspheres loaded with acid labile-STP for maximum loading and controlled topical release for prolonged period. This statistical technique allows scientists to examine more than one independent variable at a time. The microsphere size and entrapment was highly dependent on the selected variables. Eudragit RS100 (X1) had positive effect on size of microspheres, EAP (X2) exerted negative effect on entrapment efficiency whereas stirring speed (X3) effected both responses with negative effect on size and positive on entrapment efficiency. Thus STP loaded Eudragit RS100 microspheres were successfully prepared for sustained release upto 5 days with retention of its proteolytic activity. Further studies are required to establish optimum formulation in terms of improved long-term stability and in vivo therapeutic effects.
The authors are thankful to M/s Advanced Enzyme technologies Ltd., Nasik, India, for the gift sample of Serratiopeptidase; Eudragit RS100 (Rohm Pharma, Germany); SIF, AIIMS, New Delhi, India, for Scanning electron micrography; Director, University Institute of Pharmacy, Pt. Ravishankar Shukla University, Raipur (C.G.) India for providing all necessary facilities for carrying out this work.