Application of Box–Behnken Design and Desirability function in the Optimization of Aceclofenac-Loaded Micropsonges for Topical Application

 

Anjali Sharma^1,2, Kumar Guarve¹, Ranjit Singh^2*

¹Guru Gobind Singh College of Pharmacy, Yamunanagar-135001, Haryana.

²Adarsh Vijendra Institute of Pharmaceutical Sciences, Shobhit University,

Gangoh, Saharanpur-247001, Uttar Pradesh.

 

ABSTRACT:

Background: The aim of the present investigation was to develop optimized Aceclofenac-loaded microsponges using Box-Behnken design (BBD) and desirability function. Material and Method: Aceclofenac-loaded microsponges were developed using ethyl cellulose, ethanol and polyvinyl alcohol (PVA). Initially, a trial batch was developed using quasi-emulsion solvent diffusion method, and by optimizing the drug-polymer ratio. A 3-level, 3-factor BBD was used to investigate the effect of PVA, ethanol and stirring speed on particle size and entrapment efficiency (EE). The models used for the optimization were analyzed through ANOVA and diagnostic plots. Finally, the desirability function was used for the selection of optimized formulation composition. Results: A drug-polymer ratio of 1.5:1 was taken as optimized ratio for all the formulations. The developed microsponges were of the spherical shape having size and %EE in the range of 22.54±2.85 µm to 49.08±5.01 µm and 70.57±4.19% to 86.43±2.58 %, respectively. The amounts of PVA, ethanol and stirring speed were noted to have a significant impact on particle size and %EE. Finally, an optimized formulation (size-22.69 and %EE-86.42) was developed with a desirability value of 0.9967. Conclusion: The BBD is a valuable tool for the development of optimized microsponges with desired properties.

 

 

INTRODUCTION:

Aceclofenac (ACF) is a nonsteroidal anti-inflammatory drug used in the management of pain and inflammation due to rheumatoid arthritis, osteoarthritis and ankylosing sponylitis.¹Aceclofenac is chemically 2- [(2,6 -dichlorophenyl) amino] phenylacetoxyacetic acid, a non-selective COX inhibitor that stimulates glycosaminoglycan in human osteoarthritic. In addition, through suppression of metalloprotease production and proteoglycan release in rheumatoid synovial cells, it shows chondroprotective effects.² 

 

A previous study showed that the ACF and its metabolite (4′-hydroxyaceclofenac) have a significant impact on COX-2 and have less effect on COX-1.³ In comparison to Diclofenac, oral and parenteral administration of ACF is well tolerated with minimal gastrointestinal side effects.^4,5 However, ACF has a low water solubility and a low mean plasma elimination half-life (about 4 hours). Frequent dosing is needed which have been associated with various side effects including gastrointestinal irritation and gastrointestinal bleeding.^6,7

 

To overcome these limitations, transdermal delivery of ACF via microsponges could be a promising strategy.^8,9Microsponges are a sponge-like porous polymeric system with a size range of 5- 300 µm¹⁰. The microsponges prepared to date, showed excellent tolerability against a wide range of temperature (up to 130ºC) and pH (1-11). In addition, they are non-allergenic, non-mutagenic, non-irritating and have greater entrapment efficiency and excellent compatibility with a wide range of excipients.^11-14As compared to conventional formulations, microsponges required less amount of API and able to release the entrapped drug in a sustained manner.^15,16With respect to liposomal formulations, microsponges have greater entrapment efficiency, simple processing and extended stability.¹⁷

 

To date, the regulatory bodies have made quality by design (QbD) mandatory to maintain the desired quality in the end products^18,19. The International Conference on Harmonization (ICH) covers QbD aspects in its three guidelines, namely ICH Q8 (pharmaceutical development), ICH Q9 (pharmaceutical risk management), and ICH Q10 (pharmaceutical quality systems).^20-23 In the present investigation, Box-Behnken design (BBD) was used to develop an optimized formulation of ACF-loaded microsponges. The BBD is response-surface deign used to illustrate the response function that is difficult to be described by a linear function. In comparison to other optimization designs, BBD is a more efficient and economical method having wide applications including energy application and for analytical method.^24,25

 

In this study, initially, a trial batch was developed to confirm that the developed microsponges are spherical and of small size. The drug-polymer ratio was optimized in terms of size, shape and entrapment efficiency (%EE). A three-factor, three-level Box-Behnken design was used for the design of experiment (DoE). The effect of variables, such as amount of polyvinyl alcohol (PVA), ethanol and stirring time on different responses, such as particle size and EE was investigated. The models used for the response analysis were analysed using ANOVA, lack of fit, degree of freedom, sum of square and diagnostic plots. The variables effects were illustrated using 2D contour and 3D response surface plots. Finally, optimized formulation properties and composition were selected as per the desirability function value. The desirability value for the optimized formulation was illustrated via contour plots considering each variable.

 

MATERIAL AND METHODS:

Materials:

Aceclofenac was received as a gift sample from Orison Pharma International (Kala Amb, HP) Ethyl cellulose and polyvinyl alcohol were purchased from SD Fine Chemicals, Mumbai. All other chemicals and reagents used were of analytical grade.

 

Drug-excipients compatibility study:

Drug-excipients compatibility study was carried out by FTIR (Perkin Elmer Spectrum, BX II) spectrophotometer. The FTIR spectra of the drugs alone, physical mixtures (ACF+EC and ACF+PVA) and ACF-loaded microsponges were recorded using the potassium bromide (KBr) dispersion method. The test samples were added to KBr (1:1) and examined at the range of 400 to 4000 cm⁻¹ with the resolution of 4 cm⁻¹.

 

Preparation of plain microsponges:

Initially, a trial batch of plain microsponges was developed using quasi emulsion solvent diffusion method.^26-.28 Briefly, the internal phase was prepared by dissolving ethyl cellulose (300 mg) in ethanol (10 ml) followed by bath sonication (5 min) for complete solubilization. For the external phase, polyvinyl alcohol (100 mg) was dissolved in distilled water (150 ml). The internal phase was then added dropwise to external phase with continuous stirring (1000 rpm) for 2.5 hr. Finally, the solution was filtered, and the microsponges were separated and dried at room temperature, kept in a glass vial and stored in a desiccator before further analysis. The developed microsponges were examined under a microscope for their shape and size.

 

Drug-polymer ratio optimization:

The ACF-loaded microsponges were developed with the same method that was used for the fabrication of plain microsponges. However, the internal phase was prepared by dissolving a predetermined ACF and ethyl cellulose ratios (1:1, 1:1.5, 1.5:1 and 2:1) in ethanol (10 ml). Finally, the drug-polymer ratio was optimized in terms of particle shape, size and percentage entrapment efficiency (%EE).

 

Formulation optimization:

In the present work, 3-factor, 3-level Box-Behnken design (total of 17 experimental runs with 5 center points) was used to assess the overall effect of the variables on the properties of ACF-loaded microsponges. The amounts of polyvinyl alcohol (A), ethanol (B) and stirring speed (C) were selected as independent variables. The particle size (Y1) and %EE (Y2) were selected as dependent variables or responses. The responses were analyzed using Design-Expert software (Trial version 11.0.5.0, Stat-Ease Inc., MN). The levels of variables and responses used for the optimization of ACF-loaded microsponges are illustrated in Table 1.

 

Table 1: Design of experiment

Characterization of microsponges:

Particle size by optical microscopy:

The eye piece micrometer was calibrated with the help of a stage micrometer. The diameters of more than 100 microsponges were measured randomly. The average particle size was determined by using Edmondson’s equation. D = ∑ nd /∑ n Where, n = Number of microspheres observed; D = Mean of the size range.

 

Surface morphology:

The surface morphology of the developed ACF-loaded microsponges was investigated using scanning electron microscopy (JSM-6100). ACF-loaded microsponges were mounted on the double-faced adhesive tape and coated with a thin gold palladium layer with the help of sputter-coated unit and were analyzed then for surface morphology.

 

Percentage entrapment efficiency:

The percent entrapment efficiency of drug entrapment for each batch was calculated using following formula,

 

%EE = (Practical drug loading/Theoretical drug loading) X 100

 

Theoretical drug loading was determined by assuming that the entire drug got entrapped in micropsonges and no loss occurs at any stage of preparation. Practical drug loading was determined by crushing 100 mg of Aceclofenac-loaded micropsonges in a dry glass mortar followed by addition of 50 ml of hydroalcoholic solution. Resultant dispersion was kept aside in a volumetric flask for 3 h and then sonicated for 1 h in a bath sonicator. The dispersion was filtered through Whatman filter paper (0.22 mm) and the filtrate was analyzed by using a UV/ Vis spectrophotometer at 256 nm.

 

Figure 1: Drug: excipients compatibility studies; Spectra of Acelofenac(a), Acelofenac+ethyl cellulose (b), Acelofeac+polyvinyl alcohol (c), and Acelofenac-loaded microsponges (d).

 

Statistical analysis:

The BBD was used to investigate the effect of variables on the responses. The statistical model used for the optimization of the ACF-loaded microsponges was studied in terms of fit summary, lack of fit test, the sum of the square, correlation coefficient (R²), adjusted R² and predicted R². The model used to evaluate the effect of variables on responses was analyzed by ANOVA, F-value, P-value, lack of fit, degree of freedom (df), Adjusted R² (_AdjR²) and predicted R² (_PredR²). The goodness of fit of the proposed model was investigated by plotting diagnostic plots, such as externally studentized residuals vs. predicted plot, predicted vs. actual plot, normal probability plot, and externally studentized residuals vs. run number plot.  The effect of variables on the responses was evaluated via response surface plots (3D) and contour plots (2D). The optimized values of the variables were estimated within the set criterion of desirability and further analyzed through overlay plots. Finally, optimized values of variables were selected for the development of final formulation.

 

RESULTS:

Drug-excipients compatibility studies:

The drug-excipients compatibility was evaluated using FTIR spectroscopy (Fig 1). The FTIR spectrum of ACF showed characteristic peaks at 3459.29 cm^-1 (N-H stretching, amine group), 3319.39 cm^-1 (N-H stretching, amine group), 1717.57 cm^-1 (C=O stretch, carbonyl group), 1508.85 cm^-1 (C-C stretch, aromatic), 1437.86 cm^-1 (C-H stretch, alkane), 1256.21 cm^-1 (C-O stretch, ester group), 1142.65 cm^-1 (C-O-C stretch, ether group) and 1053.86 cm^-1 (N-H bending, alkyl amine); Fig 1(a). These characteristic peaks of ACF were also prominent in the FTIR spectrum of physical mixtures and ACF-loaded microsponges; Fig.1(b-d). This confirms the compatibility of drug with the selected excipients and their suitability for their inclusion in the formulation.

 

Development of trial batch:

In order to confirm the formation of microsponges, an initial trial batch of plain microsponges was developed using quasi emulsion solvent diffusion method²⁹. The microsponges with desired morphology and size were successfully prepared and harvested. The microscopic studies exhibited that the developed microsponges are of spherical shape with uniform size (Figure-2).

 

Drug-polymer ratio optimization:

The drug polymer ratios were optimized by evaluating the effect of different ratios on the shape, size and %EE of the microsponges (Table-2). A total of four formulations (MSP-1 to MSP-4) were developed having spherical microsponges with size and %EE in a range of 28.56 ± 2.8 µm to 35.22 ± 3.2 µm and 72.77 ± 4.3 % to 81.54 ± 2.7 %, respectively (Table-2). The formulation MSP-3 with a drug polymer ratio of 1.5:1 was considered as optimized formulation due to smaller size and highest entrapment efficiency.

 

Experimental design:

A 3-factor, 3-level BBD was used for the optimization of ACF-loaded microsponges. The independent variables selected were, amount of PVA (A), amount of ethanol (B) and stirring speed (C). The effect of these independent variables on particle size (Y1) and %EE (Y2) was assessed (Table-1).

 

Figure 2: Development of the trial batch: (a) Dropwise addition of internal phase into external phase, (b) Continuous stirring, (c) Collection of dried microsponges and (d) Prepared microsponges

 

Table-2 Different formulations and their evaluation

 

Table 3: Composition of different ACF-loaded microsponges formulations

 

Figure 3: SEM images of the developed microsponges and the porous surface.

 

In total 17 formulations (MS1-MS17) were developed as per the BBD designed using quasi emulsion solvent diffusion method (Table-3). The developed formulations were characterized for particle size and %EE (Table-3).

 

Characterization of the ACF-loaded microsponges:

The developed formulations (MS1-MS17) were characterized for size, morphology and %EE. The microsponges were spherical in shape with a particle size range of 22.54±2.85 µm to 49.08±5.01 µm. The %EE of the developed formulations was found in the range of 70.57±4.19% to 86.43±2.58 %. The scanning electron microscopy images showed that the developed ACF-loaded microsponges were spherical shape with porous structure (Figure-3).

 

Model analysis:

Effect on size:

According to the fit summary, sequential model sum of square, model summary statistics and fit summary details (predicted R² (_PredR²), adjusted R²(_AdjR²), F-value, p-value and degree of freedom), quadratic model was selected to evaluate the effect of variables on the size. The factors affecting the particle size, such as (A) quantity of PVA (B) volume of ethanol and (C) stirring time are included in the following equation,

 

Size (Y1) = +29.81+4.51*A-0.5162*B-5.35*C-1.61*AB-4.00*AC-1.19*BC-0.2730*A²- 2.88*B²+3.19*C ²

 

The model used to evaluate the effect on particle size was analyzed through analysis of variance (ANOVA). The ANOVA indicated the suitability andacceptance of the used model with F-value (12.14), p-value of 0.0017 (P<0.0500) and lack of fit values of 0.0001 (p<0.0500). The fit statistical analysis showed an adequate precision (ratio of signal to noise) value of 10.925 (greater than 4), which is desirable for the model to navigate the design space. The goodness of fit of the proposed model for the particle size was investigated using diagnostic plots; Fig 4 (a-d)). The normal plots of residuals showed a majority of the colored points indicating the value of particle size were located around the normal probability straight line. This further supports the normalcy of residuals and relevant analysis of response data; Fig.4 (a). The residual vs predicted values showed that the particle size lies within the set upper and lower limits. The plot showed a random distribution of the studentized residuals, which indicates that the assumption of constant variance is true; (Fig.4 (b). The predicted vs actual plots showed that the experimentally observed values of particle size are in close agreement with predicted values; Fig.4 (c).

 

The effect of variables (A: amount of PVA, B: amount of ethanol and C: stirring time) on the particle size was evaluated using contour (2D) and response-surface (3D) plots. Contour and response-surface plots between amounts of PVA and ethanol, keeping stirring time constant showed that ethanol has no significant impact on the size. Whereas, increase in the amount of PVA resulted in an increase in the size; Fig.4 (d, g). Contour and response-surface plots between stirring speed and amount of PVA keeping the amount of ethanol constant showed that an increase in the stirring speed, decreases the particle size. Whereas, an increase in the amount of PVA increases the particle size; Fig.4 (e, h). Contour and response-surface plots between stirring speed and quantity of ethanol showed that an increase in stirring speed decreases the particle size. However, amount of ethanol had no significant effect on the particle size; Fig.4 (f, i).

 

Figure 4: Effect of different variables on size. (a) normal plot of residuals, (b) residual vs predicted plot, (c) predicted vs actual plot, (d-f) 2D contour plots and (g-i) response surface 3D plots.

 

Effect on %EE:

According to the fit summary, sequential model sum of square, model summary statistics and fit summary details (_PredR², _AdjR², F-value, p-value, PRESS and df), quadratic model was selected to evaluate the effect of variables on the %EE. The factors affecting the %EE are, (A) quantity of PVA (B) amount of ethanol and (C) stirring time as given in the following equation,

 

%EE (Y2) = +84.07-2.56*A+0.5125*B+4.20*C-0.3575*AB+3.63*AC-2.48*BC+1.44*A²-          2.45*B²-3.78*C²

 

 

The model used to evaluate the effect on %EE was analyzed through analysis of variance (ANOVA). The ANOVA proved the suitability and acceptability of the used model with F-value (60.23), p-value of 0.0001 (P<0.0500) and lack of fit values 0.0212 (p<0.0500). The fit statistical analysis showed adequate precision (ratio of signal to noise) value of25.867 (greater than 4), which is desirable for the model to navigate the design space. The goodness of fit of the proposed model used to study the effect on %EE was investigated using diagnostic plots; Fig.5 (a-c). The normal plots of residuals showed a majority of the colored points indicating the value are located around the normal probability straight line. This indicates the normalcy of residuals and relevant analysis of response data; Fig.5 (a). The residual vs predicted value plots showed that all the values indicating the % EE lie within the set upper and lower limits. The plot showed a random distribution of the studentized residuals, which indicates that the assumption of constant variance is true; Fig.5 (b). The predicted vs actual plot showed that the experimentally observed values of particle size are in close agreement with predicted values; Fig.5 (c).

 

The effect of variables (A: amount of PVA, B: amount of ethanol and C: stirring time) on the %EE was evaluated using contour (2D) and response-surface (3D) plots. Contour and response-surface plots between amounts of PVA and ethanol, keeping stirring time constant showed that ethanol has a significant impact on the %EE. Increase in the ethanol volume resulted in a significant increase in the %EE.

 

Figure 5: Effect of different variables on %EE. (a) normal plot of residuals, (b) residual vs predicted plots, (c) predicted vs actual plots, (d-f) 2D contour plots and (g-i) response surface 3D plots.

 

Figure 6: Contour plots showing desirability value and desired properties of the microsponges with different compositions

 

However, with an increase in amount of PVA, %EE was found to be decreased; Fig.5(d, g). Contour and response-surface plots between stirring speed and amount of PVA, keeping ethanol contents constant showed a rapid increase in %EE with an increase in stirring speed. Moreover, the increase in amount of PVA also increases the %EE in a linear manner; Fig.5(e, h). Contour and response-surface plots between stirring speed and ethanol contents showed a significant increase in % EE on increasing in stirring speed. There was a slight increase in %EE with an increase in ethanol content; Fig.5(f,i).

 

Optimization and desrability function:

Desirability function was analyzed to prepare an optimized formulation composition, which was obtained using the set predetermined desired quality parameters of the final product, such as small size and maximum %EE.  A desirability value near to one is needed to obtain an optimized formulation composition. Desirability analysis helped in getting an optimized formulation composition (PVA- 400mg, ethanol-6.301 ml and stirring speed-1306 rpm) resulting in a formulation with the desired properties (size-22.69µm and %EE-86.43) with a desirability value of 0.997 (Fig. 6).

 

DISCUSSION:

The present investigation was aimed to utilize the QbD design models to evaluate the effect of variables on the quality attributes of the ACF-loaded microsponges. Moreover, desirability function was used to develop a formulation having desired properties. Initially, the drug-excipients compatibility was confirmed via FTIR spectroscopy (Fig. 1).  Test formulations in the form of plain microsponges were developed to confirm the suitability of the selected method (Quasi-emulsion diffusion method). The developed plain microsponges were found to be spherical in shape and exhibited predominantly uniform size distribution (Fig. 2). Thereafter, the drug-polymer ratio was optimized for the development of desired formulations.

 

In total four formulations were developed with variable drug-polymer ratios and evaluated for size and %EE (Table-3). The results showed that with an increase in the polymer concentration, the microsponge size and % EE were also increased from 32.42µm to 35.22µm and 73.21% to 77.12%, respectively (Table-3). An increase in the drug amount (1.5:1) led to the decrease in size (28.56µm) and an increase in %EE (81.54%). However, further increase in the drug amount (2:1) resulted in an insignificant increase in particle size (30.72µm) and decrease in %EE (72.77µm), (Table-3). Finally, the drug-polymer ratio (1.5:1) was selected as an optimized ratio.

A 3-factor, 3-level BBD was used for the optimization of ACF-loaded microsponges. The effect of variables (amount of PVA andethanol and stirring speed) on microsponge properties, such as size and %EE was evaluated. Based on BBD, total seventeen formulations (MS1-MS17) were developed with variable compositions and at different stirring speed (Table-2). The developed formulations were evaluated for their size, %EE and morphology. The developed formulation showed the size and % EE ranging between 22.54±2.85 µm to 49.08±5.01 µm and 70.57±4.19% to 86.43±2.58 %, respectively. In addition, the developed microsponges were spherical in shape with porous structure(Fig. 3). 

 

A quadratic model was selected to investigate the effect of the variables on the microsponge size. The ANOVA and fit statistical analysis showed that the selected model was significant (P<0.0500) and suitable for analyzing variables effects (signal to noise ratio-10.925). The diagnostic plots for analyzing the goodness of fit of the selected model showed the observed values of the developed formulations within the acceptable limits; Fig 4 (a-c). The contour and response plots showed an increase in size with an increase in PVA amount. However, with an increase in the stirring speed, the size decreased; Fig 4 (d-i). A high, stirring speed resulted in high turbulence that caused frothing and subsequent adhering of the microsponges to the container wall resulting in decrease in size. At low stirring speed, the microsponges tend to adhere to each other resulting in increased size.³⁰ Moreover, an increase in the amount of PVA increased the microsponge size. This could be due to an increase in an apparent viscosity which resulted in larger droplets and hence larger microsponges.³¹

 

The quadratic model used to investigate the effect of variables on % EE was found significant and the observed values were found under acceptable limits as per the diagnostic plots; Fig 5 (a-c). The contour and response plots showed a decrease in %EE with an increase in amount of PVA. However, there was an increase in %EE with an increase in ethanol concentration; Fig 5 (d-i). An increase in the amount of PVA resulted in a slight increase in viscosity. As a result, when the solvent diffused out, the dispersed phase converted into microsponges of large size with greater %EE.³¹ The reduced diffusion rate of ethanol in a concentrated solution resulted in extended time for droplet formation and an increased precipitation of the drug. This led to an increase in %EE. A slow diffusion of ethanol from the concentrated polymeric solution resulted in a longer time for droplet formation which in turn may increase the drug precipitation in the microsponges and hence the %EE.³² Finally, the optimized formulation composition with desired properties was established through desirability function. The optimized formulation composition was noted to be PVA- 400mg, ethanol-6.301 ml prepared at a stirring speed of 1306 rpm. This formulation showed the desired properties (size-22.69µm and %EE-86.43) (Fig 6). The desirability function value for this formulation was found to be 0.997.

 

CONCLUSION:

A topical ACF-loaded microsponge formulation based on ethyl cellulose was successfully developed using quasi-emulsion solvent diffusion method. The formulation for topical use was developed in order to reduce the frequency of administration, irritation reactions and other side effects associated with conventional oral ACF formulations. It was noted that the amount of PVA and ethanol along with stirring speed significantly affect the microsponge size and % EE. These variables can be optimized to customize the formulation with desired properties. It can be concluded that the QbD approach is a valuable tool for the development of an optimized microsponge formulation with desired size and %EE for topical application.

 

ACKNOWLEDGEMENT:

The authors acknowledge the management of Guru Gobind Singh College of Pharmacy, Yamuna Nagar, Haryana, India for providing the research facilities. One of the authors (AS) is pursuing PhD at AVIPS, Shobhit University, Gangoh, Saharanpur, UP.

 

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Accepted on 03.04.2021           © RJPT All right reserved

Research J. Pharm. and Tech 2021; 14(12):6295-6303.