Greenness Assessment of High-Performance Liquid Chromatography Method for Simultaneous Estimation of Apigenin, Apocynin and Gallic Acid with Quality by Design Approach

doi:10.21203/rs.3.rs-4962953/v1

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Greenness Assessment of High-Performance Liquid Chromatography Method for Simultaneous Estimation of Apigenin, Apocynin and Gallic Acid with Quality by Design Approach

https://doi.org/10.21203/rs.3.rs-4962953/v1

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This study presents the development of an eco-friendly high-performance liquid chromatography (HPLC) method for the simultaneous determination of apigenin, apocynin, and gallic acid, integrating Analytical Quality by Design (AQbD) and Green Analytical Chemistry (GAC) principles. The objective was to enhance method efficiency while reducing environmental impact, energy consumption, and solvent use. An AQbD approach was employed, starting with quality risk assessment and scouting analysis, followed by optimization using Box–Behnken Design (BBD) to screen three key chromatographic parameters. The optimal chromatographic conditions were determined using a C18 Column (250 mm × 4.6 mm, 5 μm) with a mobile phase of Methanol: Water (80:20 %v/v), a flow rate of 1 mL/min, UV detection at 228 nm, and a temperature of 25 °C. The method demonstrated linearity over a concentration range of 1–5 μg/mL for the target compounds, with gallic acid as an internal standard. The method’s greenness profile was assessed using various evaluation tools, confirming its compliance with green analytical chemistry principles. The results indicate that the proposed HPLC method is not only effective and reliable but also environmentally sustainable, making it suitable for routine use in quality control laboratories. The integration of AQbD and GAC principles highlights the method's contribution to more sustainable and efficient analytical practices.

Apigenin

Gallic acid

Apocynin

High-Performance Liquid Chromatography

Quality by Design

Herbal formulation

Green Analytical Chemistry

Apigenin is a flavonoid widely found in many fruits, vegetables, and herbs. It has been the subject of numerous studies exploring its anti-inflammatory, antioxidant, anti-apoptotic, and anti-tumor properties. However, its effect on acute liver injury caused by carbon tetrachloride (CCl4) remains unclear. In this study, we examined the role of apigenin in CCl4-induced acute liver injury, with a focus on its impact on inflammation and oxidative stress. Additionally, we investigated the involvement of the noncanonical NF-κB pathway to better understand the mechanism by which apigenin exerts its effects (Yue et al., 2020). Apocynin, also known as Acetovanillone, is a natural compound structurally similar to vanillin. It was initially discovered in the Picrorhiza kurroa plant during efforts to isolate immune-modulating components from the plant extract. Apocynin is recognized for its potent antioxidant properties. Its treatment has been shown to significantly reduce systemic oxidative stress, suppress hepatic lipid peroxidation, and enhance the body's overall antioxidant capacity (Rahman et al., 2017). Gallic acid, also referred to as 3,4,5-trihydroxybenzoic acid, is one of the most abundant phenolic acids found within the plant kingdom. (Anand et al., 1997). Various chromatographic techniques have been employed to isolate gallic acid from a wide range of plant species, including those from the Quercus and Punica genera. (Fig. 1).

A review of the literature suggests that several analytical techniques, such as UV spectroscopy, HPLC, and HPTLC, have been effective in estimating individual compounds like apigenin, apocynin, and gallic acid in polyherbal formulations. However, there appears to be no established method for simultaneously estimating apigenin, apocynin, and gallic acid using high-performance liquid chromatography (HPLC) with a greenness quality by design approach in hepatoprotective polyherbal formulations. (Li et al., 2005; Cai et al., 2006; Ting et al., 2007; Shen et al., 2010; Sawant et al., 2010; Watak et al., 2012; Wang et al., 2013; Kardani et al., 2013; Rajasekaran et al., 2014; Mesquita et al., 2015; Mallick et al., 2015; Kučera et al., 2016; Byun et al., 2016; Sharafi et al., 2016; de Oliveira et al., 2017; Zahiruddin et al., 2017b; Zahiruddin et al., 2017a; Liang et al., 2019; Gomathy et al., 2020; Jeong Kim et al., 2021; Parikh & Kothari, 2021; Shetti & Jalalpure, 2021; Mevada, Patel, et al., 2024; Mevada, Shukla, et al., 2024; Sawant & Chavan, n.d.).

The primary goal of the Analytical Quality by Design (AQbD) approach is to establish meaningful product quality specifications that are directly linked to clinical performance. This approach involves a systematic and proactive process that integrates quality considerations throughout the entire product lifecycle, from initial conception to final production. Pharmaceutical Quality by Design (QbD) has evolved with the introduction of key guidelines, including ICH Q8 (R2) for Pharmaceutical Development, ICH Q9 for Quality Risk Management, and ICH Q10 for Pharmaceutical Quality Systems (Patel et al., 2023).

In the context of analytical methods, green chemistry, or "greenness," refers to the practice of designing methods that minimize or eliminate the use of hazardous substances or the generation of harmful byproducts. Green Analytical Chemistry (GAC) focuses on developing analytical procedures that not only effectively determine specific compounds in a sample but also emphasize the reduction of hazardous substances. This approach incorporates principles such as atom economy and energy efficiency to create more environmentally friendly and sustainable analytical methods (Sangshetti et al., 2017).

A greenness assessment of analytical procedures is crucial because not all techniques exhibit the same level of environmental friendliness. The environmental impact of a proposed method can be evaluated using tools like the Green Analytical Procedure Index (GAPI) and the AGREE metrics. These metrics help to assess and quantify the degree to which an analytical approach aligns with the principles of green chemistry, ensuring that the method minimizes environmental harm while maintaining analytical effectiveness (Pena-Pereira et al., 2020; Płotka-Wasylka & Wojnowski, 2021). The Blue Applicability Grade Index (BAGI) is a metric used to assess the practicality of a method in analytical chemistry. This tool evaluates how feasible and applicable a method is by assigning a score ranging from 25 to 100, with higher scores indicating greater practicality and ease of implementation in real-world scenarios (Manousi et al., 2023). The Blue Applicability Grade Index (BAGI) can effectively highlight the strengths and weaknesses of an analytical method in terms of its practicality and facilitate comparisons between different methods. Utilizing Quality by Design (QbD) principles in the development of greener analytical techniques provides valuable insights into the use of environmentally friendly chemicals and their impact on method performance. According to ICH guidelines (ICH Q8), QbD is defined as "a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and control, based on sound science and quality risk management." In the context of analytical methods, Analytical Quality by Design (AQbD) focuses on defining the quality of the analytical target profile (ATP), identifying Critical Quality Attributes (CQAs) and Critical Analytical Attributes (CAAs), and optimizing these attributes for optimal chromatographic conditions using Design of Experiments (DoE).

Good resolution and specificity in a method facilitate routine analysis and measurement in quality control laboratories. Based on this study, it can be concluded that the commercial and industrial research and testing departments have accepted and are prepared to implement these formulations in their testing protocols.

2.1 Chemicals

Gallic acid standard gift sample were provided by Twinkle Chemi lab PVT. LTD, Badlapur, Maharashtra. Apocynin and Apigenin sample was buy from BLD Pharmatech PVT. LTD, Hyderabad, Telangana and Zeta Scientific LLP., Mumbai, Maharashtra. Liv ON, Liv 12 and Liv- first formulations were obtained from Local Pharmacy for assay. HPLC grade methanol and water were purchased from Merck, Mumbai, India, for the study.

2.2 Instrumentation and chromatographic conditions

For method development, a Shimadzu high-performance liquid chromatography (HPLC) system was employed. This system included a quaternary solvent manager, solvent degasser, sample manager, and a PDA detector, all controlled by LC Solution software. Additionally, it featured a cooling auto-sampler and a column oven to control the temperature of the analytical column.

Precision weighing was carried out using a Shimadzu analytical balance. Chromatographic separation was achieved using a reverse-phase Waters C18 column (250 mm × 4.6 mm, 5 µm). The method utilized isocratic elution with a mobile phase composed of Methanol and Acetonitrile in an 80:20% v/v ratio. The flow rate was maintained at 1 ml/min, and the eluents were monitored with a PDA detector set at 228 nm. Design Expert 13.0 trial version was used for designing the experiments.

2.3 Preparation of standard stock solution

Stock solutions of apigenin, apocynin, and gallic acid were prepared by dissolving 1 mg of each compound in 10 mL of methanol. From these stock solutions, various working solutions with concentrations ranging from 1 to 5 µg/mL were prepared through serial dilution with methanol. All solutions were filtered through a 0.45 µm nylon filter before being subjected to chromatographic analysis to create calibration plots.

2.4 Preparation of sample solution

To prepare the sample for analysis, the content of twenty-five tablets was weighed and thoroughly crushed. An accurately weighed 100 mg of the tablet powder was transferred to a 100 mL volumetric flask containing 70 mL of methanol and sonicated for 20 minutes. The solution was then filtered, and the solvent was evaporated to obtain a dry residue. The resulting residue was dissolved in 10 mL of methanol, sonicated for an additional 15 minutes, filtered, and the final solution was used for further analysis.

2.5 Selection of detection wavelength

Using UV spectrophotometry, a solution containing 1 µg/mL of apigenin, apocynin, and gallic acid was scanned across a wavelength range of 200–800 nm. All three compounds exhibited absorbance at 228 nm, which was therefore selected as the optimal wavelength for further marker determination.

2.6 Method development through QbD approach

2.6.1 Defining the quality target profile

In analytical method development, the Analytical Target Profile (ATP) is crucial for identifying the key variables required to ensure the quality characteristics and objectives of the method. The ATP encompasses factors such as the analyst's expertise, the sample characteristics, and the analytical techniques employed. These components collectively contribute to the effective development and optimization of the analytical method.

2.6.2 Determine critical quality attributes (CQA)

In Analytical Quality by Design (AQbD) analysis, Critical Quality Attributes (CQAs) are chosen based on the analytical techniques employed for method development, such as Reverse Phase High-Performance Liquid Chromatography (RP-HPLC), and the intended purpose of the method (e.g., assay and drug release determination). For an assay determination method, key CQAs include retention time and resolution, as they are crucial for ensuring the accuracy and reliability of the analytical results.

2.6.3 Identify critical method parameters (CMP) and risk assessment of method

Critical Method Parameters (CMPs) are the sensitivities associated with an analytical method and have a direct relationship with Critical Quality Attributes (CQAs). CMPs vary depending on the analytical technique used, such as Gas Chromatography (GC), High-Performance Liquid Chromatography (HPLC), or High-Performance Thin-Layer Chromatography (HPTLC). For a Reverse Phase HPLC (RP-HPLC) method, key CMPs include the flow rate (mL/min), the concentration of the mobile phase (e.g., 80% methanol), column oven temperature, and injection volume (µL). Risk assessment is used to identify potential interactions with CMPs and evaluate the likelihood of method failures. An Ishikawa (fishbone) diagram was employed to identify potential risks and root causes that could impact the performance of the RP-HPLC method.

2.6.4 Design of experiment (DOE)

For the design selection, the response surface methodology (RSM) approach was chosen over factorial design. Specifically, the Box-Behnken design was selected due to its advantages in optimizing a response variable (such as yield or purity) influenced by multiple factors. This design is particularly effective when dealing with a moderate number of factors and understanding their interactions. The Box-Behnken design was preferred over the central composite design because it provides a good balance between the number of factors and levels with a minimal number of experimental runs. This design allows for efficient exploration of the interaction between three factors at three levels: low (-1), intermediate (0), and high (+ 1). In this study, Box-Behnken design was used to optimize method performance by selecting Critical Method Parameters (CMPs)—flow rate (mL/min), mobile phase concentration (80% methanol), and injection volume (µL)—as input variables. Critical Quality Attributes (CQAs)—retention time and resolution—were used as response variables in the Design of Experiments (DOE). Integrating Quality by Design (QbD) with Box-Behnken Design (BBD) enhances the rigor and reliability of the research, leading to the development of high-quality, optimized processes or methodologies across various scientific disciplines.

A design matrix for 15 experimental runs was created using Design Expert software. This matrix allowed for the investigation of various interaction and quadratic effects of the Critical Method Parameters (CMPs)—flow rate (mL/min), mobile phase concentration (80% methanol), and injection volume (µL)—on the Critical Quality Attributes (CQAs), specifically retention time and resolution. The Box-Behnken design was utilized to systematically explore these effects and optimize the analytical method.

2.7 Analytical method development

The developed method was validated as per ICH guidelines for linearity, accuracy, precision, specificity and robustness, limit of detection and limit of quantification.

2.7.1 Linearity

A range 1–5 µg/mL of concentrations for the calibration curve was analyzed to ascertain the linearity response for Apigenin, Apocynin and Gallic acid respectively. Plot the peak area vs. concentration calibration curve for Apigenin, Apocynin and Gallic acid and find the regression line and correlation coefficient.

2.7.2 Precision

The mixture solution of Apigenin, Apocynin and Gallic acid was injected three times into an HPLC apparatus as 1 µg/mL, 2 µg/mL, and 3 µg/mL solutions at different time intervals on the same day (intraday precision) and on three different day(s) (Inter-day precision). Calculate the % RSD of the all data.

2.7.3 Recovery studies

The measure of accuracy is how closely the test results produced by the procedure match the actual value. In order to conduct recovery studies, standard drugs were added to the sample at three different concentration levels (80% (1 µg/mL), 100% (2 µg/mL) and 120% (3 µg/mL)), taking into consideration the percentage of recovered bulk drug samples. Further, recovery and % RSD were calculated to check the accuracy of data.

2.7.4 Limit of quantification (LOQ) & Limit of detection (LOD)

From the set of 6 calibration curves that were used to assess the linearity of the procedure, the LOQ & LOD was estimated. The LOQ & LOD could be computed as

LOQ = 10 σ/s

LOD = 3.3 σ/s

Where, σ = the SD of the response.

S = the slope of the calibration curve

2.7.5 Robustness

The robustness study was performed to evaluate influence of small but deliberate variation in the chromatographic conditions. The robustness was checked by changing small variation in parameters i.e. Flow rate (± 0.2 mL/min.), Injection volume (± 5 µg/mL) and Mobile phase concentration of Acetonitrile.

The integration of Analytical Quality by Design (AQbD) and Green Analytical Chemistry (GAC) principles in the HPLC process enhances long-term viability and sustainability. By combining these approaches, we have developed a robust and reliable method for analyzing apocynin, apigenin, and gallic acid. This approach represents a novel contribution to the field, as our literature review indicates that no previous HPLC method has applied these concepts simultaneously for these specific compounds.

3.1 Method development studies

All three markers—apocynin, apigenin, and gallic acid—showed absorbance at 228 nm, which was selected for further marker determination. For the chromatographic analysis, methanol was used as the primary solvent (Mobile Phase A: 80%), with acetonitrile as the secondary solvent (Mobile Phase B: 20%). This combination ensured adequate peak symmetry and minimized peak tailing. The method was developed using a flow rate of 1 mL/min and a C18 column (250 mm × 4.6 mm, 5 µm) to establish the chromatographic conditions.

3.2 Utilization of BBD method for Optimization of RP-HPLC method

The analytical target profile selected was retention time and resolution for optimization of HPLC conditions. The Box–Behnken design was used for further the optimization of various parameters within the design space. The data analysis was carried out by selecting the quadratic model for main and interaction effects. The design carried out 15 experimental design and three central points. Variable are included flow fate (mL/min), mobile phase (concentration of methanol) (80 mL) and injection volume (µL), and 15 optimized experimental runs. The responses were conducted and summarized in Table 1. The R² (adjusted) value was found to be closer to 1. The model validation performed with ANOVA that given in. Significance was found to be < 0.05. The % coefficient of variance < 10% of the R² (adjusted) was found to be high. This shows an effective relationship between the obtained experimental data and models. R² values adjusted have the limits of R ≥ 0.70, which is within the acceptable limits showing that obtained experimental data fits with the equations these equations with the components and factors are given in Table 2.

3D- resonse surface plots for each variable in explain effect of the CMPs on CAAs. Critical parameters such as flow fate (mL/min), mobile phase (concentration of methanol) (80 mL) and injection volume (µL) are significant interaction on peak area. The curve graphs indicate the significant effect of all three parameters over retention time and resolution. Equation for retention time of Apigenin (Y1): + 4.11 − 0.0150 A + 0.1738 B + 0.1087 C + 0.1575 AB + 0.0525 AC + 0.1800 BC + 0.0475 A² +0.2550 B² + 0.1250 C². The positive coefficient of factor B and C suggests that the Rt of Apigenin is directly proportional to Mobile phase (Methanol: Water) (80:20) (B) and Injection Volume (C). The lower value of A factor indicates less or no significant effect of A. Hence Rt of Apigenin increases with increase in Mobile phase (Methanol: Water) (80:20) (B) and Injection Volume (C) (Fig. 2).

Equation for retention time of Apocynin (Y2): + 3.27 + 0.0937 A + 0.2138 B − 0.0850 C + 0.0350 AB + 0.2725 AC + 0.3825 BC + 0.0417 A² − 0.1233 B² + 0.3642C². The positive coefficient of factor A and B suggests that the Rt of Apocynin is directly proportional to Flow Rate (A) and Mobile phase (Methanol: Water) (80:20) (B). The lower value of C factor indicates less or no significant effect of C. Hence Rt of Apocynin increases with increase in Flow Rate (A) and Mobile phase (Methanol: Water) (80:20) (B) (Fig. 3).

Equation for retention time of Gallic Acid (Y3): + 2.11 + 0.1200 A + 0.2175 B + 0.1100 C + 0.0900 AB -0.1100 AC + 0.2150 BC -0.0250 A² + 0.0400 B² + 0.1850 C². The positive coefficient of factor A, B and C suggests that the Rt of Gallic Acid is directly proportional to Flow Rate (A), Mobile phase (Methanol: Water) (80:20) (B) and Injection Volume (C). The lower value of C factor indicates less or no significant effect of C. Hence Rt of Gallic Acid increases with increase in Flow Rate (A) and Mobile phase (Methanol: Water) (80:20) (B) (Fig. 4).

Resolution between Apigenin & Apocynin (Y4): + 5.26–0.0088 A + 0.1625 B + 0.1037 C + 0.1000 AB + 0.0225 AC + 0.1100 BC − 0.0237 A² + 0.2038 B² + 0.1062 C². The positive coefficient of factor B and C suggests that the Resolution between Apigenin & Apocynin is directly proportional to Mobile phase (Methanol: Water) (80:20) (B) and Injection Volume (C). The lower value of A factor indicates less or no significant effect of A. Hence Resolution between Apigenin & Apocynin increases with increase in Mobile phase (Methanol: Water) (80:20) (B) and Injection Volume (C) (Fig. 5).

Resolution between Apocynin & Gallic acid (Y5): + 3.85 − 0.0300 A + 0.0725 B + 0.0700 C + 0.0425 AB + 0.0625 AC + 0.0225 BC − 0.0654 A² +0.0446 B² − 0.0104 C². The positive coefficient of factor B and C suggests that the Resolution between Apocynin & Gallic acid is directly proportional to Mobile phase (Methanol: Water) (80:20) (B) and Injection Volume (C). The lower value of factor indicates less or no significant effect of A. Hence Resolution between Apocynin & Gallic acid increases with increase in Mobile phase (Methanol: Water) (80: 20) (B) and Injection Volume (C) (Fig. 6).

Table 1

Box–Behnken rotatable design arrangement and responses
	Factor 1	Factor 2	Factor 3	Response 1	Response 2	Response 3	Response 4	Response 5
	Flow rate (mL/min)	Mobile phase (Methanol: water) (80:20)	Injection volume (µL)	RT for API (min)	RT for APO (min)	RT for GA (min)	Resolution between Apigenin & Apocynin	Resolution between Apocynin & Gallic acid
6	0.9	75	20	4.45	3.01	1.98	3.86	5.42
12	0.9	80	15	4.26	3.91	1.96	3.75	5.31
14	0.9	85	20	4.39	3.31	2.12	3.89	5.46
15	0.9	80	25	4.35	3.11	2.25	3.84	5.41
2	1	85	25	5.01	4.09	3.01	4.03	6.02
3	1	80	20	4.08	3.23	2.07	3.87	5.26
4	1	80	20	4.12	3.31	2.11	3.86	5.24
8	1	85	15	4.41	3.41	2.21	3.92	5.53
9	1	75	25	4.21	2.84	2.03	3.81	5.39
10	1	80	20	4.13	3.26	2.15	3.83	5.28
13	1	75	15	4.33	3.69	2.09	3.79	5.34
1	1.1	80	15	4.11	3.69	2.51	3.59	5.23
5	1.1	85	20	4.69	3.43	2.45	3.89	5.66
7	1.1	75	20	4.12	2.99	1.95	3.69	5.22
11	1.1	80	25	4.41	3.98	2.36	3.93	5.42

Table 2

Summary of results of regression analysis for models and responses
	Source	Std. Dev.	R-Squared	Adjusted R-Squared	Predicted R-Squared	%CV	P-value	Precision (Adequate)
Y1	Quadratic	0.06	0.9773	0.9365	0.6589	1.46	0.0014	17.17
Y2		0.11	0.9692	0.9139	0.5300	3.26	0.0029	13.10
Y3		0.13	0.9160	0.7649	-0.3026	6.08	0.0307	9.08
Y4		0.05	0.9013	0.7238	-0.5010	1.44	0.0442	9.72
Y5		0.06	0.9608	0.8902	0.3908	1.27	0.0051	13.83

Table 3

Comparison of experimental and predictive value of different experimental runs under optimum conditions
Optimum Conditions	Response	Responses (predicted)	Responses (observed)	Predicted error %
1	RT for API	4.084	4.074	0.245
	RT for APO	3.225	3.238	0.403
	RT for GA	2.072	2.067	0.241
	Resolution between Apigenin & Apocynin	3.840	3.862	0.569
	Resolution between Apocynin & Gallic acid	3.840	3.862	0.569

The % predicted error showcased a desirability value (D = 1) at the optimum conditions, which furnished a set of coordinates given in (Table 3).

The optimized solution showed the mobile phase composition containing 80:20% v/v mixture of Menthol: Water, detection 228 nm and flow rate 1 ml/min yielded desirability close to 1.0 along with all the CAAs in the desired range (Table 4). Under the chromatographic condition employed standard apigenin, apocynin, and gallic acid have shown sharp peaks and good separation show in Fig. 7.

Table 4

Optimized Chromatographic condition for the estimation of Apigenin, Apocynin and Gallic acid by HPLC
Parameter	Apigenin	Apocynin	Gallic acid
Retention time	4.074	3.238	2.067
Tailing factor	1.087	1.166	0.801
Theoretical Plate	36739	38522	41085
Resolution	3.862	5.237	-

3.3 Method validation

3.3.1 Linearity

The development of HPLC method for estimation of apigenin, apocynin, and gallic acid showed a good correlation coefficient (r² = 0.998), (r² = 0.999) and (r² = 0.998) in the concentration range 1–5 µg/mL. Plot the calibration curve of peak area vs concentration (Table 5).

3.3.2 Precision

The measurement of the peak area showed a low value of % RSD was range found to be range of 0.636–0.838% of apigenin, 0.479–0.886% of apocynin and 0.540–0.990% of gallic acid for intraday and 0.935–1.538% of apigenin, 0.479–1.668% of apocynin and 0.418–1.213% of gallic acid for interday.

3.3.3 Recovery studies

There was use of the standard addition method to carry out the % recovery experiment. Pre-quantified sample solutions of apigenin, apocynin, and gallic acid were supplemented with known concentrations of standard solutions at 80%, 100%, and 120% levels of LIV 12 and LIV first. The proposed method when used for extraction and subsequent estimation of apigenin, apocynin, and gallic acid from the formulation afforded recovery of 100.3–101.5%, 98.33–100.5%, 99.2 − 100.6% (Table 6).

3.3.4 Limit of detection and limit of quantification

The LOD for apigenin, apocynin, and gallic acid were 0.119, 0.091 and 0.311 µg/ml respectively. The LOQ for apigenin, apocynin, and gallic acid were 0.361, 0.030 and 0.945 µg/ml respectively. Indicate very high sensitivity of the developed method for quantification of apigenin, apocynin, and gallic acid (Table 5).

3.3.5 Robustness

The robustness was studied by the slight but deliberate change in parameters such as mobile phase, flow rate and injection volume. The % RSD of all parameters were found to be within limit, i.e. less than 2%.

3.3.6 Applicability of developed method

The amount of apigenin, apocynin, and gallic acid from the LIV 12 was found 0.0018, 0.0017, and 0.1323 mg ad from the LIV first was found 0.0014, 0.0089, and 0.0270 mg respectively. The chromatogram of the sample solution showed other peaks than those of the standards, which might be due to the presence of other minor or major phytoconstituents present in the extracts in various concentrations (Table 7, Fig. 8, 9).

Table 5

Method validation parameters for the estimation of apigenin, apocynin and gallic acid by HPLC
Parameters	Apigenin	Apocynin	Gallic acid
Concentration Range	1–5 µg/mL	1–5 µg/mL	1–5 µg/mL
Regression equation	y = 13771x + 2488	y = 34118x - 4288	y = 9462 x + 6634
Correlation co-efficient	0998	0.999	0.998
LOD (n = 5) (ng/band))	0.361	0.030	0.945
LOQ (n = 5) (ng/band))	0.119	0.091	0.311

Table 6

Accuracy data for Apigenin, Apocynin and Gallic acid by HPLC method
Constituent		Amt. Taken (µg/ml)	Amt. added (µg/ml)	Amt. Found (n = 3) ± S.D. (µg/ml)	Recovery (%) (n = 3)	% RSD
Apigenin	0%	2	0	2.03 ± 0.011	101.5	0.566
	80%	2	1	3.01 ± 0.005	100.3	0.191
	100%	2	2	4.06 ± 0.011	101.5	0.283
	120%	2	3	4.02 ± 0.011	100.4	0.229
Apocynin	0%	2	0	1.99 ± 0.011	99.5	0.582
	80%	2	1	2.95 ± 0.017	98.33	0.585
	100%	2	2	4.02 ± 0.040	100.5	0.999
	120%	2	3	4.99 ± 0.028	99.8	0.580
Gallic acid	0%	2	0	20.3 ± 0.121	101.5	0.599
	80%	2	1	29.76 ± 0.102	99.2	0.343
	100%	2	2	39.05 ± 0.086	101.2	0.220
	120%	2	3	50.33 ± 0.015	100.6	0.030

Table 7

Assay of Formulations by HPLC method
Formulation	Constituents	Assay (%w/w)	Assay (mg)	%RSD
Liv 12	Apigenin	5.003	0.0018	0.489
	Apocynin	0.259	0.0017	0.769
	Gallic acid	9.003	0.1323	0.245
Liv First	Apigenin	9.859	0.0014	1.488
	Apocynin	0.268	0.0089	1.102
	Gallic acid	5.489	0.0270	0.998

3.4 Assessment of the environmental profile

Two greenness assessment methods are applied in the presented study that can be summarized as follows:

3.4.1 Green analytics procedure index (GAPI)

The Green Analytics Procedure Index (GAPI) is an advanced tool designed to evaluate the environmental impact and hazard tolerance of analytical procedures. Building on the previous NEMI tool, GAPI introduces an upgraded system with 11 different color classifications to represent various levels of environmental friendliness and hazard tolerance.

Features of GAPI:

Color Classification: GAPI uses a color spectrum, including green, yellow, and red, to indicate different levels of environmental impact and hazard tolerance.
15 Stages: The tool evaluates 15 distinct stages of the analytical process to provide a comprehensive assessment. The GAPI consists of 13 phases, the first of which includes solvents and reagents and the second of which includes instrument energy. These stages are detailed in Fig. 10.

Usage:

Software Development: The GAPI tool requires specialized software to be developed and utilized effectively.
Evaluation: By analyzing the various stages of an analytical procedure, GAPI provides a nuanced view of its environmental and hazard impact.

This detailed approach ensures a thorough assessment of analytical methods, helping to promote greener and more sustainable practices in analytical chemistry.

3.4.2 Analytical GREEnness metric approach (AGREE)

The Analytical GREEnness metric approach (AGREE) software provides a visual representation of the method's environmental friendliness using a circular diagram. This diagram is oriented clockwise and features numbers from 1 to 12 around its edge, each representing one of the 12 principles of green analytical chemistry.

Results Visualization:

AGREE Diagram: The circular figure displays the outcomes for each of the 12 principles, with weighted agreement scores ranging from 0 to 1.
Color Coding:The final score, represented at the core of the AGREE diagram, is visualized using a color spectrum:
- Dark Green: Scores close to 1.
- Red: Scores close to 0.

For the proposed method, the AGREE metrics program yielded a score of 0.82. This score, being close to the maximum value of 1, indicates a high level of adherence to green chemistry principles. Consequently, the method's greenness profile is well-established, reflecting strong environmental sustainability. It is clearly mentioned in Fig. 10.

3.4.3 Blue Applicability Grade Index (BAGI)

The Blue Applicability Grade Index (BAGI) is a straightforward and rapid tool for evaluating the practicality of analytical methods, whether conventional, state-of-the-art, or newly developed. This tool provides two types of results:

1. Pictogram Representation:

The BAGI tool generates an asteroid-shaped pictogram with a central number.
Hue Scale: The color of the pictogram reflects the method’s compliance with the set criteria:
- Dark Blue: High compliance
- Blue: Medium compliance
- Light Blue: Low compliance
- White: No compliance

2. Numerical Score:

The number inside the pictogram indicates the overall score of the analytical method, ranging from 25 to 100.
- Score of 25: Indicates the lowest performance in terms of applicability.
- Score of 100: Represents excellent performance.

For the proposed method, the BAGI metrics program yielded a score of 72.5, covering a total of 9 pictograms. This result is illustrated in Fig. 12. The BAGI tool is expected to gain significant attention, trust, and acceptance within the chemical community due to its simplicity and effectiveness in evaluating method practicality.

The developed eco-friendly HPLC method for estimating apigenin, apocynin, and gallic acid demonstrates significant advancements in analytical chemistry. By integrating a Diode Array Detector (DAD) and employing gradient elution, the method ensures accurate and efficient separation of these compounds. The optimization process utilized Quality by Design (QbD) principles, specifically the Box-Behnken Design (BBD), to systematically refine key parameters such as flow rate, mobile phase composition, and injection volume.

The application of both Analytical Quality by Design (AQbD) and Green Analytical Chemistry (GAC) principles has enhanced the method's sustainability and reliability. The Analytical GREEnness metric approach (AGREE), Green Analytical Procedure Index (GAPI) and Blue Applicability Grade Index (BAGI) metrics confirmed the method's high environmental friendliness and practicality, with scores indicating strong compliance and effective performance.

The method's robust resolution and specificity make it suitable for routine analysis in Quality Control (QC) laboratories. Furthermore, the reduced retention times contribute to lower experimental costs, making the method both economically and operationally efficient.

Overall, the successful development and validation of this HPLC method, along with its acceptance by commercial and industrial research and testing departments, underscore its potential for widespread adoption in both research and quality control settings.

Author Contribution

All 3 authors are give their contribution. A and C wrote the manuscript also do data complication and formatting. B doing proofreading after manuscript prepared. 1-3. All authors reviewed the manuscript

Acknowledgement:

I’m very thankful to Department of pharmaceutical quality Assurance, Graduated School of Pharmacy; I would also like to thank the Principal Sir, for providing the necessary facilities to carry out this work.

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No competing interests reported.

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Greenness Assessment of High-Performance Liquid Chromatography Method for Simultaneous Estimation of Apigenin, Apocynin and Gallic Acid with Quality by Design Approach

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Abstract

Figures

1 INTRODUCTION

2 MATERIALS AND METHODS

2.1 Chemicals

2.2 Instrumentation and chromatographic conditions

2.3 Preparation of standard stock solution

2.4 Preparation of sample solution

2.5 Selection of detection wavelength

2.6 Method development through QbD approach

2.6.1 Defining the quality target profile

2.6.2 Determine critical quality attributes (CQA)

2.6.3 Identify critical method parameters (CMP) and risk assessment of method

2.6.4 Design of experiment (DOE)

2.7 Analytical method development

2.7.1 Linearity

2.7.2 Precision

2.7.3 Recovery studies

2.7.4 Limit of quantification (LOQ) & Limit of detection (LOD)

2.7.5 Robustness

3 RESULTS AND DISCUSSIONS

3.1 Method development studies

3.2 Utilization of BBD method for Optimization of RP-HPLC method

3.3 Method validation

3.3.1 Linearity

3.3.2 Precision

3.3.3 Recovery studies

3.3.4 Limit of detection and limit of quantification

3.3.5 Robustness

3.3.6 Applicability of developed method

3.4 Assessment of the environmental profile

3.4.1 Green analytics procedure index (GAPI)

3.4.2 Analytical GREEnness metric approach (AGREE)

3.4.3 Blue Applicability Grade Index (BAGI)

4 CONCLUSION

Declarations

Author Contribution

Acknowledgement:

References

Additional Declarations

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