Ecofriendly analytical quality by design-based method for determining Metronidazole, Lidocaine and Miconazole using RP-HPLC in semisolid dosage form

Abstract

The article deals with the development of an analytical method based on AQbD and GAC for the simultaneous determination of Metronidazole (MET), Lidocaine (LID) and Miconazole (MIC) in oval dosage form using RP-HPLC. The separation was achieved on the stationary phase Zorbax C18 150 mm × 4.6 mm (i.d); 5 μm, using a gradient mobile phase containing Ethanol and Phosphate buffers. The linearity has been obtained for MET's 240-390 μg/mL, 32-52 μg/mL for LID and 64-104 μg/mL for MIC, with their LOD and LOQ as 31.25, 94.705 μg/mL (MIC) 1.463, 4.433 μg/mL (LID) and 7.981, 24.187 μg/mL for MIC, respectively. Finally, the method assessed for greenness using the three tools showed that the developed method was eco-friendly. The developed method is simple and reliable with a design space that helps for further improvement and is eco-friendly, making this method unique for adoption in regular quality control analysis.

KEYWORDS:

• Analytical quality by design
• Green analytical chemistry
• HPLC
• Lidocaine
• Metronidazole
• Miconazole

1. Introduction

Metronidazole (MET) belongs to the nitroimidazole class of antibiotics, which is chemically 1H-Imidazole-1-ethanol, 2-methyl-5-nitro-, radical ion(1-) (Figure 1a) with a pKa of 15.42 and 2.81 and the log P value of −0.15 and −0.46. It is frequently used to treat gastrointestinal infections through intravenous and oral routes. MET is also formulated as a vaginal suppository for treating several specific bacterial infections. It mainly inhibits the bacteria by interacting with DNA, breaking the helix structure and making it inactive, which makes MET the preferred drug of choice in many bacterial infections [1–3].

Figure 1. Chemical structure of (a) Metronidazole (b) Lidocaine (c) Miconazole.

Lidocaine (LID) is chemically acetamide, 2-(diethylamino)-N-(2,6-dimethyl phenyl) (Figure 1b) with a pKa value of 13.78 and 7.75 and the log P value of 1.81 and 2.84, which also known as lignocaine, used as a local anaesthetic. It works by penetrating the axoplasm combined with the hydrogen undergoing ionization. The ionized cation prevents nerve depolarization by binding with sodium channels reversibly and locking them in the open state. It has a faster onset than other types of local anaesthetics because of its high pKa value [4–6].

Miconazole (MIC) is chemically 1-2-[(2,4-Dichlorobenzyl) oxy]-2-(2,4-chlorophenyl) ethyl-1H-imidazole (Figure 1c) with a pKa value of 6.48 and the log P value 5.86 used in the treatment of fungal infections caused by candidiasis like agents affecting the skin, mouth, and vagina. The mechanism of MIC acts in three ways by inhibiting an enzyme called CYP450 14α-lanosterol demethylase, increasing the reactive oxygen species, or increasing the levels of farnesol [7,8].

Even though each of these drugs had advantages on its own, the combination of these drugs produced a unique treatment for severe vaginal infections that are bacterial or fungal and cause terrible pain. Ovules are a dosage form used to treat women requiring infections, mostly when the oral route seems ineffective. This route is effective and has fewer side effects caused by the drugs. Thus, analyzing this dosage form for uniform drug content is essential as the drug readily reaches systemic circulation.

The drug can be analyzed using several instruments; among them, HPLC is preferable due to the accuracy and reliability of the results. To enhance the HPLC analysis more accurately, the ICH has recommended the existing regulations and made it mandatory to develop a method based on the Analytical Quality by Design (AQbD) principles which not only helps to develop a robust method but also helps the analyst to monitor the method and improve the method from the same condition which helps to decrease the time for revalidation [9–13].

The literature review suggests that no analytical method has been developed for this combination using HPLC or UV-vis spectroscopy. This article aimed to develop an analytical method using RP-HPLC to analyse these drugs. To complement the developed method using HPLC, applying AQbD with Green analytical chemistry (GAC) Principles makes the method more sustainable and lasts longer without further changes. The method developed here for this ternary combination of drugs by combining the GAC with AQbD in HPLC makes the method economical, dependable, and sustainable for the quality control labs to use and grow from those paths to extend further improvement [14–21].

2. Materials and methods

2.1. Instruments, software, and chemicals

The HPLC used to analyze the ternary combination was Agilent 1220 with an autosampler, binary pump, and detection by PDA detector. The software used to process the HPLC data was open lab 2.6. The green assessment was performed for GAPI assessment using GAPI chart generator version 0.1 beta and AGREE metric by AGREE calculator. MET, LID, and MIC have been ordered from Carbanio and supplied by the Research Lab Fine Chem Industries and Prince Scientific India with a 98–99% purity. HPLC-grade ethanol was purchased from the Hayman group limited, U. K. The remaining chemicals were analytical grade and procured from Sisco research laboratory, India.

2.2. Solutions preparations

2.2.1. Preparation of standard and sample

Standards of 75 mg MET, 20 mg MIC and 10 mg LID have weighed accurately and transferred into a 50 ml volumetric flask (VF), dissolved in ethanol, and further sonicated for 10 min, made up to the mark by ethanol and marked as stock. From the stock solution, 2 ml was transferred into 10 ml VF and made the volume with ethanol with the final concentration obtained at 300, 20, and 10 µg/ml of MET, MIC, and LID, respectively. Finally, the standard solution has transferred to the vial through a 0.22 µ polyvinylidene fluoride (PVDF) filter.

One suppository was weighed and dissolved in 50 ml of warm ethanol and sonicated for 5 min; a clear solution was obtained and marked as sample stock. From the above solution, 0.2 ml was taken and made up to 10 ml with ethanol, then transferred to the vial by filtering through a 0.22 µ PVDF filter. The above process was repeated six times for two days for each of the three samples per day.

2.2.2. Preparation of buffer

3.4 g of potassium dihydrogen phosphate and 0.5 ml of orthophosphoric acid (OPA) dissolved in 1000 ml of water. The final solution has run through a 0.45 µm suction filtration system and sonicated for 15 min.

2.2.3. Final HPLC conditions

The final optimized method selected after applying the central composite design (CCD) consisting of gradient mobile phase ethanol (A) and phosphate buffer (B) at different proportions starts at 0 min with 10: 90, 5.79 min changed to 75:25. The flow rate has kept constant throughout the run time as 1.14 ml/min. Three drugs are determined at an λ max of 220 nm. The column temperature has been kept at 30 °C throughout the analysis. The fundamental research has been conducted in the stationary phase Zorbax C18 150 mm × 4.6 mm (i.d.); 5 µm. The ICH Q14 was used to confirm that the optimized chromatographic conditions were valid.

2.2.4. Forced degradation studies

One suppository was weighed and dissolved in 50 ml of warm ethanol and sonicated for 5 min; a clear solution was obtained and marked as sample stock. From the above stock solution, 0.2 ml was taken and transferred to four different 10 ml volumetric flasks and added 1 ml of 0.1 M HCl, 1 ml of 0.1 M NaOH, 1 ml of 1% H₂O₂ respectively, and the fourth flask was kept in the 60 °C. Further, the flasks have made up to the mark with ethanol, then transferred to the vial by filtering through a 0.22 µ polyvinylidene fluoride (PVDF) filter. Each sample was injected at intervals of one hour until degradations from the standard peaks were discovered.

2.1.5. Validation protocol

The validation protocol has been done accordingly with ICH Q14 guidelines, a recent amendment to the Q2 R2. The accuracy has performed at around 80–120% concentration by keeping the label claim value at 100.

Linearity calculation has been performed at six different concentrations of 80–130% at a concentration range of 240 - 390 µg/mL for MET, 32–52 µg/mL for LID, and 64–104 µg/mL for MIC, respectively. Based on the results obtained from the linearity limit of detection and quantification has obtained. Method precision has been done for inter and intraday and determined for any variation in the method. Further solution stability was determined for the three days by having a comparison with the fresh sample preparations.

3. Results and discussion

Anastas has portrayed GAC principles from green chemistry by certain modifications and made a noteworthy moment in the analytical field. Nowadays, most analytical chemists have been very keen on developing methods based on the twelve principles of the GAC [22–26]. Since it can be challenging to adhere to all of the GAC principles in liquid chromatography, the guiding principles of the current work, such as the reduction and replacement of solvents, were chosen to be satisfactory.

The significant difficulty in developing an analytical method based on the GAC principles is the selection of solvents, especially in HPLC. The primary solvents available for the HPLC were acetonitrile and methanol. However, this solvent has the advantage of compatibility with the instrument and most of the sample; these solvents are unsafe for the analyst and the ecosystem. These solvents were categorized as toxic according to the harmful regulatory inventory list. Since the alternative for these solvents is still in the bay. Alternatives to these solvents, such as ethanol (the most popular green alternative solvent), propylene carbonate, water, etc., have not been widely employed by analysts; nonetheless, given its compatibility and availability, ethanol is the only option at this time. These principles have guided the developed method’s ecological beginnings, which began with the method development phase.

3.1. Method development phase

The choice of the stationary phase was tried with the C18 column as it has the advantage of having high retention capacity compared with C8. The shorter column (150 mm in length) was chosen because a decrease in column size will decrease the mobile phase consumption; selecting smaller columns and reducing the mobile phase has not compromised the retention or capacity factor. The method was designed in such a way as to obtain the results according to the standard guidelines. The second important factor in method development is the mobile phase. As discussed earlier, the choice of organic mobile phase for the study was restricted to ethanol, and the drugs were also highly soluble in ethanol (MET 5 mg/mL, LID-50 mg/mL, and MIC– 1 mg/mL). The selection of buffer is based on the pKa values of the drugs because these drugs were previously tried using a pH of 3 (1% acetic acid), but peak broadening and splitting had been noticed at various mobile phase compositions. In this regard, it was observed that phosphate buffer at pH 4 worked best for determining three drugs in a single run.

Initially, the mobile phase composition of ethanol and buffer (1:1 v/v) was tried, and the MET has a good capacity factor. Although the peak properties are under the standards and the MIC was eluted at a rate of 1 mL/min for more than 15 min, this went against the GAC principles and raised serious doubts about the choice of the above isocratic mobile phase in any ratio. The method was more customizable and more accessible since the mobile gradient phase was chosen to cut down on both time and mobile phase consumption. The composition of the mobile phase and the evolution of the organic phase’s composition have been supported by a number of experimental basis. It was found that the mobile phase needs to be adjusted at 10% ethanol and 90% buffer at 0 min to ensure that MET elution has a sufficient interaction with the column and a proper capacity factor. Then, the increase in the organic phase (75%) has done at 6 min so that the MIC could elute within 10 min. Since the temperature was held constant at 35 °C ± 0.5 °C throughout the experiment, none of the selectivity characteristics have changed, despite the column’s ambient temperature varying from 30 to 35 °C. All three drugs were simultaneously detected at 220 nm due to the relatively low concentration of LID present. Finally, the developed method has been used to discover how factors interact with the response using the Design of Expert software programme.

3.2. Application in design of expert software

The developed method has enhanced system suitability parameters, improved separation between the three drugs and stress degradation peaks. Hence, it has been applied to the Design of Expert Software to build the design space and their interactions. The choice of criteria for optimisation was crucial for the overall study and for enhancing the method’s quality in the future based on the specifications. So, the factors selected for the method development were changes in mobile phase ratio and flow rate. The CCD has been utilised to optimise the method because of its advantage, allowing alpha values to be selected with fewer runs. Thus, the method factors were set as +α, 0, and -α. The time of flow rate has been set at +α (7), 0 (6), and –α (5). The flow rate was set as +α (0.8), 0 (1), and –α (1.2). Thirteen runs of these values have been made to determine how the factors interact with the responses. The design table depicted in Table 1 shows the different levels of interaction of robust factors and their relative responses.

Table 1. Different levels of interaction of robust factors and their relative responses.

	Factor 1	Factor 2	Response 1	Response 2	Response 3
Run	A: Time	B: Flow Rate	Rs	LID Peak Height	Rt of MIC
1	6.00* (0)	1.00 (0)	7.697	300.8	8.3
2	6.71 (+1)	0.86 (−1)	7.381	385.3	7.3
3	6.71 (+1)	1.14 (+1)	7.761	324.7	8.2
4	6.00* (0)	1.00 (0)	7.731	305.5	8.3
5	5.29 (+1)	1.14 (+1)	7.761	236.4	9.4
6	6.00 (0)	1.00 (0)	7.596	289.4	8.3
7	5.29 (+1)	0.86 (−1)	7.656	283.8	8.4
8	7.00(+α)	1.00 (0)	7.564	370.7	7.5
9	6.00 (0)	0.80(-α)	7.338	341.6	7.7
10	6.00 (0)	1.20(+α)	7.907	278.2	9.1
11	5.00(-α)	1.00 (0)	7.868	259.1	9.1
12	6.00* (0)	1.00 (0)	7.709	300.0	8.3
13	6.00* (0)	1.00 (0)	7.686	300.4	8.3

*Center values in the design

3.2.1. Analysis of interaction between factors and responses

The interaction of the selected factors was determined with the help of three critical responses like resolution between MET and LID, the peak height of LID, and finally, the retention of MIC. The response has been selected based on a better resolution between the MET and LID so that the degradant peaks generated may not elute with the other drug. The concentration of LID was significantly low in the dosage form since the peak height appearance may hinder by the presence of MET, so the response of LID height has also been considered. Finally, the retention of MIC has been assessed based on green analytical principles to make the elution faster. Thirteen runs have been executed practically, and the obtained responses have been applied to the design of experiment software.

3.2.1.1. Effect of factors on resolution (Rs)

The quadratic process order shows an F-value of 14.87, which demonstrates the significance of the model. There is a 0.13% chance that noise will result in an F-value. P-values < 0.05 demonstrate the model’s efficacy. A and B factors show that the model is significant. The Lack of Fit F-value of 2.17 implies that the Lack of Fit is insignificant relative to the pure error. The perturbation, 2D, and 3D contour plots in Figure 2 show that while resolution decreases with increasing time, resolution rises with increasing flow rate. This indicates that the factors have a definite effect on the resolution.

Figure 2. Interaction of factors and responses with their perturbation (a) Factors on response 1 (b) Factors on response 2 (c) Factors on response 3; 2D contour plots for (a) Factors on response 1 (b) Factors on response 2 (c) Factors on response 3; 3D surface plots for (a) Factors on response 1 (b) Factors on response 2 (c) Factors on response 3.

3.2.1.2. Effect of factors on LID peak height

The quadratic process order shows an F-value of 79.70, which suggests the model is significant. There is only a 0.01% chance that an F-value might happen due to noise. P-values < 0.0500 specify model terms are essential. A, B, and A² factors show that the model is significant. The Lack of Fit F-value of 2.06 implies that the Lack of Fit is insignificant relative to the pure error. Perturbation, 2D, and 3D contour plots (Figure 2) show precisely opposite to the responses shown on resolution. An increase in time causes a rise in LID peak height, whereas an increase in flow rate causes a decrease. This further demonstrates that the parameters do have an effect on the resolution.

3.2.1.3. Effect of factors on MIC retention time

The quadratic process order has shown an F-value of 66382.54 suggests the model is significant. The possibility of noise affecting an F-value is incredibly unlikely (0.01%).

P-values < 0.0500 specify the model’s terms are essential. A, B, and A² factors show that the model is significant. The Lack of Fit F-value of 2.10 implies that it is insignificant relative to the pure error. The perturbation, 2D, and 3D contour plots in Figure 2 reflect the responses based on resolution. In the case of MIC retention time, increases in time duration have a beneficial effect on MIC retention time whereas increases in flow rate have a negative effect. This indicates that the factors have a definite effect on the resolution.

The actual coding for the factors on the three responses was as follows. (1) $\begin{array}{rl}\text{Rs}& =10.1435+-1.02151\times \text{Time}\\ & +0.817433\times \text{Flow}\text{Rate}+0.6875\\ & \times \text{Time}\times \text{Flow}\text{Rate}+0.01745\\ & \times \text{Tim}{\text{e}}^2+-1.90125\times \text{Flow}\text{Rat}{\text{e}}^2\end{array}$(1) (2) $\begin{array}{rl}\text{LID}\text{Peak}\text{Height}& =592.222+-64.9094\times \text{Time}+\\ & -390.58\times \text{Flow}\text{Rate}+-33.16\\ & \times \text{Time}\times \text{Flow}\text{Rate}+13.2929\\ & \times \text{Tim}{\text{e}}^2+207.434\times \text{Flow}\text{Rat}{\text{e}}^2\end{array}$(2) (3) $\begin{array}{rl}\text{Rt}\text{of}\text{MIC}& =9.60353+-0.43949\times \text{Time}\\ & +1.44086\times \text{Flow}\text{Rate}+-0.325\\ & \times \text{Time}\times \text{Flow}\text{Rate}+-0.00385\\ & \times \text{Tim}{\text{e}}^2+1.97875\times \text{Flow}\text{Rat}{\text{e}}^2\end{array}$(3)

3.3. Optimization of design

According to the design study, the selected parameters interacted with one another and had a noticeable impact on the replies. The method must be optimized to find the best suitable design space to work further. The optimization can be performed using a two-step process, numerical and graphical. Numerical data guarantees that the models accurately estimate the actual response surface (Figure 3a). It helps to set goals for the factors and responses that can be desired for optimal results. The criteria set for the present method are depicted in Table S1, which generated 63 solutions with desirability of 1. One solution has selected randomly, executed practically, and found less than a 10% deviation between theoretical and practically achieved results. Finally, the same solution has been validated according to ICH Q14 guidelines.

Figure 3. Desirability plot for the proposed method (a) and Overlay plot for the proposed design (b).

The next step in design optimization is the graphical part, which gives a clear idea of the design space to numerical data. Once the criteria are set in the numerical data, the solution is portrayed on the design space overlay plot. The yellow-coloured region in the overlay plot can consider as a design space with maximum desirability to obtain desired results. The other region coloured in grey indicates the range was out of the design space. The overlay plot depicted in Figure 3b.

3.4. Forced degradation studies

The samples were exposed to various degradation conditions and then injected into an HPLC for analysis. The obtained results were compared to the control sample. Forced degradation studies were conducted on MET, LID, and MIC under different stressed conditions. The results showed that the drugs remained stable in all conditions, with less than 20% degradation after prolonged exposure. MET experienced a 10% degradation after eight hrs. of exposure to alkali, LID degraded after 12 hrs. of exposure to acid, while MIC degraded in peroxide after 12 hrs. of exposure. Thermal degradation did not cause any changes in the peak area, even when kept at 60°C for three days. The degraded products did not interfere with the drug peaks, confirmed using peak purity and UV-visible spectra for the specified peaks. The degraded peaks, purity plots and individual spectra for confirmation are depicted in Figures S1–S4. The standard chromatogram, without any stress, along with the peak purity and spectral peaks, is also depicted in Figure 4.

Figure 4. Standard chromatogram without degradation along with the purity plots indicating green colour and their respective UV spectrum for the three drugs.

3.5. Validation of the proposed method

The developed and optimized method using the design expert was further validated according to the ICH guidelines Q2 (R1), and the results for different validation parameters have been discussed in the supplementary file.

3.5.1. Assay

The assay was performed using the procedure outlined in the materials and methods section, and the chromatograms illustrating the assay are depicted in Figure 5. The results of the assay demonstrate that the method does not exhibit any interference with the drug excipients. It assures that the method can readily analyze dosage forms containing MET, LID, and MIC drugs at different concentrations. Further, the assay results were compared with the reported individual methods using one-factor ANOVA, which indicated no significant difference between those methods. The complete results of ANOVA are depicted in the Table 2.

Figure 5. RP-HPLC chromatogram for MET (2.563), LID (3.535), and MIC (7.702) in the pharmaceutical dosage form.

Table 2. Single factor ANOVA comparison for the proposed method and reported method.

	MET		LID		MIC
Source of Variation	Between Groups	Within Groups	Between Groups	Within Groups	Between Groups	Within Groups
SS	5.713	4.163	0.220	43.303	22.702	18.011
Df	1.000	4.0	1.000	4.000	1.000	4.000
MS	5.713	1.041	0.220	10.826	22.702	4.503
F	5.489		0.020		5.042
P-value	0.079		0.893		0.088
F crit	7.709		7.709		7.709

4. Greenness assessment for the proposed method

The greenness assessment for the proposed method has been performed by using three green analytical metrics: 1) Green Analytical Procedure Index (GAPI), which is a qualitative method; 2) Analytical Eco Scale (ESA), a semi-quantitative method, and 3) Analytical Greenness metrics (AGREE metrics); a quantitative method.

4.1. GAPI

GAPI is an analytical tool used to assess the greenness of an analytical method based on pictographic representation. GAPI has 15 pictograms that represents various analytical approach ideas. It is divided into three categories: sample preparation, reagent and solvents used, and the instrument. Based on these specifications, a developed method was utilised to create a pictogram that symbolises green and suggests that it is an eco-friendly (Figure 6a). The increase in red in the pictograms indicates that the method has some hazards that need to be modified in order to make it more environmentally friendly and green.

Figure 6. Greenness assessment for the proposed method: (a) GAPI and (b) AGREE.

4.2. EAS

It is a semi-quantitative method in which the final value may be represented by using the number. However, the selection of all the criteria was purely based on the number of pictograms, making little complex and making this method semi-quantitative. EAS calculation was based on solvent usage, an instrument, and solvent wastage. The calculation was done based on those criteria, as depicted in Table 3, and the final EAS score was 90. According to EAS, any score which attains more than 75 by a method is considered a green method, and the present method has scored a score of 90, indicating the method has more merits to contain its adaptability to be greener.

Table 3. Calculation of Analytical Eco Scale for the proposed method.

S. No.	Type of penalty	PP	Total
1	Solvent or reagent
	A. Ethanol has used less than 10 ml used for a single run, so sub-total PP = 1	A. 2 pictograms (danger)	A. 4
	B. Potassium dihydrogen phosphate	B. 1 pictogram (warning)	B. 1
	C. Ortho Phosphoric acid	C. 1 pictogram (danger)	C. 2
2	Instrument (HPLC)	1 PP	1
3	Occupational Hazard	0 PP	0
4	Waste and recycling (Passivation)	2 PP	2
TOTAL PP			10
EAS score = 100 – PP			90

4.3. AGREE on metrics

AGREE metrics is a software-based analytical greenness metric used to quantify the method greenness with a score range of 0–1. A score close to 1 indicates the method was greener. The AGREE metrics software consists of 12 steps to be filled, and each step suggests a principle of GAC and covers most aspects to calculate the greenness score. The present method was applied to the AGREE tool and obtained a score of 0.85 (Figure 6b), indicating the method has more green analytical aspects, depicted under the GAC principles.

4.4. Greenness comparison

We have developed a greenness method for determining three drugs simultaneously in the ovules dosage form for the first time. Since we have successfully separated three drugs simultaneously, there are no reported methods published simultaneously separating three-drug combinations since the developed method compared with other reported methods for simultaneous separation of two-drug combinations, as shown in Table 4; the comparative results demonstrate that the current approach has the highest level of greenness.

Table 4. Assessment of greenness for the proposed method vs. reported methods using respective chromatographic conditions.

S. No.	Developed by	Chromatographic Conditions	GAPI	EAS	AGREE
1	N. Erk et al. [27].	HPLC method, for determination of MET, MIC, using Methanol: water: phosphoric acid (30:70:0.20 v: v) as mobile phase		7 + 1 + 3 + 3  = 14 Σ =  86
2	Cemal Akay et al. [28]	HPLC method, for determination of MET, MIC, using Methanol + water (40+60, v/v) as mobile phase		6 + 1 + 3 + 3  = 13 Σ =  87
3	Tarek S. Belal et al [29]	HPLC method, for determination of MET, MIC, using buffer: Acetonitrile as mobile phase in gradient elution		5 + 1 + 3 + 3  = 12 Σ =  88
4	Hasan S A et al [30]	HPLC method, for determination of MET, MIC, using Methanol + water (40+60, v/v) as mobile phase		6 + 1 + 3 + 3  = 13 Σ =  87
5	Dibya Ranjan Sahoo et al [31]	HPLC method, for determination of Benzoic Acid, MET, and MIC, using buffer: Acetonitrile (65: 35) as mobile phase		5 + 1 + 3 + 3  = 12 Σ =  88
6	Proposed method	HPLC method, for determination of MET, MIC, and LID, using Ethanol and Phosphate buffer (3.5 pH) (72: 28)		7 + 1 + 0 + 2  = 10 Σ =  90

EAS =  PP for pictograms + Instrument energy + occupational hazard + Wastage.

5. Conclusion

Pharmaceutical regulatory agencies require strict policies for safe and effective medication delivery. The ICH’s updated drug development guidelines (ICH Q14) emphasize using the AQbD approach to develop analytical methods. This allows for reproducible, reliable methods and reduces revalidation needs for minor method modifications. Combining AQbD with Green Analytical Chemistry principles ensures adherence to guidelines while promoting eco-friendliness and sustainability. This study developed an AQbD-based analytical method using GAC to identify a ternary combination of MET, LID, and MIC in bulk and ovules dosage forms. The method was optimized, validated according to ICH Q2 (R1), and assessed using green values, providing a reliable and easily adaptable approach for pharma industries. Despite the fact that there are no known green methods for simultaneously separating three-drug combinations, comparisons with other two-drug combinations show that the current method has the highest level of greenness.

Acknowledgement

The authors thank the Centre for Advanced Research Organic Materials (Sona-AROMA), Department of Chemistry, Sona College of Technology. SRM College of Pharmacy and SRMIST for supporting this research work.

Disclosure statement

No potential conflict of interest was reported by the author(s).