AQbD‐Based Development and Optimization of a Stability‐Indicating RP‐HPLC Method for Imeglimin With Integration of Lean Six Sigma Principles

ABSTRACT

An accurate and green RP‐HPLC stability indicating method has been developed for estimation of Imeglimin with an AQbD approach. Method optimization was done based on Box–Behnken design by changing factors like mobile phase, pH, and flow rate for optimum resolution and retention time. Lean Six Sigma (DMAIC) and statistical process control techniques ensured the consistency of the method. Water–methanol in the ratio of 65:35 (v/v) adjusted to pH 4.0 was used as the mobile phase at a flow rate of 0.8 mL/min, having an approximate retention time of 3.7 min. Method validation was performed as per the ICH guidelines, and the method exhibited a high correlation coefficient (R ² = 0.9994), low %RSD (< 2%), and good accuracy (99.45%–100.35%). LOD and LOQ were found to be 1.04 and 3.15 μg/mL, respectively. The results of forced degradation experiments showed significant degradation in the presence of alkaline, oxidative, and thermal stress conditions, whereas Imeglimin remained stable under photolytic stress conditions. HR‐MS and FTIR studies indicated that N‐dealkylation and N‐demethylation were possible degradation pathways. The greenness assessment of the developed method was carried out using AGREE, GAPI, BAGI, and CLICK indices.

Abbreviations

AGREE

Analytical GREEnness Metric Approach

ANOVA

analysis of variance

API

active pharmaceutical ingredient

AQbD

analytical quality by design

ATP

analytical target profile

BAGI

Blue Applicability Grade Index

BBD

Box–Behnken Design

CACI

CLICK Analytical Chemistry Index

CLICK

Analytical Chemistry Index Tool

CMPs

critical method parameters

CQAs

critical quality attributes

DMAIC

define, measure, analyze, improve, control

DoE

design of experiments

FMEA

Failure Mode and Effects Analysis

FTIR

Fourier Transform Infrared Spectroscopy

GAPI

Green Analytical Procedure Index

HR‐MS

High‐Resolution Mass Spectrometry

ICH

International Council for Harmonization

LOD

limit of detection

LOQ

limit of quantification

MS

mass spectrometry

PDA

photodiode array detector

RP‐HPLC

reverse phase high‐performance liquid chromatography

RSD

relative standard deviation

SD

standard deviation

SIAM

stability‐indicating analytical method

SPC

statistical process control

T2DM

type 2 diabetes mellitus

UV

ultraviolet

1. Introduction

Imeglimin (brand name Twymeeg) (Figure 1), a first‐in‐class oral antidiabetic agent from the novel tetrahydro‐triazine class, was approved in Japan in 2021 for treating type 2 diabetes mellitus (T2DM) (Dubourg et al. 2022; Nowak and Grzeszczak 2022). It exerts a unique dual mechanism of action—enhancing glucose‐stimulated insulin secretion (GSIS) with protection of β‐cell function, and simultaneously increasing insulin sensitivity while preventing gluconeogenesis (Pirags et al. 2012; Hallakou‐Bozec et al. 2021; Yaribeygi et al. 2020; Yendapally et al. 2020). At the cellular level, Imeglimin improves mitochondrial bioenergetics, reduces oxidative stress, and restores NAD⁺/ATP homeostasis, thereby targeting fundamental pathophysiological mechanisms of T2DM (Hallakou‐Bozec et al. 2021; Yendapally et al. 2020; Fouqueray et al. 2022). Clinical trials have demonstrated significant HbA1c reduction and favorable safety when administered with standard therapies (Pirags et al. 2012; Singh et al. 2023).

FIGURE 1.

Structure of Imeglimin.

There is a need for the establishment of reliable stability indicating assay methods that will guarantee the quality of Imeglimin formulations. RP‐HPLC has been described as one of the most popular methods of quantification due to its accuracy, reproducibility, and suitability in stability studies. Various methods have been described for the determination of Imeglimin in raw material and formulations using RP‐HPLC. These methods mainly emphasize validation parameters including accuracy, precision, sensitivity, and linearity as per the ICH guideline Q2(R1).

Several analytical methods have been reported for the determination of Imeglimin in bulk drug and pharmaceutical dosage forms, including RP‐HPLC, UHPLC, and stability‐indicating chromatographic methods (Mubeen et al. 2024; Jain et al. 2023; Chikhale et al. 2024; Mansour et al. 2026). Recently, a stability‐indicating green RP‐HPLC method employing ion‐pair chromatography and PDA detection was reported for the simultaneous determination of Imeglimin and its related impurity, along with comprehensive evaluation of method greenness. Despite these advances, most reported methods have primarily focused on method development and validation, with relatively limited emphasis on systematic method optimization, process performance monitoring, degradation pathway elucidation, and multidimensional sustainability assessment.

Analytical Quality by Design (AQbD) has emerged as an effective strategy for developing robust analytical methods through a systematic understanding of critical method variables and their impact on analytical performance (Peraman et al. 2015). Similarly, Lean Six Sigma tools and Statistical Process Control (SPC) provide structured approaches for continuous process improvement and monitoring (Laureani and Antony 2015), yet their application in the development of stability‐indicating chromatographic methods for Imeglimin remains scarcely explored. Furthermore, detailed characterization of degradation products using complementary HR‐MS and FTIR techniques, together with comprehensive sustainability assessment employing AGREE, GAPI, BAGI, and CLICK tools, has received limited attention in the literature (Peña‐Pereira et al. 2020; Gałuszka et al. 2013; Mansour et al. 2025).

In this context, the present study describes an integrated analytical framework for the development of a stability‐indicating RP‐HPLC method for Imeglimin. The proposed approach combines AQbD‐based method optimization, Lean Six Sigma (DMAIC) methodology, SPC‐based performance monitoring, degradation‐product characterization using HR‐MS and FTIR, and multidimensional sustainability evaluation. By integrating analytical, quality, operational, and environmental perspectives within a single workflow, the study provides a comprehensive strategy that extends beyond the scope of previously reported methods (Mubeen et al. 2024; Jain et al. 2023; Chikhale et al. 2024; Mansour et al. 2026).

2. Materials and Methods

2.1. Materials

Imeglimin (purity 99.5%, Batch No.: IME2025A) was kindly supplied by Lupin Pharmaceutical Ltd., Aurangabad, India. Commercial tablet formulations containing Imeglimin (brand name Twymeeg 500 mg) were procured from the local market. HPLC‐grade water was obtained from a Milli‐Q purification system and used throughout the study. Analytical reagent‐grade solvents and chemicals employed for forced degradation studies were procured from Merck (Mumbai, India).

2.2. Instrumentation

The HPLC system used in this study comprised a Jasco PU‐2089 Plus Quaternary Gradient Pump. Injection of samples was performed through auto‐sampler with an injection volume of 20 μL. Chromatographic analysis was carried out using a Jasco HPLC system equipped with a PDA detector (Jasco MD‐2018 Plus). Quantitative measurements were performed at 245 nm, while spectral data were simultaneously acquired over the wavelength range of 200–400 nm for peak characterization and specificity assessment. Data collection and processing were done using ChromNAV software, connected to LC–Net II/ADC systems.

High‐resolution mass spectrometric analysis was performed using a Shimadzu LCMS‐9030 quadrupole time‐of‐flight (QTOF) mass spectrometer (Shimadzu, Kyoto, Japan). Data were acquired using an electrospray ionization (ESI) source operated in positive ionization mode. Stressed samples were directly analyzed by HR‐MS for degradation‐product characterization without chromatographic separation. Mass spectra were recorded over an m/z range of 50–1000. Instrument control, data acquisition, and processing were carried out using LabSolutions LCMS software (Shimadzu, Kyoto, Japan). Additionally, FTIR spectra were recorded using Bruker Alpha II FTIR spectrometer fitted with sensitivity detector. It was done for analysis of both solid and liquid samples in mid‐infrared region.

Mobile phase optimization was done through the Box–Behnken Design (BBD) method. This is a very robust approach in experimental design that aids in determining how several factors affect the response of interest. Chromatographic separation was done on HiQ Sil C18 HS column (4.6 mm ID × 250 mm length, 5.0 μm particle size, and 100 Å pore size). The mobile phase consisted of water and methanol (65:35, v/v). After mixing, the pH of the mobile phase was adjusted to 4.0 using dilute orthophosphoric acid and verified using a calibrated digital pH meter. The prepared mobile phase was filtered through a 0.45‐μm membrane filter and degassed before use.

2.3. Preparation of Standard Stock Solution

Imeglimin reference (10 mg) was precisely measured and dissolved in 10 mL of water (1000 μg/mL). Subsequently, 1 mL of this preparation was taken and put in a 10‐mL volumetric flask. This was later diluted with water to 100 μg/mL. Finally, the solution was further diluted with water to yield a 20‐μg/mL solution.

2.4. Quality by Design (QbD) Application in RP‐HPLC Method Development

The RP‐HPLC method was devised using AQbD principles as recommended by ICH Q8(R2).

2.4.1. Analytical Target Profile (ATP)

The ATP was defined to set the target for the analytical method. The method was targeted to provide accurate, precise, and stability‐indicating quantitation of Imeglimin, with desired chromatographic attributes such as a suitable retention time, symmetric peaks (asymmetry factor close to 1), and adequate resolution from degradation products.

2.4.2. Risk Assessment Using Fishbone (Ishikawa) Diagram

The risk analysis was done through the preparation of the Fishbone (Ishikawa) diagram, after which the Failure Mode and Effects Analysis (FMEA) was conducted as indicated in Figure 2. Based on the risk assessment, the critical method parameters (CMPs) that affect the RP‐HPLC method were identified as the percentage of organic modifier (methanol), the mobile phase's pH, and the flow rate, as they are the most dominant in affecting the critical quality attributes (CQAs), including the retention time, peak shape, and column efficiency, respectively. FMEA was employed to identify CMPs and assess their potential impact on chromatographic performance. Each parameter was evaluated based on three criteria: Severity (S), representing the impact of a parameter on method performance; Occurrence (O), indicating the likelihood of variation occurring during analysis; and Detectability (D), reflecting the probability of detecting the variation before it affects the analytical result. Scores ranging from 1 (lowest risk) to 5 (highest risk) were assigned to each criterion based on prior knowledge and experimental experience. The Risk Priority Number (RPN) was calculated using the following relationship:

$$\mathrm{RPN}=\text{Severity}\times \text{Occurrence}\times \text{Detectability}$$

FIGURE 2.

Risk assessment and identification of critical method parameters.

Parameters with higher RPN values were considered more critical and were prioritized for further evaluation during method optimization. Based on the calculated RPN values, factors were categorized as high‐risk, moderate‐risk, or low‐risk parameters.

After calculation, it was found that there is a high level of risk associated with the mobile phase composition and the pH, while there is a moderate level of risk associated with the flow rate and other factors. The application of the risk assessment tool in the development of the RP‐HPLC method was instrumental in the design of experiments (DoE), hence ensuring the development of an efficient and reliable analytical method through the QbD approach, as well as the development of the control strategy and the design space.

2.4.3. Identification of Critical Parameters

Based on the Fishbone (Ishikawa) diagram (Figure 2) and the preliminary experimental investigations, the major factors affecting the analytical method performance have been identified. The CMPs are the organic modifier (percentage of methanol), flow rate, and pH of the mobile phase, as these parameters have major effects on the analytical chromatographic performance. However, the CQAs are the retention time, the peak symmetry or tailing factor, and the theoretical number of plates, which are the key determinants influencing the analytical performance of the method and are crucial factors for developing the analytical method. These parameters are then subjected to optimization to develop the analytical method.

2.4.4. DoE Using BBD

A BBD was utilized for investigating the effects of CMPs, including mobile phase composition (A), flow rate (B), and pH (C), on CQAs, such as retention time and asymmetry factor. The BBD was composed of 17 runs (Table 1), including three factors at three levels: −1, 0, +1, including center points as replicates to guarantee the reliability and reproducibility of the results obtained from the experiments. The results obtained from the experiments were utilized to establish a quadratic polynomial equation between the independent variables and the chromatographic results obtained.

TABLE 1.

Optimization of various parameters (DOE).

Run	Factor 1 A: Mobile phase mL	Factor 2 B: Flow rate mL	Factor3 C: pH	Response 1: Retention time (min)	Response 2: Asymmetry factor
1	40	0.7	4	2.1	1.42
2	35	0.8	4	3.7	1.21
3	35	0.9	5	3.3	1.12
4	35	0.7	5	3.6	1.19
5	35	0.8	3	3.6	1.22
6	40	0.9	4	2.4	1.91
7	35	0.9	4	4.3	1.02
8	40	0.8	3	2.6	1.01
9	35	0.7	3	4.2	1.23
10	30	0.7	4	5.1	2.12
11	35	0.7	4	4.1	1.21
12	30	0.9	4	4.5	1.52
13	30	0.8	5	5.1	1.08
14	30	0.8	3	5.1	2.23
15	35	0.9	3	3.4	1.18
16	40	0.8	5	2.6	1.15
17	35	0.8	4	3.6	1.03

The models developed were statistically verified by analysis of variance (ANOVA), where the significance of the model F‐value (p < 0.05) and the non‐significance of the lack‐of‐fit value confirmed the reliability of the models developed. To visualize the effects of individual variables as well as the interaction between the variables, contour plots and response surface plots were constructed (Figures 3 and 4), allowing a better understanding of the results obtained. Perturbation plots (Figure 5) were also utilized to evaluate the sensitivity of each response variable to small changes in the method parameters, allowing the identification of the most sensitive variable.

FIGURE 3.

Contour plot of Imeglimin. (a) Influence of mobile phase and flow rate on Asymmetry factor. (b) Influence of mobile phase and pH on Asymmetry factor. (c) Influence of flow rate and pH on Asymmetry factor.

FIGURE 4.

Contour Plot of Imeglimin. (a): Influence of mobile phase and Flow rate on Retention time. (b): Influence of mobile phase and pH on Retention time. (c): Influence of flow rate and pH on Retention time.

FIGURE 5.

Perturbation plots obtained by Box–Behnken design. (a) Retention time, (b) Asymmetry.

The predictability of the model was confirmed by the predicted vs. actual plots (Figure 6), where there was good correlation between the experimental and predicted values. Finally, numerical optimization by the use of desirability functions was conducted to obtain the optimal chromatographic conditions. The optimal conditions obtained, i.e., mobile phase composition (65/35 v/v % water/methanol), flow rate (0.8 mL/min), and pH (4), were then used to obtain the design space (Figure 7), defined as the multi‐dimensional space where all CQAs satisfy predetermined acceptance criteria, thus ensuring robust, reliable, and consistent method performance, in accordance with the AQbD approach.

FIGURE 6.

Predicted versus actual plots for (a) Retention time (b) Asymmetry.

FIGURE 7.

Design space obtained by factor optimization.

The experimental design and statistical analysis were performed using Design‐Expert software (Version 13.0, Stat‐Ease Inc., Minneapolis, MN, United States). Each experimental run was conducted in triplicate, and chromatographic responses were recorded as mean values. The acceptance criteria for method optimization included a retention time of less than 4.0 min, an asymmetry factor between 0.9 and 1.5, and adequate chromatographic separation of Imeglimin from its degradation products. To verify the predictive capability of the developed model, confirmation experiments were performed under the optimized chromatographic conditions (water:methanol, 65:35 v/v; pH 4.0; flow rate 0.8 mL/min). The experimentally obtained responses showed close agreement with the predicted values, with percentage prediction errors below 5%, thereby confirming the adequacy, robustness, and reproducibility of the optimized method.

2.4.5. Lean Six Sigma (DMAIC) Framework Integration

The optimized method for Imeglimin by RP‐HPLC was developed according to the Lean Six Sigma DMAIC methodology to ensure optimization. The DMAIC stands for Define, Measure, Analyze, Improve, and Control. The Define phase of the Lean Six Sigma DMAIC methodology established an ATP with a target retention factor less than 4 min and an asymmetry factor close to 1.0. The Measure phase included preliminary experiments to develop baseline information and estimate variability for key method parameters. The Analyze phase included a FMEA to identify CMPs like mobile phase composition, flow rate, and pH. The FMEA results were further confirmed by a BBD and ANOVA to investigate their impact on CQAs. The Improve phase included response surface methodology and numerical optimization to develop optimized chromatographic conditions. The Control phase included robustness testing by establishing a design space and validating the method according to ICH guidelines.

2.4.6. SPC for Method Performance Monitoring

SPC was employed to evaluate the consistency and reproducibility of the developed RP‐HPLC method. The SPC analysis was based on six assay observations obtained during the precision study conducted under identical analytical conditions. The % assay values were used to calculate the mean, standard deviation, and control limits. The upper control limit (UCL) and lower control limit (LCL) were established using the ±3σ criterion around the process mean. An X̄ control chart was subsequently constructed to assess process stability and identify any potential sources of analytical variability. This approach enabled continuous monitoring of method performance and provided additional evidence of method reliability.

An X̄ control chart was created to check the stability and potential variability in the process. All the calculations were done by applying general statistical calculations. The control charts created were used to check whether the analytical method was within the desired performance limits during its usage (Figure 8).

FIGURE 8.

Statistical process control (SPC) Chart for analytical method precision.

2.4.7. Establishment of Optimized Stability‐Indicating Chromatographic Conditions

A stability‐indicating HPLC method was developed through systematic optimization. The method was applied to analyze Imeglimin and its degradation products. Various ratios of water to methanol were evaluated, and the optimal mobile phase was established as water: methanol (65:35, v/v). The selected flow rate was 0.8 mL/min. The finalized method successfully achieved clear separation of Imeglimin from its degradation products, providing well‐defined retention times and satisfactory theoretical plate numbers, indicating good column efficiency (Figure 9a).

FIGURE 9.

HPLC chromatograms of forced degradation study.

2.5. Validation of the Method

2.5.1. Linearity

For the determination of linearity, six different concentrations of Imeglimin in the range of 5–30 μg/mL were prepared. Each solution was analyzed in triplicate, maintaining a fixed volume injection of 20 μL per concentration. The relationship between the peak area (y‐axis) and the concentration of the drug (x‐axis) was plotted.

The selected calibration range (5–30 μg/mL) was considered appropriate for routine assay and stability analysis of Imeglimin. The calculated LOD and LOQ values indicate that the method possesses sufficient sensitivity to detect and quantify the analyte at concentrations below the working range, while maintaining excellent linearity within the concentrations relevant to its intended application.

2.5.2. Precision

Analytical procedure precision refers to the level of agreement (or variation) observed among multiple measurements obtained from a homogeneous sample. Three types of precision may be evaluated: repeatability, intermediate precision, and reproducibility.

2.5.3. Limit of Detection and Limit of Quantitation

LOD is the lowest quantity of analyte that can be detected but cannot be quantitatively determined. LOQ is the lowest quantity of analyte that can be determined with a certain degree of precision and accuracy, which can be used to quantify minor impurities or degradation products. In accordance with ICH Q2(R1), LOD and LOQ were calculated according to the calibration curve method. The calculations of residual standard deviation of the regression line were made using the following formulas:

$$\mathrm{LOD}=3.3\mathrm{\sigma }/\mathrm{S}$$

$$\mathrm{LOQ}=10\mathrm{\sigma }/\mathrm{S}$$

where:σ: SD of the regression lineS: slope of the regression line.

2.5.4. Robustness of the Method

A robust method is one that is not significantly affected by minor and intentional changes in the parameters of the method.

Robustness was assessed by introducing minor, deliberate variation in method parameters such as flow rate, methanol content in the mobile phase, and solvents sourced from different lots. Further, drug resolution in stressed sample mixture was evaluated using an alternative chromatographic system.

2.5.5. Specificity

The specificity of the HPLC method was tested by isolating Imeglimin from its decomposition products under several stress conditions. Retention time, asymmetry factor, capacity factor, tailing factor, and theoretical plates were determined. All values were within acceptable limits, indicating the method's ability to distinctly identify the drug in the presence of its degradants.

The specificity of the HPLC method was evaluated by assessing the separation of Imeglimin from its degradation products generated under various stress conditions. In addition to chromatographic parameters such as retention time, asymmetry factor, capacity factor, tailing factor, and theoretical plates, peak purity analysis was performed using the PDA detector. Spectral homogeneity of the Imeglimin peak was assessed by comparing spectra across the leading edge, apex, and trailing edge of the chromatographic peak. The peak purity values obtained were used to confirm the absence of co‐eluting impurities or degradation products.

2.5.6. Accuracy

In order to conduct the recovery test, 20 tablets containing Imeglimin (500 mg) were weighed and the mean value was determined, after which the tablets were ground. A volume of the ground substance containing 25 mg of Imeglimin was carefully weighed and transferred into three different volumetric flasks each having a capacity of 25 mL. A sample equivalent to 25 mg of Imeglimin was spiked with 20, 25, and 30 mg of the reference standard, corresponding to 80%, 100%, and 120% recovery levels, respectively. After dilution to volume, stock solutions containing 1800, 2000, and 2200 μg/mL of Imeglimin were obtained. Appropriate dilution of these stock solutions with water yielded final analytical concentrations of 18, 20, and 22 μg/mL, which were analyzed in triplicate for recovery determination.

2.5.7. Assay of Marketed Formulation

Imeglimin tablets (500 mg) were obtained commercially from a local chemist shop. The 20 tablets were individually weighed to get their average mass, after which the tablets were reduced to fine powder form. An amount of the drug that is equal to 25 mg Imeglimin was weighed using an analytical balance and was dissolved in 25 mL volumetric flask. The obtained solution was centrifuged at 3000 rpm for 5 min to get a supernatant having a concentration of 1000 μg/mL. The obtained supernatant was filtered using a 0.45‐μm membrane filter and subsequently diluted with water to obtain a 20‐μg/mL concentration. A 20‐μL sample was analyzed using the HPLC system.

2.6. Forced Degradation Studies

Stock solution was made using Imeglimin by dissolving 10 mg of the drug in 10 mL of water.

This was then followed by forced degradation studies where the stock solution prepared was used in order to assess the ability of the method to indicate stability. The solutions were further diluted in order to achieve a concentration of 20 μg/mL.

2.6.1. Acid and Base‐Induced Degradation

Acidic and alkaline degradation tests were conducted by withdrawing 1 mL of the Imeglimin stock solution (1000 μg/mL) and refluxing the sample in 1 N HCl at 60°C for 1.5 h. Alkaline degradation was conducted by reacting 1 mL of the Imeglimin stock solution (1000 μg/mL) with 1 N NaOH for 1.5 h at 60°C. Neutralization followed immediately after stress testing and the sample was then diluted to 10 mL in volumetric flasks.

2.6.2. Oxidative Degradation

Oxidative degradation test was carried out by preparing Imeglimin (1000 μg/mL) stock solution in a volumetric flask. Two milliliters of 3% hydrogen peroxide was added, followed by incubation for 30 min under ambient conditions.

2.6.3. Photochemical Degradation

Photodegradation of Imeglimin was done by exposing Imeglimin solution (concentration of 1000 μg/mL) to the direct sun for 30 days. This was done simultaneously with exposure of the solid‐state Imeglimin to the sun to investigate the degradation rate in solid state.

2.6.4. Thermal Degradation

Initial thermolysis study was done by exposing the solid state of Imeglimin to dry heat at 60°C for 5 h but no significant degradation was seen. In order to make a meaningful assessment, solution state of Imeglimin (1000 μg/mL in water) was exposed to the heat for 5 h at 60°C by refluxing.

2.6.5. Neutral Hydrolysis

Hydrolysis in neutral conditions was investigated by refluxing the Imeglimin (1000 μg/mL in water) for 5 h at 60°C. After the refluxing procedure, the solution is cooled and diluted with water to make a final concentration of 20 μg/mL.

The degraded samples were all diluted using distilled water in such a way that their concentrations became 20 μg/mL. Further, 20 μL from each sample was injected into HPLC equipment.

2.7. Development of HPLC Method and Characterization of Degradation Products by MS and FTIR

HPLC method was developed and validated for the analysis of degradation products, followed by characterization using MS and FTIR. The molecular weights and m/z values of the separated compounds were analyzed in positive ESI mode. The MS fragmentation patterns were further examined to gain insights into the structural features and degradation behavior of the drug. For degradation‐product characterization, aliquots of the stressed samples generated during forced degradation studies were suitably diluted and subjected to HR‐MS analysis. The HR‐MS investigation was performed as a complementary technique to obtain accurate mass information and facilitate structural elucidation of degradation products. The stressed samples were analyzed directly without isolation of individual degradation products, and the observed molecular ions were interpreted in conjunction with chromatographic and FTIR data to propose degradation pathways.

In addition to MS analysis, FTIR spectroscopy was employed to identify characteristic functional groups present in the degradation products. The use of IR spectroscopy helped identify certain bond vibrations and was important in determining whether or not specific functional groups were present.

The combined use of MS and FTIR allowed for a comprehensive understanding of the degradation products. The molecular weight data, fragmentation patterns, and IR spectral features were collectively used to propose possible structures and degradation pathways.

Degradation products under stress should be identified in order to understand the stability of the drug. These potential degradation products, generated during forced degradation studies, provide critical insights for the design and validation of robust analytical methods. This ensures the reliability, safety, and efficacy of pharmaceutical formulations during their entire period of storage.

2.8. Greenness Assessment Methods

2.8.1. AGREE Metric: Assessing the Greenness of Analytical Methods

The AGREE, which stands for Analytical GREEnness, is an assessment index for evaluating the environmental friendliness of analytical procedures in relation to the 12 Principles of Green Analytical Chemistry (GAC). AGREE provides a unified and transparent approach in contrast to previous greenness assessment methods, which frequently only addressed a few GAC principles or employed strict scoring systems.

By combining all 12 GAC principles into a single algorithm, AGREE produces a visual circular diagram with each segment representing a GAC principle and a numerical score between 0 and 1. An analytical method's ecological impact can be quickly, objectively, and reproducibly assessed.

2.8.2. Greenness Assessment Using the GAPI Tool

Green Analytical Procedure Index (GAPI) refers to a comprehensive and graphical tool developed to analyze the environmental effect of analytical processes at all stages of the analytical procedure. The GAPI uses a five‐pentagram pictogram, where each pentagram corresponds to a particular stage of the analytical process: sampling, sample preparation, usage of reagents and solvents, instrumentation, and waste management. Each stage of the analytical process is indicated by a certain color that represents the degree of its environmental impact—from green (low) to yellow (moderate) and red (high).

In total, 15 parameters are evaluated, including factors such as sample transportation, preservation methods, extraction scale, solvent toxicity, energy consumption, and waste generation and handling. This semi‐quantitative and visually intuitive approach allows researchers to quickly identify environmentally critical stages within an analytical method. GAPI supports the development, optimization, and comparison of methods by highlighting areas that can be modified to enhance sustainability. It is especially useful in promoting greener decision‐making during method selection and validation.

2.8.3. Methods Blueness Assessment

In addition to the conventional greenness evaluations, the Blue Application Grade Index (BAGI) is a new metric created to evaluate the “blueness” or practical application of analytical techniques. BAGI places more emphasis on aspects of method performance, such as sample throughput, automation level, operational ease, necessary sample and reagent amounts, and overall cost‐effectiveness than instruments that only consider environmental impact. After evaluating and scoring each parameter, a visual representation is produced that makes it easier to compare procedures quickly based on how simple they are to use in standard laboratory conditions. BAGI encourages the selection of analytical techniques that are not only ecologically sustainable but also effective, scalable, and useful for practical applications by incorporating blueness into method evaluation.

2.8.4. CLICK Analytical Chemistry Index

GAC CLICK Index Tool was used to determine the greenness of the developed analytical method. The CLICK tool is an open‐source software program that allows users to estimate the environmental sustainability of an analytical method based on certain parameters such as type and amount of solvent used in the method, toxicity of reagents used in the method, amount of waste generated in the method, etc.

Relevant information regarding the analytical method was inserted in the CLICK tool software program, which then produced a cumulative score that indicated the greenness of the RP‐HPLC method. The results obtained from the CACI tool are discussed in the Results section.

3. Results and Discussion

3.1. Application of QbD in RP‐HPLC Method Development

3.1.1. Quality Risk Assessment by Risk Filtering and Ranking

The risk assessment outcomes identified mobile phase composition, pH, and flow rate as the most influential method parameters affecting chromatographic performance. Based on their high risk scores, these variables were selected for subsequent optimization using the BBD. The assessment facilitated a systematic understanding of method variability and supported the development of a robust analytical procedure.

3.1.2. HPLC Method Development for Imeglimin by QbD Approach

A robust, reproducible, and efficient reverse‐phase HPLC method for the estimation of Imeglimin was established through a systematic approach of AQbD. This was achieved by first establishing the ATP, where the retention time was expected to be < 4 min and the peak asymmetry factor close to 1.0.

Evaluation of the impact and interaction of chosen method parameters was conducted by executing a three‐factor, three‐level BBD using Design‐Expert software. In total, 17 batches were conducted, of which five were center points to validate model adequacy and reproducibility. The retention time was varied from 2.1 to 5.1 min, and the peak asymmetry factor varied from 1.01 to 2.23, indicating the significance of the chosen parameters.

The statistical analysis revealed that the developed models were highly significant, with p < 0.05, and lack of fit was insignificant, suggesting high values for R ² (> 0.95) and precision (> 10) (Tables 2 and 3). Regression equations were formulated based on the following coded values:

$$\begin{array}{l}\mathbf{\text{Retention Time}}\left(\mathbf{min}\right)\mathbf{=}3.8178-1.2625\mathrm{A}-0.1200\mathrm{B}-0.0576\mathrm{C}\hfill \\ +0.2250\mathrm{AB}+0.0000\mathrm{AC}+0.1250\mathrm{BC}\hfill \\ -0.0758{\mathrm{A}}^2-0.0732{\mathrm{B}}^2-0.0357{\mathrm{C}}^2\hfill \end{array}$$

$$\begin{array}{l}\mathbf{\text{Asymmetry Factor}}\mathbf{=}1.0734-0.1825\mathrm{A}-0.0420\mathrm{B}-0.1506\mathrm{C}\hfill \\ +0.2725\mathrm{AB}+0.3225\mathrm{AC}-0.0050\mathrm{BC}\hfill \\ +0.4399{\mathrm{A}}^2+0.1821{\mathrm{B}}^2-0.0989{\mathrm{C}}^2\hfill \end{array}$$

TABLE 2.

ANOVA for quadratic model for Response 1: Retention time.

Source	Sum of squares	df	Mean square	F‐value	p
Model	13.23	9	1.47	10.32	0.0028	Significant
A‐Mobile phase	12.75	1	12.75	89.47	< 0.0001
B‐Flow rate	0.1440	1	0.1440	1.01	0.3483
C‐pH	0.0290	1	0.0290	0.2037	0.6654
AB	0.2025	1	0.2025	1.42	0.2721
AC	0.0000	1	0.0000	0.0000	1.0000
BC	0.0625	1	0.0625	0.4385	0.5290
A2	0.0232	1	0.0232	0.1625	0.6989
B2	0.0190	1	0.0190	0.1332	0.7259
C2	0.0048	1	0.0048	0.0338	0.8593
Residual	0.9977	7	0.1425
Lack of fit	0.9927	6	0.1654	33.09	0.1323	Not significant
Pure error	0.0050	1	0.0050
Cor total	14.23	16

TABLE 3.

ANOVA for Quadratic model for Response 2: Asymmetry factor.

Source	Sum of squares	df	Mean square	F‐value	p
Model	2.04	9	0.2270	5.50	0.0175	Significant
A‐Mobile phase	0.2665	1	0.2665	6.46	0.0386
B‐Flow rate	0.0176	1	0.0176	0.4276	0.5340
C‐pH	0.1988	1	0.1988	4.82	0.0642
AB	0.2970	1	0.2970	7.20	0.0314
AC	0.4160	1	0.4160	10.09	0.0156
BC	0.0001	1	0.0001	0.0024	0.9621
A2	0.7794	1	0.7794	18.89	0.0034
B2	0.1177	1	0.1177	2.85	0.1350
C2	0.0369	1	0.0369	0.8937	0.3760
Residual	0.2888	7	0.0413
Lack of Fit	0.2726	6	0.0454	2.80	0.4278	Not significant
Pure Error	0.0162	1	0.0162
Cor Total	2.33	16

These models can be linear or nonlinear in nature. In the case of retention time, the composition of the mobile phase (A) was the most dominant factor, as the retention times decreased with the increase of the organic composition of the solvent. The flow rate (B) and pH (C) played a minor role compared with the composition of the mobile phase.

In the case of the asymmetry factor, the composition of the mobile phase (A) and pH (C) played a dominant role in the symmetry of the peak shape, whereas the flow rate (B) played a minor role. The interaction terms AB and AC indicated the effect of the combination of the parameters on the peak shape, which was negative at extreme conditions.

To further elucidate the influence of CMPs and their interactions, contour plots and response surface plots were analyzed, as shown in Figures 3 and 4. These plots revealed that mobile phase composition had the greatest influence on retention time, while mobile phase composition and pH had significant effects on peak symmetry. The interaction effects between the parameters confirmed the need for optimization.

Perturbation plots, as shown in Figure 5, revealed that mobile phase composition had the greatest influence on the responses, as confirmed by the steepest curvature, while flow rate and pH had moderate effects. The adequacy and predictability of the models were further confirmed by using predicted vs. actual plots, as shown in Figure 6. The close alignment of the data points along the diagonal line confirmed the excellent agreement between experimental and predicted values.

Numerical optimization of the method using desirability functions resulted in optimal chromatographic conditions, which include a mobile phase of water: methanol (65:35, v/v), a flow rate of 0.8 mL/min, and a pH of 4.0. This resulted in a retention time of approximately 3.7 min and an asymmetry factor of approximately 1.1 with a desirability value higher than 0.95. From the experimental validation, it is evident that there is a strong correlation between the predicted and experimental values, validating the robustness of the method.

To further verify the reliability of the developed model, confirmation experiments were performed under the optimized chromatographic conditions obtained through numerical optimization. The experimentally observed retention time and asymmetry factor showed close agreement with the predicted values generated by the model, with prediction errors below 5%. These results demonstrate the adequacy, predictive capability, and robustness of the developed AQbD model and confirm the reproducibility of the optimized chromatographic conditions.

To further verify model reliability, confirmation experiments were conducted under the optimized conditions identified by numerical optimization. The observed retention time and asymmetry factor were found to be in close agreement with the predicted values, with prediction errors below 5%. These findings confirmed the adequacy of the developed model and demonstrated the reproducibility of the optimized chromatographic method.

3.1.3. Establishment of Three‐Dimensional Design Space Using Overlay Plot Analysis

The design space of the developed RP‐HPLC method was defined by overlay plot analysis, as presented in Figure 7a–c. As seen in the figure, these plots are two‐dimensional representations of the multidimensional design space, where the interaction of two CMPs is presented while holding the third one constant. The yellow‐colored area in each plot shows the region where all CQAs, including retention time and asymmetry factor, are within the predetermined limits.

In Figure 7a, the interaction of mobile phase composition (A) and flow rate (B), while holding pH constant, shows the importance of both parameters in the retention time of the analytes. As the composition of the mobile phase increases, the retention time decreases, while higher flow rates also result in faster elution of the analytes. However, it is clear that the region of interest is small, indicating the importance of optimization.

In Figure 7b, the interrelation of mobile phase composition (A) and pH (C) under fixed flow rate is represented. It was found that the two factors play an important role in determining peak symmetry. Peak symmetry can be maximized using a particular pH value and medium organic content.

In Figure 7c, the interaction between flow rate (B) and pH (C) at a fixed mobile phase composition shows a relatively lower effect on the responses; however, extreme conditions of these factors may result in peak symmetry variations.

It is evident from the figure that the relatively narrow design space observed in all the overlay plots indicates that the method is sensitive to changes in critical parameters, particularly mobile phase composition. The optimized chromatographic conditions of water: methanol (65:35 v/v), 0.8 mL/min flow rate, and pH 4.0 were found to be well within the design space, thus confirming the robustness and reliability of the method.

3.1.4. Application of Lean Six Sigma (DMAIC) Framework in Method Optimization

The DMAIC framework provided a structured approach for method development and performance improvement. Through systematic identification of critical variables, evaluation of analytical performance, implementation of optimization strategies, and establishment of control measures, the framework contributed to enhanced method robustness, consistency, and reliability.

3.2. Validation of the Stability Indicating Method

The developed stability‐indicating method by employing HPLC technique with a water and methanol mixture in a 65:35 ratio showed excellent validation results according to ICH guidelines (Figure 9a). Linearity was demonstrated with a good correlation coefficient (R ² = 0.9994) between 5 and 30 μg/mL concentrations. Precision was also shown to be high as the % RSD was below 2%. LOD and LOQ were calculated as 1.04 and 3.15 μg/mL, respectively. Robustness was observed to be good as slight variations in the HPLC parameters did not affect much the peak areas and retention times of Imeglimin. The method demonstrated excellent specificity, as Imeglimin was well resolved from all degradation products, with resolution values exceeding 3 under the applied stress conditions. Furthermore, PDA‐based peak purity analysis confirmed the spectral homogeneity of the Imeglimin peak, with peak purity values consistently close to 0.999. These findings indicate the absence of co‐eluting degradation products or interfering components and confirm the stability‐indicating capability of the developed method. The results of the recovery study ranged from 99.45% to 100.35%, showing excellent accuracy of the method. The summary of validation study is depicted in Table 4.

TABLE 4.

Summary of validation parameters.

Sr. no.	Parameters	Imeglimin (brand name Twymeeg 500 mg)
1	Linearity	Regression coefficient R 2 linearity concentrations	y = 4719.92× + 4734.066 0.9994 5–30 μg/mL
2	Precision	Actual concentration (μg/mL)	Recovery ± % RSD
		Intra‐day precision studies
		10 μg/mL 20 μg/mL 30 μg/mL	99.80% ± 0.99% 100.05% ± 0.61% 99.57% ± 1.01%
		Inter‐day precision studies
		10 μg/mL 20 μg/mL 30 μg/mL	101.00% ± 0.71% 99.30% ± 1.31% 100.10% ± 0.87%
3	LOD	—	1.04 μg/mL
	LOQ	—	3.15 μg/mL
4	Robustness	%RSD	Less than 2%
5	Specificity	Resolution > 3; Peak purity≈0.999
6	Accuracy	Actual concentration (%)	Mean Recovery ± % RSD
		80%	99.67 ± 1.12
		100%	100.35 ± 0.96
		120%	99.45 ± 0.84

3.2.1. Analysis of Marketed Formulation

On average, the Imeglimin tablet formulation yielded a mean drug content of 100.21% ± 0.02 for two lots. Table 5 gives a brief overview of the recommended methods as well as the results.

TABLE 5.

Analysis of commercial formulation.

Imeglimin (brand name Twymeeg 500 mg)	Imeglimin found (mg per tablet)
	Mean ± SD (n = 6)	Recovery (%)
1st lot	500.95 ± 214.3	100.19
2nd lot	501.11 ± 157.2	100.22

3.3. SPC for Method Performance Monitoring

SPC was applied to further assess the reproducibility and reliability of the proposed RP‐HPLC method for Imeglimin. The analysis was performed using six assay measurements obtained during the method precision study. These observations were used to construct an X̄ control chart and evaluate the stability of the analytical process over repeated measurements.

The mean assay value was calculated as 99.97%, while the upper and LCLs, determined using the ±3σ approach, were found to be 101.65% and 98.29%, respectively. All six observations were within the established control limits, indicating that the analytical process remained under statistical control throughout the study. No abnormal trends, shifts, or out‐of‐control points were observed, suggesting consistent method performance (Figure 8).

The absence of outliers and systematic variation confirms the robustness and reproducibility of the developed method. Although the SPC evaluation was based on a limited dataset generated during method validation, the results provide supportive evidence that the method is suitable for routine quality‐control applications and can consistently generate reliable analytical results.

3.4. Stability Indicating Property

A stress testing study of Imeglimin was conducted on HPLC using a mobile phase consisting of water: methanol (65:35, v/v). Degradation characteristics are summarized in Table 6.

TABLE 6.

Summary of stress degradation.

Sr. no.	Stress condition	Exposure condition	Rt of standard (min)	Rt of degradation products (min)	% degradation
1.	Acid hydrolysis	1 N HCl, refluxed for 1.5 h at 60°C	3.50	4.2	8.6
2.	Base hydrolysis	1 N NaOH, refluxed for 1.5 h at 60°C	3.50	4.3	19.9
3.	Oxidative	3% H2O2, 30 min, room temperature	3.71	3.06	17.9
4.	Thermal	Oven at 60°C for 5 h	3.75	4.3, 4.9	15.5
5.	Neutral	Water, 5 h refluxed at 60°C	3.72	4.2	9.5
6.	Photolytic	Exposure to direct sunlight for 30 days	—	—	—

3.4.1. Acid and Base Induced Degradation

Imeglimin exhibited greater stability under acidic conditions compared with alkaline conditions. Since there was no degradation observed when Imeglimin was introduced to 0.5 N hydrochloric acid, the normality of acid was increased. After Imeglimin was exposed to 1 N hydrochloric acid and refluxed at 60°C for 1 h 30 min, a substantial rise in degradation product (8.6%) was seen. The retention time of the degradation product was 4.2 min (Figure 9b).

Drug degradation was observed under alkaline conditions; however, no detectable degradation was observed when Imeglimin was introduced to 0.5 N sodium hydroxide, and the normality of the base was increased. When Imeglimin was exposed to 1 N sodium hydroxide and refluxed at 60°C for 1 h 30 min, a significant degradation was (19.9%) observed. The retention time of the degradation product was 4.3 min (Figure 9c).

The mass spectral results indicated that acid and base hydrolysis produced the same degradation products, with the major product showing a molecular ion at m/z 113.08. Figure 10b,c illustrates the mass spectrum of Imeglimin under both acidic and basic conditions.

FIGURE 10.

Mass spectra of Imeglimin standard, acid, base, oxidative, thermal and neutral degradation.

3.4.1.1. Mechanism for Acid and Base Hydrolysis of Imeglimin. Under acidic and alkaline stress conditions, Imeglimin (C₆H₁₃N₅; m/z 156.12) underwent hydrolytic degradation, resulting in the formation of a major degradation product at m/z 113.08. The observed mass loss suggests cleavage of the exocyclic aminoalkyl moiety through N‐dealkylation, yielding a lower molecular weight triazine derivative. This proposed transformation was supported by HR‐MS data and further corroborated by FTIR spectral changes, particularly in the amine‐related absorption bands, indicating alteration of the side‐chain amino functionality while preserving the triazine ring system. Based on these findings, the major degradant was tentatively identified as 4‐methyl‐4,5‐dihydro‐1,3,5‐triazin‐2‐amine (C₄H₈N₄; m/z 113.08), formed through degradation of the side‐chain substituents with retention of the triazine core structure (Figure 11a,b).

FIGURE 11.

(a) Mechanism for acid hydrolysis of Imeglimin. (b) Mechanism for base hydrolysis of Imeglimin. (c) Mechanism for oxidative hydrolysis of Imeglimin. (d) Mechanism for thermal degradation of Imeglimin. (e) Mechanism for neutral degradation of Imeglimin.

3.4.2. Hydrogen Peroxide‐Induced Degradation

The reaction was initially carried out in 3% H₂O₂ for 10 min at ambient temperature; however, no degradation was observed. Therefore, the exposure time was extended, and the drug was treated with 3% H₂O₂ for 30 min under the same conditions. This resulted in the formation of degradation products, accounting for approximately 17.9%, with a retention time of 3.06 min (Figure 9d).

The mass spectral analysis revealed that oxidative degradation resulted in a major product exhibiting a molecular ion peak at m/z 113.08. Figure 10d presents the mass spectrum of Imeglimin under oxidative conditions.

3.4.2.1. Mechanism for Oxidative Hydrolysis of Imeglimin. Under oxidative stress conditions, Imeglimin (C₆H₁₃N₅; m/z 156.12) exhibited degradation with the formation of a major product at m/z 113.08. The generation of the same degradation product observed under hydrolytic conditions suggests that the exocyclic amino groups are particularly susceptible to oxidative attack. HR‐MS analysis indicated the loss of the aminoalkyl side chain, while FTIR spectra revealed modifications in the characteristic amine stretching bands. These observations support an oxidative N‐dealkylation pathway leading to the formation of a stable triazine derivative. Based on the spectral evidence, the major degradant was tentatively identified as 4‐methyl‐4,5‐dihydro‐1,3,5‐triazin‐2‐amine (C₄H₈N₄; m/z 113.08), with preservation of the triazine core structure.

Although slight differences in chromatographic retention were observed for the degradation product formed under oxidative conditions compared with those generated under acidic, alkaline, and neutral stress conditions, HR‐MS analysis consistently identified the same major ion at m/z 113.08 corresponding to the molecular formula C₄H₈N₄. Therefore, the assignment of a common degradation product was based primarily on mass spectral evidence and the proposed degradation pathway rather than retention time alone (Figure 11c).

3.4.3. Photochemical Degradation

In the study of photochemical degradation, Imeglimin appeared to be stable. No formation of degradation products was observed even after exposing the drug solutions to sunlight for 30 days.

3.4.4. Thermal Degradation

When the reaction was carried out in oven at 60°C for 2 h, no degradation was observed. Therefore, the duration was extended to 5 h, which led to the formation of degradation products amounting to approximately 15.5%, with retention times at 4.3 and 4.9 min (Figure 9e).

Figure 10e presents the mass spectrum of Imeglimin under thermal conditions.

3.4.4.1. Mechanism for Thermal Degradation of Imeglimin. Under thermal stress, Imeglimin (C₆H₁₃N₅; m/z 156.12) underwent progressive degradation, resulting in the formation of degradation products at m/z 139.09 and m/z 113.08. The sequential decrease in molecular mass observed in the HR‐MS spectra indicates stepwise removal of methyl substituents from the parent molecule. FTIR analysis further supported this transformation through changes in alkyl C—H stretching vibrations, consistent with demethylation reactions. Based on the combined spectral evidence, thermal degradation is proposed to proceed through sequential N‐demethylation, initially forming N²,6‐dimethyl‐1,6‐dihydro‐1,3,5‐triazine‐2,4‐diamine (m/z 139.09), followed by further demethylation to yield 4‐methyl‐4,5‐dihydro‐1,3,5‐triazin‐2‐amine (m/z 113.08). The results indicate that thermal degradation primarily affects the alkyl substituents while maintaining the integrity of the triazine ring system (Figure 11d).

3.4.5. Neutral Degradation

Refluxing the reaction mixture at 60°C for 2 h did not produce any detectable degradation. However, extending the reflux duration to 5 h resulted in approximately 9.5% degradation, with the degradant eluting at a retention time of 4.2 min (Figure 9f).

The mass spectral analysis revealed that neutral degradation resulted in a major product exhibiting a molecular ion peak at m/z 113.08. Figure 10f presents the mass spectrum of Imeglimin under oxidative conditions.

3.4.5.1. Mechanism for Neutral Hydrolysis of Imeglimin. Under neutral stress conditions, Imeglimin (C₆H₁₃N₅; m/z 156.12) exhibited limited but detectable degradation upon prolonged reflux, producing a major degradation product at m/z 113.08. The HR‐MS data suggest cleavage of the exocyclic aminoalkyl side chain, resulting in the formation of a lower molecular weight triazine derivative. FTIR spectra showed corresponding changes in amine‐associated absorption bands, indicating modification of the side‐chain functionality while preserving the triazine nucleus. The similarity of the degradation product observed under neutral, acidic, alkaline, and oxidative conditions suggests a common degradation pathway involving N‐dealkylation. Accordingly, the major degradant was tentatively identified as 4‐methyl‐4,5‐dihydro‐1,3,5‐triazin‐2‐amine (C₄H₈N₄; m/z 113.08), formed through side‐chain cleavage with retention of the triazine core (Figure 11e).

3.5. Characterization of Degradation Products by Mass Spectrometry and Fourier Transform Infrared Spectroscopy

The HR‐MS analysis of Imeglimin under various stress conditions revealed the formation of several degradation products with different molecular weights and molecular formulas, indicating structural modifications of the parent compound (Table 7). HR‐MS spectra were acquired directly from the stressed samples without prior chromatographic isolation of individual degradation products. Therefore, the spectra contained ions corresponding to both the parent drug and the degradation products formed under the respective stress conditions. The proposed degradation products were assigned based on the characteristic m/z values observed in the stressed samples and correlated with the results obtained from the forced degradation studies.

TABLE 7.

Summary of HR‐MS of Imeglimin and its degradation products.

Sr. no.	Compound	Molecular weight	Molecular formula	IUPAC name
1	Imeglimin	156.12	C6H13N5	6‐dimethylamino‐2,4‐diamino‐1,3,5‐triazine
2	Acid degradation DP I	113.08	C4H8N4	4‐methyl‐4,5‐dihydro‐1,3,5‐triazin‐2‐amine
3	Alkaline degradation DP I	113.08	C4H8N4	4‐methyl‐4,5‐dihydro‐1,3,5‐triazin‐2‐amine
4	Neutral degradation DP I	113.08	C4H8N4	4‐methyl‐4,5‐dihydro‐1,3,5‐triazin‐2‐amine
5	Oxidative degradation DP I	113.08	C4H8N4	4‐methyl‐4,5‐dihydro‐1,3,5‐triazin‐2‐amine
6	Thermal degradation DP I	139.09	C5H10N5	N2,6‐dimethyl‐1,6‐dihydro‐1,3,5‐triazine‐2,4‐diamine
7	Thermal degradation DP II	113.08	C4H8N4	4‐methyl‐4,5‐dihydro‐1,3,5‐triazin‐2‐amine

FTIR spectra of Imeglimin under different stress conditions are presented in Figure 12a–f. A summary of the FTIR spectral interpretation, together with the corresponding structural changes and proposed degradation products, is provided in Table 8. FTIR analysis served as a complementary tool to support the structural interpretation derived from HR‐MS data. As the degradation products were not isolated prior to analysis, the FTIR spectra were used primarily to monitor changes in characteristic functional groups and to provide supporting evidence for the proposed degradation pathways.

FIGURE 12.

(a) FTIR spectra of Imeglimin standard. (b) FTIR spectra of Imeglimin acid degradation product. (c) FTIR spectra of Imeglimin base degradation product. (d) FTIR spectra of Imeglimin oxidative hydrolysis degradation product. (e) FTIR spectra of Imeglimin thermal degradation product. (f) FTIR spectra of Imeglimin neutral degradation product.

TABLE 8.

FTIR interpretation of Imeglimin acid degradation product.

Degradation condition	N—H stretch (cm−1)	C‐H stretch (sp3 CH2/CH3) (cm−1)	C=N stretch (cm−1)	C—H bend/CH2 bend (cm−1)	C—N stretch (cm−1)
Acid	3357.30	2947.05, 2834.99	1654.94	1452.71,1408.53	1112.64,1016.70
Base	3381.17	—	1641.45	1457.76, 1406.49	1016.47
Oxidative hydrolysis	3385.56	2935.52, 2839.18	1646.12	1402.77	1017.71
Thermal	3306.27	2946.34, 2834.25	1648.25	1452.77, 1408.73	1113.14, 1017.18
Neutral	3306.27	2946.34, 2834.25	1648.25	1452.77, 1408.73	1113.14, 1017.18

3.6. Greenness Assessment Method

3.6.1. AGREE

A greenness evaluation of the proposed analytical method was determined using the AGREE scoring tool (Figure 13a). The proposed method was assigned an AGREE score of 0.74, a result lying in the green zone. The pictogram shows that most criteria were well satisfied, with only minor limitations in certain areas (principle 9 being less favorable). The AGREE score of 0.74 reflects good compliance with the principles of GAC, indicating that the developed method offers a favorable balance between analytical performance and environmental sustainability.

FIGURE 13.

(a) Greenness Assessment using the AGREE tool. (b) Greenness Assessment using the GAPI tool. (c) Blueness Assessment using the BAGI tool. (d) CLICK Analytical Chemistry Index tool.

3.6.2. GAPI

The environmental sustainability of the developed analytical method is shown in Figure 13b. The figure displays mostly green and yellow areas, with no red zones, indicating that the overall environmental impact is low to moderate. The central yellow pentagon suggests a slight ecological burden during the sample preparation stage, likely due to solvent use or extraction steps. The GAPI assessment revealed predominantly green and yellow zones with no red regions, indicating a low environmental impact. The results confirm that the developed method is environmentally acceptable and aligned with GAC principles.

3.6.3. BAGI

The BAGI score of 70.0 indicates that the method is practical and suitable for routine laboratory applications. Strong performance was observed for sample treatment and time efficiency, while cost effectiveness showed comparatively lower performance, suggesting an area for future improvement as shown in Figure 13c.

3.6.4. CLICK

The CLICK assessment yielded an overall score of 81, reflecting the strong analytical performance of the proposed method. High scores for sensitivity and sample size highlight its efficiency for routine analysis, whereas parameters related to sample preparation and operational convenience offer opportunities for further optimization (Figure 13d).

Overall, the combined outcomes of AGREE, GAPI, BAGI, and CLICK demonstrated that the proposed RP‐HPLC method possesses a favorable balance between analytical performance, environmental compatibility, operational efficiency, and sustainability. Although each assessment tool evaluates different aspects of method greenness, their collective findings consistently support the suitability of the developed method for routine pharmaceutical analysis with reduced environmental impact and improved sustainability characteristics.

4. Conclusion

A robust and stability‐indicating RP‐HPLC method for the quantification of Imeglimin has been successfully developed by adopting the AQbD strategy. CMPs have been identified by risk assessment, which were optimized by BBD, providing reliable HPLC conditions in a well‐defined design space. The incorporation of Lean Six Sigma (DMAIC) and SPC has provided assurance in terms of consistency and reliability in the developed analytical method.

This method has been validated as per the ICH guidelines, showing excellent results in terms of linearity, precision, accuracy, specificity, and sensitivity, confirming the suitability of the method for routine quality‐control and stability‐testing applications. The forced degradation study showed substantial degradation in alkaline, oxidative, and thermal conditions, while photolytic stability has been observed. The structural characterization by HRMS and FTIR has shown degradation by N‐dealkylation and demethylation, with the formation of a common triazine core degradation product with m/z 113.08.

The greenness and practical applicability of the analytical method were demonstrated through AGREE, GAPI, BAGI, and CLICK assessments, showing the suitability, sustainability, and efficiency of the analytical method with minor scope for improvement in cost and sample handling. The integrated sustainability assessment using AGREE, GAPI, BAGI, and CLICK further confirmed that the developed method combines analytical reliability with environmental responsibility, supporting its application as a sustainable analytical tool for routine quality‐control and stability studies of Imeglimin.

A key strength of the present work lies in the integration of multiple contemporary analytical quality concepts within a single study. Unlike previously reported methods for Imeglimin, the developed approach combines AQbD‐driven optimization, Lean Six Sigma methodology, SPC‐based process monitoring, detailed degradation product characterization using HR‐MS and FTIR, and comprehensive sustainability assessment using AGREE, GAPI, BAGI, and CLICK metrics. This integrated framework enhances method understanding, reliability, and practical applicability for routine pharmaceutical analysis.

In conclusion, the present study provides a robust, stability‐indicating, and environmentally conscious analytical strategy for the determination of Imeglimin, highlighting the value of integrating AQbD principles, advanced characterization techniques, and sustainability assessment into pharmaceutical analysis.

Funding

The authors have nothing to report.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.