Abstract
Several studies have revealed that the fruit mucilage of different species of Cordia is found to be an excellent binder in tablet manufacturing. However, the binding ability of the mucilage of Cordia Africana (CM) is not yet established. Therefore, this study aimed to evaluate the tablet-binding ability of the CM using paracetamol as a model drug. The fruit of Cordia Africana was extracted with the maceration technique and characterized for different properties, such as its compatibility with the model drug (paracetamol) using the Fourier transform infrared spectroscopy (FTIR) and its crystallinity nature with X-ray Diffractometer (XRD). Granules were prepared using the wet granulation method and compressed into tablets. The prepared tablets were evaluated for their hardness, disintegration time, friability, and drug release profile. The effect of these independent variables was further studied and optimized using the central composite design (CCD). The yield of extracted CM powder was 29%. The FTIR study revealed that the CM is compatible with the model drug. The loss in drying and moisture sorption studies was 6 ± 0.02% and 1.3–6.7%, respectively. The granules exhibited good flowability (angle of repose < 30 °) and compressibility properties with Carr’s and Hausner ratios of < 10 and < 1.11, respectively. All the prepared tablets have shown a hardness value of less than 100 N and a disintegration time in the range of 0.55 to 10.27 min. The ANOVA analysis for model adequacy testing confirmed the adequacy of the optimization model. Accordingly, the model provided an optimum formulation at 5.32% of CM concentration, 5% of disintegrant (Starch), and 76.71 N of compression force (CF). Under this condition, the software predicted 83.3% drug release at 30 min and 0.63 of friability. The validity of this optimum formulation was confirmed experimentally. The CM can be used as an alternative binder for tablets, and the optimization model was proven to be effective in identifying the optimum concentration of the CM along with CF and ST.
Keywords: Cordia africana, Cordia mucilage, Central composite design, Response surface methodology, Optimization, Paracetamol
Subject terms: Pharmaceutics, Drug development
Introduction
Binders are a type of excipient that are added in a tablet formulation to enhance the mechanical properties and release profile of the tablet through inter-particle bonding. They can be natural, synthetic, or semisynthetic. Synthetic and semi-synthetic pharmaceutical binders are expensive, less available, and nonrenewable1. Research indicated that these synthetic additives are hazardous not only when taken in vivo but also for the environment2.
On the contrary, nearly all plant binders are carbohydrates in nature and composed of repeating monosaccharide units that make them safer than synthetic ones. In addition, natural polymers remain attractive primarily because they are inexpensive, readily available, capable of a multitude of chemical modifications, and potentially biodegradable and compatible due to their origin3.
Paracetamol was used as a model drug to test the binding ability of Cordia mucilage (CM) in this study. It is categorized as analgesic and antipyretic. It is the most common drug for the alleviation of mild to moderate pain. It is an odorless white crystalline solid with a bitter taste4. Paracetamol was chosen as it is easily accessible and cheap. Moreover, paracetamol has a known characteristic of poor compressibility and flowability, which usually requires wet granulation to be employed for its formulation as a tablet5,6. Thus, finding alternative binders is important to ensure accessibility, suitability, and affordability of binders.
Cordia africana is a widely distributed plant in Ethiopia. It has been used as a source for traditional medicine, as a beverage, and has great importance in the furniture industry. Therefore, the proper utilization of the native Cordia Africana plant will have a big positive impact on the country’s economy7. In this study, the CM, as part of the plant, was assessed for its use as a tablet binder using an optimization technique by Response Surface Methodology (RSM).
RSM is one of the most commonly used optimization methods in the design of experiments8. It shows the interactions of independent variables with one or more response variables9. In this method, the effect of variables can be shown through the surface adjustment by observing where the surface lies based on the graphical illustrations. Among the methods under RSM, the central composite design (CCD) was used. The CCD includes the minimum and maximum values with repeatable central points and two additional star points per factor. The central points are for the estimation of the pure errors, while the star points are used to navigate the quadratic relationship between the study variables and the response. Compared with a Full factorial study, CCD has a good ability to predict numerical optimization. Moreover, CCDs are important in capturing curvature (quadratic effect), which cannot be detected using a two-level full factorial design10.
In this research, CM was evaluated, characterized, and optimized using CCD for its binding effect along with other determinant factors, the ST and CF.
Results
The physicochemical characterization of the cordia mucilage
The extracted CM was found to be reddish-brown in color, odorless, and smooth in texture. Moreover, as shown in Table 1, Starch was absent in the CM as confirmed by the iodine test. The CM did not exhibit swelling in distilled water, HCl, or phosphate buffer pH 5.8. The loss on drying was 6 ± 0.02%, which is within the pharmacopeial limit for natural gums and CM(≤ 15%)11. The pH of the CM was 6.8 ± 0.01. The total Ash value in this study was found to be 1.96%± 0.05.
Table 1.
The physicochemical properties of CM expressed as Mean ± SD for n = 3.
| No. | Parameter | Results | ||||
|---|---|---|---|---|---|---|
| 1 | Yield | 29%± 0.9 | ||||
| 2 | Color | Reddish-Brown | ||||
| 3 | Loss on Drying | 6% ± 0.02 | ||||
| 4 | Total Ash | 1.96%± 0.05 | ||||
| 5 | Swelling ratio | No | ||||
| 6 | PH | 6.8 ± 0.01 | ||||
| 7 | Presence of Starch | No | ||||
| 8 | Solubility | % soluble mass | ||||
| Ethanol | Acetone | Chloroform |
Distilled water (hot) |
Distilled water | ||
| 0 | 0 | 0 | 100 | 100 | ||
The solubility profile of the CM was investigated using different solvents (Fig. 1). Accordingly, the CM was found to be completely soluble in distilled water (D &E in Fig. 1) and it has shown no solubility in other solvents of lower polarity (ethanol, chloroform, and acetone), indicated with letters A, B, and C, respectively.
Fig. 1.
The solubility profile of CM using Ethanol (A), chloroform (B), Acetone(C), hot distilled water (D), and Distilled water at room temperature (E).
The XRD has shown a noisy signal with a wide peak from 20°- 30° as shown in Fig. 2A. The percentage of moisture uptake was found between 1.3 and 6.7 for the range of 20–100% relative humidity, respectively (Fig. 2B). The highest viscosity profile of the CM was 5231 mPa.s (millipascal-seconds) at a concentration of 12.7% (Fig. 2C).
Fig. 2.
The X-ray diffractometry(A), Relative humidity (B), and Rheology (C) of CM.
Drug-excipient compatibility study
Fourier transform infrared spectroscopy (FTIR) analysis
FTIR did not show any shift in the functional groups of paracetamol. The spectrum of paracetamol showed the vibrational peaks at 3320 and 3164 − 3098 cm-1, characteristic of O-H and CH3 stretching, respectively. A vibrational peak at 1653 cm-1 was due to C = O stretching, while 1610 cm-1 is attributed to C = C stretching. Moreover, peaks at 1566, 1506, and 1442 cm-1 were designated to N-H bending, and asymmetrical bending in C-H and C-C stretching, respectively. In addition, peaks at 1369 − 1328, 1260 − 1227, and 1170–1107 cm-1 were attributed to symmetrical bending in C-H bend, C-N, and C-O stretching, respectively. The Vibrational peaks 966 and 837 cm-1 are assigned to C-N (amide) stretching and para-disubstituted aromatic ring, respectively (Fig. 3A). Figure 3B shows the combination spectrum of paracetamol and CM at a 1:1 ratio mixed physically.
Fig. 3.
The FTIR spectra of pure paracetamol (A) and a Combination of Paracetamol and CM (B).
Evaluation of the granule of paracetamol tablets
The angle of repose of granules was found to be less than 30° for all batches. The Carr’s index and the Hausner’s ratio were found to be in the range of 5.01 ± 0.07 to 9.53 ± 0.39 and 1.00 ± 0.12 to 1.10 ± 0.02, respectively (Table 2). Moreover, the granule size distribution was found to be between 416.8 μm and 805.77 μm.
Table 2.
Micromeritics properties of granules.
| Formulation | Bulk density(g/ml) | Tapped density(g/ml | Flowrate (g/sec) | Angle of repose (o) | Carr’s index | Hauser’s ratio |
|---|---|---|---|---|---|---|
| F1 | 0.47 ± 0.01 | 0.51 ± 0.04 | 4.19 ± 0.09 | 25.36 ± 0.04 | 7.84 ± 0.07 | 1.08 ± 0.00 |
| F2 | 0.45 ± 0.06 | 0.49 ± 0.01 | 4.11 ± 0.23 | 26.66 ± 0.34 | 8.16 ± 0.12 | 1.07 ± 0.09 |
| F3 | 0.48 ± 0.04 | 0.53 ± 0.09 | 5.19 ± 0.12 | 26.77 ± 0.55 | 9.43 ± 0.14 | 1.09 ± 0.01 |
| F4 | 0.51 ± 0.08 | 0.56 ± 0.12 | 4.48 ± 0.27 | 28.44 ± 0.11 | 8.92 ± 0.02 | 1.10 ± 0.02 |
| F5 | 0.46 ± 0.03 | 0.48 ± 0.19 | 4.83 ± 0.06 | 25.82 ± 0.26 | 4.16 ± 0.38 | 1.03 ± 0.04 |
| F6 | 0.49 ± 0.07 | 0.52 ± 0.06 | 4.17 ± 0.11 | 27.78 ± 0.72 | 5.76 ± 0.29 | 1.06 ± 0.12 |
| F7 | 0.54 ± 0.03 | 0.57 ± 0.04 | 5.12 ± 0.01 | 28.26 ± 0. 09 | 5.26 ± 0.46 | 1.04 ± 0.02 |
| F8 | 0.43 ± 0.05 | 0.47 ± 0.00 | 4.09 ± 0.06 | 26.49 ± 0.64 | 8.51 ± 0.89 | 1.07 ± 0.00 |
| F9 | 0.46 ± 0.01 | 0.48 ± 0.07 | 4.61 ± 0.19 | 26.16 ± 0.17 | 4.16 ± 0.28 | 1.03 ± 0.05 |
| F10 | 0.46 ± 0.09 | 0.48 ± 0.03 | 4.74 ± 0.18 | 25.36 ± 0.04 | 4.16 ± 0.19 | 1.04 ± 0.15 |
| F11 | 0.48 ± 0.09 | 0.50 ± 0.01 | 5.22 ± 0.19 | 29.19 ± 0.18 | 4.01 ± 0.58 | 1.04 ± 0.12 |
| F12 | 0.51 ± 0.06 | 0.53 ± 0.06 | 5.17 ± 0.08 | 25.59 ± 0.45 | 3.77 ± 0.51 | 1.03 ± 0.03 |
| F13 | 0.46 ± 0.02 | 0.51 ± 0.09 | 4.08 ± 0.01 | 25.21 ± 0.31 | 9.80 ± 0.23 | 1.09 ± 0.00 |
| F14 | 0.45 ± 0.03 | 0.49 ± 0.08 | 4.56 ± 0.12 | 27.46 ± 0.25 | 8.16 ± 0.08 | 1.07 ± 0.04 |
| F15 | 0.50 ± 0.05 | 0.53 ± 0.05 | 4.88 ± 0.09 | 25.26 ± 0.02 | 5.66 ± 0.39 | 1.06 ± 0.08 |
| F16 | 0.51 ± 0.01 | 0.54 ± 0.06 | 4.74 ± 0.19 | 27.65 ± 0.06 | 5.01 ± 0.35 | 1.05 ± 0.00 |
| F17 | 0.50 ± 0.09 | 0.55 ± 0.02 | 4.49 ± 0.24 | 29.37 ± 0. 15 | 9.01 ± 0.23 | 1.09 ± 0.07 |
| F18 | 0.53 ± 0.07 | 0.56 ± 0.06 | 5.00 ± 0.05 | 28.32 ± 0.51 | 5.35 ± 0.58 | 1.04 ± 0.01 |
| F19 | 0.50 ± 0.08 | 0.53 ± 0.09 | 4.45 ± 0.06 | 26.55 ± 0. 45 | 5.66 ± 0.07 | 1.06 ± 0.06 |
| F20 | 0.49 ± 0.07 | 0.54 ± 0.04 | 4.92 ± 0.07 | 29.37 ± 0. 19 | 9.25 ± 0.69 | 1.10 ± 0.14 |
Evaluation of paracetamol tablet: weight uniformity and thickness
The weight uniformity test for all batches was between 648.4 ± 2.12 and 653.8 ± 1.95 mg, and thickness values were between 4 mm and 5 mm.
Hardness, friability, and disintegration time of paracetamol tablets
In all batches, the tablet hardness has shown a directly proportional relationship with friability and an inverse proportion with disintegration time, as shown in Table 3. Except for F1, F3, and F13, the other formulations meet the friability test limit (< 1%). Moreover, all batches are in line with the specific pharmacopeial requirement for the disintegration time of conventional tablets (< 15 min)12.
Table 3.
Tablet Hardness, Friability, and disintegration profile time of Paracetamol Tablets.
| Formulation | Hardness N(± SD) | Friability%(± SD) | Disintegration time (Minutes) |
|---|---|---|---|
| F1 | 59.61 ± 3.09 | 1.1 ± 0.25 | 0.59 |
| F2 | 69.21 ± 2.54 | 0.7 ± 0.18 | 2.51 |
| F3 | 59.22 ± 2.48 | 1.05 ± 0.21 | 0.54 |
| F4 | 71.71 ± 3.18 | 0.68 ± 0.08 | 2.57 |
| F5 | 83.70 ± 2.53 | 0.47 ± 0.15 | 7.24 |
| F6 | 89.11 ± 3.96 | 0.33 ± 0.12 | 7.12 |
| F7 | 70.2 ± 3.2 | 0.5 ± 0.14 | 4.37 |
| F8 | 84.1 ± 3.36 | 0.4 ± 0.13 | 7.21 |
| F9 | 57.4 ± 2.48 | 0.9 ± 0.21 | 1.23 |
| F10 | 86.3 ± 3.69 | 0.39 ± 0.20 | 6.39 |
| F11 | 73.90 ± 3.87 | 0.6 ± 0.12 | 5.22 |
| F12 | 78.81 ± 1.77 | 0.57 ± 0.15 | 0.55 |
| F13 | 58.41 ± 3.31 | 1.2 ± 0.34 | 0.46 |
| F14 | 87.03 ± 3.63 | 0.31 ± 0.14 | 10.27 |
| F15 | 78.51 ± 1.06 | 0.59 ± 0.16 | 2.41 |
| F16 | 76.15 ± 3.77 | 0.52 ± 0.09 | 2.19 |
| F17 | 79.21 ± 1.51 | 0.52 ± 0.11 | 2.39 |
| F18 | 76.31 ± 1.39 | 0.55 ± 0.08 | 2.51 |
| F19 | 75.08 ± 3.25 | 0.56 ± 0.21 | 3.12 |
| F20 | 75.81 ± 3.44 | 0.54 ± 0.13 | 2.53 |
Construction of a calibration curve of paracetamol tablet
The linear regression equation was found to be Y = 0.05402x-0.01724, while the adjusted R-squared is 0.99991 as indicated in Fig. 4.
Fig. 4.
The calibration curve of paracetamol plotted at concentration against absorbance at 267 nm with 95% confidence level (R2 = 0.99991).
In vitro drug release
All the batches of Paracetamol Tablet showed various patterns in the release at different extents, as shown in Fig. 5, indicated as A, B, C, and D to represent a series of formulations. All six central point formulations (see F15-20 in Fig. 5D) have shown close-release profiles of approximately 85% at 30 min. F2, F4, and F9 showed optimum drug release, which is about 85% at 30 min. F14 releases greater than 80% of its content in 15 min drug content.F5 to F11 showed a release profile of less than 80% of its total content within 30 min. F7 showed marginally optimum drug release at 30 min (78.57%).
Fig. 5.
Drug Release Profiles of Paracetamol Tablet Formulations denoted from F1-F20 for 20 formulations.
Model diagnosis of the optimization study
The actual versus the predicted values exhibited closer values that indicated the model’s fitness. (Fig. 6).
Fig. 6.
Predicted vs. Actual plots for drug release at t-30 (A) and Friability (B).
Model adequacy
The ANOVA for a reduced quadratic model of drug release at t = 30 is summarized in Table 4, while the ANOVA for a reduced quadratic model of Friability is summarized in Table 5. The lack of fit values were 0.2278 and 0.2116 for drug release at 30 min and Friability, respectively. In this study, the lowest Predicted Residual Sum of Squares (PRESS) value is exhibited for the quadratic model than other models for both responses, which is a good indicator for the fit of the model13.
Table 4.
ANOVA of the reduced quadratic model for Drug-Release at 30 min.
| Source | Sum of Squares | df | Mean Square | F-value | p-value | |
|---|---|---|---|---|---|---|
| Model | 1886.48 | 7 | 269.50 | 70.09 | < 0.0001 | Significant |
| A-CF | 294.80 | 1 | 294.80 | 76.67 | < 0.0001 | |
| B-ST | 215.72 | 1 | 215.72 | 56.10 | < 0.0001 | |
| C-CM | 1187.41 | 1 | 1187.41 | 308.79 | < 0.0001 | |
| AC | 19.44 | 1 | 19.44 | 5.06 | 0.0441 | |
| BC | 44.76 | 1 | 44.76 | 11.64 | 0.0052 | |
| A² | 34.61 | 1 | 34.61 | 9.00 | 0.0111 | |
| C² | 99.32 | 1 | 99.32 | 25.83 | 0.0003 | |
| Residual | 46.14 | 12 | 3.85 | |||
| Lack of Fit | 34.10 | 7 | 4.87 | 2.02 | 0.2278 | Not significant |
| Pure Error | 12.05 | 5 | 2.41 | |||
| Cor Total | 1932.63 | 19 |
Table 5.
ANOVA of the reduced quadratic model for Friability.
| Source | Sum of Squares | df | Mean Square | F-value | p-value | |
|---|---|---|---|---|---|---|
| Model | 1.18 | 5 | 0.2360 | 194.39 | < 0.0001 | Significant |
| A-CF | 0.2554 | 1 | 0.2554 | 210.41 | < 0.0001 | |
| C-CM | 0.8104 | 1 | 0.8104 | 667.57 | < 0.0001 | |
| AC | 0.0351 | 1 | 0.0351 | 28.92 | < 0.0001 | |
| A² | 0.0136 | 1 | 0.0136 | 11.22 | 0.0048 | |
| C² | 0.0703 | 1 | 0.0703 | 57.88 | < 0.0001 | |
| Residual | 0.0170 | 14 | 0.0012 | |||
| Lack of Fit | 0.0135 | 9 | 0.0015 | 2.12 | 0.2116 | Not significant |
| Pure Error | 0.0035 | 5 | 0.0007 | |||
| Cor Total | 1.20 | 19 |
The measurements for signal-to-noise ratio, called adequate precision, were found to be 29.0714 and 42.981 for drug release at t-30 min and friability, respectively. Regarding the impact of independent factors, in both the drug release and friability analysis, the concentration of CM was found to be more influential than other factors.
Overlay plots
The best combination of variables for the maximum possible response exhibited a desirability of one. The yellow-shaded region in Figure 7 indicates the area that satisfies the imposed criteria. In the shaded region, the optimum conditions were CF of 76.7134 N, concentration of starch 1500 (ST) (5%), and concentration of CM(5.32871%), as the combined effect of ST and CF (Fig.7A), CM and ST (Fig. 7B), CM and CF (Fig.7C). Under these conditions, the software predicts 83.333133% drug release at t-30 and 0.634242% friability.
Fig. 7.
The overlay plot of the optimization study.
The interaction among variables is further noticed using the 3D and Contour plots. The 3D plot and contour plot depicting the impact of CM and CF on drug release at 30 min are shown in Fig. 8A and B, respectively. The impact of ST and CM is illustrated in Fig. 8D and C, respectively. The 3D and contour plots showing the impact of CM and CF on Friability are shown in Fig. 8E and F, respectively.
Fig. 8.
The 3D and Contour plots for variable interactions.
The Perturbation curve indicates influential factors in response variables. In both drug release (Fig. 9 − 1) and Friability (Fig. 9 − 2), Curve C (CM), which represents the binder (the mucilage), is shown to have the highest influence.
Fig. 9.
Perturbation Curve of variables on Drug release at 30 min (1) and Friability (2), where C, A, and B stand for CM, CF, and ST, respectively.
Validation of the experimental design
The relative error (RE) values for the selected checkpoints for all optimized Tablets (OT) were found between − 3.17% and 4.02%, which ensures the validity of the model (Table 6).
Table 6.
Validation of the experimental design using 5 checkpoints.
| Code | Optimized formulation composition | Response variable | Experiment al value (%) | Predicated value (%) | %RE |
|---|---|---|---|---|---|
| OT-1 | CF = 76.713 N | Friability | 0.65 | 0.634 | −2.5 |
| ST = 5% |
Drug release at t-30 |
84.221 | 83.331 | −1.06 | |
| CM = 5.329% | |||||
| OT-2 | CF = 76.710 N | Friability | 0.65 | 0.63 | −3.17 |
| ST = 5% |
Drug release at t-30 |
83.11 | 83.2 | 0.1 | |
| CM = 5.375% | |||||
| OT-3 | CF = 76.713 N | Friability | 0.61 | 0.634 | 3.78 |
| ST = 5.257% |
Drug release at t-30 |
82 | 83.494 | 1.78 | |
| CM = 5.329% | |||||
| OT-4 | CF = 74.894 N | Friability | 0.62 | 0.646 | 4.02 |
| ST = 5.007% |
Drug release at t-30 |
84.56 | 83.719 | −1 | |
| CM = 5.329% | |||||
| OT-5 | CF = 71.978 N | Friability | 0.67 | 0.665 | −0.751 |
| ST = 5% | Drug release at t-30 | 85.34 | 84.294 | −1.24 | |
| CM = 5.329% |
Evaluation of the optimized tablet (OT) (mean ± SD)
The angle of repose (< 30o), Carr’s compressibility index (within 5–15%), and Hauser’s ratio (< 1.1) of granules were found within the acceptable ranges. All batches of the optimized tablet have shown to release 80% of their content in 30 min (Fig. 10A) and a friability between 0.61% and 0.67% (Fig. 10B).
Fig. 10.
Confirmatory Study on Drug Release (A) and Friability (B) of the optimized tablet.
Discussion
Physicochemical characteristics of cordia mucilage
The swelling index study provides the moisture-water absorption capacity of the CM14. Furthermore, it indicates the mechanism of drug release. For example, the drug release retards as the swelling power increases due to the thick gel layer formed, which could delay the drug release15. This inability of the CM to absorb moisture suggests that the drug release profile of the tablet is not affected due to the swelling power.
The loss of moisture on drying (Loss on Drying) of CM was found to be less compared to Cordia dicothoma, with a value of 15.6%, indicating that CM has a lower moisture content than other mucilages in the same family. The pH of CM was also closer to that of Cordia dicothoma, which has a pH of 616.
The ash value is comparable to the impurity level of a substance. This purity level of CM was within the normal range of most mucilage in the Cordia family (0.7–6.86%)17. Moreover, the ash value of CM was higher than the purity level of Cordia myxia, which has shown the ash value of 5.86% in another study18. The CM was tested to be insoluble in non-polar solvents (acetone, ethanol, methanol, and ether) as shown in Fig. 2. A similar finding was observed in the research on the mucilage of Cordia dichotoma, as it was found to be insoluble in non-polar solvents18.
In a powder of good flowability, the values of bulk and tapped density would be closer to each other, which lowers the value of the Carr’s index19. In this study, the values of Carr’s index and Hausner’s ratio were below 15 and 1.25, respectively, depicting the better flowability of the granules20.
The moisture sorption study indicates a less hygroscopic nature of the CM (Fig. 3B). In addition, the peak obtained in this study is similar to the characteristic peak (J-shaped) of sugar-containing foods21. Moreover, the viscosity of the CM is directly proportional to its concentration due to the possible intricacy of the CM in the solution (Fig. 3C)11. A similar finding was reported in the research conducted on the rheological property of C. myxa that showed the concentration was a determinant of its viscosity18.
The XRD study
The XRD pattern of the CM was measured to get information on the crystalline structure. Due to the direct relationship between the crystallinity and stability of a substance, the determination of the nature of the CM as amorphous or crystalline through XRD analysis would be of great importance22. As presented in Fig. 3A, the CM has shown a noisy signal with a wide peak from 20°- 30° due to its amorphous structure. This finding was similar to other findings, as a peak at 20° was also observed in xanthan gum23.
Compatibility study
The FTIR study confirmed the compatibility of the CM and paracetamol. All the characteristic peaks of paracetamol appear both when the FTIR runs for paracetamol only (Fig. 3A) and are unchanged when combined with CM (Fig. 3B).
Evaluation of granules paracetamol tablet
The granule size distribution is very important as it has an impact on the flowability, weight uniformity, and dissolution profile of the tablet. It is also essential to ascertain the quality of the final dosage forms. The granule size distribution between 416.8 μm and 805.77 μm is within the range required for tablets (300–1000 μm)24.
Evaluation of paracetamol tablets
Tablet thickness
A range of thickness values was observed due to the variation in compression force (CF). For example, F1, F3, F5, and F7 have depicted higher thickness values (all slightly greater than 5 mm) due to the low CF(50 N), whereas F9 has the highest value (5.66 ± 0.13) due to the lowest CF(32.9 N) used. On the contrary, F10 showed the smallest of all thickness values (3.78 ± 0.12) due to the maximum CF used (117.045 N).
Tablet hardness, disintegration, and friability study
The tablet hardness has shown a direct relationship with friability and an inverse relationship with disintegration time. The other formulations meet the friability test limit (< 1%). However, F1, F3, and F13 failed to meet this criterion because these formulations had the lowest binder concentration and minimum compression force. On the other hand, all formulations meet the hardness test (< 100 N). Moreover, all batches are in line with the specific pharmacopeial requirement for the disintegration time of conventional tablets (< 15 min)12.
Drug release profile of paracetamol tablet
The drug release profile varied due to differences in the magnitude of compression force, level of the binder, and the ST. For instance, F1 released its 80% content in 15 min, while F3 and F13 released 85% of the drug before 10 min. This may be due to the low CF(50 N) and low binder concentration (3%) in both F3 and F13. While the magnitude of the CF is high in F13, it exhibits high drug release due to the low binder concentration (0.61%).
Optimization study
The diagnosis of the optimization model was supported with different tests to ensure the predictability of the model25. In the Design Expert software, externally studentized residuals are more sensitive to finding outliers than internally studentized residuals26,27. Therefore, the software was adjusted for externally studentized residuals for the whole process of the diagnosis.
The actual vs. predicted plots are shown to be closer to each other (Fig. 7) in both responses. This depicts that the prediction could satisfy the analysis28. Both the adjusted(> 0.8) and predicted R2 (> 0.8) were good enough to prove the model’s fitness29. The R2 value is also an indicator of the fitness of the regression model, and the closer the value is to one, the higher the predictability of the response variables30. In both drug release at t-30 and friability, the R² were 0.9779 and 0.9907, respectively. The difference between the adjusted and predicted R2 is also within the limit (< 0.2) for both drug release at t-30 and friability, which is a good indicator for the fitness of the model in both responses31,32.
The F value stands for residual error and estimates the accuracy of the model33. This value for both drug release at t-30 and friability is 70.09 and 194.39, respectively. The higher the F value of each term, the greater the significance of causing an effect will be. Accordingly, the values obtained in this study satisfied the model34. Moreover, the lack of fit values for both responses is shown to be greater than 0.1 (0.2278 and 0.2116 for drug release at t-30 min and friability, respectively), which is a good indicator of the good fit of the model35.
The adequate precision, measurements for signal-to-noise ratio, were found to be 29.07 and 42.98 for drug release at t-30 min and friability, respectively, which is much greater than 4, indicating an adequate signal to explore the design space36. The sum of squares of the mean (SEM) value indicates factors that have a more influential effect. The larger SEM value indicates the factor has a more influential effect. In both the drug release and friability analysis, the concentration of CM was found to be more influential than other factors. The level of the impact of each independent variable on the selected responses was further confirmed using the perturbation curve. while all variables are at the center point and the curve with higher change is perceived to be the one with a bigger impact on the response than others37. As indicated in Fig. 10, factor C (the binder) has shown the highest impact on both responses (drug release at t-30 and Friability), and factor B (the ST) is found to have the least or insignificant effect on responses.
Moreover, the ST (B) was found to be insignificant for the determination of friability, while the interaction between the CF(A) and the ST (B) was insignificant for the determination of the drug release at t-30 min. Therefore, these factors were avoided using a backward model reduction approach to improve the model prediction ability33. Accordingly, the model terms, A, B, C, AC, BC, A², and C² were significant for drug release at t-30 min, and A, C, AC, A², and C² for friability, as the p-values were found to be less than 0.05 for every model term mentioned above.
Interaction among variables
The graphical presentations using contour and 3D plots have also shown the interactions of variables (Fig. 9A-F). The 3D graphical presentations indicate the effect of different factors on response and show how sensitive the response surface is to the change of every factor36. The change in the response surface was observed as two factors interact in different settings while maintaining the third variable at the center (average)33. The discussion above is further supported by the contour plots that show the relationship among variables using a two-dimensional plane. As the concentration of CM and CF decreases, the drug release improves, and the concentration of disintegration is directly proportional to the drug release. Both the concentration of CM and CF was found to have an inverse proportional relationship with friability.
Materials and methods
Paracetamol was obtained from Cadila Pharmaceuticals (PLC)as a gift and other ingredients such as lactose monohydrate (BDH Chemicals Ltd Poole, England), starch 1500 (Neolab Life Science Co., India), Talc (BDH Chemicals Ltd Poole, England), magnesium stearate (BDH Chemicals Ltd Poole, England), acetone (99%) (Riedel-de Haen, Germany), disodium hydrogen phosphate dehydrate (99.0%) (CARLO ERBA Reagents S.A.S,), potassium dihydrogen phosphate (KH2PO4, 99.0%) (Guangdong Guanghua Sci-Tech Co., Ltd, China) were bought from the local market. Fruits of C. africana were collected from a wild source around Ayra, Gondar city, central Gondar zone.
Methods
Collection and authentication of cordia Africana fruit
The fruit of C. africana was collected from Ayra, Gondar City, Central Gondar, and Amhara Regional State, and authenticated by the Department of Biology, University of Gondar.
Extraction of cordia Africana fruit mucilage
The collected Cordia fruit was washed to avoid any potential contaminants. Then, a kilogram of the fruit was measured, peeled, and macerated in distilled water by maintaining a 1:10 fruit-to-distilled water ratio, and was left for 24 h. Finally, the macerate was allowed to pass through the muslin cloth to filter the fruit and other debris materials38.
Purification of the isolated mucilage
The dried CM was powdered using a mortar and pestle and passed through a sieve number 60 μm. After this, the powdered CM was solubilized using distilled water. Then, the concentrated solution was precipitated using acetone, and the supernatant fluid was decanted, and the precipitate was dried in a tray oven dryer (Kottermann® 2711, Germany) at 60 °C. Finally, the dried CM was pulverized and kept in a closed container for further use as a binder38.
Physicochemical characterization of the dried mucilage
Loss on drying of CM
About five grams of CM was dried by a tray oven dryer (Kottermann® 2711, Germany) at 105 °C for 2 h till a constant weight was obtained. Then, the loss on drying was determined by using Eq. 139.
 |
1 |
Where W1 is the initial weight and W2 is the second weight after drying.
Moisture sorption study
A 2 g of the pre-dried CM was added to a dried Petri dish of known weight and then transferred into different desiccators containing a relative humidity of 20%, 40%, 60%, 80%, and 100% which were maintained using different saturated solutions of KC2H3O2, K2CO3, KI, KCl, and K2SO4, respectively. The weight of each sample was measured every day until a constant weight was achieved in two successive samples. Then, the Moisture sorption was calculated as follows using Eq. 214.
 |
2 |
where W1 is the initial weight, and W2 is the final weight obtained.
Drug-excipient compatibility study
The drug-mucilage compatibility test was done using FTIR to identify if there was any chemical shift or change while combining the paracetamol and the CM.
Characterization of the granules
Bulk and tap density
Granules of 30 g Paracetamol Tablet were poured into a 250 ml measuring cylinder, and the volume occupied was recorded (Vo). Then, the sample in the measuring cylinder was tapped 500 times using the taping machine (ERWEKA SVM), and the final volume was noted (VT). Then both tap and bulk density were calculated using Eq. 340.
 |
3 |
Flowability and compressibility of the granule
The compressibility and flow properties of the granule were determined using Carr’s index and Hausner’s ratio through Eq. 4 & Eq. 5, respectively41.
 |
4 |
 |
5 |
Where is tapped density and is bulk density.
Preparation and characterization of granules
Preparation of granules
The granules were prepared using paracetamol, CM, ST, talc, magnesium stearate, and lactose as presented in Table 7 below. First, the dough was made by mixing paracetamol, lactose (as needed), the desired CM concentration, and only half of the desired ST using a tubular mixer (Willy A. Bachofen AG, Turbula 2TF, Basel, Switzerland). Then, distilled water was added as granulating liquid, and the final mass was subjected to passing through a 1.6 mm sieve. The wet mass was then transferred to a Petri dish and dried in an oven (Kottermann 2711, Germany) at 105 °C for 30 min. (There is a lack of a step to convert the dried mass into granules.) Finally, the dried granules were mixed using Willy A. Bachofen AG, Turbula 2TF, Basel, Switzerland, with the rest of the ingredients: magnesium stearate, talc, and half of the dry ST, and passed through a 1 mm sieve.
Table 7.
The concentration range of each ingredient.
| No. | Ingredients | Amount (%) |
|---|---|---|
| 1 | Paracetamol | 77 |
| 2 | CM | 3–10 |
| 3 | ST | 3–12 |
| 4 | Magnesium stearate | 1 |
| 5 | Talc | 1 |
| 6 | Lactose | q.s to100. |
Angle of repose
The powder angle of repose (θ) of granules was determined by a fixed funnel system to assess the flow property. The granules were allowed to flow freely through the funnel with an inner diameter of 10 mm at the bottom and 100 mm at the top, onto a flat surface from a height of 10 cm. The diameter (d), the height (h) of the powder pile, and the time taken for the CM powders to flow through the orifice were noted. Then, the angle of repose was calculated as per Eq. 642.
 |
6 |
Where h is the height of the powder pile, r is the radius of the pile.
Flow rate determination
First, a 30 g sample was measured and subjected to passing through the funnel fixed at a height of 10 cm from the base. Then, the time it took for the granules to pass through the funnel was recorded. A thrice repetition was done for each batch of the formulation, and the flow rate was calculated based on Eq. 743.
 |
7 |
Where WG is the weight of the granule and FT is the flow time for the granule.
Preparation of paracetamol tablet
The tablet was prepared with the indicated range of concentration for each ingredient study (Table 7).
Accordingly, 20 batches of the granules were prepared based on various concentration-based combinations of ingredients generated using Design Expert software, following the formula 2ⁿ + 2n + 6 as per the Central Composite Design (CCD) for three independent variables (Table 8). The concentrations of paracetamol, talc, and magnesium stearate were kept constant throughout the study, while lactose was used as a filler to adjust the total weight to 650 mg.
Table 8.
The formulation of 20 batches of Paracetamol tablets as per the CCD.
| Formulation | Space Type | CF | ST | CM |
|---|---|---|---|---|
| N | % | % | ||
| F1 | Factorial | 50 | 5 | 3 |
| F2 | Factorial | 100 | 5 | 3 |
| F3 | Factorial | 50 | 15 | 3 |
| F4 | Factorial | 100 | 15 | 3 |
| F5 | Factorial | 50 | 5 | 10 |
| F6 | Factorial | 100 | 5 | 10 |
| F7 | Factorial | 50 | 15 | 10 |
| F8 | Factorial | 100 | 15 | 10 |
| F9 | Axial | 32.9552 | 10 | 6.5 |
| F10 | Axial | 117.045 | 10 | 6.5 |
| F11 | Axial | 75 | 1.59104 | 6.5 |
| F12 | Axial | 75 | 18.409 | 6.5 |
| F13 | Axial | 75 | 10 | 0.613725 |
| F14 | Axial | 75 | 10 | 12.3863 |
| F15 | Center | 75 | 10 | 6.5 |
| F16 | Center | 75 | 10 | 6.5 |
| F17 | Center | 75 | 10 | 6.5 |
| F18 | Center | 75 | 10 | 6.5 |
| F19 | Center | 75 | 10 | 6.5 |
| F20 | Center | 75 | 10 | 6.5 |
Evaluation of paracetamol tablet
Granule size distribution
A sample of 30 g was added to a set of sieves (ERWEKA, Type AR 401, Germany) arranged with the widest mesh wire size at the top and the smallest size at the bottom. After shaking the sieve for 2 min, the granules retained in each sieve were weighed and expressed as a percentage. The procedure was repeated three times for all batches, and the average value was calculated along with the standard deviation44.
Weight uniformity test
The weight of 20 tablets was measured individually from each batch using an analytical balance (ADAM, AAA 160 L analytical balance). The mean weight, as well as the deviation, was calculated accordingly38.
Thickness
The mean thickness and standard deviation were determined with 10 tablets, which were selected randomly from each batch, and the thickness of each tablet was measured using a sliding caliper scale (Nippon Sokutei, Japan).
Tablet hardness test
The tablet hardness was tested using a tablet hardness tester (CALEVA Tablet Hardness Tester). For each batch, 10 tablets were subjected to the force exerted by the blunt tip of the machine until it just got broke, and the force used to break the tablet was recorded accordingly44.
Friability test
Randomly selected 20 tablets from each batch were weighed in an analytical balance. Then, transferred into the friability tester (Erweka TAR 20) and subjected to the tumbling action of the instrument at 25 rpm for 4 min. Finally, the tablets were taken out of the friability tester, dedusted, and weighed once again. The friability was then calculated using Eq. 8.
 |
8 |
Where W1 is the initial weight and W2 is the second weight after the completion of the friability tester.
Construction of a calibration curve for paracetamol
The calibration curve was done with 5 concentrations, which were prepared from the stock concentration of 0.02%w/v (4 µg/ml, 6 µg/ml, 8 µg/ml, 10 µg/ml, and 12 µg/ml) of paracetamol, which were prepared using a phosphate buffer of pH 5.8, and the absorbance for each concentration was measured by a UV-Visible spectrophotometer (UV-visible spectrophotometer, CM2203, Belarus λ max of 267 nm. Then the linear equation for the calibration curve was noted at the end of the reading (absorbance vs. concentration).
In vitro drug release study
The USP type II dissolution apparatus (ERWEKA, DT600, Germany) at a paddle rotation speed of 50 rpm was employed to study drug release from the prepared tablets. The dissolution medium was 900 mL of phosphate buffer with a pH of 5.8 maintained at a temperature of 37 ± 0.5 °C45. six tablets were added into the dissolution medium in each cup, and 5 mL of samples were taken at a prescheduled time (5, 10, 15, 30, 60, 120 min). Each withdrawn sample was replaced with an equal volume of fresh dissolution medium. The withdrawn samples were filtered and suitably diluted, and UV absorbance was measured using UV-visible spectroscopy (UV-visible spectrophotometer, CM2203, Belarus) at 267 nm. The concentration was calculated from the formula obtained from the calibration curve.
Optimization of the variables
The optimization was done using the CCD, one of the most widely used techniques in response surface methodology (RSM). The study was conducted using the Design Expert software 13.0(Stat-Ease Inc., Minneapolis, MN, USA). RSM is a sequential procedure that starts with model fitness checking, the Analysis of variance (ANOVA) analysis for model adequacy, diagnosis, and ends with optimization and validation35. Among RSM methods, the CCD is the most widely used one as compared to its common alternatives, such as Box–Behnken which lacks augmentation with star points46. The CCD includes the minimum and maximum values with repeatable central points and two additional star points per factor. The central points are for the estimation of the pure errors, while the star points are used to navigate the quadratic relationship between the study variables and the response10. Therefore, the total experiment run was calculated by using the following formula as per the requirement of this design, which is 2n + 2n + no, where n is the number of factors and n number of repetitions. Therefore, the total numbers of experimental runs were summed up to be 20, substituting 3 for n and 6 for no, as the repetition for three independent variables is six47.
Optimization of independent factors for the desired response
Both numerical and graphical optimization techniques of the design expert software were used to find the optimal point48. Among various response variables, drug release at 30 min and Friability were selected for the optimization study. The criteria were set as a minimum for CM, ST, and friability, whereas the maximum criterion was selected for drug release, and the criterion for CF was left to be in the range between 50 and 100 N (Table 9).
Table 9.
The criteria set for the optimization of the Paracetamol tablet.
| Variables | Criteria | Goal |
|---|---|---|
| CF | 50–100 N | in range |
| ST | 5–15% | Minimum |
| CM | 3–10% | Minimum |
| Friability | 0–1% | Minimum |
| Drug Release at t-30 | 80–100% | Maximum |
Validation of the experimental design
The experimental design was validated through the comparison of the actual values obtained in the experiment with the predicted values given by the software, and the percent relative error was calculated using Eq. 9.
 |
9 |
Where PV stands for predicted values and EV stands for experimental values.
Statistical analysis
Origin 7 Software (Origin Lab Corporation, MA, USA) and Design Expert Software 13.0 (Stat-Ease Inc., Minneapolis, MN, USA) were used to statistically analyze the data. Results were compared using a one-way analysis of variance (ANOVA). P-values of < 0.05 were noted as statistically significant for a 95% confidence interval.
Conclusion
In this study, the CM was extracted from Cordia africana fruit and characterized as well as evaluated as a tablet binder in paracetamol tablet preparation. The FTIR test revealed that the fruit CM with paracetamol was found to be compatible. The result of the optimization experiment depicted that the role of the ST concentration was negligible on friability, while all three factors (CF, CM, and ST) were found to be determinants for drug release.
The optimization study showed that the optimal drug release at 30 min was 83.33% while the friability was found to be 0.63% for factor combinations of 76.7 N, 5.32%, and 5% for the CF, the CM, and the ST, respectively. The confirmatory run has also shown that all results are within 5% of the relative error, which ensured that the predictions made by the design expert software are realistic and reputable. In conclusion, the CM can be used as an alternative binder for tablets, and the optimization model was proven to be effective in identifying the optimum concentration of the CM along with other factors.
Acknowledgements
We acknowledged the institutions that supported us during the research.
Author contributions
Conceptualization, T.A.T., and F.M.; methodology, T.A.T, F.M., and N.M.J.; software, T.A.T. and N.M.J.; validation, F.M., and N.M.J.; formal analysis, T.A.T and F.M.; investigation, T.A.T, F.M, and N.M.J.; resources, T.A.T., F.M., and N.M.J.; data curation, T.A.T., F.M. and N.M.J; writing—original draft preparation, T.A.T., N.M.J and F.M.; writing—review and editing, T.A.T., F.M., and N.M.J.; visualization, T.A.T.; supervision, F.M., and N.M.J.; project administration N.M.J.
Funding
No funding available.
Data availability
The data supporting the findings of this study will be made available by the authors upon request. The corresponding author will be responsible for providing data at any time.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Debnath, S. et al. A review of natural binders used in pharmacy. Asian J. Pharm. Res.9 (1), 55–60 (2019). [Google Scholar]
- 2.Rajeswari, S. et al. Natural polymers: A recent review. World J. Pharm. Pharm. Sci.6 (8), 472–494 (2017). [Google Scholar]
- 3.Milivojevic, M., Pajic-Lijakovic, I., Bugarski, B., Nayak, A. K. & Hasnain, M. S. Gellan gum in drug delivery applications. Natural polysaccharides in drug delivery and biomedical applications. Jan1, 145–186 (2019). [Google Scholar]
- 4.National Center for Biotechnology Information (NCBI)[Internet]. Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information, 1–8 (1988).
- 5.Šimek, M., Grünwaldová, V. & Kratochvíl, B. Comparison of compression and material properties of differently shaped and sized paracetamols. Kona Powder Part. J.34, 197–206 (2017). [Google Scholar]
- 6.Al-Dulaimi, A., Al-kotaji, M. & Abachi, F. Paracetamol/naproxen co-crystals; a simple way for improvement of flowability, tableting and dissolution properties. Iraqi J. Pharm.18 (1), 1–9 (2021). [Google Scholar]
- 7.Gebreegziabher, S. T. Cordia africana (Lam.) fruit and its uses (Doctoral dissertation, PhD Thesis. Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway). 1-138 (2014). ISSN 1503–1667.
- 8.Kleijnen, J. P. Response surface methodology. In Handbook of Simulation Optimization 81–104 (Springer, 2014). [Google Scholar]
- 9.Zhou, W. et al. RSM-Optimized IRAE sample pretreatment and HPLC simultaneous determination of tryptanthrin, indigo, and indirubin from Chinese herbal medicine radix isatidis. Acta Chromatographica. 25 (2), 297–315 (2013). [Google Scholar]
- 10.Narukulla, S. et al. Comparative study between the full Factorial, Box–Behnken, and central composite designs in the optimization of metronidazole immediate release tablet. Microchem. J.207, 111875 (2024). [Google Scholar]
- 11.Kalegowda, P., Chauhan, A. S. & Urs, S. M. Opuntia dillenii (Ker-Gawl) Haw cladode mucilage: Physico-chemical, rheological, and functional behavior. Carbohydr. Polym.157, 1057–1064 (2017). [DOI] [PubMed] [Google Scholar]
- 12.Malallah, O. et al. Digital image disintegration analysis: A novel quality control method for fast disintegrating tablets. AAPS PharmSciTech. 22 (7), 219 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Islam Shishir, M. R., Taip, F. S., Aziz, N. A., Talib, R. A. & Hossain Sarker, M. S. Optimization of spray drying parameters for Pink guava powder using RSM. Food Sci. Biotechnol.25, 461–468 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Gebresamuel, N. & Gebre-Mariam, T. Comparative physico-chemical characterization of the mucilage of two cactus pears (Opuntia spp.) obtained from Mekelle, Northern Ethiopia. J. Biomater. Nanobiotechnol. 3 (8) (2012).
- 15.Poosarla, A. & Muralikrishna, R. Viscosity, swelling index, and moisture content in gum Karaya. Int. J. Sci. Res. (IJSR). 6 (4), 1189–1192 (2015). [Google Scholar]
- 16.Pawar, H. A., Gavasane, A. J. & Choudhary, P. D. Extraction of polysaccharides from fruits of cordia dichotoma G. Forst using the acid precipitation method and its physicochemical characterization. Int. J. Biol. Macromol.115, 871–875 (2018). [DOI] [PubMed] [Google Scholar]
- 17.Hashemi Gahruie, H. et al. Characterization of novel edible films and coatings for food preservation based on gum cordia. J. Food Qual.2020 (1), 8883916 (2020). [Google Scholar]
- 18.Keshani-Dokht, S., Emam-Djomeh, Z., Yarmand, M. S. & Fathi, M. Extraction, chemical composition, rheological behavior, antioxidant activity, and functional properties of cordia Myxa mucilage. Int. J. Biol. Macromol.118, 485–493 (2018). [DOI] [PubMed] [Google Scholar]
- 19.Pawar, H. A. & Lalitha, K. G. Extraction, characterization, and molecular weight determination of Senna Tora (L.) seed polysaccharide. Int. J. Biomaterials. 2015 (1), 928679 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Düsenberg, B., Schmidt, J., Sensoy, I. & Bück, A. Flowability of plant-based food powders: Almond, chestnut, chickpea, coconut, hazelnut and rice. J. Fooplant-based Eng.357, 111606 (2023). [Google Scholar]
- 21.Isaac, N., Owino, W., Ambuko, J. & Imathiu, S. Moisture sorption properties of two varieties of dehydrated Mango slices as determined by the gravimetric method using Guggenheim–Anderson–de Boer model. J. Food Process. Preserv.45 (1), e15041 (2021). [Google Scholar]
- 22.Kang, Y. R., Lee, Y. K., Kim, Y. J. & Chang, Y. H. Characterization and storage stability of chlorophylls microencapsulated in different combinations of gum Arabic and maltodextrin. Food Chem.272, 337–346 (2019). [DOI] [PubMed] [Google Scholar]
- 23.Wang, L., Xiang, D., Li, C., Zhang, W. & Bai, X. Effects of lyophilization and low-temperature treatment on the properties and conformation of Xanthan gum. Food Hydrocoll.112, 106352 (2021). [Google Scholar]
- 24.Shekunov, B. Y., Chattopadhyay, P., Tong, H. H. & Chow, A. H. Particle size analysis in pharmaceutics: principles, methods and applications. Pharm. Res.24, 203–227 (2007). [DOI] [PubMed] [Google Scholar]
- 25.Donghui, L., Tabil, L. G., Decheng, W., Guanghui, W. & Zhiqin, W. Optimization of binder addition and compression load for pelletization of wheat straw using response surface methodology. Int. J. Agricultural Biol. Eng.7 (6), 67–78 (2014). [Google Scholar]
- 26.Borhani, M. R. & Kermani, F. Investigating the method of tungsten leaching from hydrocracking catalyst HC-102 W/Ni/Al2O3/SiO2. Results Eng.23, 102488 (2024). [Google Scholar]
- 27.Abdel-Raoof, A. M. et al. Fabrication of an (α-Mn 2 O 3: Co)-decorated CNT highly sensitive screen printed electrode for the optimization and electrochemical determination of Cyclobenzaprine hydrochloride using response surface methodology. RSC Adv.10 (42), 24985–24993 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Bunny, R. S., Pathak, Y. D., Arya, D. & Dutt, N. Optimization of gasoline yield in FCC riser unit using RSM. Res. J. Pharm. Biol. Chem. Sci.6 (4), 1269–1278 (2015). [Google Scholar]
- 29.Ojewumi, M. E., Oyekunle, D. T., Ekanem, G. P., Obanla, O. R. & Owolabi, O. M. Extraction of oil from selected plants using response surface methodology [RSM]. J. Phys. Conf. Ser.1378(4), 042019 (2019). [Google Scholar]
- 30.Bo, R. et al. Optimization of conditions of lycium barbarum polysaccharides liposome by RSM and its effects on the peritoneal macrophages function. Carbohydr. Polym.117, 215–222 (2015). [DOI] [PubMed] [Google Scholar]
- 31.Bhasin, R. K. & Ghosh, P. K. Design and development of Ondansetron orally disintegrating tablets and their optimization using the design of experiments. Int. J. Pharm. Sci. Res.3 (3), 840 (2012). [Google Scholar]
- 32.Deshmukh, R. K. & Naik, J. B. Diclofenac sodium-loaded Eudragit® microspheres: optimization using statistical experimental design. J. Pharm. Innov.8, 276–287 (2013). [Google Scholar]
- 33.Saeid, S., Behnajady, M. A., Tolvanen, P. & Salmi, T. Optimization of photooxidative removal of phenazopyridine from water. Russ. J. Phys. Chem. A. 92, 876–883 (2018). [Google Scholar]
- 34.Xie, Y., Chen, L. & Liu, R. Oxidation of AOX and organic compounds in pharmaceutical wastewater in RSM-optimized-Fenton system. Chemosphere155, 217–224 (2016). [DOI] [PubMed] [Google Scholar]
- 35.Pankaj, V. P. & Awasthi, M. Optimization of growth condition for chlorella vulgaris using response surface methodology (rsm). Int. J. Eng. Sci. Adv. Technol.4 (5), 492–500 (2014). [Google Scholar]
- 36.Huiduan, L. & Jianzhong, Y. Optimal Enzyme-Assisted ethanol extraction of flavonoids from broccoli by RSM and research on antioxidant effects. Chem. Bimolecular Eng.1 (1), 12–20 (2016). [Google Scholar]
- 37.Shahabipour, S. & Bohlooli, S. Modeling/Optimization of a RP-HPLC method via response surface strategy: A case study on the analysis of Paracetamol in tablet dosage forms. Drug Adv. Sci. J.1 (1), 9–16 (2019). [Google Scholar]
- 38.Vidyasagar, G., Jadhav, A. G., Narkhede, S. P. & Narkhede, S. B. Isolation and comparative evaluation of cordia dichotoma Forst. Mucilage as a binding agent with a standard binder. J. Chem. Pharm. Res.2 (4), 722–726 (2010). [Google Scholar]
- 39.Patil, D. N., Kulkarni, A. R., Hatapakki, B. C. & Patil, B. S. Preparation and evaluation of cordia fruit gum as tablet binder. Phcog Net. 1, 143–145 (2009). [Google Scholar]
- 40.Traina, K. et al. Flow abilities of powders and granular materials evidenced from dynamical tap density measurement. Powder Technol.235, 842–852 (2013). [Google Scholar]
- 41.Kalman, H. Effect of moisture content on flowability: angle of repose, tilting angle, and Hausner ratio. Powder Technol.393, 582–596 (2021). [Google Scholar]
- 42.Apeji, Y. E. et al. Effect of mode of superdisintegrant incorporation on tableting properties of metronidazole granules. Pharm. Sci. Asia. 46, 25–32 (2019). [Google Scholar]
- 43.Kebebe, D., Belete, A. & Gebre-Mariam, T. Evaluation of two olibanum resins as rate-controlling matrix-forming excipients in oral sustained release tablets. Ethiop. Pharm. J.28 (2), 95–109 (2010). [Google Scholar]
- 44.Segun, P., Ajala, O. T. & Aremu, O. I. The effect of formulation variables on the release kinetics of Paracetamol tablet formulations. J. Pharm. Health Sci.6 (2), 103–111 (2018). [Google Scholar]
- 45.USP. Dissolution Methods Database. In United States Pharmacopeia 42 – National Formulary 37, Second Supplement. Rockville, MD: United States Pharmacopeial Convention (2019).
- 46.Almasi, A. et al. Removal of penicillin G by a combination of sonolysis and photocatalytic (sonophotocatalytic) process from aqueous solution: process optimization using RSM (Response surface Methodology). Electron. Physician. 8 (9), 2878 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Muluken Nigatu, M. N., Nisha Mary Joseph, N. M. & Anteneh Belete, A. B. Evaluation, formulation, and optimization study of myrrh (Commiphora myrrha) resin as a rate-controlling excipient in sustained release matrix tablets of Theophylline. Br. J. Pharm. Res.8 (6), 20780 (2015). [Google Scholar]
- 48.Elhawi, M. M., Hassan, W. S., El-Sheikh, R. & El-Sayed, H. M. Multivariate analysis of perampanel in pharmaceutical formulations using RP-HPLC. Chromatographia83, 1335–1343 (2020). [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting the findings of this study will be made available by the authors upon request. The corresponding author will be responsible for providing data at any time.