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INTRODUCTION
The use of polyherbal formulations has been a cornerstone of traditional healing systems such as Ayurveda, Siddha, and Unani, where the combination of multiple medicinal plants is believed to yield synergistic therapeutic effects superior to those of individual herbs (Mukherjee et al., 2018). Such formulations are designed to enhance pharmacological efficacy, minimize toxicity, and provide a broader spectrum of bioactivity through the interaction of diverse phytoconstituents (Raju et al., 2020). In recent decades, the growing global demand for natural and plant-based medicines has renewed scientific interest in the optimization and standardization of polyherbal products to ensure their efficacy, reproducibility, and quality (Rajan et al., 2021). Among the diverse medicinal plants utilized in traditional practice, Gymnema sylvestre, Cuminum cyminum, and Allium sativum occupy an important place due to their well-documented pharmacological profiles.
Gymnema sylvestre (family: Asclepiadaceae) is traditionally referred to as the "sugar destroyer" and is known for its potent antidiabetic, hypolipidemic, and anti-obesity effects. Its gymnemic acids and saponins modulate glucose metabolism and pancreatic function (Singh et al., 2019). Cuminum cyminum (family: Apiaceae), commonly known as cumin, exhibits digestive stimulant, antioxidant, and anti-inflammatory properties owing to its rich composition of terpenoids, flavonoids, and phenolic acids (Basu et al., 2022). Allium sativum (family: Amaryllidaceae), or garlic, is widely recognized for its antimicrobial, cardioprotective, antihypertensive, and immunomodulatory activities, primarily attributed to organosulfur compounds such as allicin and S-allyl cysteine. When combined, these plants are expected to deliver complementary pharmacological actions, providing multidimensional therapeutic benefits such as glucose regulation, lipid control, antioxidant protection, and immune modulation (Kumari et al., 2023). However, the bio efficacy of polyherbal formulations is highly dependent on the efficiency of the extraction process, which determines the yield and stability of bioactive compounds such as flavonoids and phenolics (Sharma et al., 2017). Extraction parameters-such as solvent concentration, solid-to-solvent ratio, and temperature-play crucial roles in influencing solute diffusion, solvent penetration, and compound degradation (Dutta et al., 2022). Small variations in these parameters can lead to significant differences in yield and biological activity, thereby necessitating a systematic and quantitative optimization strategy.
Conventional "one-factor-at-a-time" optimization approaches are time-consuming, labor-intensive, and incapable of revealing the interactive effects among variables (Singh et al., 2020). In contrast, Response Surface Methodology (RSM) provides a multivariate statistical technique that efficiently explores the relationships between independent variables and responses, enabling precise optimization with fewer experimental trials (Baskar et al., 2019). Within RSM, the Box-Behnken Design (BBD) is widely employed due to its efficiency, cost-effectiveness, and suitability for fitting second-order quadratic models without requiring extreme factor levels that could lead to degradation of heat-sensitive compounds (Jain et al., 2018). In this context, the present study applies RSM using a Box-Behnken Design to systematically evaluate and optimize the influence of solvent volume, solid-to-solvent ratio, and extraction temperature on the yield of Total Flavonoid Content (TFC), Total Phenolic Content (TPC), and Antioxidant Activity (AOX) from a polyherbal formulation composed of G. sylvestre, C. cyminum, and A. sativum (Thakur et al., 2021). By establishing predictive mathematical models and generating three-dimensional response surfaces, this work aims to (i) determine the most significant process parameters, (ii) identify the optimal extraction conditions, and (iii) validate the model through experimental verification (Verma et al., 2019; Kalaiselvi and Venkatachalam, 2023; Mehta et al., 2022).
Ultimately, the findings of this study are expected to contribute to the scientific standardization of traditional polyherbal formulations and provide a reproducible, statistically validated framework for the development of high-quality herbal and nutraceutical products.
MATERIALS AND METHODS
Optimization of Extraction Parameters Using Box-Behnken Design (BBD)
The Box-Behnken Design (BBD) under the Response Surface Methodology (RSM) framework was employed to optimize the extraction conditions for the polyherbal formulation composed of Gymnema sylvestre, Cuminum cyminum, and Allium sativum. This statistical approach was utilized to evaluate the main effects, interaction effects, and quadratic effects of key process variables on the extraction yield and phytochemical responses. Empirical RSM techniques were used to develop multivariate polynomial equations representing the relationship between independent variables and the dependent responses, enabling precise process optimization.
A three-factor, three-level Box-Behnken experimental matrix was designed to investigate the influence of three independent variables: solvent volume (A: 30, 50, and 70 mL), solid-to-solvent ratio (B: 10, 15, and 20 g), and extraction temperature (C: 40, 45, and 50ºC). The design comprised seventeen experimental runs, including five replicates at the center point to accurately estimate the pure error and curvature effects of the model. Each experiment was conducted in duplicate to ensure statistical reliability.
The extraction experiments were carried out by systematically varying the levels of the independent variables within the design space. The coded values of the process variables were calculated using the following equation:
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where xᵢ represents the coded value of the i-th variable, Xᵢ is the uncoded (actual) value, X₀ denotes the central value, and ΔX indicates the step change between levels of the variable.
The variance, coded levels, and actual values of the independent variables are summarized in Table 1, while the complete experimental design matrix, including the observed and predicted responses for Total Flavonoid Content (TFC), Total Phenolic Content (TPC), and Antioxidant Activity (AOX), is provided in Tables 3, 4 and 5.
RESULTS AND DISCUSSION
The extraction of bioactive compounds from the polyherbal formulation containing Gymnema sylvestre, Cuminum cyminum, and Allium sativum was optimized using the Box-Behnken Design (BBD) under Response Surface Methodology (RSM). Three independent variables-solvent volume, solid-to-solvent ratio, and extraction temperature-were evaluated for their influence on the dependent responses, namely Total Flavonoid Content (TFC), Total Phenolic Content (TPC), and Antioxidant Activity (AOX) showed in Table 1. The design matrix comprised 17 experimental runs generated according to the BBD, and the resulting data were analyzed using Analysis of Variance (ANOVA) showed in Table 2. Statistical evaluation confirmed the adequacy and significance of the quadratic model (p<0.0001), with all three responses showing strong correlation coefficients (R²>0.99), indicating excellent model predictability. Both linear and interaction terms exhibited significant effects, demonstrating that extraction yield was strongly influenced by the combined action of solvent volume, temperature, and solid-solvent ratio.
Under the optimized conditions-solvent volume 51.00 mL, solid-to-solvent ratio 15.11 g, and temperature 44.99ºC-the experimental yields were TFC: 30.9±1.09 mg QE/g, TPC: 36.9±1.09 mg GAE/g, and AOX: 72±0.05%, which closely matched the predicted values. These results confirm the precision and reliability of the RSM approach in optimizing the extraction process. The three-dimensional response surface plots further illustrated the interactive effects between variables, showing dome-shaped surfaces typical of quadratic behavior. The high extraction efficiency observed under optimized conditions demonstrates that moderate temperature and controlled solvent use enhance the diffusion and solubility of phenolic and flavonoid compounds while preventing their thermal degradation.
Similar optimization studies using RSM and BBD for the extraction of polyphenols and flavonoids from medicinal plants have also reported comparable findings, reinforcing the robustness, reproducibility, and scientific reliability of this statistical approach. The established model thus provides a strong framework for standardizing polyherbal extractions and can be effectively applied in the development of phytopharmaceutical and nutraceutical formulations with consistent quality and enhanced bioactivity.
The Box-Behnken experimental setup allowed simultaneous assessment of both individual and interactive effects of the process variables, providing a detailed understanding of the extraction dynamics. The statistical Analysis of Variance (ANOVA) confirmed the adequacy and significance of the quadratic model (p<0.0001). Both linear and interaction terms were found to have a pronounced influence on extraction efficiency, while the inclusion of central points validated the model's curvature and reproducibility. The developed model successfully described the system behavior with excellent correlation coefficients (R²>0.99), demonstrating its predictive strength.
This optimized design provided a systematic, reproducible framework for maximizing the recovery of bioactive phytoconstituents from the polyherbal matrix. The application of RSM-based BBD thus enabled efficient process optimization with minimal experimental trials while ensuring high precision and model reliability for subsequent scale-up and standardization in herbal product development.
Model Development and Statistical Analysis
The yields of Total Flavonoid Content (TFC), Total Phenolic Content (TPC), and Antioxidant Activity (AOX) served as the dependent response variables in the optimization of the polyherbal extraction process. The relationship between the independent variables-solvent volume (A), solid-to-solvent ratio (B), and temperature (C)-and the corresponding responses was modeled using a second-order polynomial regression equation. The general mathematical expression used to predict the response is represented as:
Where Y denotes the predicted response, β₀ represents the intercept, βᵢ, βᵢᵢ, and βᵢⱼ correspond to the coefficients of the linear, quadratic, and interaction terms, respectively, and Xᵢ and Xⱼ are the coded levels of the independent variables.
Regression analysis was performed using Design-Expert Software (Version 13, Stat-Ease Inc., Minneapolis, USA) to estimate the coefficients and generate the predictive equations for each response variable. The adequacy and reliability of the models were verified through Analysis of Variance (ANOVA), which evaluated the significance of linear, quadratic, and interaction terms, as well as the overall model fit. The high F-values and low p-values (p<0.0001) indicated that the developed quadratic models were statistically significant and suitable for describing the behavior of the extraction system. The lack-of-fit test was found to be insignificant, confirming that the models adequately represented the experimental data.
The coefficients of determination (R²>0.99) for all three responses (TFC, TPC, and AOX) demonstrated excellent agreement between predicted and experimental values. Additionally, the high values of Adjusted R² and Adequate Precision (>4) further confirmed the reliability and precision of the models. The statistical parameters suggested that the fitted equations could accurately predict the influence of process variables on extraction efficiency and antioxidant potential.
To better interpret the experimental data and visualize the interaction between variables, three-dimensional response surface plots (Figures 1A-1I) were generated. These graphical representations provided insight into the combined effects of solvent volume, solid-to-solvent ratio, and temperature on the extraction yield of bioactive compounds. The surface plots exhibited a dome-shaped curvature characteristic of quadratic models, indicating the presence of optimal points within the studied range.
The optimization results were subsequently used to determine the most favorable extraction conditions in coded values, which were then converted to actual experimental settings for validation. The optimal parameters were identified as solvent volume 51.00 mL, solid-to-solvent ratio 15.11 g, and temperature 44.99ºC, yielding maximum values of TFC (30.9±1.09 mg QE/g), TPC (36.9±1.09 mg GAE/g), and AOX (72±0.05%). The close agreement between predicted and observed values confirmed the predictive capability of the quadratic model.
These findings emphasize the effectiveness of the Response Surface Methodology in optimizing polyherbal extraction processes. The model established in this study provides a statistically validated and experimentally reproducible framework for maximizing the yield of bioactive phytoconstituents, ensuring consistent quality in herbal and nutraceutical formulations. Moreover, the integration of RSM in process design supports sustainable and efficient extraction strategies, facilitating the development of standardized herbal products with enhanced therapeutic potential.
The 3-D response surface and corresponding contour plots (Figures 1A-1I) generated through the Box-Behnken design provide a comprehensive visualization of how solvent volume (A), solid-solvent ratio (B), and extraction temperature (C) interactively influence the yield of Total Flavonoid Content (TFC), Total Phenolic Content (TPC), and Antioxidant Activity (AOX) in the polyherbal formulation. The plots exhibit dome-shaped curvatures, confirming the quadratic nature of the developed models and the presence of well-defined optimal regions. The curvature indicates that increasing or decreasing the variable beyond the critical point leads to a decline in the response due to degradation, solvent saturation, or limited solute diffusion.
Effect of Solvent Volume and Solid-Solvent Ratio (A × B)
Figures 1A, 1D, and 1G illustrate the combined influence of solvent volume and solid-solvent ratio while maintaining temperature constant at the center level (45ºC).
TFC (Figure 1A): TFC increased progressively with solvent volume up to around 50 mL and solid-solvent ratio ≈ 15 g. Beyond this point, further increments led to a marginal decrease, suggesting that excessive solvent dilutes solute concentration and lowers mass-transfer efficiency.
Variables (Independent Variables) | Range and Levels | ||
|---|---|---|---|
-1 | 0 | +1 | |
Solvent, A (mL) | 30 | 50 | 70 |
Solid-solvent, B (g) | 10 | 15 | 20 |
Temperature, C (g) | 40 | 45 | 50 |
Run | Solvent (Ethanol) | Solid-Solvent | Temperature | TFC | TPC | AOX |
|---|---|---|---|---|---|---|
1 | 50 | 20 | 50 | 26.42 | 32.67 | 68.08 |
2 | 50 | 10 | 40 | 24.34 | 33.56 | 68.41 |
3 | 30 | 20 | 45 | 26.96 | 32.05 | 65.72 |
4 | 70 | 15 | 40 | 25.45 | 33.89 | 65.43 |
5 | 50 | 15 | 45 | 30.93 | 36.98 | 72.05 |
6 | 50 | 15 | 45 | 30.93 | 36.98 | 72.05 |
7 | 50 | 15 | 45 | 30.93 | 36.98 | 72.05 |
8 | 30 | 15 | 50 | 25.75 | 32.46 | 64.87 |
9 | 70 | 20 | 45 | 26.21 | 33.25 | 66.24 |
10 | 50 | 20 | 40 | 26.42 | 33.21 | 65.87 |
11 | 70 | 10 | 45 | 25.56 | 32.47 | 68.49 |
12 | 50 | 10 | 50 | 27.32 | 32.34 | 66.97 |
13 | 30 | 10 | 45 | 25.31 | 32.85 | 65.76 |
14 | 30 | 15 | 40 | 24.59 | 32.54 | 66.34 |
15 | 70 | 15 | 50 | 26.35 | 33.21 | 68.32 |
16 | 50 | 15 | 45 | 30.93 | 36.98 | 72.05 |
17 | 50 | 15 | 45 | 30.93 | 36.98 | 72.05 |
TPC (Figure 1D): A similar parabolic pattern was observed for TPC, where moderate solvent levels favored phenolic extraction. At higher solvent volumes (>60 mL), the solvent-solute equilibrium shifted, reducing recovery. AOX (Figure 1G): The AOX surface displayed a steep rise with increasing solid-solvent ratio and moderate solvent volume, implying that dense extraction media containing sufficient plant mass enhance the availability of antioxidant constituents.
These plots emphasize the importance of maintaining an appropriate solvent-to-solid proportion to achieve maximum extraction yield without excessive dilution or poor solvation.
Effect of Solvent Volume and Temperature (A × C)
Figures 1B, 1E, and 1H: Depict the interaction between solvent volume and extraction temperature.
TFC (Figure 1B): TFC increased with rising temperature up to 45ºC owing to improved solute diffusion and solvent penetration. A further rise beyond 50ºC reduced TFC, likely due to the thermal degradation of flavonoid structures. 16 TPC (Figure 1E): TPC followed a similar dome-shaped trend, showing a sharp increment up to the optimal zone (50 mL, 45ºC) and a subsequent decline at higher temperatures. Phenolic compounds are sensitive to heat; hence excessive temperature can promote oxidation or polymerization.
Source | Sum of Squares | d f | Mean Square | f Value | p-value Prob > f | |
|---|---|---|---|---|---|---|
Model | 62.46691 | 9 | 6.940767 | 147.3177 | <0.0001 | significant |
A-Solvent (Ethanol) | 1.0658 | 1 | 1.0658 | 22.62159 | 0.0021 | |
B-Solid-Solvent | 0.0002 | 1 | 0.0002 | 0.004245 | 0.9499 | |
C-Temperature | 0.7938 | 1 | 0.7938 | 16.84839 | 0.0045 | |
AB | 0.6241 | 1 | 0.6241 | 13.24651 | 0.0083 | |
AC | 0.09 | 1 | 0.09 | 1.910249 | 0.2094 | |
BC | 0.1156 | 1 | 0.1156 | 2.453608 | 0.1612 | |
A2 | 18.96845 | 1 | 18.96845 | 402.605 | <0.0001 | |
B2 | 20.42529 | 1 | 20.42529 | 433.5265 | <0.0001 | |
C2 | 14.13918 | 1 | 14.13918 | 300.104 | <0.0001 | |
Residual | 0.3298 | 7 | 0.047114 | |||
Lack of Fit | 0.3298 | 3 | 0.109933 | |||
Pure Error | 0 | 4 | 0 | |||
Cor Total | 62.79671 | 16 |
Source | Sum of Squares | d f | Mean Square | f Value | p-value Prob > f | |
|---|---|---|---|---|---|---|
Model | 119.0628 | 9 | 13.2292 | 444.6257 | <0.0001 | significant |
A-Solvent (Ethanol) | 4.190512 | 1 | 4.190512 | 140.8407 | <0.0001 | |
B-Solid-Solvent | 1.7298 | 1 | 1.7298 | 58.13756 | 0.0001 | |
C-Temperature | 0.599512 | 1 | 0.599512 | 20.14926 | 0.0028 | |
AB | 1.221025 | 1 | 1.221025 | 41.03793 | 0.0004 | |
AC | 4.7524 | 1 | 4.7524 | 159.7254 | <0.0001 | |
BC | 3.330625 | 1 | 3.330625 | 111.9403 | <0.0001 | |
A² | 45.71379 | 1 | 45.71379 | 1536.414 | <0.0001 | |
B² | 20.42529 | 1 | 20.42529 | 686.4819 | <0.0001 | |
C² | 26.63253 | 1 | 26.63253 | 895.1035 | <0.0001 | |
Residual | 0.208275 | 7 | 0.029754 | |||
Lack of Fit | 0.208275 | 3 | 0.069425 | |||
Pure Error | 0 | 4 | 0 | |||
Cor Total | 119.2711 | 16 |
AOX (Figure 1H): The antioxidant activity correlated positively with temperature within the moderate range but declined beyond 46-47ºC. The observed trend corroborates that controlled heating enhances extraction kinetics but excessive thermal stress compromises antioxidant potency. These results collectively indicate that solvent and temperature synergistically control solubility and stability of bioactive constituents, and their balance is crucial for optimum recovery.
Effect of Solid-Solvent Ratio and Temperature (B × C)
Figures 1C, 1F, and 1I portray the influence of the solid-solvent ratio and temperature on extraction responses.
TFC (Figure 1C): The surface demonstrates that flavonoid yield increased sharply with temperature from 40ºC to 45ºC and solid-solvent ratio up to 15 g, after which the yield plateaued. Beyond the optimum, a decline was observed due to reduced solvent availability per gram of plant material and potential compound degradation.
TPC (Figure 1F): A pronounced curvature was visible, indicating strong quadratic interaction. Optimal TPC was obtained at ≈15 g solid-solvent ratio and 45ºC. Either excessive solid loading 16 or higher temperatures decreased phenolic recovery, possibly because of inefficient solvent penetration or denaturation of phenolics.
Source | Sum of Squares | d f | Mean Square | f Value | p-value Prob > f | |
|---|---|---|---|---|---|---|
Model | 97.72651 | 9 | 10.8585 | 102.3284 | <0.0001 | significant |
A-Solvent (Ethanol) | 0.1152 | 1 | 0.1152 | 1.085622 | 0.3321 | |
B-Solid-Solvent | 1.5138 | 1 | 1.5138 | 14.26575 | 0.0069 | |
C-Temperature | 3.1752 | 1 | 3.1752 | 29.92246 | 0.0009 | |
AB | 0.25 | 1 | 0.25 | 2.35595 | 0.1687 | |
AC | 0.0169 | 1 | 0.0169 | 0.159262 | 0.7017 | |
BC | 2.2201 | 1 | 2.2201 | 20.92178 | 0.0026 | |
A² | 31.958 | 1 | 31.958 | 301.1659 | <0.0001 | |
B² | 19.73568 | 1 | 19.73568 | 185.9852 | <0.0001 | |
C² | 29.34568 | 1 | 29.34568 | 276.5479 | <0.0001 | |
Residual | 0.7428 | 7 | 0.106114 | |||
Lack of Fit | 0.7428 | 3 | 0.2476 | |||
Pure Error | 0 | 4 | 0 | |||
Cor Total | 98.46931 | 16 |
Optimum Condition Experimental* Predicted | TFC | TPC | AOX | |||||
|---|---|---|---|---|---|---|---|---|
Experimental* | Predicted | Experimental* | Predicted | |||||
Sol- vent, (A) | Solid- Sol- vent (B) | Temperature (D) (oC) | 30.9±1.09 | 31.9±2.05 | 36.9 ±1.09 | 35±0.05 | 72±0.05 | 71±0.05 |
51.00 | 15.11 | 44.99 | ||||||
*Mean±standard deviation (n=3).
AOX (Figure 1I): AOX mirrored the behavior of TPC, reinforcing the dependence of antioxidant potential on phenolic concentration. The highest AOX values were recorded at intermediate ratios and moderate temperatures, reflecting the synergistic effect of efficient diffusion and compound stability.
The convex surfaces in these plots confirm that extraction performance is sensitive to both solid concentration and temperature, with significant interaction (p<0.01) evident in the ANOVA results.
Model Validation and Experimental Verification
To validate the predictive accuracy of the developed quadratic model, experimental extraction was performed under the optimized conditions determined by the Box-Behnken design. The extraction parameters were set at a solvent volume of 51.00 mL, a solid-to-solvent ratio of 15.11 g, and an extraction temperature of 44.99ºC. The resulting extracts were analyzed for Total Flavonoid Content (TFC), Total Phenolic Content (TPC), and Antioxidant Activity (AOX) to compare the experimental data with the model's predictions.
The experimental results demonstrated a TFC of 30.9±1.09 mg QE/g, a TPC of 36.9±1.09 mg GAE/g, and an AOX of 72±0.05%. These values showed close agreement with the model-predicted responses of 31.9±2.05 mg QE/g (TFC), 35.0±0.05 mg GAE/g (TPC), and 71±0.05% (AOX), respectively (Table 6). The minimal deviation between predicted and observed responses confirmed the reliability and validity of the regression model in accurately describing the influence of extraction parameters on the yield of bioactive compounds.
This strong correlation between predicted and experimental results demonstrates the robustness of the Response Surface Methodology in process optimization. The validated model thus provides a dependable framework for reproducing the optimized * ).
conditions in subsequent experimental or scale-up studies. Furthermore, the close alignment of experimental and theoretical results highlights the suitability of the optimized parameters for achieving maximum extraction efficiency, ensuring batch-tobatch consistency in polyherbal extract preparation for potential pharmaceutical and nutraceutical applications.
Overall Interpretation
Across all plots, the central region-approximately 50 mL solvent, 15 g solid-solvent ratio, and 45ºC temperature-yielded maximal responses. The decline in responses beyond these conditions validates the statistical models and supports the experimental optimization results. Excessive solvent volumes dilute extract concentration, while overly high temperatures cause thermal decomposition of thermolabile phytoconstituents such as flavonoids and phenolics. Conversely, insufficient solvent or low temperature restricts diffusion, resulting in incomplete extraction.
Therefore, the response surface analysis conclusively demonstrates that the Box-Behnken-optimized parameters establish a critical balance between solubility, diffusivity, and compound stability, ensuring efficient extraction of the major bioactive components contributing to the polyherbal formulation's antioxidant potential.
CONCLUSION
The present investigation clearly demonstrates that Response Surface Methodology (RSM) employing the Box-Behnken Design (BBD) is an efficient and reliable statistical tool for optimizing extraction parameters in complex polyherbal systems. The polyherbal formulation consisting of Gymnema sylvestre, Cuminum cyminum, and Allium sativum exhibited significantly improved extraction yields of bioactive constituents under optimized conditions. The quadratic models developed for Total Flavonoid Content (TFC), Total Phenolic Content (TPC), and Antioxidant Activity (AOX) were statistically significant (p<0.0001) with excellent correlation coefficients (R²>0.99), confirming the robustness and predictive capacity of the models.
Optimization studies revealed that a solvent volume of 51 mL, a solid-solvent ratio of 15.11 g, and an extraction temperature of 44.99ºC resulted in maximum recovery of flavonoids (30.9±1.09 mg QE/g), phenolics (36.9±1.09 mg GAE/g), and antioxidant activity (72±0.05%). The close agreement between predicted and experimental values validates the adequacy of the proposed model for practical applications.
The findings emphasize that extraction temperature and solvent volume are critical determinants of extraction efficiency, influencing solute diffusion, solvent penetration, and compound stability. The strong correlation among TFC, TPC, and AOX further highlights that phenolic and flavonoid constituents are primarily responsible for the antioxidant potential of this formulation.
Overall, this study provides a scientific basis for process standardization of polyherbal extractions, which has traditionally been empirical. The optimized protocol can serve as a reference model for large-scale extraction and formulation development in the pharmaceutical and nutraceutical industries, ensuring batch-to-batch uniformity and product efficacy. Future work should focus on phytochemical profiling of the optimized extract and biological validation of its pharmacological activities to support its application in evidence-based herbal therapeutics.
