1. INTRODUCTION
Polycystic ovarian syndrome (PCOS) is a prevalent, multifactorial, and heterogeneous endocrinological-metabolic disorder impacting women of reproductive age, with a global prevalence estimated between 5% and 21% [1]. Conventional treatments, including ovulation induction, androgen suppression, and insulin sensitizers, are often associated with adverse effects such as nausea, fatigue, and gastrointestinal discomfort, resulting in suboptimal patient adherence [2]. Therefore, substitute therapeutic approaches are vital to mitigate undesirable side effects and improve therapy efficacy.
Daidzein (DAI), (Fig. 1), is a naturally occurring isoflavonic phytoestrogen sourced from leguminous plants [3] known for its anti-inflammatory, antioxidative, and neuroprotective properties [4,5]. Its unique estrogen-like ability [6] helps to regulate estrogen levels and alleviate hormonal imbalances, a hallmark of PCOS. Owing to its poor oral absorption and associated side effects, there is a need to develop advanced delivery systems for DAI to enhance its absorption, bioavailability, and site-specific delivery, thereby realizing its full therapeutic potential in PCOS management.
 | Figure 1. Structure of DAI. [Click here to view] |
Cubosomes are nanostructured lipid-based carriers belonging to lyotropic liquid crystalline systems, composed of amphiphilic lipids forming bicontinuous cubic phases stabilized by polymeric surfactants. Their unique architecture with interpenetrating aqueous channels within a continuous lipid bilayer enables the concurrent encapsulation of hydrophilic, lipophilic, and amphiphilic molecules, offering high drug-loading efficiency, sustained release, mucoadhesion, and thermodynamic stability [7,8]. Compared with previously reported DAI formulations such as nanosuspensions [9], Poly (lactic-co-glycolic acid) nanoparticles [10], cocrystals [11], and solid lipid nanoparticles [12], cubosomes provide superior bioavailability, prolonged release, and enhanced tissue targeting, particularly beneficial for reproductive tissue delivery in PCOS. Notably, no reports exist on DAI-loaded cubosomes or their comprehensive physicochemical and analytical evaluation, highlighting a clear research gap in this domain.
Quantitative analysis of DAI in both bulk and nano formulated states is crucial for rational formulation design, process optimization, and quality control. Several analytical techniques, including Ultraviolet (UV) spectrophotometry [13], high-performance liquid chromatography (HPLC) [14], and ultra-high-performance liquid chromatography [15], have been reported; most existing methods are tailored to food or biological matrices and predominantly utilize acetonitrile-rich mobile phases, raising significant environmental and safety concerns. These conventional approaches often lack the sensitivity, selectivity, and robustness required for complex lipid-based nanocarriers like cubosomes, frequently exhibiting poor resolution of excipient–drug interferences [16,17]. Moreover, they seldom incorporate quality by design (QbD) principles or assess environmental sustainability within the framework of Green Analytical Chemistry (GAC), limiting their applicability to advanced drug delivery systems.
In response to these limitations, the present investigation aims to establish a cost-effective, QbD-oriented, and environmentally benign reverse-phase high-performance liquid chromatography (RP-HPLC) method specifically optimized for the quantification of DAI in cubosomal matrices. The method employs a methanol–water mobile phase, providing superior UV transparency at DAI’s λ_max (249–254 nm), efficient solute–solvent interactions, reduced toxicity, and minimal organic waste relative to acetonitrile-based systems [18]. Systematic optimization of critical chromatographic attributes was performed using Taguchi and central composite design (CCD) models to ensure analytical robustness and reproducibility. Furthermore, the developed efficient valid green (EVG) HPLC method underwent a comprehensive quantitative evaluation of its environmental sustainability using advanced green assessment frameworks, including the complex green analytical procedure index (GAPI), Analytical GREEnness Metric (AGREE), Eco-Scale Assessment (ESA), and Blue Applicability Grade Index (BAGI), thereby substantiating its alignment with contemporary principles of GAC.
Collectively, these tools affirm the method’s strong environmental compatibility and analytical robustness. This investigation pioneers an integrated and forward-looking approach that unites the development of DAI-loaded cubosomes for targeted PCOS therapy with a validated, QbD-oriented, and environmentally sustainable RP-HPLC method. This dual innovation not only strengthens the analytical foundation for lipidic nanocarrier research but also exemplifies the transition toward green, quality-centric methodologies in contemporary pharmaceutical analysis.
2. MATERIALS AND METHODS
2.1. Materials
DAI (97%) was procured from Chemsenses Ltd., Thane, Maharashtra, India. Poloxamer 407 and glyceryl monooleate (GMO) were provided as complimentary samples by Mohini Organics Pvt. Ltd., Mumbai, India, while HPLC-grade methanol was sourced from HiMedia Laboratories Pvt. Ltd. A polyvinylidene difluoride filter membrane (0.45 μm; Millex HV®, Millipore, USA) was utilized, and HPLC-grade water was maintained throughout the study to ensure analytical accuracy.
2.2. Instrument and chromatographic specifications
Method development and forced degradation studies were executed utilizing an advanced “Shimadzu HPLC system (LC-2010, Kyoto, Japan)” featuring an “LC-20AD pump, DGU-20A5” degasser, and “CTO-10ASVP column oven, SIL-20ACHT auto-injector, SPD-M20A Photodiode Array Detector (PDA) detector, and Lab Solutions software (v1.25)”. Reversed-phase separation was conducted via a Phenomenex Luna C18 analytical column (150, 5 μm, × 4.6 mm) maintained at 30°C. The isocratic elution utilized a methanol–water mixture (64.52:35.48, v/v) at a flow rate of 1.08 ml/min. Detection was conducted at 249 nm with a 10 µl injection volume. To ensure analytical accuracy, the mobile phase was filtered through a 0.45-µm nylon filter and degassed using an ultrasonic bath. Peak purity assessments were facilitated through advanced spectral analysis, while forced degradation samples were evaluated in PDA scan mode across a spectral range of 200–400 nm.
2.3. Standard solution preparation
A DAI stock solution (1,000 µg/ml) was thoroughly composed by dissolving 10 mg of DAI in methanol within a 10 ml volumetric flask. Subsequently, a working solution of 10 µg/ml was achieved by accurately diluting the stock solution with the mobile phase, facilitating the preparation of calibration standards ranging from 2 to 10 µg/ml. To ensure purity, all samples were filtered through a 0.22 µm Millipore membrane before chromatographic analysis for calibration curve construction. Solutions were stored in tightly sealed, amber-coloured volumetric flasks, protected from light, until analysis.
2.4. Quality-centric RP-HPLC method design
2.4.1. Target method quality profile (TMQP) and critical analytical attributes (CAAs)
The TMQP, also referred to as the analytical target profile (ATP), forms the foundation of the A-QbD approach by defining the intended method purpose and performance requirements. Based on the TMQP goals (Table 1), the CAAs were established as retention time (RT), tailing factor (TF), and theoretical plates (TP), which collectively determine chromatographic efficiency, peak symmetry, and analytical throughput [19]. These CAAs were selected in alignment with compendial expectations to ensure that the final method delivers rapid analysis, acceptable peak symmetry, and adequate column efficiency consistent with United States Pharmacopeia (USP) and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) standards.
Table 1. Essential TMQP for an effective liquid chromatographic approach for quantification of DAI.
| Procedure variables | Goal | Rationale |
|---|---|---|
| Analysed sample | DAI /Lipid nanocarriers | Formulation of a robust analytical technique for quantifying DAI in bulk API and cubosomal formulation for routine quality control and stability evaluation. |
| Specimen type | Dissolved phase | The analyte must be transitioned into the fluid phase to guarantee absolute miscibility and seamless integration. |
| Analytical methodology | RP-HPLC | A resilient and highly efficacious methodology employing a nonpolar stationary phase to augment molecular retention and ensure superior chromatographic performance. |
| Equipment specifications | HPLC system fitted with a quaternary pump and a UV detector. | The quaternary pump ensures precise mobile phase mixing for superior chromatographic resolution, while the UV detector enables accurate analyte detection at an optimal wavelength. |
| Specimen procedure | Systematic dilution series in a linear assay | Precise sample dilution guarantees optimal separation. |
| Assay | DAI quantification | The technique should efficiently assay DAI in bulk and cubosomal formulations for routine analysis and stability evaluation. |
| Quality attributes (CQA) | Target values | Justification |
| RT | <10 minutes | Ensure that the chromatographic method meets compendial quality expectations, delivering rapid analysis, acceptable peak symmetry, and adequate column efficiency in accordance with USP <621> and ICH Q2(R1) guidelines. |
| TF | <2.0 | |
| TP | >2,000 plates |
2.4.2. Risk evaluation and factor identification analyses
Preliminary risk assessment is crucial for identifying factors that may influence method performance before chromatographic optimization [20]. Utilizing Minitab® 18 software (Minitab, LLC, USA), an Ishikawa fishbone diagram was meticulously constructed to elucidate the interrelation between critical method parameters (CMPs) and CAAs within the ATP/TMQP framework. Based on chromatographic theory, prior experimental experience, and literature evidence related to DAI analysis, seven potential CMPs, mobile phase composition, flow rate, column temperature, column dimensions, injection volume, detection wavelength, and flow pattern were shortlisted. These factors were statistically screened using a Taguchi L8 (27) factorial design with predefined high (+1) and low (-1) levels to assess their effects on RT, TF, and TP. The studied range values for each factor are presented in Table 2, along with the experimental design matrix [21].
Table 2. Risk assessment of DAI utilizing Taguchi design.
| Run | Organic phase ratio | Flow rate | Column temperature | Injection volume | Column dimension | Wavelength | Flow type |
|---|---|---|---|---|---|---|---|
| 1 | 55 | 0.9 | 28 | 8 | 148 | 247 | Low |
| 2 | 55 | 1.1 | 32 | 12 | 152 | 247 | Low |
| 3 | 65 | 1.1 | 28 | 12 | 148 | 247 | High |
| 4 | 65 | 0.9 | 32 | 12 | 148 | 251 | Low |
| 5 | 55 | 1.1 | 32 | 8 | 148 | 251 | High |
| 6 | 65 | 1.1 | 28 | 8 | 152 | 251 | Low |
| 7 | 55 | 0.9 | 28 | 12 | 152 | 251 | High |
| 8 | 65 | 0.9 | 32 | 8 | 152 | 247 | High |
| Level of factor study | |||||||
| Mobile phase | (Organic: aqueous) | Low (−1) | High (+1) | ||||
| Flow rate | (ml/min) | 0.9 | 1.1 | ||||
| Column temperature | (C) | 28 | 32 | ||||
| Injection volume | (μl) | 8 | 12 | ||||
| Column dimension | (mm) | 148 | 152 | ||||
| Wavelength | (λmax) | 247 | 251 | ||||
| Flow type | Low | High | |||||
2.4.3. Response optimization analysis
Two CMPs were identified due to their profound impact on CAAs: (A) mobile phase composition (X1) and (B) flow rate (X2). These parameters underwent further refinement through response surface methodology (RSM), employing a CCD with three uniformly spaced levels—lower (−1), mid-level (0), and upper (+1)—distributed across 13 experimental runs (Table 3). All additional variables were maintained at their optimal conditions, and a standard concentration was used throughout the study [22]. The resulting data were evaluated based on three key response factors: RT(Y1), TF(Y2), and TP(Y3).
Table 3. Influence of central composite design independent variables on dependent variables.
| Run | Variable (X1) | Variable (X2) | Effect (Y1) | Effect (Y2) | Effect(Y3) |
|---|---|---|---|---|---|
| A: % organic phase ratio | B: Flow rate | RT | TF | TP | |
| % | min | min | |||
| 1 | +1 | +1 | 4.6 | 1.175 | 4,660 |
| 2 | −1 | 0 | 8.45 | 1.11 | 6,527 |
| 3 | +1 | 0 | 4.92 | 1.162 | 4,935 |
| 4 | −1 | +1 | 7.3 | 1.1 | 6,193 |
| 5 | +1 | −1 | 5.51 | 1.16 | 5,194 |
| 6 | 0 | 0 | 6.24 | 1.15 | 5,655 |
| 7 | 0 | 0 | 6.21 | 1.16 | 5,609 |
| 8 | 0 | 0 | 6.33 | 1.163 | 5,603 |
| 9 | 0 | +1 | 5.68 | 1.161 | 5,393 |
| 10 | −1 | −1 | 9.38 | 1.07 | 6,879 |
| 11 | 0 | 0 | 6.206 | 1.166 | 5,582 |
| 12 | 0 | 0 | 6.201 | 1.172 | 5,496 |
| 13 | 0 | −1 | 6.9 | 1.17 | 5,863 |
Organic phase; Methanol: water (+1) = (65:45), (0) = 60:40, and (−1) = 55:45
Flow rate - (+1) = 1.1, (0) = 1, and (−1) = 0.9.
2.4.4. Optimization using CCD
Data evaluation, refinement, and model verification were meticulously conducted using Design Expert® 13 (M/s Stat-Ease Inc., Minneapolis, MN) software. A quadratic model was employed to evaluate primary and interaction effects, with data suitability assessed through ANOVA, lack of fit, and correlation coefficients (R², R²-adj, R²-pred). The well-correlated model exhibited an insignificant lack-of-fit (p > 0.05), while a significant value (p < 0.05) indicated poor alignment with the data. Factor-response relationships were visually interpreted using three-dimensional response surface plots and two-dimensional contour graphs. Model assessment utilized mathematical optimization segments to refine chromatographic conditions by minimizing RT and TF while maximizing TP. The final optimization was confirmed graphically within the defined design space [23].
2.5. Method validation
The analytical methodology underwent rigorous authentication in adherence to “ICH Q2 (R2)” guidelines, encompassing the evaluation of linearity, accuracy, precision, detection limits (LODs), and quantification limits (LOQs) [24,25]. System suitability was determined by injecting six replicates of a 10 µg/ml standard solution, ensuring compliance with established criteria for RT, TF, and TP. Selectivity studies assessed interference from formulation excipients and degradation products, while spectral integrity was confirmed by dual-wavelength UV detection. Linearity was established across 0.2–10 µg/ml, with peak area mapped versus concentration via linear regression, and each concentration analysed in triplicate (n = 3). The LOD and LOQ were determined using the signal-to-noise ratio. Precision was assessed through intra-day (triplicate analysis at three intervals) and inter-day (over 3 consecutive days) evaluations at 2, 6, and 10 µg/ml. Robustness and ruggedness were examined by varying conditions such as organic phase proportion (±2%), flow rate (±0.2 ml/min), column temperature (±2°C), and detection wavelength (±2 nm). Accuracy was confirmed via recovery experiments at 50%, 100%, and 150% spiking levels, with relative standard deviation (%RSD) calculated. Specificity was verified by injecting a 10 µg/ml standard solution alongside a blank to detect any solvent interference.
2.6. Forced degradation studies
Stability-indicating properties of the validated method were evaluated through forced degradation studies [26]. DAI was exposed to diverse stress environments, including acidic, basic, thermal, photolytic, and oxidative degradation. Acid-base stress was induced using 0.1 N HCl and 1 N NaOH, while oxidative degradation was examined with 30% v/v hydrogen peroxide. For thermal degradation, sealed volumetric flasks containing the drug solution were heated at 80°C for 2 hours. Photochemical degradation was assessed by exposing the sealed volumetric flask to direct sunlight for 6 hours. Following degradation, the samples were passed through 0.2 μm syringe filters and examined via HPLC analysis.
2.7. Methodology applicability
2.7.1. Formulation of DAI incorporated cubosomes
Cubosomes were engineered using the top-down approach [27], wherein DAI-loaded cubosomes were formulated with GMO (3%), Poloxamer 407 (PF127) (1.5%), and Tween 80(0.5%) in an aqueous medium. The lipid phase was molten and incorporated with the drug, followed by emulsification with the aqueous phase. The dispersion underwent sequential high-speed homogenization, ultrasonication, and equilibration before being stored at 4°C. The physicochemical attributes of the developed cubosomes were characterized, including particle size, zeta potential, and polydispersity index (PDI), using dynamic light scattering (Zetasizer, Malvern Instruments, UK). Encapsulation efficiency was assessed via ultracentrifugation, with quantification performed through HPLC analysis [28].
2.7.2. Sample processing for DAI-loaded cubosomes
A precisely measured 30 ml of the cubosomes, equivalent to 10 mg of the drug, was meticulously dissolved in methanol and subsequently blended with the designated mobile phase to obtain a 100 ml stock solution. The mixture underwent ultrasonicated for 15 minutes for complete dispersion, then a 10 ml aliquot was diluted with the mobile phase to achieve the required concentration and analysed for drug content and encapsulation efficiency (DAI) using a validated HPLC technique.
2.8. Appraisal of the environmental compatibility and sustainability
The ecological viability of the optimized analytical approach was appraised utilizing advanced green assessment metrics, including Complex GAPI, AGREE, Analytical Eco-Scale, and BAGI frameworks.
2.8.1. ComplexGAPI software for GAPI evaluation
Complex GAPI, an enhanced iteration of the GAPI index, represents a cutting-edge tool for assessing the environmental sustainability of analytical methodologies. It evaluates critical parameters, including sample processing, reagent classification, energy utilization, waste production, and overall ecological footprint. The system assigns quantitative scores, enabling comparative analysis and selection of environmentally responsible techniques through a color-coded scheme: red (elevated risk), yellow (moderate risk), and green (minimal risk) [29,30].
2.8.2. Analytical GREEnness tool
The AGREE approach is a key software for assessing the ecological burden of analytical methodologies, grounded in the 12 principles of GAC. It quantifies sustainability through a 0 to 1 scoring system, with higher values reflecting superior adherence to green practices. The evaluation considers factors such as energy and reagent efficiency, waste minimization, and safety, integrating results into a radar chart for a comprehensive visual representation. This approach facilitates sustainability improvements by highlighting key areas for optimization [31,32].
2.8.3. Eco-scale analytical
The ESA is a crucial tool for evaluating the Sustainability impact of analytical procedures and quantifies the factors such as material consumption, residual output, and safety, assigning an Eco-Scale Score (ESS) ranging from 0 to 100. This score, calculated as ESS = 100 – total penalty points, reflects the method’s sustainability, with deductions based on environmental impact. By identifying areas for improvement, ESA promotes the adoption of greener, more efficient analytical practices [33,34].
2.8.4. Blue applicability grade index
The BAGI is a key framework in white chemistry, assessing the efficiency and relevance of analytical procedures. It evaluates 10 performance criteria, generating a numerical score and pictogram to represent overall practicality. A higher BAGI score indicates superior reliability and suitability for analytical tasks. By integrating environmental and operational assessments, BAGI provides a comprehensive evaluation, reinforcing sustainability and efficiency in analytical practices [35].
3. RESULT AND DISCUSSION
3.1. Initial method development
The RP-HPLC technique was systematically developed by evaluating crucial factors, including the composition of the mobile phase, phase proportion, column temperature, and flow rate, to achieve an ideal peak profile, shortened analysis time, minimized peak tailing, enhanced column efficiency, and superior separation. Due to DAI’s limited aqueous solubility, organic solvents like methanol and acetonitrile were explored, with methanol proving most effective for complete dissolution. Consequently, methanol was chosen as the organic phase for chromatographic analysis. Extensive trials were conducted with varying organic phase ratios (70:30, 65:35, 60:40, 55:45, 50:50, 45:55, 40:60, 35:65, 30:70, 20:80), along with adjustments in flow rate and temperature, to optimize peak performance. The Phenomenex Luna C18 column (150 × 4.6 mm, 5 µm) was selected for its highly stable, end-capped Octadecylsilane stationary phase stationary phase, which ensures strong hydrophobic interactions and symmetrical peaks for DAI. Its optimised 5 µm particle size provides an efficient balance between resolution and backpressure, supporting robust and reproducible chromatographic performance [13]. Initial chromatograms displayed moderate asymmetry, slight tailing, and limited TP count. To refine separation and enhance peak characteristics, a QbD strategy incorporating Design of experiments (DoEs) methodologies was utilized. This systematic strategy facilitated fine-tuning of chromatographic variables, ensuring a robust, reproducible, and high-performance analytical method.
3.2. Efficacy (EVG 1st pillar)
3.2.1. Risk evaluation analysis
Preliminary trials were conducted under varied chromatographic conditions, including stationary phase type, flow rate, mobile phase composition, injection volume, and column dimensions. Based on these observations, a structured risk evaluation framework was established to systematically analyse the interplay between CMPs and CAAs, as illustrated by the Ishikawa fishbone diagram (Fig. 2). Comparable quality risk assessment strategies have been documented in previous studies [36,37] wherein Ishikawa fishbone diagrams were effectively utilized to guide chromatographic method optimization. The evaluation identified the mobile phase organic ratio and flow rate as the most critical determinants of chromatographic performance, while column temperature and dimensions exhibited moderate influence. Statistical analysis confirmed their significance, with (RT, p = 0.0020), (TF, p = 0.0003), and (TP, p = 0.0102) all demonstrating highly significant effects (p < 0.05). These pivotal parameters were subsequently subjected to systematic optimization via RSM to ensure a robust, precise, and reproducible analytical method.
 | Figure 2. Ishikawa fish bone diagram. [Click here to view] |
3.2.2. Taguchi design strategy for parameter screening
Key method variables (CMPs) were identified through a Taguchi design approach to assess their impact on CAAs (RT, TF, and TP). The Taguchi model included seven pre-determined factors, and their assessment was aided by a half-normal probability plot and Pareto diagrams (Fig. 3A and B), offering a comprehensive depiction of parameter influences. Statistical analysis (p < 0.05) revealed that the % of the organic phase and flow rate exerted a significant impact on the CAAs. Pareto analysis identified the mobile phase ratio, buffer pH, and flow rate as the most influential parameters, necessitating their inclusion in optimization. Factors exceeding the Bonferroni threshold demonstrated a pronounced impact, particularly organic phase percentage, flow rate, and column temperature. Consequently, these variables were chosen for subsequent process refinement to improve method durability and efficiency.
 | Figure 3. (a) Half-normal probability plots and (b) Pareto diagrams illustrating the influence on the CAAs: (i) RT, (ii) TP count, and (iii) TF. [Click here to view] |
3.2.3. Analytical method refinement and response surface studies
Response surface optimisation using a CCD was undertaken to refine the analytical method by systematically assessing the influence of organic phase proportion (A) and flow rate (B) identified as critical variables during risk assessment, across three coded levels (–1, 0, +1), corresponding to 55:45–0.9 ml/min, 60:40–1.0 ml/min, and 65:35–1.1 ml/min, respectively. Evaluation of the 13 CCD experimental runs (Table 3) confirmed that both factors exerted statistically significant effects on the CAAs (RT, TF, and TP), as validated by the fitted quadratic polynomial models (Eqs. 1–3).
RT = + 6.24 −1 .68A − 0.7017B + 0.2925AB + 0.4282A2 + 0.0030B2 (1)
TF = + 1.16 + 0.0362A + 0.0060B − 0.0037AB − 0.0325A2 − 0.0030B2 (2)
TP = + 5594.31 − 801.67A − 281.67B + 38.00AB + 123.41A2 + 20.41B2 (3)
A negative linear effect of A and B on RT and TP suggests that increasing these parameters initially reduces retention and column efficiency, while positive quadratic and interaction terms indicate that their combined optimization enhances performance. For TF, minor positive linear effects and negative quadratic terms imply that excessive increases may impair peak symmetry. These trends align with previous studies, which report that higher organic content can prolong RT due to stronger analyte-stationary phase interactions, elevated flow rates may distort peak shape, and balanced mobile phase conditions improve column efficiency [38,39]. The fitted polynomial models and 3D response plots confirm the nonlinear, interdependent nature of these variables, supporting robust method refinement.
The experimental responses were analysed using different kinetic models, with the nonlinear regression model exhibiting the optimal fit (p < 0.0001) and the highest R² value, approaching closely to 1. Table S1 provides a concise overview of the evaluation report for all three outcomes using CCD. A 3D response surface plot and a polynomial model were employed to depict the correlation between the mobile phase (A) and flow rate (B) concerning RT, TF, and TP. Response surface analysis for Y1 (RT) revealed that an increment in both organic phase concentration and flow rate resulted in an extended retention period (Fig. 4A). The effect of these independent variables on the TF (Y2) is illustrated in Figure 4B. The results indicate that organic phase composition and flow rate play a pivotal role in influencing TP count. A higher organic phase ratio resulted in an increased number of TPs, whereas a lower ratio led to a reduction. For Y3 (TPs), simultaneous enhancement of organic phase concentration and flow rate contributed to a greater TP count, demonstrating a positive influence, as depicted in Figure 4C.
 | Figure 4. 3D response surface visualization for (a) Y1 (RT), (b) Y2 (TF), and (c) Y3 (TP). [Click here to view] |
3.2.4. Analytical operational domain
Quantitative and visual optimization using (DoE, v13.0, Stat Ease, Inc.) established the optimal chromatographic conditions for DAI, achieving an organic phase composition of 64.52% v/v and a flow rate of 1.08 ml/min, with a desirability score of 0.997. The overlay plot (Fig. S1) highlights the successful operating ranges within the design space. Under these conditions, the method exhibited superior performance with an RT of 4.6 minutes, TF of 1.16, and 4,772 TPs, (Fig. 5), demonstrating excellent precision (n = 6, deviation <5%). Compared to previous reports, which involved longer RTs, suboptimal peak symmetry, or higher solvent toxicity [40–42], the present method provides enhanced efficiency, peak shape, and environmental compatibility, attributable to systematic response surface optimization of critical chromatographic parameters. The comparison data are represented in Table 5, showing the chromatographic conditions along with their application to the existing methods.
 | Figure 5. Chromatogram of DAI under the optimized analytical conditions. [Click here to view] |
Table 4. Overview of validation measures.
| SL. No | Validation attributes | DAI |
|---|---|---|
| 1. | Linearity range | 2 to 12 μg/ml |
| Correlation coefficient | 0.9985 | |
| 2. | LOD | 0.3219 |
| LOQ | 0.9756 | |
| 3. | Precision (%RSD) Intra-day Inter- day | 0.127 0.091 |
| 4. | Robustness (%RSD) Organic phase ratio Flow rate Wavelength | 0.293 0.271 0.23 |
| 5. | Accuracy(%RSD) 50% recovery 100 % recovery 150% recovery | 100.29 ± 1.126 101.80 ± 1.994 99.65 ± 0.357 |
Table 5. Comparison data of the HPLC methods published.
| Mobile phase and flow rate (ml/min) | Detection (nm) | RT (min) | Application | References |
|---|---|---|---|---|
| Methanol–water (55:45, v/v) 1.0 ml/min | 249 | 10 | HPLC method for the pharmacokinetic study of DAI -loaded nanoparticle formulations after injection to rats | [40] |
| Methanol–0.1% acetic acid (53:47, v/v) 1.0 ml/min | 254 | 6 | HPLC method for free aglycones DAI and genistein in soybeans (glycine max (l.) Merr.) | [41] |
| Methanol–water–acetonitrile (60:35:5, v/v/v) 1.0 ml/min | 251 | 5–5.1 | HPLC method, for determination of DAI in soy sauce | [42] |
3.3. Method validation (EVG 2nd pillar)
A robust linear correlation was established through the construction of a calibration curve, graphing the mean analyte DAI peak area against its concentration range of 2–12 µg/ml (Fig. S2 and Table S2). The corresponding linear regression equation was employed to calculate the LOD and LOQ. In addition, the method's reliability was substantiated by evaluating the percentage % RSD for critical analytical metrics, including peak area, RT, TP, and TF. Notably, all % RSD values remained below 2%, affirming the method’s precision and analytical validity. Method robustness was examined by inducing slight variations in critical HPLC parameters, such as detection wavelength, column oven temperature, and organic phase composition. The results demonstrated that the % RSD for both peak area and RT remained well within the stringent acceptance limit of ≤2%. Moreover, intra-day and inter-day precision studies further reinforced the method's reproducibility. Accuracy, evaluated through percentage recovery at 50%, 100%, and 150% concentrations, substantiated the method's exceptional precision. Collectively, these findings underscore the reliability and robustness of the optimized analytical method, as depicted in Table 4.
3.4. Force degradation studies
Forced degradation studies were conducted under acidic, alkaline, oxidative, thermal, and photolytic conditions as per ICH Q1 (R2) guidelines to assess the stability-indicating potential of the developed method. The chromatograms in Figure 6A–E and data in Table S3 highlight the extent of degradation and RTs under each condition. DAI showed the highest degradation under oxidative (22.13%) and acidic (21.97%) stress, followed by alkaline (13.28%), thermal (8.70%), and photolytic (6.98%). In all cases, the main DAI peak remained well-resolved with consistent RTs (4.3–4.7 min), and no additional peaks were observed, suggesting minimal formation of detectable degradation products. While the method proved effective in quantifying DAI under stress, the lack of identifiable degradation peaks limits its classification as fully stability-indicating.
 | Figure 6. Degradation chromatograms: (a) Acidic hydrolysis, (b) Alkaline hydrolysis, (c) Oxidative degradation, (d) Thermal decomposition, and (e) Photodegradation. [Click here to view] |
3.5. Method applicability
3.5.1. Characterization of prepared cubosomes
Cubosomes were developed using the top-down method and subsequently characterized for key evaluation parameters. The DAI-loaded cubosomes exhibited a particle diameter of 124.24 ± 0.24 nm, an encapsulation efficiency of 86.32% ± 0.53%, a PDI value of 0.23 ± 0.11, and a zeta potential of −22 mV, as illustrated in Figure S3. The PDI values indicated a uniform particle size distribution, while the zeta potential measurements suggested enhanced colloidal stability of the cubosomes. Transmission electron microscopy was employed to analyse surface topology and structural attributes, revealing that the cubosomes possessed a cubic geometry with a uniform morphology, as depicted in Figure S4.
3.5.2 Recovery analysis of formulated DAI-loaded cubosomes
The developed analytical strategy was successfully utilized to determine DAI in the formulated cubosomes, with six replicate analyses performed. The findings demonstrated excellent recovery rates ranging from 99.85% to 98.52%. Quantitative assessment revealed distinct peaks for both compounds, highlighting the method’s outstanding sensitivity, accuracy, and precision. This confirms the reliability and robustness of the acquired data, as illustrated in Figure 7.
 | Figure 7. Chromatogram of cubosomes containing standard DAI solution (10 µg/ml). [Click here to view] |
3.6. Greenness (EVG 3rd Pillar)
3.6.1. Assessment of eco-friendliness and purity of the designed methodology
The Complex GAPI assessment verified that the proposed method poses minimal environmental risk. A color-coded pictogram visually represents the assessment, incorporating five pentagrams corresponding to sample preparation, reagent and solvent consumption, instrumentation, and method classification, alongside a hexagon denoting pre-analysis procedures. Green indicates a low environmental footprint, yellow represents a moderate impact, and red highlights critical areas requiring intervention. The optimized HPLC method achieved eight green, six yellow, and one red classification, with an E-factor of 2.1 (Fig. 8A). An advanced greenness calculator, encompassing 12 analytical criteria, generated an AGREE pictogram structured as a clock face, offering a visual representation of the environmental ramifications on a spectrum from deep green (eco-friendly) to deep red (high environmental burden). The developed method attained an AGREE score of 0.77, predominantly aligning with the green zone (Fig. 8B). Furthermore, the Analytical Eco-Scale, a semi-quantitative framework assigning penalty points for ecological limitations—including excessive waste generation, substantial energy consumption, and hazardous substance usage—classified the methodology as highly sustainable, with an outstanding Eco-Score of 85. Additional validation employing the BAGI tool yielded a score of 75 (Fig. 8C), reaffirming the method's sustainability, operational feasibility, and environmental stewardship.
 | Figure 8. Assessing the environmental sustainability and efficiency of the proposed method using (a) ComplexGAPI, (b) AGREE, and (c) BAGI metrics. [Click here to view] |
3.7. EVG framework for method evaluation
The EVG framework offers a multidimensional assessment of analytical methods across three synergistic pillars: Efficiency (E), Validation (V), and Greenness (G). This integrative model ensures that the method is not only analytically robust and reproducible but also environmentally responsible. Each pillar is quantitatively scored (0–3) across five defined criteria (A–E), with quartile stratification (Q1–Q4) used to evaluate inter-pillar equilibrium. Concordance within or across adjacent quartiles signifies methodological harmony, whereas divergence highlights areas necessitating refinement [43,44].
- Efficiency: Encompasses the strategic application of DoE, optimization of CMPs and CAAs, minimization of runtime and resource consumption, and maximization of analytical throughput.
- Validation: Captures the method’s analytical rigor through precision, accuracy, sensitivity, and robustness under controlled perturbations, ensuring consistent and high-fidelity performance.
- Greenness: Assesses ecological sustainability based on sample preparation complexity, chemical hazard classification (GHS pictograms), energy demand, and waste output, in alignment with GAC principles.
The radar chart (Fig. 9) illustrates balanced performance, with Validation consistently scoring 3, confirming high precision, accuracy, and robustness. Greenness shows moderate scores [1,2], indicating eco-conscious design with scope for improvement in reagent safety and preparation complexity. Efficiency displays variability [1–3], performing well in design and throughput but moderately in runtime and resource use. Overall, the method demonstrates strong analytical reliability and sustainable potential, with minor areas for refinement.
 | Figure 9. Radar chart of proposed EVG-HPLC method. [Click here to view] |
4. CONCLUSION
A rigorously validated, QbD-driven RP-HPLC method has been successfully developed for the precise quantification of DAI in both bulk drug and cubosomal formulations. Employing CCD for multivariate optimization, the study identified organic phase composition and flow rate as CMPs, yielding an optimized RT of 4.6 minutes—representing a 40%–55% reduction compared to conventional protocols. The method demonstrated exceptional chromatographic performance, including a TF of 1.16, a TP count of 4,772, and a desirability index of 0.997. Validation in accordance with ICH Q2(R2) guidelines confirmed its analytical rigor, with broad linearity, low detection thresholds, accuracy within 99%–102%, and precision %RSD application to cubosomal matrices confirmed the method’s robustness and reliability for routine quality control, enabling accurate dose verification, stability profiling, and batch-to-batch consistency, critical for the future development of flavonoid-based nanotherapeutics. Due to shared chromophoric and hydrophobic properties, the method is readily adaptable to structurally related flavonoids, requiring only minor adjustments to detection wavelength or mobile phase composition. Environmental sustainability was rigorously evaluated using multiple green chemistry metrics. The method achieved a Complex-GAPI E-factor of 2.1, AGREE score of 0.77, ESS of 85, and BAGI score of 75, collectively affirming its minimal ecological footprint and high operational feasibility. Thus, the RP-HPLC platform offers a rapid, precise, and environmentally responsible analytical solution for DAI and related bioactive flavonoids, aligning with contemporary demands for sustainable pharmaceutical analysis and advanced formulation science.
5. ACKNOWLEDGMENTS
The authors extend their sincere gratitude to Chemsenses, Maharashtra, India, for providing the drug and to Mohini Organics Pharmaceuticals, Mumbai, for the generous gift of GMO. They are equally appreciative to KLE Academy of Higher Education and Research, the Department of Pharmaceutics at KLE College of Pharmacy, Belagavi, and KAHER Dr. Prabhakar Kore Basic Science Research Centre, Belagavi, India, for their support.
6. AUTHOR CONTRIBUTIONS
All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agreed to be accountable for all aspects of the work. All the authors are eligible to be an author as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.
7. FINANCIAL SUPPORT
There is no funding to report.
8. CONFLICTS OF INTEREST
The authors report no financial or any other conflicts of interest in this work.
10. ETHICAL APPROVAL
This study does not involve experiments on animals or human subjects.
11. DATA AVAILABILITY
All the data is available with the authors and shall be provided upon request.
12. PUBLISHER’S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of the publisher, the editors and the reviewers. This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.
13. USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY
The authors declare that they have not used artificial intelligence (AI)-tools for writing and editing of the manuscript, and no images were manipulated using AI.
14. SUPPLEMENTARY MATERIAL
The supplementary material can be accessed at the link here: [https://japsonline.com/admin/php/uploadss/4729_pdf.pdf].
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