Sustainable QbD-Driven RP-HPLC Method for the Simultaneous Estimation of Azelnidipine and Metoprolol Succinate with Green and Red Analytical Assessments

Sustainable QbD-Driven RP-HPLC Method for the Simultaneous Estimation of Azelnidipine and Metoprolol Succinate with Green and Red Analytical Assessments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sustainable QbD-Driven RP-HPLC Method for the Simultaneous Estimation of Azelnidipine and Metoprolol Succinate with Green and Red Analytical Assessments Saurabh Shukla, Ravikumar Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7154441/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A sustainable, stability-indicating Reverse Phase High Performance Liquid Chromatography (RP-HPLC) method was developed and validated for the simultaneous estimation of Azelnidipine (AZL) and Metoprolol Succinate (MET) using an Analytical Quality by Design (AQbD) approach. Method optimization was performed using Central Composite Design (CCD), focusing on critical method parameters including flow rate, mobile phase composition, and detection wavelength. The optimized method employed a C18 column with a mobile phase of acetonitrile: water (70:30, v/v) under gradient elution and detection at 230 nm. The method showed excellent linearity, accuracy, precision, and robustness across a defined concentration range and was confirmed to be stability-indicating through forced degradation studies. To assess the environmental and safety performance of the method, two modern evaluation tools were applied: Analytical Green Star Area (AGSA) and Rapid Assessment of Performance Indicators (RAPI). The AGSA tool confirmed strong adherence to the 12 Principles of Green Analytical Chemistry, while the RAPI score reflected high analytical reliability and occupational safety in line with Red Analytical Chemistry principles. The integration of QbD with green and red assessments ensures both regulatory compliance and sustainable laboratory practice. This validated method offers a practical and comprehensive solution for the routine quality control of AZL and MET in pharmaceutical formulations. Azelnidipine Metoprolol succinate RP-HPLC Analytical Quality by Design (AQbD) Stability-indicating method Red Analytical Chemistry and Green Analytical Chemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction The highest risk factor for cardiovascular disease (CVD) and mortality is hypertension; treatment with this condition can significantly reduce the higher risk associated with blood pressure elevation. Antihypertensive drugs that reduce blood pressure and associated harm to target organs [ 1 ]. The long-acting dihydropyridine-based calcium antagonist Azelnidipine (AZL) was just approved and is used to treat myocardial infarction-related cardiac remodeling and ischemic heart disease; however, its impact on hyperglycemia-induced cardiac damage has not been investigated [ 2 ] (Fig. 1 ). A common β-adrenergic antagonist, metoprolol is essential to cardiovascular pharmacology. Heart failure, arrhythmias, angina pectoris, and hypertension are the main conditions for which metoprolol is prescribed. The drug works by specifically blocking β-1 adrenergic receptors, which lowers blood pressure, heart rate, and cardiac contractility [ 3 ] (Fig. 1 ). In contrast, Red Analytical Chemistry is a relatively recent concept that emphasizes the safety, health, and well-being of analysts and laboratory personnel. While GAC addresses ecological sustainability, Red Analytical Chemistry highlights human-centric concerns, focusing on reducing occupational exposure to toxic reagents, improving laboratory ergonomics, and ensuring safer analytical practices [ 4 – 6 ]. It underscores the ethical responsibility of chemists to design not only environmentally friendly but also safe and risk-conscious analytical procedures. Together, GAC and Red Analytical Chemistry offer a comprehensive framework for developing modern analytical methods that are both eco-efficient and human-safe. Their integration is particularly vital in pharmaceutical analysis, where large volumes of solvents and reagents are routinely used. GAC's main objective is to reduce or eliminate the use of dangerous chemicals in analytical procedures in order to improve health and the environment without compromising method performance [ 7 , 8 ]. The most popular analytical method in pharmaceutical quality control (QC) for characterizing active pharmaceutical ingredients (APIs) and their contaminants in biological fluids and pharmaceutical formulations is high performance liquid chromatography (HPLC). These methods are ideal for regular analysis, allowing for the accurate determination of multiple components in pharmaceutical formulations [ 9 , 10 ]. To improve accuracy and robustness, the International Council for Harmonization (ICH) places a strong emphasis on Analytical Quality by Design (AQbD) in method development. The concepts of AQbD aid in the development of trustworthy techniques, allowing for ongoing improvement and lowering the need for revalidation. In contrast to conventional trial-and-error techniques, AQbD combines quality to reduce failures and results that are not up to par and guarantees resilience early in the development process [ 11 – 15 ]. A detailed literature survey, it was revealed that numbers of method have been reported in literature for the individual analysis of Azelnidipine [ 3 , 11 , 16 , 17 ] and Metoprolol succinate [ 18 – 24 ] by various analytical methods. However, no Stability indicating RP-HPLC method have been reported with Green and Red assessment for simultaneous estimation of AZL and MET utilizing the Analytical Quality by Design approach. The objective of this study is to develop reliable and accurate analytical techniques for figuring out how much AZL and MET are present in mixes made in a lab. These methods offer an economical and effective choice for simultaneous analysis, making a substantial contribution to pharmaceutical research, upholding quality control, and streamlining accurate dosage calculation. We used AGSA and RAPI tools to do a green and red profile assessment in order to investigate the environmental impact of the recently developed Stability indicating RP-HPLC method utilizing the Analytical Quality by Design approach. Taking into account variables including solvent usage, chemical compounds, energy consumption, and waste formation, this evaluation confirmed the methods' environmental friendliness. 2 Materials and Method 2.1 Reagents and chemicals Reference standard of AZL and MET were procured from Zydus LifeScience, Ahmedabad. Acetonitrile and Methanol (HPLC grade) was used as solvents in this method. All the glass wares were calibrated before using. 2.2 Instrumentation Chromatographic analysis was carried out on a prominence HPLC Method: LC-2010AHT series binary pump systems, Auto sampler injection, temperature controller (column oven) system controller and a UV detector (LC-2010). CLASS-VP (version 2.42) software was used to acquire and process the data. 2.3 Sample and standard preparation Standard Solution of AZL (8 µg/mL) and MET (25 µg/mL) were prepared separately by dissolving 8 mg of AZL and 25 mg of MET in 10 mL of mobile Phase respectively. The standard solution was subsequently diluted to prepare different concentrations 4–20 µg/mL and 12.5–62.5 µg/mL of AZL and MET respectively. 2.4 Selection of elution mode reverse phase chromatography is recommended for ionic and moderate to polar compounds, it is not only easy to use and convenient, but it also performs better in terms of efficiency, stability, and reproducibility. The C18 column was chosen because it is less polar than the C4 and C8 columns, allowing polar compounds to be eluted more quickly than non-polar ones. As a foundation for method development, a 250 × 4.6 mm column of 5 µm particle packing was chosen. Because of its resilience with regard to extended column stability and ease of application, gradient mode was selected. For the majority of separations, this arrangement offers a wide range of potential plate values. 2.5 Preparation of mobile phase The mobile phase consisted of mixture of Acetonitrile: Water (70: 30% v/v) the mode for was gradient. The mobile phase was filtered through a 0.22 µm nylon membrane filter and degassed prior to use. 2.6 Chromatographic separation Standard and sample solutions were injected in column using ultrafast autosampler. The chromatogram was run for appropriate time duration with degassed mobile Acetonitrile: Water (70: 30% v/v) using UV detector (LC-2010) at wavelength 230 nm. The chromatogram was stopped after separation was achieved completely. 2.7 Method development using AQbD framework 2.7.1 Establishing Analytical Target Profiles and Critical Quality Attributes In the context of AQbD, the term "Analytical Target Profile" (ATP) refers to a thorough explanation of the intended performance characteristics and requirements of an analytical technique. It serves as a road map for the methodical advancement and improvement of analytical techniques, guaranteeing that they satisfy established quality standards and legal obligations. By carefully selecting elements like Critical Quality Attributes (CQAs), which have a direct impact on the analytical procedure's quality and safety, the ATP serves as a first step in implementing a QbD-oriented approach. The ATP element targets and their rationale are shown in Table 2 . The retention duration, tailing factor, theoretical plates, and resolution are the CQA components that correspond to ATPs and are in charge of effective HPLC analysis. 2.7.2 Risk assessment studies The primary risk factors influencing the advancement of analytical techniques are to be identified and addressed by this study. To determine how different situations would affect the approach's performance, a thorough risk analysis was conducted. To identify possible hazards, an analysis was done to look at the relationship between ATP and Critical Method Parameters (CMP). 2.7.3 Factor screening study To find CMPs that had a major influence on the CAAs, screening study was carried out using a Central Composite design (CCD) with Design-Expert® Software version 13. Three center points and two levels—represented by the symbols + 1 and − 1 for high and low levels, respectively—were used to alter all dependent variables. In order to screen six CMPs, the CCD recommended fifteen experiments: flow rate (0.8–1.2 min), acetonitrile volume (65–75), injection volume (15–25 µL), and detection wavelength (229–232 nm). Retention duration, peak area, number of theoretical plates, tailing factor, and resolution were among the factors whose effects on CAAs were examined. 2.7.4 Optimization study Critical parameters influencing the HPLC method's critical quality attributes (CAAs) were determined by the screening research and risk assessment. Central Composite Design (CCD) was then used to do an optimization analysis. The critical material parameters (CMPs) for the CAAs, which comprised retention duration, number of theoretical plates, and resolution, were determined to be the Flow Rate (mL/min) (X1), Mobile Phase (Volume of Acetonitrile (70 mL)) (X2), and Detection wavelength (nm) (X3). A design matrix with 17 trial runs with chosen CAAs ranging at three levels high (+ 1), midrange (0), and low (-1) was suggested by Design-Expert® version 13. 2.8 Force Degradation In order to demonstrate the stability of both the standard and sample solutions during analysis, both solutions were analyzed over a period of 24 h at room temperature. The results indicated that for both the solutions, the retention time and peak area of AZL and MET did not show much% difference. There was no significant degradation within the indicated period. Hence, it was concluded that both the solutions were stable for 24 h at room temperature. 2.9 Method validation 2.9.1 System suitability studies The system suitability was evaluated by five replicate analyses of AZL and MET mixture at concentration of 16 µg/mL of AZL and 50 µg/mL of MET. The column efficiency, resolution, and peak asymmetry were calculated for the standard solutions. 2.9.2 Linearity Linearity of the proposed method was assessed by scanning concentrations at five equidistance levels 6–20 µg/mL and 12.5–62.5 µg/mL for HPLC method for Azelnidipine and Metoprolol succinate respectively. A graph of Concentration vs. Peak Area and Concentration vs. Absorbance was plotted for HPLC method respectively and regression equation was obtained. 2.9.3 Precision 2.9.3.1 Repeatability The Repeatability precision was performed by evaluating lowest concentration for six replicates; 16 µg/mL and 50 µg/mL for HPLC for Azelnidipine and Metoprolol succinate respectively. 2.9.3.2 Intraday precision The Intraday precision was performed by evaluating three concentration levels on same day for three replicates; 4, 12 and 20 µg/ mL and 12.5, 37.5 and 62.5 µg/mL for HPLC method for Azelnidipine and Metoprolol succinate respectively. 2.9.3.3 Interday precision The Interday precision was performed by considering three concentration levels (same as intraday precision) on three different days for three replicates. All solutions were prepared form different stocks prepared on different days. The standard deviation and% RSD were calculated. 2.9.4 Recovery Studies It was determined by calculating the recovery of AZL and MET by standard addition method. Accuracy was performed by% recovery study at 50%, 100% and 150% by spiking the API to the placebo. 2.9.5 Limit of quantification (LOQ) & Limit of detection (LOD) As per ICH guideline, limit of detection and quantitation of the developed method were calculated from the standard deviation of the response (σ) and slope of the calibration curve (S) of drug using the formula; Limit of detection = 3.3* σ/S and Limit of quantitation = 10* σ/S. 2.9.6 Robustness Robustness of the method was determined by subjecting the method to slight change in the method condition individually. For HPLC method, robustness parameters were Pump flow rate (1 ml/min ± 0.2 ml/min) and Mobile Phase Composition. The% RSD was calculated for all the parameters. 2.9.7 Analysis of marketed formulation Take HPMC (4 mg), MCC (190 mg), Magnesium stearate (4 mg), Talc (2 mg). Role of HPLC-Film forming agent, MCC- Directly compressible material, MS, Gliding agent, Talk, Lubricating agent, AZL (80 mg), and MET (250 mg) was taken into the volumetric flask (100 ml) and volume of the flask was raised to 100 ml with methyl alcohol to give stock solution containing 800 µg/ml of AZL, and 2500 µg/ml of MET. Withdraw 100 µl from above filtrate in 10 mL volumetric flask; make up the volume with mobile phase, which contain AZL + MET = 8 + 25 µg/ml. 3 Result and discussion 3.1 Selection of wavelength The 230 nm isobestic wavelength of AZL and MET was selected as the detection wavelength for HPLC 3.2 Mobile phase selection Asymmetric peaks and a delayed retention time of MET were the results of using multiple mobile phases with varying ratios of different solvents and pH. The ideal polarity for appropriate migration, separation, and resolution of AZL and MET peaks was supplied by the 70:30 v/v acetonitrile: water mixture. The eluted peaks were clear, distinct, and tailing-free under these circumstances. 3.3 Utilization of CCD method for Optimization of RP-HPLC method Retention duration and resolution were the analytical target profiles chosen for HPLC condition optimization. To further optimize several factors inside the design space, the Central Composite Design was employed. The quadratic model for main and interaction effects was chosen in order to analyze the data. Three primary points and twenty experimental designs were implemented. 20 optimized experimental runs, the mobile phase (volume of acetonitrile (70 mL)), the detection wavelength (nm), and the flow fate (mL/min) are among the variables. Table 1 provides a summary of the replies that were obtained. The model was validated using the provided ANOVA. The significance level was less than 0.05. The corrected R2 was found to have a high coefficient of variation (≥ 10%). This demonstrates a strong correlation between the models and the experimental data that was acquired. The collected experimental data fits the equations with the components and factors listed in Table 2 as indicated by the R2 values adjusted with limits of R ≥ 0.70, which are within acceptable bounds. Each variable's 3D response surface plots illustrate how the CMPs affect CAAs. Peak area is significantly influenced by critical parameters like flow fate (mL/min), mobile phase (volume of acetonitrile (70 mL), and detection wavelength (nm). The curve graphs show how each of the three parameters significantly affects resolution and retention time. The retention period of AZL appears to be directly related to the flow rate (A) and detection wavelength (C), as indicated by the positive coefficients of factors A, B, and C. B has a less significant effect when the Mobile Phase (Volume of Acetonitrile (70 mL)) (B) factor is lower. Therefore, As the flow rate (A) and detection wavelength (C) increase, the AZL retention duration also increases (Fig. 2 ). The retention period of MET appears to be directly proportional to the mobile phase (volume of acetonitrile (70 mL)), as indicated by the positive coefficients of factors A, B, and C (B). The flow rate (A) and detection wavelength (C) variables' negative or lower values suggest that A and C have little to no effect. Therefore, As the volume of acetonitrile (70 mL) in the mobile phase increases, so does the retention period of MET (B) (Fig. 3 ). Resolution between AZL and MET is directly proportional to Flow Rate (A) and Detection wavelength (C), according to the positive coefficient of factors A, B, and C. Thus, Resolution between AZL and MET increases as Flow Rate (A) and Detection wavelength (C) increase. A negative or less value of the Mobile Phase (Volume of Acetonitrile (70 mL)) (B) factor indicates less or no significant effect of B (Fig. 4 ). Table 1 Central Composite rotatable design arrangement and responses Factor 1 Factor 2 Factor 3 Response 1 Response 2 Response 3 Run A: Flow Rate B: Mobile Phase (Volume of Acetonitrile (70 mL)) C: Detection wavelength Rt of AZE Rt of MET Resolution AZE & MET min/mL mL Nm Min Min 1 1.2 75 232 5.993 3.862 6.79 2 1.2 75 228 5.836 4.256 6.38 3 1 70 233.364 5.582 3.315 5.25 4 1 70 230 5.884 3.218 6.78 5 1.2 65 228 6.002 3.589 6.36 6 1 61.591 230 5.586 3.836 7.59 7 0.8 65 232 4.982 3.998 6.79 8 1 70 230 5.589 3.325 6.71 9 1 70 230 5.398 3.359 6.98 10 0.8 75 232 5.962 3.986 6.61 11 1.2 65 232 5.981 3.058 6.93 12 1 70 230 5.584 3.319 6.73 13 1.33636 70 230 5.984 3.325 6.58 14 0.8 65 228 5.298 3.698 6.28 15 0.663641 70 230 5.289 3.389 6.48 16 1 70 230 5.514 3.198 7.14 17 1 78.409 230 5.589 4.296 6.98 18 1 70 226.636 5.189 3.398 5.98 19 0.8 75 228 5.369 3.456 6.45 20 1 70 230 5.525 3.302 6.75 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 0.1835 0.8044 0.6284 -0.0692 3.27 0.0132 7.8262 Y2 0.0873 0.9693 0.9417 0.8183 3.48 0.0001 22.2434 Y3 0.2974 0.7925 0.6058 -0.3608 4.49 0.0170 8.8649 Table 3 Comparison of experimental and predictive value of different experimental runs under optimum conditions Optimum Condition Response Responses (predicted) Responses (observed) Predicted error % 1 Retention time of AZL 5.587 5.584 -0.053 Retention time of MET 3.312 3.318 -0.181 Resolution between AZL and MET 6.77 6.7 1.033 Under ideal circumstances, the percentage anticipated error displayed a desirability value (D = 1), which provided a set of coordinates provided in Table 3 . The mobile phase composition of the optimized solution, which had a 70:30% v/v mixture of acetonitrile and water, at a detection wavelength of 230 nm and a flow rate of 1 ml/min, produced a desirability that was nearly 1.0 and all of the CAAs were within the intended range (Table 4 ). Standard AZL and MET have demonstrated distinct peaks and satisfactory separation under the chromatographic conditions used in Fig. 5 . Table 4 Optimized Chromatographic condition for the estimation of AZL and MET Succinate by HPLC Drug Retention time (min) Tailing Factor Theoretical Plates Resolution AZL 3.318 min 1.03 19821.00 6.78 MET 5.584 min 1.13 13751.67 6.78 3.4 Force Degradation study The capacity of the optimized approach to separate all degradation products in the presence of the active ingredient was determined to be a stability indicator (Fig. 6 , Fig. 7 , Fig. 8 , Fig. 9 ) In stressed samples, no degradation product was discovered to obstruct the estimate of MET and AZE. imposed that the percentage of degradation seen was predictive in nature (below 15%), even the stress imposed was determined to be optimal (Table 5 ). Table 5 Evaluation Table of Forced Degradation Studies Stress Condition Area AZL MET % Degradation (AZL) % Degradation (MET) Acid Hydrolysis Standard Area 34587 72856 15.79% 14.23% Observed Area 29124 62485 Base Hydrolysis Standard Area 34587 72856 13.99% 13.19% Observed Area 29745 63241 Oxidative Stress Standard Area 34587 72856 12.58% 11.02% Observed Area 30234 64827 Thermal Degradation Standard Area 34587 72856 10.66% 10.13% Observed Area 30897 65471 3.5 Method validation 3.5.1 System suitability data The standard solutions' column efficiency, resolution, and peak asymmetry were computed and compiled in Table 4 . 3.5.2 Linearity The HPLC overlain chromatogram for AZL succinate and MET succinate was displayed in Table 5 and ranged from 4 to 20 µg/mL and 12.5 to 62.5 µg/mL, respectively, with retention times of 5.58 and 3.32 minutes. 3.5.3 Precision Table 6 summarized the results of the HPLC and Absorbance Correction UV method's repeatability, intraday precision, and interday precision. 3.5.4 Accuracy According to the results described in Table 6 , the recovery percentage, which was determined to be between 98% and 102%, validates the correctness of the devised HPLC and Absorbance Correction UV method. Table 6 Validation parameter for RP-HPLC method Sr. No. Parameter AZL MET 1 Linearity Range 4–20 µg/ml 12.5–62.5 µg/ml 2 Co- relation Coefficient 0.998 0.998 3 Precision 1. Repeatability (n = 5) 2. Intra-day precision (n = 3) 3. Inter-day precision (n = 3) 1.04 0.47–1.02 0.63–1.16 0.85 0.59–0.82 0.62–1.03 4 Accuracy (% Recovery) 98.75–99.31 98.60-99.56 5 Limit of detection (LOD) (µg/mL) 0.24 0.44 6 Limit of quantification (LOQ) (µg/mL) 0.73 1.35 3.5.5 Limit of quantification (LOQ) & Limit of detection (LOD) AZL's LOD and LOQ were determined to be 0.24 µg/mL and 0.73 µg/mL, while MET's were 0.44 µg/mL and 1.35 µg/mL (Table 6 ). 3.5.6 Robustness The devised HPLC technique was found to be robust when the percentage RSD value for all robustness parameters was less than 2%. Table 7 provided a summary of the findings. Table 7 Robustness data for AZL and MET Parameter Level of Change Effect on assay volume AZL MET Assay ± SD RSD Assay ± SD RSD Flow rate ( ± 0.1) 0.9 mL/min 98.52 ± 0.24 0.25 99.47 ± 0.32 0.32 1.1 mL/min 98.41 ± 0.42 0.42 99.49 ± 0.26 0.26 Mobile Phase Composition (70:30) 72:28 98.35 ± 0.20 0.20 99.52 ± 0.26 0.26 68:32 98.77 ± 0.53 0.53 98.94 ± 0.97 0.98 3.5.7 Assay By adding standard excipients to a synthetic mixture, the assay was estimated and examined. Table 8 presented the assay results, which fell between 98% and 102% of the acceptance criteria. Table 8 Assay of Marketed Formulation Drug Amount taken (µg/mL) Amount found (µg/mL) % Assay AZL 8 7.95 ± 0.10 99.42 ± 1.26 MET 25 24.74 ± 0.11 98.96 ± 0.43 3.6 Green Assessment profile: Analytical Green Star Area (AGSA) The Analytical Green Star Area (AGSA) is a novel tool developed to assess the environmental impact of analytical chemistry methods in a structured, visual, and objective way. Unlike existing metrics such as the Analytical Eco-Scale (AES) and GAPI, AGSA integrates both a total scoring system and a visual star-shaped diagram to evaluate how well a method aligns with the 12 Principles of Green Analytical Chemistry (GAC). Each principle is assessed through specific questions with standardized scoring from 1 to 3, allowing for consistent and reproducible evaluations. AGSA reduces user bias by providing clear guidelines and combines both numeric results and visual outputs to make comparison between methods easier and more intuitive. The final greenness score is out of 36 points, representing 100% alignment with green practices. A larger star area on the AGSA diagram indicates a greener method. Additionally, AGSA builds upon the Green Star Area concept used in green chemistry, making it suitable for cross-disciplinary evaluations. AGSA represents a practical, effective, and user-friendly solution for promoting greener, safer, and more sustainable analytical procedures. The output score of the suggested method was 75 according to the AGSA tool. Figure 10 makes explicit reference to it. The chemical community will pay more attention to, trust, and accept the AGSA tool. 3.7 Red Assessment profile: Rapid Assessment of Performance Indicators (RAPI) RAPI (Rapid Assessment of Performance Indicators) is a Python-based, open-source tool designed to assess the quantitative performance of analytical methods quickly and objectively. It follows the structure of BAGI and operates through a simple interface where users select values from drop-down menus. RAPI focuses on ten key criteria based on ICH validation guidelines and general good laboratory practices: repeatability, intermediate precision, reproducibility, trueness, recovery and matrix effects, LOQ, working range, linearity (R²), ruggedness, and selectivity. Each parameter is scored on a five-level scale from 0 to 10, with 0 also assigned when a parameter hasn’t been tested. The overall method score, ranging from 0–100, is shown at the center of a star-shaped pictogram, where the color intensity visually reflects the score for each criterion. RAPI ensures fairness by giving equal weight to all criteria and adjusts expectations based on analyte concentration using the Horwitz model. It encourages comprehensive validation by penalizing missing data, promoting better laboratory practices. While it provides valuable comparative insight, RAPI should not be the only factor in determining method suitability. It is most useful when comparing methods applied to the same analyte and matrix, ensuring context-appropriate decisions in method development and selection. The tool thus supports laboratories in reducing their environmental footprint while maintaining the high standards of accuracy and reliability required in analytical chemistry show in Fig. 12 . 4 CONCLUSION A robust, eco-friendly, and cost-effective RP-HPLC method was successfully developed and validated for the simultaneous estimation of Azelnidipine and Metoprolol Succinate using a Quality by Design (QbD) approach. The method demonstrated excellent precision, accuracy, linearity, robustness, and stability, making it suitable for routine pharmaceutical quality control. Notably, the greenness and safety of the developed method were comprehensively evaluated using Analytical Green Star Area (AGSA) and Rapid Assessment of Performance Indicators (RAPI), confirming its strong environmental and occupational health profile. This integrated green–red assessment underscores the method’s alignment with modern analytical goals, combining sustainability, performance, and analyst safety. The proposed method not only supports regulatory compliance and reliability but also contributes to sustainable laboratory practices, offering a valuable tool for the pharmaceutical industry. Declarations Acknowledgement I’m very thankful to Department of pharmaceutical quality Assurance, Gokul Pharmacy College, Gokul global University, Sidhdhpur. I would also like to thank the Principal Sir, for providing the necessary facilities to carry out this work. Conflicts of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article. Funding Declaration: No funding Clinical trial number: Not Applicable Consent to Publish declaration: Not Applicable Consent to Participate declaration: Not Applicable Ethics declaration: Not Applicable Author Contribution SS wrote the main manuscript, prepared figures, and collected and formatted data.RP supervises all. References Kain V, Kumar S, Puranik AS, Sitasawad SL (2010) Azelnidipine protects myocardium in hyperglycemia-induced cardiac damage. Cardiovasc Diabetol 9:1–9. https://doi.org/10.1186/1475-2840-9-82/FIGURES/4 Morris J, Awosika AO, Dunham A (2024) Metoprolol. xPharm Compr Pharmacol Ref 1–7. https://doi.org/10.1016/B978-008055232-3.62174-9 Darji H, Dedania Z (2023) Simultaneous estimation of Azelnidipine and Metoprolol succinate with greenness assessment using HPLC and UV-spectrophotometric methods. Green Anal Chem 7:100079. https://doi.org/10.1016/J.GREEAC.2023.100079 Miladinović SM (2024) Green analytical chemistry: integrating sustainability into undergraduate education. Anal Bioanal Chem 417:665. https://doi.org/10.1007/S00216-024-05680-4 Nowak PM, Wojnowski W, Manousi N, et al (2025) Red analytical performance index (RAPI) and software: the missing tool for assessing methods in terms of analytical performance. Green Chem 27:5546–5553. https://doi.org/10.1039/D4GC05298F Mansour FR, Bedair A, Belal F, et al (2025) Analytical Green Star Area (AGSA) as a new tool to assess greenness of analytical methods. Sustain Chem Pharm 46:102051. https://doi.org/10.1016/J.SCP.2025.102051 Koel M, Kaljurand M (2021) Editorial overview: A closer look on green developments in analytical chemistry: Green analytical chemistry is going mainstream. Curr Opin Green Sustain Chem 31:. https://doi.org/10.1016/J.COGSC.2021.100541 Gałuszka A, Migaszewski Z, Namieśnik J (2013) The 12 principles of green analytical chemistry and the SIGNIFICANCE mnemonic of green analytical practices. TrAC - Trends Anal Chem 50:78–84. https://doi.org/10.1016/J.TRAC.2013.04.010 Dhull P, Dunuweera S, Bietsch J, et al (2025) Recent advances and application of liquid chromatography in pharmaceutical industry. J Liq Chromatogr Relat Technol. https://doi.org/10.1080/10826076.2024.2448692 (PDF) applications in HPLC in pharmaceutical analysis. https://www.researchgate.net/publication/338166319_applications_in_HPLC_in_pharmaceutical_analysis. Accessed 5 Mar 2025 Agrawal R, Kotadiya R (2024) AQbD-guided stability indicating HPLC method for azelnidipine and chlorthalidone fixed-dose combination tablet: a green approach. J Taibah Univ Sci 18:2415156. https://doi.org/10.1080/16583655.2024.2415156 Mevada S, Shukla S, Patel H (2024) Simultaneous estimation of andrographolide, apigenin, apocynin, and gallic acid by high-performance thin layer chromatography method with Greenness quality by design approach. Sep Sci Plus 7:2300109. https://doi.org/10.1002/SSCP.202300109 Mevada S, Shukla S, U Patel H (2025) Greenness Assessment of High-Performance Liquid Chromatography Method for Simultaneous Estimation of Apigenin, Apocynin, and Gallic Acid With Quality by Design Approach. Sep Sci Plus 8:e202400335. https://doi.org/10.1002/SSCP.202400335 Mevada S, Patel H, Shukla S (2024) Simultaneous equation method for the estimation of gallic acid and apigenin by UV–visible spectrophotometry. Accredit Qual Assur 29:11–17. https://doi.org/10.1007/S00769-023-01558-7 Mevada S, Patel H, Shukla S (2025) Development and validation of simultaneous equation method for the estimation of andrographolide and apocynin in hepatoprotective polyherbal formulation using UV–visible spectrophotometry. Accredit Qual Assur 1–8. https://doi.org/10.1007/S00769-025-01633-1/METRICS Author C, Mane YM (2024) Development And Validation Of UV Method For Simultaneous Estimation Of Metoprolol Succinate And Azelnidipine In Pharmaceutical Dosage Form. Int J Pharm Sci 02:292. https://doi.org/10.5281/ZENODO.12658344 Agrawal S, Nizami T (2021) METHOD DEVELOPMENT AND VALIDATION FOR THE SIMULTANEOUS DETERMINATION OF AZELNIDIPINE AND TELMISARTAN IN TABLET DOSAGE FORM BY RP- HPLC. Int J Pharm Sci Med 6:26–36. https://doi.org/10.47760/IJPSM.2021.V06I10.003 Pekamwar SS, Kalyankar TM, Kokate SS (2014) RP-HPLC method development and validation for simultaneous estimation of bromhexine and ciprofloxacin in tablet dosage form. Der Pharma Chem 6:90–97 Hussain S, Amjad M, Khan A, Hussain S (2021) HPLC Method Development and Validation for the Simultaneous Estimation of Atorvastatin Calcium and Sildenafil. Int J Pharm Res 13:. https://doi.org/10.31838/ijpr/2021.13.01.715 Krittanawong C, Khawaja M, Ul H, et al (2024) Strategies for chronic coronary disease: A brief guide for clinicians. npj Cardiovasc Heal 2024 11 1:1–15. https://doi.org/10.1038/s44325-024-00006-w Cato T. Laurencin, Farmington C, (US); Lakshmi Sreedharan Nair A, (US) C (2005) Immobilized Metallic Nanoparticles as Unique Materials for Therapeutic and Biosensor Applications. 2: Bawane S, Telrandhe R, Pande SD (2018) Formulation and Evaluation of Oral Fast Dissolving Film of Bisoprolol Fumarate. Int J Pharm Drug Anal 6:105–115 Zhang K, Liu X (2016) Mixed-mode chromatography in pharmaceutical and biopharmaceutical applications. J Pharm Biomed Anal 128:73–88. https://doi.org/10.1016/J.JPBA.2016.05.007 Marie AA, Hammad SF, Salim MM, et al (2023) Deduction of the operable design space of RP-HPLC technique for the simultaneous estimation of metformin, pioglitazone, and glimepiride. Sci Rep 13:1–13. https://doi.org/10.1038/s41598-023-30051-x Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7154441","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501884211,"identity":"7bc19d9a-dc6b-498a-b70f-78fddcf1bccd","order_by":0,"name":"Saurabh Shukla","email":"","orcid":"","institution":"Swaminarayan University","correspondingAuthor":false,"prefix":"","firstName":"Saurabh","middleName":"","lastName":"Shukla","suffix":""},{"id":501884212,"identity":"d1ea41c4-d1cc-4f68-b19e-b0329003c9d4","order_by":1,"name":"Ravikumar Patel","email":"data:image/png;base64,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","orcid":"","institution":"Swaminarayan University","correspondingAuthor":true,"prefix":"","firstName":"Ravikumar","middleName":"","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2025-07-18 06:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7154441/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7154441/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89579605,"identity":"6826c5e7-2adf-40b4-9143-3c8b685c105a","added_by":"auto","created_at":"2025-08-21 13:48:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructure of (a) Azelnidipine and (b) Metoprolol\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/7186cd29476b8888fef9db46.jpeg"},{"id":89579606,"identity":"7d46602c-5d14-4737-b9da-e05545721309","added_by":"auto","created_at":"2025-08-21 13:48:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":651056,"visible":true,"origin":"","legend":"\u003cp\u003e3D “plots of Rt of AZE against C and B (A)”, 3D “plots of Rt of AZE against A and C (B)” and Contour “plots of Rt of AZE against A and B (C)”\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/dec160ab0da0be328c7237b9.png"},{"id":89580764,"identity":"b4c196d6-3982-4e1a-b7cc-eb28a242d323","added_by":"auto","created_at":"2025-08-21 14:04:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":580954,"visible":true,"origin":"","legend":"\u003cp\u003e3D “plots of Rt of MET against C and B (D)”, 3D “plots of Rt of MET against A and C (E)” and 3D “plots of Rt of MET against A and B (F)”\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/f0a0a9ae662c38b41937aaea.png"},{"id":89579609,"identity":"b1ad5d2a-c18f-4135-bbf9-c977b75f8fa1","added_by":"auto","created_at":"2025-08-21 13:48:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":651056,"visible":true,"origin":"","legend":"\u003cp\u003e3D “plots of Resolution AZE \u0026amp; MET against C and B (G)”, 3D “plots of Resolution AZE \u0026amp; MET against A and B (H)” and 3D “plots of Resolution AZE \u0026amp; MET against C and A (I)”\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/9ea5c45af4e3ba9722ce5471.png"},{"id":89580560,"identity":"50399026-13d8-4c8d-aaf4-feb02cac0b2a","added_by":"auto","created_at":"2025-08-21 13:56:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":29716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh performance liquid chromatography of standard AZE + MET (16+50 μg/ml)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/851ddeb0cfd8a54928341246.png"},{"id":89580765,"identity":"abdf5f1a-c159-4227-9abb-94044a3e5116","added_by":"auto","created_at":"2025-08-21 14:04:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":240972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAcid Degradation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/57815ecda060716ea617b172.png"},{"id":89580569,"identity":"e63b1f15-b28e-48dc-a57f-2e92007a7034","added_by":"auto","created_at":"2025-08-21 13:56:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":94992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBase degradation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/ea2e2ef2fca156cee9ecdfef.png"},{"id":89579638,"identity":"036eda19-2b56-4350-95f4-bb2361d6a3ec","added_by":"auto","created_at":"2025-08-21 13:48:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":101824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOxidation degradation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/a0c1cc1fdacf3e94f66d379d.png"},{"id":89580568,"identity":"b5f4a269-91e2-4ff1-961f-478fab003684","added_by":"auto","created_at":"2025-08-21 13:56:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":193085,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThermal degradation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/df122ffb66f744156a99c8d3.png"},{"id":89579621,"identity":"50535838-507f-413b-b267-940bcdeb9e98","added_by":"auto","created_at":"2025-08-21 13:48:54","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":99702,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResult of Analytical Green Star Area (AGSA)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/3a744ac6964876396e9951ed.png"},{"id":89579618,"identity":"da02ef9e-0e1c-4e2f-819e-78bcf875ee03","added_by":"auto","created_at":"2025-08-21 13:48:54","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":188671,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 12\u003c/strong\u003e \u003cstrong\u003eResult of Red Analytical Procedure Index (RAPI) matrix\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/126de033492aba7f385bfd02.png"},{"id":89897977,"identity":"e9f420da-5738-4e22-b01d-eff12e9cfa93","added_by":"auto","created_at":"2025-08-26 08:48:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4017353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7154441/v1/e001ea0f-1f0c-488c-8093-6c46c4d1d2bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sustainable QbD-Driven RP-HPLC Method for the Simultaneous Estimation of Azelnidipine and Metoprolol Succinate with Green and Red Analytical Assessments","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe highest risk factor for cardiovascular disease (CVD) and mortality is hypertension; treatment with this condition can significantly reduce the higher risk associated with blood pressure elevation. Antihypertensive drugs that reduce blood pressure and associated harm to target organs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The long-acting dihydropyridine-based calcium antagonist Azelnidipine (AZL) was just approved and is used to treat myocardial infarction-related cardiac remodeling and ischemic heart disease; however, its impact on hyperglycemia-induced cardiac damage has not been investigated [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A common β-adrenergic antagonist, metoprolol is essential to cardiovascular pharmacology. Heart failure, arrhythmias, angina pectoris, and hypertension are the main conditions for which metoprolol is prescribed. The drug works by specifically blocking β-1 adrenergic receptors, which lowers blood pressure, heart rate, and cardiac contractility [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, Red Analytical Chemistry is a relatively recent concept that emphasizes the safety, health, and well-being of analysts and laboratory personnel. While GAC addresses ecological sustainability, Red Analytical Chemistry highlights human-centric concerns, focusing on reducing occupational exposure to toxic reagents, improving laboratory ergonomics, and ensuring safer analytical practices [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It underscores the ethical responsibility of chemists to design not only environmentally friendly but also safe and risk-conscious analytical procedures. Together, GAC and Red Analytical Chemistry offer a comprehensive framework for developing modern analytical methods that are both eco-efficient and human-safe. Their integration is particularly vital in pharmaceutical analysis, where large volumes of solvents and reagents are routinely used. GAC's main objective is to reduce or eliminate the use of dangerous chemicals in analytical procedures in order to improve health and the environment without compromising method performance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe most popular analytical method in pharmaceutical quality control (QC) for characterizing active pharmaceutical ingredients (APIs) and their contaminants in biological fluids and pharmaceutical formulations is high performance liquid chromatography (HPLC). These methods are ideal for regular analysis, allowing for the accurate determination of multiple components in pharmaceutical formulations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo improve accuracy and robustness, the International Council for Harmonization (ICH) places a strong emphasis on Analytical Quality by Design (AQbD) in method development. The concepts of AQbD aid in the development of trustworthy techniques, allowing for ongoing improvement and lowering the need for revalidation. In contrast to conventional trial-and-error techniques, AQbD combines quality to reduce failures and results that are not up to par and guarantees resilience early in the development process [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA detailed literature survey, it was revealed that numbers of method have been reported in literature for the individual analysis of Azelnidipine [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Metoprolol succinate [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] by various analytical methods. However, no Stability indicating RP-HPLC method have been reported with Green and Red assessment for simultaneous estimation of AZL and MET utilizing the Analytical Quality by Design approach.\u003c/p\u003e\u003cp\u003eThe objective of this study is to develop reliable and accurate analytical techniques for figuring out how much AZL and MET are present in mixes made in a lab. These methods offer an economical and effective choice for simultaneous analysis, making a substantial contribution to pharmaceutical research, upholding quality control, and streamlining accurate dosage calculation. We used AGSA and RAPI tools to do a green and red profile assessment in order to investigate the environmental impact of the recently developed Stability indicating RP-HPLC method utilizing the Analytical Quality by Design approach. Taking into account variables including solvent usage, chemical compounds, energy consumption, and waste formation, this evaluation confirmed the methods' environmental friendliness.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2 Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Reagents and chemicals\u003c/h2\u003e\u003cp\u003eReference standard of AZL and MET were procured from Zydus LifeScience, Ahmedabad. Acetonitrile and Methanol (HPLC grade) was used as solvents in this method. All the glass wares were calibrated before using.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Instrumentation\u003c/h2\u003e\u003cp\u003eChromatographic analysis was carried out on a prominence HPLC Method: LC-2010AHT series binary pump systems, Auto sampler injection, temperature controller (column oven) system controller and a UV detector (LC-2010). CLASS-VP (version 2.42) software was used to acquire and process the data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sample and standard preparation\u003c/h2\u003e\u003cp\u003eStandard Solution of AZL (8 \u0026micro;g/mL) and MET (25 \u0026micro;g/mL) were prepared separately by dissolving 8 mg of AZL and 25 mg of MET in 10 mL of mobile Phase respectively. The standard solution was subsequently diluted to prepare different concentrations 4\u0026ndash;20 \u0026micro;g/mL and 12.5\u0026ndash;62.5 \u0026micro;g/mL of AZL and MET respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Selection of elution mode\u003c/h2\u003e\u003cp\u003ereverse phase chromatography is recommended for ionic and moderate to polar compounds, it is not only easy to use and convenient, but it also performs better in terms of efficiency, stability, and reproducibility. The C18 column was chosen because it is less polar than the C4 and C8 columns, allowing polar compounds to be eluted more quickly than non-polar ones. As a foundation for method development, a 250 \u0026times; 4.6 mm column of 5 \u0026micro;m particle packing was chosen. Because of its resilience with regard to extended column stability and ease of application, gradient mode was selected. For the majority of separations, this arrangement offers a wide range of potential plate values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Preparation of mobile phase\u003c/h2\u003e\u003cp\u003eThe mobile phase consisted of mixture of Acetonitrile: Water (70: 30% v/v) the mode for was gradient. The mobile phase was filtered through a 0.22 \u0026micro;m nylon membrane filter and degassed prior to use.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Chromatographic separation\u003c/h2\u003e\u003cp\u003eStandard and sample solutions were injected in column using ultrafast autosampler. The chromatogram was run for appropriate time duration with degassed mobile Acetonitrile: Water (70: 30% v/v) using UV detector (LC-2010) at wavelength 230 nm. The chromatogram was stopped after separation was achieved completely.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Method development using AQbD framework\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.7.1 Establishing Analytical Target Profiles and Critical Quality Attributes\u003c/h2\u003e\u003cp\u003eIn the context of AQbD, the term \"Analytical Target Profile\" (ATP) refers to a thorough explanation of the intended performance characteristics and requirements of an analytical technique. It serves as a road map for the methodical advancement and improvement of analytical techniques, guaranteeing that they satisfy established quality standards and legal obligations. By carefully selecting elements like Critical Quality Attributes (CQAs), which have a direct impact on the analytical procedure's quality and safety, the ATP serves as a first step in implementing a QbD-oriented approach. The ATP element targets and their rationale are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The retention duration, tailing factor, theoretical plates, and resolution are the CQA components that correspond to ATPs and are in charge of effective HPLC analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.7.2 Risk assessment studies\u003c/h2\u003e\u003cp\u003eThe primary risk factors influencing the advancement of analytical techniques are to be identified and addressed by this study. To determine how different situations would affect the approach's performance, a thorough risk analysis was conducted. To identify possible hazards, an analysis was done to look at the relationship between ATP and Critical Method Parameters (CMP).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.7.3 Factor screening study\u003c/h2\u003e\u003cp\u003eTo find CMPs that had a major influence on the CAAs, screening study was carried out using a Central Composite design (CCD) with Design-Expert\u0026reg; Software version 13. Three center points and two levels\u0026mdash;represented by the symbols\u0026thinsp;+\u0026thinsp;1 and \u0026minus;\u0026thinsp;1 for high and low levels, respectively\u0026mdash;were used to alter all dependent variables. In order to screen six CMPs, the CCD recommended fifteen experiments: flow rate (0.8\u0026ndash;1.2 min), acetonitrile volume (65\u0026ndash;75), injection volume (15\u0026ndash;25 \u0026micro;L), and detection wavelength (229\u0026ndash;232 nm). Retention duration, peak area, number of theoretical plates, tailing factor, and resolution were among the factors whose effects on CAAs were examined.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.7.4 Optimization study\u003c/h2\u003e\u003cp\u003eCritical parameters influencing the HPLC method's critical quality attributes (CAAs) were determined by the screening research and risk assessment. Central Composite Design (CCD) was then used to do an optimization analysis. The critical material parameters (CMPs) for the CAAs, which comprised retention duration, number of theoretical plates, and resolution, were determined to be the Flow Rate (mL/min) (X1), Mobile Phase (Volume of Acetonitrile (70 mL)) (X2), and Detection wavelength (nm) (X3). A design matrix with 17 trial runs with chosen CAAs ranging at three levels high (+\u0026thinsp;1), midrange (0), and low (-1) was suggested by Design-Expert\u0026reg; version 13.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Force Degradation\u003c/h2\u003e\u003cp\u003eIn order to demonstrate the stability of both the standard and sample solutions during analysis, both solutions were analyzed over a period of 24 h at room temperature. The results indicated that for both the solutions, the retention time and peak area of AZL and MET did not show much% difference. There was no significant degradation within the indicated period. Hence, it was concluded that both the solutions were stable for 24 h at room temperature.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Method validation\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e2.9.1 System suitability studies\u003c/h2\u003e\u003cp\u003eThe system suitability was evaluated by five replicate analyses of AZL and MET mixture at concentration of 16 \u0026micro;g/mL of AZL and 50 \u0026micro;g/mL of MET. The column efficiency, resolution, and peak asymmetry were calculated for the standard solutions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e2.9.2 Linearity\u003c/h2\u003e\u003cp\u003eLinearity of the proposed method was assessed by scanning concentrations at five equidistance levels 6\u0026ndash;20 \u0026micro;g/mL and 12.5\u0026ndash;62.5 \u0026micro;g/mL for HPLC method for Azelnidipine and Metoprolol succinate respectively. A graph of Concentration vs. Peak Area and Concentration vs. Absorbance was plotted for HPLC method respectively and regression equation was obtained.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e2.9.3 Precision\u003c/h2\u003e\u003cdiv id=\"Sec19\" class=\"Section4\"\u003e\u003ch2\u003e2.9.3.1 Repeatability\u003c/h2\u003e\u003cp\u003eThe Repeatability precision was performed by evaluating lowest concentration for six replicates; 16 \u0026micro;g/mL and 50 \u0026micro;g/mL for HPLC for Azelnidipine and Metoprolol succinate respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section4\"\u003e\u003ch2\u003e2.9.3.2 Intraday precision\u003c/h2\u003e\u003cp\u003eThe Intraday precision was performed by evaluating three concentration levels on same day for three replicates; 4, 12 and 20 \u0026micro;g/ mL and 12.5, 37.5 and 62.5 \u0026micro;g/mL for HPLC method for Azelnidipine and Metoprolol succinate respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section4\"\u003e\u003ch2\u003e2.9.3.3 Interday precision\u003c/h2\u003e\u003cp\u003eThe Interday precision was performed by considering three concentration levels (same as intraday precision) on three different days for three replicates. All solutions were prepared form different stocks prepared on different days. The standard deviation and% RSD were calculated.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e2.9.4 Recovery Studies\u003c/h2\u003e\u003cp\u003eIt was determined by calculating the recovery of AZL and MET by standard addition method. Accuracy was performed by% recovery study at 50%, 100% and 150% by spiking the API to the placebo.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e2.9.5 Limit of quantification (LOQ) \u0026amp; Limit of detection (LOD)\u003c/h2\u003e\u003cp\u003eAs per ICH guideline, limit of detection and quantitation of the developed method were calculated from the standard deviation of the response (σ) and slope of the calibration curve (S) of drug using the formula; Limit of detection\u0026thinsp;=\u0026thinsp;3.3* σ/S and Limit of quantitation\u0026thinsp;=\u0026thinsp;10* σ/S.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e2.9.6 Robustness\u003c/h2\u003e\u003cp\u003eRobustness of the method was determined by subjecting the method to slight change in the method condition individually. For HPLC method, robustness parameters were Pump flow rate (1 ml/min\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 ml/min) and Mobile Phase Composition. The% RSD was calculated for all the parameters.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e2.9.7 Analysis of marketed formulation\u003c/h2\u003e\u003cp\u003eTake HPMC (4 mg), MCC (190 mg), Magnesium stearate (4 mg), Talc (2 mg). Role of HPLC-Film forming agent, MCC- Directly compressible material, MS, Gliding agent, Talk, Lubricating agent, AZL (80 mg), and MET (250 mg) was taken into the volumetric flask (100 ml) and volume of the flask was raised to 100 ml with methyl alcohol to give stock solution containing 800 \u0026micro;g/ml of AZL, and 2500 \u0026micro;g/ml of MET. Withdraw 100 \u0026micro;l from above filtrate in 10 mL volumetric flask; make up the volume with mobile phase, which contain AZL\u0026thinsp;+\u0026thinsp;MET\u0026thinsp;=\u0026thinsp;8\u0026thinsp;+\u0026thinsp;25 \u0026micro;g/ml.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3 Result and discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Selection of wavelength\u003c/h2\u003e\n \u003cp\u003eThe 230 nm isobestic wavelength of AZL and MET was selected as the detection wavelength for HPLC\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Mobile phase selection\u003c/h2\u003e\n \u003cp\u003eAsymmetric peaks and a delayed retention time of MET were the results of using multiple mobile phases with varying ratios of different solvents and pH. The ideal polarity for appropriate migration, separation, and resolution of AZL and MET peaks was supplied by the 70:30 v/v acetonitrile: water mixture. The eluted peaks were clear, distinct, and tailing-free under these circumstances.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Utilization of CCD method for Optimization of RP-HPLC method\u003c/h2\u003e\n \u003cp\u003eRetention duration and resolution were the analytical target profiles chosen for HPLC condition optimization. To further optimize several factors inside the design space, the Central Composite Design was employed. The quadratic model for main and interaction effects was chosen in order to analyze the data. Three primary points and twenty experimental designs were implemented. 20 optimized experimental runs, the mobile phase (volume of acetonitrile (70 mL)), the detection wavelength (nm), and the flow fate (mL/min) are among the variables. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provides a summary of the replies that were obtained. The model was validated using the provided ANOVA. The significance level was less than 0.05. The corrected R2 was found to have a high coefficient of variation (\u0026ge;\u0026thinsp;10%). This demonstrates a strong correlation between the models and the experimental data that was acquired. The collected experimental data fits the equations with the components and factors listed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e as indicated by the R2 values adjusted with limits of R\u0026thinsp;\u0026ge;\u0026thinsp;0.70, which are within acceptable bounds.\u003c/p\u003e\n \u003cp\u003eEach variable\u0026apos;s 3D response surface plots illustrate how the CMPs affect CAAs. Peak area is significantly influenced by critical parameters like flow fate (mL/min), mobile phase (volume of acetonitrile (70 mL), and detection wavelength (nm). The curve graphs show how each of the three parameters significantly affects resolution and retention time.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"1011\" height=\"88\"\u003e\u003c/p\u003e\n \u003cp\u003eThe retention period of AZL appears to be directly related to the flow rate (A) and detection wavelength (C), as indicated by the positive coefficients of factors A, B, and C. B has a less significant effect when the Mobile Phase (Volume of Acetonitrile (70 mL)) (B) factor is lower. Therefore, As the flow rate (A) and detection wavelength (C) increase, the AZL retention duration also increases (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"1029\" height=\"90\"\u003e\u003c/p\u003e\n \u003cp\u003eThe retention period of MET appears to be directly proportional to the mobile phase (volume of acetonitrile (70 mL)), as indicated by the positive coefficients of factors A, B, and C (B). The flow rate (A) and detection wavelength (C) variables\u0026apos; negative or lower values suggest that A and C have little to no effect. Therefore, As the volume of acetonitrile (70 mL) in the mobile phase increases, so does the retention period of MET (B) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"1037\" height=\"140\"\u003e\u003c/p\u003e\n \u003cp\u003eResolution between AZL and MET is directly proportional to Flow Rate (A) and Detection wavelength (C), according to the positive coefficient of factors A, B, and C. Thus, Resolution between AZL and MET increases as Flow Rate (A) and Detection wavelength (C) increase. A negative or less value of the Mobile Phase (Volume of Acetonitrile (70 mL)) (B) factor indicates less or no significant effect of B (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCentral Composite rotatable design arrangement and responses\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRun\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA: Flow Rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB: Mobile Phase (Volume of Acetonitrile (70 mL))\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC: Detection wavelength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRt of AZE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRt of MET\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResolution AZE \u0026amp; MET\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emin/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.663641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of results of regression analysis for models and responses\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR-Squared\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003cp\u003eR-Squared\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredicted\u003c/p\u003e\n \u003cp\u003eR-Squared\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%CV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrecision (Adequate)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQuadratic\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.8262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.2434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.3608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of experimental and predictive value of different experimental runs under optimum conditions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOptimum\u003c/p\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponses\u003c/p\u003e\n \u003cp\u003e(predicted)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponses\u003c/p\u003e\n \u003cp\u003e(observed)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredicted\u003c/p\u003e\n \u003cp\u003eerror %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention time of AZL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention time of MET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResolution between AZL and MET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eUnder ideal circumstances, the percentage anticipated error displayed a desirability value (D\u0026thinsp;=\u0026thinsp;1), which provided a set of coordinates provided in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe mobile phase composition of the optimized solution, which had a 70:30% v/v mixture of acetonitrile and water, at a detection wavelength of 230 nm and a flow rate of 1 ml/min, produced a desirability that was nearly 1.0 and all of the CAAs were within the intended range (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Standard AZL and MET have demonstrated distinct peaks and satisfactory separation under the chromatographic conditions used in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOptimized Chromatographic condition for the estimation of AZL and MET Succinate by HPLC\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRetention time\u003c/p\u003e\n \u003cp\u003e(min)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTailing Factor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTheoretical Plates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResolution\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAZL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.318 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19821.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.584 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13751.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Force Degradation study\u003c/h2\u003e\n \u003cp\u003eThe capacity of the optimized approach to separate all degradation products in the presence of the active ingredient was determined to be a stability indicator (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) In stressed samples, no degradation product was discovered to obstruct the estimate of MET and AZE. imposed that the percentage of degradation seen was predictive in nature (below 15%), even the stress imposed was determined to be optimal (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEvaluation Table of Forced Degradation Studies\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStress Condition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAZL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMET\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% Degradation (AZL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% Degradation (MET)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcid\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHydrolysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObserved Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBase\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHydrolysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObserved Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOxidative Stress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObserved Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eThermal Degradation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObserved Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Method validation\u003c/h2\u003e\n \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.1 System suitability data\u003c/h2\u003e\n \u003cp\u003eThe standard solutions\u0026apos; column efficiency, resolution, and peak asymmetry were computed and compiled in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.2 Linearity\u003c/h2\u003e\n \u003cp\u003eThe HPLC overlain chromatogram for AZL succinate and MET succinate was displayed in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and ranged from 4 to 20 \u0026micro;g/mL and 12.5 to 62.5 \u0026micro;g/mL, respectively, with retention times of 5.58 and 3.32 minutes.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.3 Precision\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e summarized the results of the HPLC and Absorbance Correction UV method\u0026apos;s repeatability, intraday precision, and interday precision.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.4 Accuracy\u003c/h2\u003e\n \u003cp\u003eAccording to the results described in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the recovery percentage, which was determined to be between 98% and 102%, validates the correctness of the devised HPLC and Absorbance Correction UV method.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValidation parameter for RP-HPLC method\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSr.\u003c/p\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAZL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMET\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinearity Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026ndash;20 \u0026micro;g/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5\u0026ndash;62.5 \u0026micro;g/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo- relation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003cp\u003e1. Repeatability (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003cp\u003e2. Intra-day precision (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003cp\u003e3. Inter-day precision (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e0.47\u0026ndash;1.02\u003c/p\u003e\n \u003cp\u003e0.63\u0026ndash;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e0.59\u0026ndash;0.82\u003c/p\u003e\n \u003cp\u003e0.62\u0026ndash;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccuracy (% Recovery)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.75\u0026ndash;99.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.60-99.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimit of detection (LOD) (\u0026micro;g/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimit of quantification (LOQ)\u003c/p\u003e\n \u003cp\u003e(\u0026micro;g/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.5 Limit of quantification (LOQ) \u0026amp; Limit of detection (LOD)\u003c/h2\u003e\n \u003cp\u003eAZL\u0026apos;s LOD and LOQ were determined to be 0.24 \u0026micro;g/mL and 0.73 \u0026micro;g/mL, while MET\u0026apos;s were 0.44 \u0026micro;g/mL and 1.35 \u0026micro;g/mL (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.6 Robustness\u003c/h2\u003e\n \u003cp\u003eThe devised HPLC technique was found to be robust when the percentage RSD value for all robustness parameters was less than 2%. Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e provided a summary of the findings.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRobustness data for AZL and MET\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLevel of Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eEffect on assay volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAZL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMET\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssay\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssay\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFlow rate\u003c/p\u003e\n \u003cp\u003e(\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9 mL/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.52\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.47\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 mL/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.41\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.49\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMobile Phase Composition\u003c/p\u003e\n \u003cp\u003e(70:30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72:28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.35\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.52\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68:32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.77\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.94\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.7 Assay\u003c/h2\u003e\n \u003cp\u003eBy adding standard excipients to a synthetic mixture, the assay was estimated and examined. Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e presented the assay results, which fell between 98% and 102% of the acceptance criteria.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssay of Marketed Formulation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmount taken\u003c/p\u003e\n \u003cp\u003e(\u0026micro;g/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmount found\u003c/p\u003e\n \u003cp\u003e(\u0026micro;g/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% Assay\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAZL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.95\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.42\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.74\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.96\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec39\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Green Assessment profile: Analytical Green Star Area (AGSA)\u003c/h2\u003e\n \u003cp\u003eThe Analytical Green Star Area (AGSA) is a novel tool developed to assess the environmental impact of analytical chemistry methods in a structured, visual, and objective way. Unlike existing metrics such as the Analytical Eco-Scale (AES) and GAPI, AGSA integrates both a total scoring system and a visual star-shaped diagram to evaluate how well a method aligns with the 12 Principles of Green Analytical Chemistry (GAC). Each principle is assessed through specific questions with standardized scoring from 1 to 3, allowing for consistent and reproducible evaluations. AGSA reduces user bias by providing clear guidelines and combines both numeric results and visual outputs to make comparison between methods easier and more intuitive. The final greenness score is out of 36 points, representing 100% alignment with green practices. A larger star area on the AGSA diagram indicates a greener method. Additionally, AGSA builds upon the Green Star Area concept used in green chemistry, making it suitable for cross-disciplinary evaluations. AGSA represents a practical, effective, and user-friendly solution for promoting greener, safer, and more sustainable analytical procedures. The output score of the suggested method was 75 according to the AGSA tool. Figure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e makes explicit reference to it. The chemical community will pay more attention to, trust, and accept the AGSA tool.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec40\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Red Assessment profile: Rapid Assessment of Performance Indicators (RAPI)\u003c/h2\u003e\n \u003cp\u003eRAPI (Rapid Assessment of Performance Indicators) is a Python-based, open-source tool designed to assess the quantitative performance of analytical methods quickly and objectively. It follows the structure of BAGI and operates through a simple interface where users select values from drop-down menus. RAPI focuses on ten key criteria based on ICH validation guidelines and general good laboratory practices: repeatability, intermediate precision, reproducibility, trueness, recovery and matrix effects, LOQ, working range, linearity (R\u0026sup2;), ruggedness, and selectivity. Each parameter is scored on a five-level scale from 0 to 10, with 0 also assigned when a parameter hasn\u0026rsquo;t been tested. The overall method score, ranging from 0\u0026ndash;100, is shown at the center of a star-shaped pictogram, where the color intensity visually reflects the score for each criterion. RAPI ensures fairness by giving equal weight to all criteria and adjusts expectations based on analyte concentration using the Horwitz model. It encourages comprehensive validation by penalizing missing data, promoting better laboratory practices. While it provides valuable comparative insight, RAPI should not be the only factor in determining method suitability. It is most useful when comparing methods applied to the same analyte and matrix, ensuring context-appropriate decisions in method development and selection. The tool thus supports laboratories in reducing their environmental footprint while maintaining the high standards of accuracy and reliability required in analytical chemistry show in Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 CONCLUSION","content":"\u003cp\u003eA robust, eco-friendly, and cost-effective RP-HPLC method was successfully developed and validated for the simultaneous estimation of Azelnidipine and Metoprolol Succinate using a Quality by Design (QbD) approach. The method demonstrated excellent precision, accuracy, linearity, robustness, and stability, making it suitable for routine pharmaceutical quality control. Notably, the greenness and safety of the developed method were comprehensively evaluated using Analytical Green Star Area (AGSA) and Rapid Assessment of Performance Indicators (RAPI), confirming its strong environmental and occupational health profile. This integrated green\u0026ndash;red assessment underscores the method\u0026rsquo;s alignment with modern analytical goals, combining sustainability, performance, and analyst safety. The proposed method not only supports regulatory compliance and reliability but also contributes to sustainable laboratory practices, offering a valuable tool for the pharmaceutical industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eI\u0026rsquo;m very thankful to Department of pharmaceutical quality Assurance, Gokul Pharmacy College, Gokul global University, Sidhdhpur. I would also like to thank the Principal Sir, for providing the necessary facilities to carry out this work.\u003c/p\u003e\n\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.\u003c/p\u003e\n\u003ch2\u003eFunding Declaration:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNo funding\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eClinical trial number:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eConsent to Publish declaration:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eConsent to Participate declaration:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eEthics declaration:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSS wrote the main manuscript, prepared figures, and collected and formatted data.RP supervises all.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKain V, Kumar S, Puranik AS, Sitasawad SL (2010) Azelnidipine protects myocardium in hyperglycemia-induced cardiac damage. Cardiovasc Diabetol 9:1\u0026ndash;9. https://doi.org/10.1186/1475-2840-9-82/FIGURES/4\u003c/li\u003e\n\u003cli\u003eMorris J, Awosika AO, Dunham A (2024) Metoprolol. xPharm Compr Pharmacol Ref 1\u0026ndash;7. https://doi.org/10.1016/B978-008055232-3.62174-9\u003c/li\u003e\n\u003cli\u003eDarji H, Dedania Z (2023) Simultaneous estimation of Azelnidipine and Metoprolol succinate with greenness assessment using HPLC and UV-spectrophotometric methods. Green Anal Chem 7:100079. https://doi.org/10.1016/J.GREEAC.2023.100079\u003c/li\u003e\n\u003cli\u003eMiladinović SM (2024) Green analytical chemistry: integrating sustainability into undergraduate education. Anal Bioanal Chem 417:665. https://doi.org/10.1007/S00216-024-05680-4\u003c/li\u003e\n\u003cli\u003eNowak PM, Wojnowski W, Manousi N, et al (2025) Red analytical performance index (RAPI) and software: the missing tool for assessing methods in terms of analytical performance. Green Chem 27:5546\u0026ndash;5553. https://doi.org/10.1039/D4GC05298F\u003c/li\u003e\n\u003cli\u003eMansour FR, Bedair A, Belal F, et al (2025) Analytical Green Star Area (AGSA) as a new tool to assess greenness of analytical methods. Sustain Chem Pharm 46:102051. https://doi.org/10.1016/J.SCP.2025.102051\u003c/li\u003e\n\u003cli\u003eKoel M, Kaljurand M (2021) Editorial overview: A closer look on green developments in analytical chemistry: Green analytical chemistry is going mainstream. Curr Opin Green Sustain Chem 31:. https://doi.org/10.1016/J.COGSC.2021.100541\u003c/li\u003e\n\u003cli\u003eGałuszka A, Migaszewski Z, Namieśnik J (2013) The 12 principles of green analytical chemistry and the SIGNIFICANCE mnemonic of green analytical practices. TrAC - Trends Anal Chem 50:78\u0026ndash;84. https://doi.org/10.1016/J.TRAC.2013.04.010\u003c/li\u003e\n\u003cli\u003eDhull P, Dunuweera S, Bietsch J, et al (2025) Recent advances and application of liquid chromatography in pharmaceutical industry. J Liq Chromatogr Relat Technol. https://doi.org/10.1080/10826076.2024.2448692\u003c/li\u003e\n\u003cli\u003e(PDF) applications in HPLC in pharmaceutical analysis. https://www.researchgate.net/publication/338166319_applications_in_HPLC_in_pharmaceutical_analysis. Accessed 5 Mar 2025\u003c/li\u003e\n\u003cli\u003eAgrawal R, Kotadiya R (2024) AQbD-guided stability indicating HPLC method for azelnidipine and chlorthalidone fixed-dose combination tablet: a green approach. J Taibah Univ Sci 18:2415156. https://doi.org/10.1080/16583655.2024.2415156\u003c/li\u003e\n\u003cli\u003eMevada S, Shukla S, Patel H (2024) Simultaneous estimation of andrographolide, apigenin, apocynin, and gallic acid by high-performance thin layer chromatography method with Greenness quality by design approach. Sep Sci Plus 7:2300109. https://doi.org/10.1002/SSCP.202300109\u003c/li\u003e\n\u003cli\u003eMevada S, Shukla S, U Patel H (2025) Greenness Assessment of High-Performance Liquid Chromatography Method for Simultaneous Estimation of Apigenin, Apocynin, and Gallic Acid With Quality by Design Approach. Sep Sci Plus 8:e202400335. https://doi.org/10.1002/SSCP.202400335\u003c/li\u003e\n\u003cli\u003eMevada S, Patel H, Shukla S (2024) Simultaneous equation method for the estimation of gallic acid and apigenin by UV\u0026ndash;visible spectrophotometry. Accredit Qual Assur 29:11\u0026ndash;17. https://doi.org/10.1007/S00769-023-01558-7\u003c/li\u003e\n\u003cli\u003eMevada S, Patel H, Shukla S (2025) Development and validation of simultaneous equation method for the estimation of andrographolide and apocynin in hepatoprotective polyherbal formulation using UV\u0026ndash;visible spectrophotometry. Accredit Qual Assur 1\u0026ndash;8. https://doi.org/10.1007/S00769-025-01633-1/METRICS\u003c/li\u003e\n\u003cli\u003eAuthor C, Mane YM (2024) Development And Validation Of UV Method For Simultaneous Estimation Of Metoprolol Succinate And Azelnidipine In Pharmaceutical Dosage Form. Int J Pharm Sci 02:292. https://doi.org/10.5281/ZENODO.12658344\u003c/li\u003e\n\u003cli\u003eAgrawal S, Nizami T (2021) METHOD DEVELOPMENT AND VALIDATION FOR THE SIMULTANEOUS DETERMINATION OF AZELNIDIPINE AND TELMISARTAN IN TABLET DOSAGE FORM BY RP- HPLC. Int J Pharm Sci Med 6:26\u0026ndash;36. https://doi.org/10.47760/IJPSM.2021.V06I10.003\u003c/li\u003e\n\u003cli\u003ePekamwar SS, Kalyankar TM, Kokate SS (2014) RP-HPLC method development and validation for simultaneous estimation of bromhexine and ciprofloxacin in tablet dosage form. Der Pharma Chem 6:90\u0026ndash;97\u003c/li\u003e\n\u003cli\u003eHussain S, Amjad M, Khan A, Hussain S (2021) HPLC Method Development and Validation for the Simultaneous Estimation of Atorvastatin Calcium and Sildenafil. Int J Pharm Res 13:. https://doi.org/10.31838/ijpr/2021.13.01.715\u003c/li\u003e\n\u003cli\u003eKrittanawong C, Khawaja M, Ul H, et al (2024) Strategies for chronic coronary disease: A brief guide for clinicians. npj Cardiovasc Heal 2024 11 1:1\u0026ndash;15. https://doi.org/10.1038/s44325-024-00006-w\u003c/li\u003e\n\u003cli\u003eCato T. Laurencin, Farmington C, (US); Lakshmi Sreedharan Nair A, (US) C (2005) Immobilized Metallic Nanoparticles as Unique Materials for Therapeutic and Biosensor Applications. 2:\u003c/li\u003e\n\u003cli\u003eBawane S, Telrandhe R, Pande SD (2018) Formulation and Evaluation of Oral Fast Dissolving Film of Bisoprolol Fumarate. Int J Pharm Drug Anal 6:105\u0026ndash;115\u003c/li\u003e\n\u003cli\u003eZhang K, Liu X (2016) Mixed-mode chromatography in pharmaceutical and biopharmaceutical applications. J Pharm Biomed Anal 128:73\u0026ndash;88. https://doi.org/10.1016/J.JPBA.2016.05.007\u003c/li\u003e\n\u003cli\u003eMarie AA, Hammad SF, Salim MM, et al (2023) Deduction of the operable design space of RP-HPLC technique for the simultaneous estimation of metformin, pioglitazone, and glimepiride. Sci Rep 13:1\u0026ndash;13. https://doi.org/10.1038/s41598-023-30051-x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Azelnidipine, Metoprolol succinate, RP-HPLC, Analytical Quality by Design (AQbD), Stability-indicating method, Red Analytical Chemistry and Green Analytical Chemistry","lastPublishedDoi":"10.21203/rs.3.rs-7154441/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7154441/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA sustainable, stability-indicating Reverse Phase High Performance Liquid Chromatography (RP-HPLC) method was developed and validated for the simultaneous estimation of Azelnidipine (AZL) and Metoprolol Succinate (MET) using an Analytical Quality by Design (AQbD) approach. Method optimization was performed using Central Composite Design (CCD), focusing on critical method parameters including flow rate, mobile phase composition, and detection wavelength. The optimized method employed a C18 column with a mobile phase of acetonitrile: water (70:30, v/v) under gradient elution and detection at 230 nm. The method showed excellent linearity, accuracy, precision, and robustness across a defined concentration range and was confirmed to be stability-indicating through forced degradation studies. To assess the environmental and safety performance of the method, two modern evaluation tools were applied: Analytical Green Star Area (AGSA) and Rapid Assessment of Performance Indicators (RAPI). The AGSA tool confirmed strong adherence to the 12 Principles of Green Analytical Chemistry, while the RAPI score reflected high analytical reliability and occupational safety in line with Red Analytical Chemistry principles. The integration of QbD with green and red assessments ensures both regulatory compliance and sustainable laboratory practice. This validated method offers a practical and comprehensive solution for the routine quality control of AZL and MET in pharmaceutical formulations.\u003c/p\u003e","manuscriptTitle":"Sustainable QbD-Driven RP-HPLC Method for the Simultaneous Estimation of Azelnidipine and Metoprolol Succinate with Green and Red Analytical Assessments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 13:48:49","doi":"10.21203/rs.3.rs-7154441/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07ab1319-c013-493b-9410-51f9169150a4","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T08:39:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 13:48:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7154441","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7154441","identity":"rs-7154441","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}