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INTRODUCTION
It is becoming more and more important in modern pharmaceutical research to look at multiple Active Pharmaceutical Ingredients (APIs) at the same time. This is especially true for combination therapies that aim to treat complicated pathophysiological conditions (Blessy et al., 2014). MTX is a folate antagonist that is commonly used in cancer chemotherapy and for autoimmune disorders. Curcumin (CUR) is a natural polyphenolic compound that has anti-inflammatory and antioxidant properties. And Etodolac (ETO) is a selective cyclooxygenase-2 inhibitor. Together, they make a therapeutically relevant combination for both inflammatory and oncological uses (Jouyban et al., 2010).
The different chemical profiles of these molecules make analysis more difficult in their own ways. MTX is stable at different pH levels and needs to be handled carefully because it is photosensitive (Alvarez-Lueje et al., 2005). Curcumin doesn't dissolve well in water and breaks down quickly in alkaline conditions. On the other hand, etodolac has a middling lipophilicity and could be affected by impurities (Tonnesen et al., 2002). Because of these different physicochemical features, we need to create a strong analysis method that can measure multiple things at once while still being selective and sensitive enough.
Trial-and-error methods are often used to build traditional analytical methods, which can take a long time and might not find the best conditions (Hibbert, 2012). Using the Design of Experiments (DOE) method gives you a structured way to improve your method. It lets you look at many factors and how they affect each other, with the goal of reducing the number of experiments you need to do and increasing the amount of information you get (Bezerra et al., 2008).
Individual chemical methods for these substances have been reported in previous studies, but there isn't a lot of research on how to figure them all out at the same time. Dhaneshwar et al. (2006) created different HPLC methods for measuring etodolac, and Anand et al., (2007) wrote about methods for analysing curcumin. Using a DOE approach, however, no complete synchronous way has been found for this three-part system.
The study's goal was to create and test a new RP-HPLC method for measuring MTX, CUR, and ETO at the same time. To do this, they used a structured DOE approach to improve the method and then did full validation according to ICH Q2(R1) standards (ICH, 2005).
MATERIALS AND METHODS
Chemicals and Reagents
We got reference standards for methotrexate (99.2% pure), curcumin (98.8% pure), and etodolac (99.5% pure) from Sigma-Aldrich in St. Louis, MO, USA. We got methanol, acetonitrile, and orthophosphoric acid that are good for HPLC from Merck in Darmstadt, Germany. A Milli-Q machine (Millipore, Bedford, MA, USA) was used to make ultra-pure water. The scientific grade was the only level of drugs used.
Tools and Equipment
Waters Corporation, Milford, MA, USA, made the HPLC system, which was made up of a Waters Alliance 2695 separation module and a Waters 2998 photodiode array detector. The Empower 3 program was used to collect and handle the data. The fixed phase was a Phenomenex Luna C18 column that was 250×4.6 mm and 5 μm thick.
Design of Experiments (DOE) Strategy
As usual (Ferreira et al., 2007), a Box-Behnken design was used to find the best conditions for chromatography. Three things were looked at in the study: the flow rate (mL/min), the temperature of the column (ºC), and the make-up of the mobile phase. The trial design grid had 15 runs and 3 middle points to help find the mistakes (Table 1).
| Run | Factor A: Mobile Phase (% ACN) | Factor B: Flow Rate (mL/min) | Factor C: Temperature (ºC) |
|---|---|---|---|
| 1 | 40 | 0.8 | 30 |
| 2 | 60 | 0.8 | 30 |
| 3 | 40 | 1.2 | 30 |
| 4 | 60 | 1.2 | 30 |
| 5 | 40 | 1 | 25 |
| 6 | 60 | 1 | 25 |
| 7 | 40 | 1 | 35 |
| 8 | 60 | 1 | 35 |
| 9 | 50 | 0.8 | 25 |
| 10 | 50 | 1.2 | 25 |
| 11 | 50 | 0.8 | 35 |
| 12 | 50 | 1.2 | 35 |
| 13 | 50 | 1 | 30 |
| 14 | 50 | 1 | 30 |
| 15 | 50 | 1 | 30 |
Sample Preparation
We made stock solutions of MTX, CUR, and ETO in methanol at a concentration of 1000 μg/mL. Working solutions were made by diluting them with the right amount of mobile phase. Every day, new mixed standard solutions with all three analytes were made and kept out of the light.
Conditions for Chromatography
The final chromatographic conditions were set as follows based on DOE optimisation:
- Mobile phase: Acetonitrile: Water (52:48, v/v).
- Flow rate: 1.0 mL/min.
- Column temperature: 30ºC.
- Injection volume: 20 μL.
- Detection wavelength: 280 nm.
- Run time: 20 min.
Method Validation
The created method was checked against the ICH Q2(R1) standards for the following factors:
Specificity: Tested by looking at blank samples, separate standard solutions, and mixed standard solutions to make sure the peak is pure and there is no disturbance.
Linearity: Six concentration levels were used for each substance to make calibration graphs. Correlation coefficient (r²), slope, intercept, and residual analysis were all types of statistical analysis.
Precision: Checked using quality control samples at low, medium, and high concentrations in tests that looked at consistency (intra-day precision) and intermediate precision (inter-day precision).
Recovery studies at 80%, 100%, and 120% of standard amounts were used to figure out the accuracy.
Robustness: Changes were made on purpose to important method factors, such as the mobile phase's make-up (±2%), the flow rate (±0.1 mL/min), and the temperature (±2ºC).
Stability: Testing the solution's stability on a bench, in the fridge, and through freeze-thaw cycles were some of the places it was kept.
RESULTS
DOE Optimization Results
The Box-Behnken design was able to find the best chromatographic conditions for separating all three analytes at the same time. Response surface methods showed that the makeup of the mobile phase had the biggest effect on resolution. Flow rate and temperature had less of an effect (Figure 1).
Chromatographic Performance
With baseline resolution, the optimised method did a great job of separating all three analytes. It looked like the chromatogram had good peak symmetry and long enough retention times for quantitative analysis (Figure 2).
Method Validation Results
Specificity
The method worked very well and didn't get messed up by typical medicinal ingredients or breakdown products. Peak purity analysis showed that all of the chemical peaks were the same.
The Linearity
For all three analytes, linear calibration curves were made with very high correlation values. The straight ranges worked well enough for the tasks they were meant for (Table 2 and Figure 3).
| Analyte | Linear Range (μg/mL) | Slope | Intercept | r² | LOD (μg/mL) | LOQ (μg/mL) |
|---|---|---|---|---|---|---|
| MTX | 5-50 | 45,231 | 234 | 0.9998 | 0.8 | 2.5 |
| CUR | 10-100 | 32,145 | 456 | 0.9997 | 1.2 | 3.8 |
| ETO | 25-250 | 28,967 | 123 | 0.9999 | 2.1 | 6.5 |
Precision
The method worked very well, and the RSD values were well within acceptable ranges for both intra-day and inter-day tests (Table 3).
| Analyte | Concentration (μg/mL) | Intra-day (n=6) | Inter-day (n=18) |
|---|---|---|---|
| MTX | 15 | 1.2 | 1.8 |
| MTX | 25 | 0.9 | 1.5 |
| MTX | 35 | 1.1 | 1.6 |
| CUR | 30 | 1.5 | 1.9 |
| CUR | 50 | 1.3 | 1.7 |
| CUR | 70 | 1.4 | 1.8 |
| ETO | 75 | 1.6 | 2 |
| ETO | 125 | 1.2 | 1.9 |
| ETO | 175 | 1.4 | 1.8 |
Accuracy
Recovery studies demonstrated excellent accuracy with mean recovery values ranging from 98.5% to 101.8% for all analytes (Table 4).
| Analyte | Spiked Level | Mean Recovery (%) | RSD (%) |
|---|---|---|---|
| MTX | 80% | 99.2 | 1.3 |
| MTX | 100% | 100.5 | 1.1 |
| MTX | 120% | 101.2 | 1.4 |
| CUR | 80% | 98.8 | 1.5 |
| CUR | 100% | 100.1 | 1.2 |
| CUR | 120% | 101.8 | 1.6 |
| ETO | 80% | 98.5 | 1.7 |
| ETO | 100% | 99.9 | 1.3 |
| ETO | 120% | 100.8 | 1.5 |
Robustness
The method demonstrated good robustness with minimal impact from deliberate variations in chromatographic conditions (Figure 4).
Stability
Solution stability studies confirmed adequate stability of all analytes under various storage conditions (Table 5).
| Storage Condition | Time | MTX | CUR | ETO |
|---|---|---|---|---|
| Bench-top (25ºC) | 24h | 99.2 | 97.8 | 99.5 |
| Refrigerated (4ºC) | 7d | 99.8 | 99.1 | 99.7 |
| Freeze-thaw cycles | 3× | 98.9 | 98.3 | 99.2 |
| Light exposure | 24h | 95.2 | 89.7 | 98.8 |
Method Comparison
The new method was compared to separate methods that had already been reported for each analyte, and it showed better or similar performance values (Table 6).
| Parameter | This Method | Literature Method 1* | Literature Method 2** |
|---|---|---|---|
| Analysis Time | 20 min | 35 min | 28 min |
| Mobile Phase | Simple | Complex gradient | Binary gradient |
| LOD (μg/mL) | 0.8-2.1 | 1.5-3.2 | 2.0-4.5 |
| Precision (RSD%) | <2.0 | <3.0 | <2.5 |
| Simultaneous Analysis | Yes | No | No |
Application to Pharmaceutical Formulations
It was possible to safely use the approved method to test synthetic pharmaceutical mixtures that contained the three analytes. The results were pretty close to what was written on the labels (Table 7).
| Formulation | Analyte | Labelled Amount (mg) | Found Amount (mg) | Assay (%) |
|---|---|---|---|---|
| Formulation 1 | MTX | 10 | 9.98 | 99.8 |
| CUR | 50 | 49.7 | 99.4 | |
| ETO | 100 | 101.2 | 101.2 | |
| Formulation 2 | MTX | 15 | 15.1 | 100.7 |
| CUR | 75 | 74.8 | 99.7 | |
| ETO | 150 | 149.5 | 99.7 |
DISCUSSION
DOE Optimization and Method Development
The application of the Box-Behnken Design (BBD) as a part of the Quality by Design (QbD) framework was pivotal in systematically optimizing the RP-HLC method parameters. BBD allowed the identification of significant factors affecting chromatographic performance-namely, mobile phase composition, flow rate, and column temperature. Among these, the composition of the mobile phase emerged as the most influential factor impacting the resolution of the analytes.
The response surface plots (Figure ) clearly demonstrated how even minor alterations in the percentage of organic solvent could significantly affect the separation quality. This behavior is expected, as changes in polarity and elution strength directly influence analyte retention and selectivity in reversed-phase chromatography. In contrast, the effects of flow rate and temperature were comparatively less pronounced but still relevant in fine-tuning retention times and peak shapes. This indicates that mobile phase optimization should be prioritized when aiming for simultaneous separation of chemically diverse compounds like Methotrexate (MTX), Curcumin (CUR), and Etodolac (ETO).
Chromatographic Performance
The optimized chromatographic conditions successfully achieved baseline separation of all three analytes within a 20-min runtime, which is significantly shorter compared to some existing methods (Table ). The chromatogram (Figure ) showed sharp and symmetric peaks with no evidence of peak tailing or co-elution, indicating efficient column performance and appropriate method parameters.
These characteristics are crucial for accurate quantification, especially in simultaneous estimation methods where resolution between peaks is essential to avoid integration errors. Additionally, the adequate retention times for all analytes prevented overlapping and minimized matrix effects, ensuring method selectivity.
Method Validation
Specificity
Specificity is a critical parameter in multi-analyte analysis. In this study, the method showed excellent specificity, with no interference observed from excipients or degradation products. This was confirmed by peak purity analysis, which demonstrated homogenous spectral profiles for all three analytes. This ensures that the method can distinguish each compound even in the presence of complex matrices like pharmaceutical formulations.
Linearity
The method demonstrated excellent linearity across a wide range of concentrations for all analytes (Table ), with correlation coefficients (r²) exceeding 0.999. These results confirm the method's suitability for accurate quantification across typical dosage levels found in pharmaceutical formulations. Moreover, the low Limits of Detection (LOD) and Quantification (LOQ) make the method sensitive enough for trace-level analysis, which is especially valuable in content uniformity and stability studies.
Precision
The method exhibited robust intra-day and inter-day precision, with RSD values well below 2% across all tested concentrations (Table ). This indicates consistent reproducibility under the same operating conditions and over multiple days. The precision results are in accordance with ICH guidelines (Q2(R1)), establishing the reliability of the method for routine analytical use.
Accuracy
Accuracy was evaluated using standard addition recovery studies at 80%, 100%, and 120% levels (Table ). The mean recovery percentages ranged from 98.5% to 101.8%, which are well within the acceptable range of 98–102% for pharmaceutical analysis. This confirms that the method can accurately recover analytes from sample matrices without significant bias.
Robustness
Robustness testing revealed that small deliberate changes in method parameters (e.g., flow rate, mobile phase ratio, column temperature) had negligible effects on the chromatographic output (Figure ). This demonstrates that the method is not sensitive to typical operational variations, making it practical and reliable for implementation in quality control laboratories.
Stability
Stability studies showed that the analytes remained stable under various conditions, including bench-top, refrigerated, freeze-thaw, and light exposure (Table ). However, curcumin showed slight degradation under light exposure, which is consistent with its known photolabile nature. Overall, the results support that the sample solutions can be reliably stored and handled under standard laboratory conditions.
Method Comparison with Existing Literature
Compared to existing single-analyte or dual-analyte HPLC methods (Table ), the developed method offers several advantages:
- Reduced analysis time (20 min vs. 28-35 min).
- Simpler mobile phase system, enhancing reproducibility and cost-effectiveness.
- Lower detection limits, allowing more sensitive detection.
- Simultaneous analysis, which significantly reduces sample preparation and analysis workload.
These advantages make the method highly valuable for quality control of combination drug products, especially in environments where efficiency and throughput are critical.
Application to Pharmaceutical Formulations
The method was applied successfully to synthetic pharmaceutical formulations containing MTX, CUR, and ETO (Table ). The found assay values were very close to the labeled amounts, with recoveries ranging between 99.4% and 101.2%, demonstrating the method’s applicability in real-world scenarios. This not only validates the method’s reliability in analyzing complex dosage forms but also highlights its potential utility in stability testing and batch release.
CONCLUSION
Using a structured DOE approach, a new RP-HPLC method for measuring methotrexate, curcumin, and etodolac all at the same time has been created and proven to work. The Box-Behnken design well improved the chromatographic conditions, which led to great separation and measurement of all three analytes. The method showed better results in a number of ways, such as:
- Very good separation efficiency with good baseline clarity and the right holding times.
- Large linear ranges that can be used in pharmacy settings.
- Very high precision and accuracy that meets ICH standards
- Good stability makes sure that the method works.
- Stability good enough for regular study.
- Less time spent on research compared to using separate ways.
- Environmental kindness by using solvents more efficiently.
It has been proven that the approved method is a good way to check the quality of medicines that contain these three active ingredients. This helps with the creation of combination treatments for inflammatory and cancerous conditions. The DOE model showed that it could be useful in developing analysis methods by giving a structured way to improve things while reducing the amount of work that had to be done in experiments.
Possible areas for future study could be:
- Increase the number of cellular models used for pharmacokinetic studies.
- Creating tools that can show when something is stable.
- Putting green analytical chemistry ideas into practice.
- Automation for research with a high throughput.