Oral pioglitazone HCl-loaded solid lipid microparticles: Formulation design and bioactivity studies

Materials

PGZ was obtained from Global Pharma (Dubai, UAE). Cetyl alcohol (CA), poloxamer (Pluronic® F-86), polysorbate (Tween 80^®), and sorbitan monooleate (Span 80^®) were obtained from Sigma-Aldrich (St. Louis, MO). Potassium dihydrogen phosphate and chloroform were purchased from Fisher Scientific (Pittsburg, PA). Streptozocin (STZ) was acquired from Shanghai Yuanye Biological Technology (Shanghai, China).

QbD approach for optimization of PGZ-LMPs

PGZ-LMP characteristics were examined using the Design-Expert^® software (Version 12.0, Stat-Ease Inc., Minneapolis, MN) utilizing a D-optimal factorial design. For the experimental design, three distinct, independent factors including amount (mg) of lipid (X₁), surfactant type (X₂), and concentration of surfactant (X₃) were selected as critical material attributes (CMAs) to assess the effect of these formulation parameters on critical quality attributes (CQAs). As a result of the selected levels of the independent variables, the drug-loaded LMPs could be processed in a feasible manner (Table 1).

Next in order, for the optimization of the studied factors and the production of the optimal formula, three responses had been tracked, including PS (Y₁), EE% (Y₂), and cumulative drug release after 8 hours (Q8) (Y₃) (Table 1). The target responses were set at the minimum size of MPs, maximum EE%, and maximum drug release rate in 8 hours. The optimal PGZ-LMP formula was then prepared using the optimal independent variables.

As shown in Table 2, the statistical design software generated 12 different experimental runs (formulations). Experimental runs were conducted randomly to increase predictability and eliminate bias in model variance. The measurements were done in triplicate (n = 3). Polynomial equations were statistically validated by analysis of variance (ANOVA) after the linear, two-factor interaction and quadratic responses were fitted simultaneously.

Preparation of PGZ-LMPs

By adopting the solvent injection technique, PGZ-LMPs were prepared following the experimental design (Schubert and Müller-Goymann, 2003). The procedure was optimized to obtain the fittest stirring speed and time. First, 30 mg of the drug and different amounts of CA were dissolved in ethanol (2 ml), while different amounts of Poloxamer 188, Tween 80, and Span 80 were dissolved in 8 ml of distilled water. The alcoholic solution containing the drug was rapidly injected into the aqueous phase after both solutions were heated to 50°C, followed by continuous stirring at 2,000 rpm for 1 hour. PGZ-LMPs were frozen at −60^oC for 4 hours and lyophilized at −50^oC and 7 × 10⁻² mbar for 48 hours, using Vertical Freeze Dryer BK-FD18S BK-FD18P (China) to yield a dry powder. The powdered samples were then sealed in firmly closed containers, wrapped with parafilm, and stored at 4^oC.

Table 1. Optimization of PGZ-LMPs based on independent variables (factors) and dependent variables (responses). [Click here to view]

Table 2. DOE and measured responses for PGZ-LMP optimization. [Click here to view]

Determination of particle size PGZ-loaded microparticles

The average particle size (z-average) of the PGZ-LMP formulations was measured by Zetasizer Nano ZS (Malvern Instruments, Malvern, UK) at a scattering angle of 173^o. A sample of 0.2 ml of PGZ-LMP dispersion was diluted with purified water (0.8 ml) and placed in the instrument to measure the PS at room temperature (Abdul Rasool et al., 2020). The PS of PGZ-LMPs was calculated as mean values ± standard deviation (SD) of triplicate measurements (n = 3).

Determination of entrapment efficiency

The loading efficiency of PGZ in the MPs was determined following the preparation of the PGZ-LMP formulae; the MPs were separated from the hydration medium by centrifugation at −10^oC and 18 × 10³ rpm for 30 minutes. The supernatant was then decanted, and the separated MPs were dissolved in 50 ml of a chloroform/methanol mixture (1:10 v/v) and sonicated for 5 minutes to obtain a clear solution (Bhosale et al., 2016). The amount of the entrapped PGZ was determined by a UV-visible spectrophotometer at λ_max 269 nm, and EE% was then computed according to the following formula:

$$\mathrm{E}\mathrm{E}\mathrm{\%}=\frac{\text{amount of PGZ entrapped}}{\text{total amount of PGZ}}\times 100(1)$$

In vitro drug release and kinetics measurements

The in vitro release of PGZ from the prepared LMPs was studied using the dialysis membrane diffusion technique (Al Shuwaili et al., 2016). A sample of 1 ml of each drug-loaded microparticle dispersion, equivalent to 3 mg PGZ, was centrifuged at −9 ± 0.5°C and 15,000 rpm for 30 minutes. The residue was then dispersed in distilled water (1 ml) and placed in the cassette dialysis bags (Slide-A-Lyzer Dialysis Cassette, USA) as a donor compartment fitted with a dialysis membrane of cut-off molecular weight equal to 10,000 da. The dialysis cassettes were then immersed in 150 ml of the dissolution media (0.1 N HCl, pH 1.2) or phosphate-buffered saline (PBS), pH 6.8. The media were stirred continuously at 100 rpm at 37°C ± 0.5. Samples of 3 mL were collected at predetermined time intervals (0.5, 1, 2, 3, 4, 5, 6, 7, and 8 hours) and substituted with the same volume of the freshly prepared phosphate buffer to control the sink condition.

After analyzing the samples spectrophotometrically at 269 nm, the amount of released PGZ at each time interval was measured by converting the absorbance into concentration using the previously prepared PGZ calibration curve in 0.1 N HCl, pH 1.2. The data was normalized against the total amount of PGZ and reported as a percentage of cumulative drug released. The experiments were conducted in triplicate (n =3).

The release kinetics of PGZ from the optimized MPs was studied by fitting the release data to various kinetic models, including the “zero-order kinetics, first-order kinetics, Korsmeyer-Peppas, Hixson–Crowell, Higuchi, and Makoid–Banakar” equations. In addition, the data collected from the in vitro release study were analyzed, and various kinetic parameters were determined using the DDSolver^® 2010 add-in program for Microsoft Excel. The model which showed the best fitting to data and could describe the PGZ release mechanism from the MPs was selected based on the correlation coefficient (R²) produced from the linear regression analysis.

Morphological examination

SEM was used to identify the surface topography and morphological features of the optimal PGZ-loaded LMP formula and the blank MPs. The test was performed by placing the dispersion on a clean microscopical glass slide under a vacuum until complete drying. The dried sample was then fixed with conductive tape on a metal stub and coated with a gold-palladium (80%−20%) coat using a Mini Sputter Coater (SC7620, Quorum Technologies, UK). The gold-coated samples were examined at an acceleration voltage of 3 kV, and photomicrographs were taken by a Thermo Scientific Apreo Field Emission SEM (FEI Company, Hillsboro, OR).

FT-IR spectroscopy studies

FT-IR spectroscopy was used to explore the chemical compatibility between the PGZ and the excipients. Samples of the pure drug, the individual formulation constituents (PGZ and CA), their physical mixture, the optimized PGZ-LMPs, and the blank formula were scanned from 4,000 to 400 cm⁻¹ at a resolution of 4 cm⁻¹ using an FT-IR spectrophotometer (JASCO FT-IR 6300, Jasco, Easton, MD). The physical mixture was prepared by mixing an equal molar ratio (1:1) of PGZ and CA with a mortar and pestle for 30 minutes. The stretching and vibrational modes represent the functional groups and the chemical bonding in the tested samples.

DSC analysis

The differential scanning calorimetry (DSC) analysis was conducted to measure possible chemical interactions between PGZ and the excipients in the prepared microspheres. Samples of PGZ, CA, their physical mixture, and the optimized formulation were placed in a flat-bottomed aluminum pan and heated in the DSC-50 (Shimadzu, Kyoto, Japan). To eliminate the oxidative and pyrolytic effects of air, nitrogen gas was pumped into the apparatus. The heating rate was 10°C/min, and the temperature ranged from 0 to 400°C. PGZ-LMPs and physical mixtures were DSC-thermogrammed, and the results were compared with the control groups containing a similar proportion of each component.

Preclinical in vivo study

Statistical analysis

The in vitro experiments were carried out in triplicate, and the results are expressed as mean ±SD. The two-tailed unpaired Student’s t-test and one-way ANOVA were employed to compare two groups and test the equality of several means, respectively. The results were statistically considered significant at p < 0.05. The Design-Expert^® software (Version 12.0, Stat-Ease Inc., Minneapolis, MN) or SPSS (SPSS^® Statistics software program, Version 17.0, International Business Machines Corp., Armonk, NY) were used for statistical analyses.

Design and preparation of PGZ-LMP formulations

To maximize the therapeutic effect of PGZ, the principle of the quality target product profiles (QTPPs) was constructed based on the characteristics of PGZ-LMPs designed for oral administration (Scalia et al., 2015). Also, depending on our prior experience with the development and optimization of LMP formulations, we achieved the critical process parameters (CPPs), CMAs, and CQAs.

A solvent injection method was used to prepare PGZ-LMPs, which is known for its ease of production and low cost (Schubert and Müller-Goymann, 2003). The stirring speed and stirring time were fixed at 2,000 rpm and 1 hour, respectively, to generate the smallest particle size. The choice of the lipid and stabilizers is very important as they affect the ability of the formulation to improve the drug solubility and control its release. The lipid that we chose, CA, is an FDA-approved solid lipid with suitable pharmaceutical and biological properties. It is biodegradable, biocompatible, and nonimmunogenic and has no taste or smell. Accordingly, it is suitable for incorporation in formulations for oral administration to control drug release (Gupta et al., 2013). Stabilizer addition is essential to facilitate the formation of uniform microparticles, enhance drug entrapment, and prevent aggregation during storage. Surface active agents (SAA) such as Poloxamer 188, Tween 80, and Span 80 were used as stabilizers because of their satisfactory solubilizing and surfactant properties. Therefore, their use is promising in various pharmaceutical formulations (Haider et al., 2020b).

The PGZ-LMPs were fabricated using process parameters selected based on preliminary trials and previous studies. Using the previously described experimental design (Table 2), the response data for each of the 12 experimental runs were obtained and multiple linear regression analysis and ANOVA were used to investigate the individual effects of the independent factors on the selected responses; in addition, a second-order polynomial equation was derived as follows:

Y = β_o + β₁X₁ + β₂X₂ β₃X₃ + β₁₁X₁² β₂₂X₂² β₃₃X₃² + β₁₂X₁X₂ + β₁₃X₁X₂ + β₂₃X₂X₃ (3)

where Y is the response, β₁, β₂, β₃, …, and β₂₃ are the regression coefficients, and X₁, X₂, and X₃ are the studied factors at the specified levels. The equation in terms of coded factors can be used to estimate the response for the given levels of each factor. Moreover, they can be used to identify the relative impact of the factors by comparing the factor regression coefficients. Three and higher-order interactions were not considered. The predicted R² values were in agreement with the adjusted R² for all responses, indicating the model’s reliability in estimating all response values (Table 3). Adequate precision was used to measure the signal-to-noise ratio to ensure that the model could navigate the design space. The resultant adequate precision was significant and more than four (the desirable value) for all responses.

Determination of particle size

There is a critical relationship between particle size and the stability of the LMPs, EE%, drug release profile, dissolution rate, drug absorption, cellular uptake, and bioavailability. Smaller particles with a narrow distribution of sizes tend to be more stable and less prone to aggregation and physical instability (Wang et al., 2016). Furthermore, particle size reduction can efficiently enhance the wettability and solubility of drugs, as particle size reduction can increase drugs’ dissolution and bioavailability to a certain extent (Zhang et al., 2020). The graphical analysis of the effects of the formulation variables on particle size displayed that the average size of the prepared PGZ-LMPs ranged from 2.57 ± 0.8 μm to 9.38 ± 0.9 μm (Table 2 and Fig. 1).

This wide range of particle sizes suggests that formulation variables, especially the type of SAA used and its concentration, are critical and significantly affect the analyzed response. Particles formed by formulations containing Poloxamer 188 were the largest, whereas those prepared using Tween 80 were the smallest. According to the statistical analysis, the type of the SAA (X₂) and its concentration (X₃) had a significant effect on particle size (p < 0.05) while the amount of CA (X₁) did not. The particle size equation obtained from the analysis was

PS = 5.99 + 0.7383 X₂ – 2.22 X₂² – 0.815 X₃. (4)

Previous studies showed that using various SAA with different hydrophilic-lipophilic balance (HLB) resulted in size variation of O/W emulsion particles (Housaindokht et al., 2012). On the other hand, the concentration of the SAA has an inverse effect on the size of PGZ-LMPs, where increasing the SAA concentration from 1% w/v to 2% w/v resulted in a significant reduction in the particle size. These results are in good correlation with previous work studying the effect of various concentrations of poloxamer on the size of the particles in nanostructured lipid carriers and observing that MPs prepared with a high ratio of surfactant to lipid had the smaller particle size (Zirak and Pezeshki, 2015). This trend might be expected at a relatively low quantity of SAA (<2% w/v) as increasing SAA concentration means more SAA particles are present to cover any newly formed LMP surfaces at a convenient rate during preparation (Qian and McClements, 2011).

Table 3. Results of the experimental design. [Click here to view]

Figure 1. Surface response chart representing the effect of type of SAA and amount of CA on particle size using 1% (A) and 2% (B) concentration of SAA. [Click here to view]

Determination of EE%

The EE% of the drug is a critical factor that helps assess the drug loading capacity of the microparticles. Moreover, EE% significantly affects drug release from LMPs, where higher drug loading increases the concentration gradient and enhances the rate of drug release.

In addition, the sufficient concentration of the drug loading may also reduce the size of the administered dose, the cost of manufacturing, and the amount of excipients needed for the manufacturing of the microparticles, specifically SAA, which has been associated with in vivo toxicity when used in large amounts (Haider et al., 2020b). The EE% is indeed highly affected by different factors such as the method of preparation, physicochemical properties of the drug, and type and concentration of lipids and SAA. Our results showed that the PGZ-LMP formulae exhibited an EE% ranging from 26.7% to 71.3%, where the maximum drug entrapment was obtained using 300 mg CA in 1% Tween 80 (Table 2). The statistical analysis proved that only the surfactant concentration had a significant effect on the drug EE% (Table 3). The polynomial predictive equation produced for the EE% was

EE% = +47.26 – 9.23X₃. (5)

As shown in Figure 2, the use of a higher concentration of SAA resulted in a significant decrease in the percentage of drugs entrapped within the LMPs (p = 0.0312).

These results were consistent with those obtained by other scientists (Rahman et al., 2010; Thakkar et al., 2005), who reported a decline in EE% in association with increasing SAA concentration due to the drug’s solubility improvement in the external medium and the associated increase in its partition from the lipid matrix of the MPs to the external medium. As mentioned earlier, LMPs prepared using Tween 80 (less than 2% w/v) generated the smallest particle size, suggesting the capability of this combination to improve the drug loading to a great extent without compromising the particle size. Also, as a result of the low crystallinity of the excipients, the lipid matrix was able to accommodate more drugs at higher drug loadings.

It was also noticed that larger MPs prepared using 1% w/v Tween 80 entrapped a bigger amount of the drug than LMPs with the larger particle size prepared using 2% w/v Tween 80 and the same amount of CA.

On the other hand, Chen et al. (2017) observed the same results when studying gefitinib-loaded PLGA MPs and suggested that this may be due to drug loss at the surface upon partitioning from the internal matrix-forming material into the external aqueous phase during the solidification of the MPs. These processes usually occur mainly at the surface of the MPs when they have not solidified yet. Once the particles solidify, the drug is entrapped within the matrix. The relatively higher surface: volume ratio of smaller particles results in relatively more drug loss and hence less EE% of smaller MPs.

In vitro drug release

The solubility of PGZ is pH dependent, where PGZ, a weak base (pK_a = 6.8), is more soluble at lower pH. In the beginning, the release of PGZ from the prepared formulations was studied in both 0.1 N HCl and PBS pH 6.8 for 8 hours. In PBS, no PGZ was detected in the release media from the prepared PGZ-LMP formulations after 8 hours of the in vitro study due to the low PGZ solubility at higher pH. On the other hand, the PGZ release from drug-loaded LMPs exhibited a distinctive biphasic release pattern in 0.1N HCl (Fig. 3).

Figure 2. Surface response chart representing the effect of type of SAA and amount of CA on EE% using 1% (A) and 2% (B) concentration of SAA. [Click here to view]

The first phase was distinguished by a faster drug release from the MPs for up to 3 hours, especially in the formulation containing 1% SAA, where more than 50% of the drug was released in a burst release pattern followed by a slower constant drug release over 5 hours of the run. A burst in drug release may result from the accumulation of PGZ in the outer surface of LMPs as a result of phase separation during the crystallization of CA and the solidification of the particles. As a result, those drug particles at the lipid/water interface dissolved faster, leading to accelerated release of the drug to the dissolution medium. However, the sustained release pattern occurred when the drug molecules embedded in the matrix of the microparticles were slowly released because of the partition between the oil and water phases, drug diffusion, or matrix erosion (Khamanga and Walker, 2012).

Calculating the Q8 values from the release profiles of different PGZ-LMPs was used to assess their ability to sustain drug release. The prepared PGZ-LMPs could prolong and control the release of the drug to a varying degree (average Q8 values between 20% and 71%), as shown in Table 2. The polynomial equation describing the correlation between the tracked independent variables and Q8 was as follows:

Q8 = 41.71 + 3.86 X₁ – 2.82 X₂¹ – 0.6725 X₂² – 9.93 X₃ − 13.21 X₁X₂¹ + 9.13 X₁X₂². (6)

As shown in Table 3 and Figure 3, SAA concentration (X₃) exerted a significant impact on the Q8 response (p = 0.0122). Increasing SAA concentration resulted in a reduction in Q8 values. LMPs prepared using 1% Tween 80 and 300 mg of CA were the highest Q8 values. This may be due to a decrease in hydrophobic interaction between SAA with high HLB when added at a low concentration and the lipid phase resulting in rapid degradation of the lipid matrix and an increment of the amount of drug released (Haider et al., 2020b). The amount of lipid incorporated in the LMP formulae did not affect the drug release. The results in Table 3 showed that increasing the amount of lipid from 150 to 300 mg had no significant effect on the drug release (p > 0.05). Similarly, no significant difference in drug release was reported when changing the type of SAA. Nevertheless, an interaction between the amount of lipid incorporated into the formulation and the type of surfactant was statistically significant (p < 0.05). According to these results, the hydrophobic interactions between lipid chains and SAA molecules affect both the rigidity and permeability of LMPs.

Selection of the optimal PGZ-LMPs

Based on the response surface analysis of the D-optimal surface design, the optimum levels of the studied factors were estimated for PGZ-LMP preparation (Fig. 4). We aimed to minimize particle size (<9 μm) to improve drug solubility and maximize EE% and Q8 to ensure optimal drug release after oral administration. Thus, simultaneous optimization of all three responses was performed, and the desirability value was used to select the optimal formulation for Y1, Y2, and Y3.

The highest desirability value (0.77) was represented in the formulation containing 300 mg CA and 1% w/v Tween 80 (Fig. 4). The optimal PGZ-LMPs selected by the statistical design were prepared and characterized for their PS, EE%, and Q8. The optimal selected formulation produced an observed PS, EE%, and Q8 of 4.64 μm, 69.55%, and 68.72%, respectively (Table 4). As a result of these findings, the optimal PGZ-LMP formulation, proposed by QbD, offered a promising formulation of suitable particle size, maximum drug-loaded LMPs, and highest drug release Q8; hence, it was selected for further investigation. For some of the experiments, PGZ-free LMPs (blank LMPs) were prepared using the optimal formulation.

Figure 3. In vitro release profiles of PGZ from different drug-loaded LMPs containing 1% (A) and 2% (B) (w/v) SAA and surface response chart representing the effect of type of SAA and amount of CA on Q8 using 1% (C) and 2% (D) (w/v) SAA. [Click here to view]

Figure 4. Selection of optimal PGZ-LMP formula (flagged) among different formulations using 1% (A) and 2% (B) concentration of SAA. [Click here to view]

Table 4. Parameters optimized along with predicted and observed responses. [Click here to view]

Morphological examination

The morphology, surface topography, and structure of the optimal PGZ-LMP formula were investigated using an optical microscope and SEM. Optical micrographs showed that the particles were spherical and existed as discrete entities in the field (Fig. 5). SEM examination of the blank microparticles and the optimized PGZ-LMPs (F₇) revealed the formation of the uniform, monodisperse, and typical spherical shape of LMPs. The existence of clusters of microparticles was also observed, which may be attributed to the incomplete solidification of the microparticles during the time frame of the preparation process. Further magnification showed that both blank microparticles and PGZ-LMPs had a rough and coarse surface which may be due to the crystallization of the lipids on the surface of the microparticles (Wu et al., 2019). In addition, the drug-loaded LMPs were significantly larger than that of the blank microparticles indicating the loading of the drug.

FT-IR spectroscopy analysis

The pure PGZ, CA, their physical mixture, and the optimal blank LMPs and PGZ-LMPs were analyzed by FT-IR spectroscopy, and the peaks relevant to the chemical functional groups were revealed in the spectra (Fig. 6). As a whole, the constituents displayed the typical bands associated with their functional groups. The major IR peaks assigned to PGZ, namely, 1,148, 1,509, 1,683, and 658 cm⁻¹, corresponded to the stretching vibration of C-O, C=N, C=O, and C-S, respectively. On the other hand, CA had two characteristic peaks at 2,847 and 2,914 cm⁻¹ representing C-H and O-H stretching vibration, respectively.

PGZ and CA peaks in the physical mixture indicate that there is no chemical interaction between PGZ and the lipid used in the preparation of the LMPs. Furthermore, in the spectrum of the optimized formula, stretching vibration bands from the lipid matrix appeared at the same wavelength, but none of the pure PGZ peaks were present, confirming the encapsulation of the pure PGZ into the LMPs.

Thermal analysis

The DSC thermograms of the pure PGZ, lipid matrix, PGZ-lipid physical mixture, blank formula, and PGZ-LMP are shown in Figure 7. The pure PGZ and CA thermograms showed a sharp endothermic peak associated with their melting at 197.7°C and 49.49°C, respectively. The PGZ-lipid physical mixture showed both endothermic peaks of PGZ and lipid at 48.28°C and 192.6°C, respectively. PGZ-LMPs showed a broad endothermic peak at 45.52°C, whereas blank LMPs showed a broad endothermic peak at 47.95°C. There was a small shift in the peak mostly due to the drug incorporation into the lipid matrix. Also, in the drug-loaded microparticles, the PGZ endothermic peak was reduced, indicating an amorphous conformation, resulting in homogeneous solid mixtures with the lipids. This could mean the improved dissolution of the drug when PGZ-LMPs were placed in aqueous media. Similarly, the encapsulation of curcumin in stearic acid and tripalmitin lipid particles showed complete drug solubilization within the lipid matrix (Behbahani et al., 2017).

Drug release kinetics modeling

PGZ in vitro release data were implemented in different kinetic models (Table 5). The optimum model would be the one with the highest correlation coefficient (R²). A model was selected based on its R² value with the closest value to 1.

According to the Makoid–Banakar model, the correlation of the observed PGZ released from the microparticles versus the predicted amount was found visually acceptable for the optimal PGZ-LMP formulation with an R² value of 0.9915 (Table 5 and Fig. 8).

DDSolver was employed to perform further analysis to confirm the kinetic model fitting to the in vitro release data by generating the “Model Selection Criterion (MSC) and Akaike Information Criterion (AIC).” The release of PGZ from the optimal LMP formula showed the best fitting to the Makoid–Banakar kinetic model with a high value of R² adjusted and MSC and a low AIC. Additionally, because of the low release exponent n, the release behavior was consistent with the Fickian diffusion mechanism (Wu et al., 2021).

Preclinical in vivo study

The in vivo pharmacological effect of the optimal PGZ-LMP formula was assessed and compared to that obtained from marketed PGZ tablets following oral administration. The low-dose STZ-induced diabetic albino Wistar rats were used for comparing the antidiabetic effect (Fig. 9a). The mean baseline BGL in the normal control group was 91.7 mg/dl, while the diabetic group showed a significant increase in BGL of up to 314.7 mg/dl. The animals treated with the marketed product and PGZ-LMPs presented mean BGL of 243.1 and 218.7 mg/dl, respectively. The PGZ-LMPs-treated group exhibited a significant reduction in BGL, 72.82% (p < 0.05*), compared to the diabetic group and the group the received the marketed PGZ. This was superior to the marketed PGZ oral tablets, which showed no significant difference between both the diabetic and PGZ solution groups. Animals treated with marketed PGZ tablets showed a 60.8% reduction in BGL over 24 hours, while treatment with blank LMPs showed a minimal decrease in BGL. The results confirmed that optimization of drug-loaded LMP formulation resulted in higher therapeutic effectiveness and a longer duration of action for the encapsulated PGZ than the marketed product.

Figure 5. PGZ-LMP morphological investigation showing a light microscopic image of PGZ-LMPs at 40× (A), SEM micrographs of blank LMPs (B and C), and PGZ-LMPs (D and E) at 2.5k× and 50k×, respectively. [Click here to view]

Figure 6. Characterization of optimal PGZ-LMPs showing FT-IR spectra of PGZ, CA, their physical mixture, and the optimal blank LMPs and PGZ-LMPs. [Click here to view]

Figure 7. Physicochemical characterization of optimal LMP formulation showing thermal analysis of the PGZ, CA, their physical mixture, and the optimal blank LMPs and PGZ-LMPs. [Click here to view]

Table 5. PGZ release mechanism from the optimal formulation. [Click here to view]

Figure 8. The correlation of PGZ released versus the predicted amount of PGZ released from PGZ-LMP of F1–F12 by the Makoid–Banakar model. [Click here to view]

Figure 9. BGL measurements in control and treatment groups (A) and % reduction in BGL (B) after each treatment for 24 hours. Data presented as a mean ± SD (n = 5). [Click here to view]