Response Surface Methodology-Assisted Development and Validation of a Stability-Indicating HPLC–PDA Method for Quantification of Medroxyprogesterone Acetate in Microcrystals

Abstract

Reliable and stability-indicating analytical methods are required to support the development and quality control of Medroxyprogesterone Acetate (MPA), particularly in emerging solid-state pharmaceutical systems such as microcrystals. In this analytical method development and validation study, a stability-indicating reversed-phase high-performance liquid chromatography method with photodiode array detection (HPLC–PDA) was developed and validated using Response Surface Methodology (RSM) based on a Box–Behnken design for the quantitative determination of MPA in microcrystalline systems. RSM was applied to systematically evaluate the effects and interactions of critical chromatographic parameters, including methanol proportion, acid modifier concentration, and flow rate, enabling simultaneous optimisation of chromatographic responses related to sensitivity, retention behaviour, and peak symmetry. The optimised method demonstrated balanced chromatographic performance and was validated in accordance with the International Council for Harmonisation guideline Q2(R1). The method exhibited excellent linearity over the concentration range of 0.5–100 µg/mL (r = 0.9999), with accuracy ranging from 99.35% to 100.36% and precision expressed as relative standard deviation (%RSD) below 1.56%. The method also demonstrated adequate sensitivity, with limit of detection (LOD) and limit of quantification (LOQ) values of 0.045 µg/mL and 0.137 µg/mL, respectively, confirming its suitability for quantitative analysis. Forced degradation studies demonstrated that MPA was particularly susceptible to alkaline–thermal stress, resulting in the formation of distinguishable degradation-related peaks adjacent to the parent compound peak, supporting the stability-indicating capability of the method. Overall, the developed RSM-assisted HPLC–PDA method is suitable for routine analysis, stability studies, and formulation-oriented pharmaceutical quality control of MPA microcrystals.

Graphical Abstract

Introduction

MPA is a synthetic progestin extensively used in long-acting contraceptive products and remains an important active pharmaceutical ingredient in modern formulation research.1 Accurate and reliable determination of MPA is essential throughout pharmaceutical development, including routine quality control, stability testing, and formulation-oriented studies.2,3 Beyond conventional dosage forms such as tablets and injectable suspensions, MPA has also been investigated in alternative pharmaceutical systems, including intravaginal devices, solid-state forms such as microcrystals, and innovative transdermal delivery systems such as dissolving microneedles, which require analytical methods capable of ensuring content uniformity, selectivity, and stability under various conditions.2–4

A variety of analytical techniques have been reported for the determination of MPA, including spectrophotometric methods,5 vibrational spectroscopic techniques,6 gas chromatography–mass spectrometry (GC–MS),7 and high-performance liquid chromatography (HPLC).8 Among these techniques, reversed-phase HPLC coupled with photodiode array (PDA) detection remains the most practical approach for routine pharmaceutical analysis because of its accessibility, robustness, and cost-effectiveness. Several HPLC methods have been developed and validated for the quantification of MPA in tablets and injectable formulations,2 intravaginal dosage forms,3 and steroid-containing pharmaceutical products,8 demonstrating acceptable analytical performance for their intended applications.

Despite the availability of various analytical methods, most reported HPLC procedures for MPA have been developed using conventional trial-and-error or one-factor-at-a-time optimisation strategies.2,3,8 Such approaches do not adequately account for interaction effects among critical chromatographic parameters, such as mobile phase composition, flow rate, and detection wavelength, which may lead to suboptimal separation and limited method robustness. In addition, existing HPLC methods are predominantly designed for bulk drug substances or finished dosage forms,2,3 whereas systematic analytical evaluation specifically tailored to microcrystalline MPA systems remains limited. Variations in solid-state properties of microcrystalline MPA, including particle size, crystal habit, and crystallinity, may influence degradation behaviour and chromatographic performance, thereby necessitating a more structured and systematic optimisation strategy.9,10

The development of a stability-indicating analytical method is an essential requirement in pharmaceutical analysis, particularly for formulation development and stability assessment. Previous studies have demonstrated that MPA is susceptible to degradation under alkaline conditions, generating degradation products that elute earlier than the parent compound and must be adequately resolved to ensure method selectivity.3,4 Although forced degradation studies of MPA using HPLC-based methods have been reported,4 these evaluations are often presented as supportive experiments, and comprehensive optimisation explicitly targeting chromatographic separation between MPA and its degradation products remains limited.

Response Surface Methodology (RSM) offers a multivariate statistical framework for analytical method optimisation, enabling the simultaneous evaluation of multiple experimental factors and their interactions with a reduced number of experiments. Compared with traditional optimisation strategies, RSM provides a systematic and efficient approach to enhance chromatographic performance, robustness, and reproducibility. However, its application to the development of stability-indicating HPLC methods for steroidal contraceptives, including MPA, remains scarce.8

Therefore, the aim of the present study was to develop and validate a robust, stability-indicating HPLC–PDA method for the quantitative determination of MPA microcrystals using a Response Surface Methodology-assisted optimisation approach. The proposed method was systematically optimised with respect to critical chromatographic parameters, validated in accordance with the International Council for Harmonisation (ICH) Q2(R1) guideline, and evaluated through forced degradation studies to confirm its stability-indicating capability.11,12

Materials and Methods

Chemicals

MPA reference standard was obtained from Indonesian Food and Drug Authority as an Indonesian Pharmacopoeia Reference Standard (Jakarta, Indonesia). MPA technical grade was obtained from PT. Harsen Laboratories (Jakarta, Indonesia). HPLC-grade methanol, acetonitrile, water, acetic acid, hydrochloric acid, and sodium hydroxide were purchased from Merck (Darmstadt, Germany) and used for sample preparation and chromatographic analysis.

Instrumentation

Chromatographic analysis was performed using a Waters Alliance 2695 Separation Module (Waters Corporation, Milford, MA, USA) coupled with a Waters 2996 photodiode array (PDA) detector and equipped with a Waters XBridge C18 column (250 × 4.6 mm, 5 μm). Data acquisition and processing were performed using Empower™ chromatography software installed on a Dell computer (Dell Inc., USA). pH measurements were performed using a pH meter (Eutech pH 510, Thermo Scientific, Sweden). All weighings were conducted using an analytical balance (GR–202, A&D, Japan). Ultrasonication was carried out using an ultrasonic bath (ElmaSonic, Germany). Vacuum filtration was performed using a Büchner funnel with a vacuum filtration system. Sample mixing was performed using a vortex mixer (Maxi Mix II, Thermo Scientific, Sweden). Magnetic stirring was performed using a magnetic stirrer (IKA, Germany), and thermal treatment was conducted using a hot air oven (Memmert, Germany). Micropipettes (Socorex, Switzerland), membrane filters (Whatman), microtubes, and standard laboratory glassware (Pyrex, USA) were used as required.

Chromatographic Conditions

Chromatographic separation of MPA was performed under isocratic elution using a reversed-phase C18 column. The mobile phase consisted of methanol and an aqueous acetate phase containing acetic acid as an acid modifier. The chromatographic conditions were optimised using response surface methodology as described in RSM-Based Optimisation of Chromatographic Conditions.

Detection was carried out using a photodiode array detector at 245 nm, corresponding to the maximum absorbance of MPA. The flow rate, mobile phase composition, and acid modifier concentration were varied during the optimisation study. The injection volume was 20 µL, and all analyses were performed at ambient temperature.

Preparation of MPA Microcrystals

MPA microcrystals were prepared using a solvent–antisolvent precipitation method with slight modification from previously reported methods.13–15 Briefly, MPA was dissolved in acetonitrile at a concentration of 30 mg/mL. The resulting solution was added dropwise at a constant rate into cold distilled water (antisolvent) previously cooled to 5 °C and maintained in an ice bath under continuous magnetic stirring at 600 rpm. The tip of the dispensing needle was positioned above the centre of the stirring vortex to ensure uniform mixing.

After completion of the addition of the MPA solution into the antisolvent, the suspension was stirred for an additional 10 min to allow crystal ageing. Ultrasonic treatment (≈40 kHz) was then applied for 3 min while maintaining the system at low temperature. The formed microcrystals were collected by vacuum filtration using a Büchner funnel, washed twice with cold distilled water (5 mL each, 5 °C) dried under desiccation, and stored until further analysis. The formation of microcrystalline material was preliminarily confirmed by optical microscopic observation prior to further analysis.

Preparation of Standard and Sample Solutions

Reference standard and microcrystalline samples of MPA were prepared using an identical procedure. An accurately weighed amount of 10.0 mg of MPA reference standard or MPA microcrystals was dissolved in methanol and diluted to 100.0 mL to obtain a stock solution. The solutions were sonicated for 30 min to ensure complete dissolution and subsequently diluted with methanol to obtain the required concentrations for the respective analyses. Prior to HPLC analysis, all solutions were filtered through a 0.45 µm syringe membrane filter to remove any insoluble particles.

RSM-Based Optimisation of Chromatographic Conditions

RSM was applied to optimise the chromatographic conditions for the analysis of MPA under normal conditions. Based on preliminary chromatographic screening experiments and literature reports, the proportion of methanol in the mobile phase (X₁), the concentration of acid modifier in the aqueous phase (X₂), and the flow rate (X₃) were selected as independent variables. The investigated ranges (65–80% methanol, 0.05–0.10% acid modifier, and 0.8–1.2 mL/min flow rate) were chosen to ensure acceptable chromatographic performance in terms of retention behaviour, peak symmetry, and analytical sensitivity while covering a practical experimental domain for optimisation. A three-factor, three-level Box–Behnken design comprising 15 experimental runs, including three replicated centre points, was employed. The replicated centre points were used to estimate pure experimental error and support model adequacy assessment through lack-of-fit analysis.

The responses evaluated in the RSM study were the retention time, the tailing factor, and the peak area of MPA. The optimisation aimed to minimise retention time and peak tailing while maximising peak area to ensure adequate chromatographic performance and sensitivity of the analytical method. Statistical analysis and numerical optimisation were performed using a desirability function approach, and the predicted optimal conditions were subsequently verified experimentally. The independent variables and responses selected for the RSM-based optimisation of chromatographic conditions are summarised in Table 1. The complete Box–Behnken design matrix, ANOVA outputs, regression equations, model statistics, and optimization results are provided in the Supplementary Tables S1–S9.

Table 1.

Factors Used for RSM-Based Optimization

Factor	Parameter	−1	0	1
X1	Methanol proportion (% v/v)	65	72.5	80
X2	Acid modifier (% v/v)	0.05	0.075	0.1
X3	Flow rate (mL/min)	0.8	1	1.2

Validation of the Analytical Method

The analytical procedure for the determination of MPA in microcrystal formulations using HPLC–PDA was validated in accordance with ICH Q2(R1) to demonstrate its suitability for assay determination.11 Validation was conducted by evaluating system suitability, specificity, linearity and range, accuracy, precision, detection limit, quantitation limit, and robustness, as recommended for quantitative assay procedures.11 Unless otherwise stated, all validation experiments were performed in triplicate (n = 3). Each replicate represents an independently prepared solution and a separately conducted analytical run, rather than repeated injections from the same preparation. The analytical method was developed and validated according to ICH Q2(R1), which served as the applicable guideline during the conduct of this study. Although ICH Q2(R2) has subsequently become effective, the validation experiments reported herein were performed according to ICH Q2(R1), which remains a widely accepted framework for analytical method validation.

System Suitability

System suitability testing was performed prior to sample analysis using MPA standard solutions at 50 µg/mL. Six consecutive injections were carried out under the optimized HPLC–PDA conditions. System suitability parameters, including retention time, peak area, tailing factor were evaluated to assess consistent chromatographic performance. Acceptance criteria were predefined based on general chromatographic system suitability requirements. The relative %RSD of peak area and retention time was set at not more than 2.0%, the tailing factor at not more than 2.0. These criteria were applied in accordance with the system suitability concept described in ICH Q2(R1).

Specificity

Specificity was evaluated to assess the ability of the analytical procedure to unequivocally determine MPA in the presence of formulation components, in accordance with ICH Q2(R1). Blank solvent, MPA reference standard, and MPA-loaded microcrystal samples were analysed under identical chromatographic conditions. Specificity assessment was performed by overlaying chromatograms obtained from the blank, reference standard, and microcrystal sample to examine potential co-eluting peaks at the retention time of MPA.

Linearity and Range

Linearity was assessed by analysing MPA standard solutions at eight concentration levels, namely 0.5, 1, 5, 10, 25, 50, 75, and 100 µg/mL, covering the concentration range of 0.5–100 µg/mL. Each concentration level was independently prepared and analysed in triplicate (n = 3), where each replicate represented a separately prepared solution and individual chromatographic injection. Calibration curves were constructed by plotting peak area against nominal concentration, and linear regression analysis was performed using the least-squares method. The analytical range was defined as the concentration interval over which the method was evaluated for linearity, accuracy, and precision, in accordance with the principles described in ICH Q2(R1).

Accuracy

Accuracy was evaluated in accordance with ICH Q2(R1) using the standard addition method at three concentration levels corresponding to 80%, 100%, and 120% of the working concentration (40, 50, and 60 µg/mL). Each level was prepared and analysed in triplicate. Accuracy was expressed as percentage recovery of the added analyte, with predefined acceptance criteria of 98–102% for assay determination. All accuracy experiments were performed in triplicate (n = 3) using independently prepared samples.

Precision

Precision was evaluated according to ICH Q2(R1) as repeatability (intra-day) and intermediate precision (inter-day). Repeatability was assessed by analysing independently prepared samples at 40, 50, and 60 µg/mL in triplicate (n = 3) within a single analytical run, while intermediate precision was evaluated by repeating the analysis on different days using newly prepared independent solutions. Precision was expressed as %RSD, with predefined acceptance criteria of ≤2.0%.

Limit of Detection (LOD) and Limit of Quantification (LOQ)

The LOD and LOQ were determined based on the standard deviation of the response and the slope of the calibration curve, following the approach described in ICH Q2(R1). LOD and LOQ were calculated using the equations LOD = 3.3σ/S and LOQ = 10σ/S, where σ represents the standard deviation of the response and S is the slope of the calibration curve.

Robustness

Robustness was evaluated in accordance with ICH Q2(R1) by introducing small, deliberate variations in chromatographic conditions, including mobile phase composition, flow rate, and acid modifier concentration. The effects of these variations on retention time, peak area were monitored. Each robustness condition was evaluated using independently prepared solutions.

Forced Degradation Studies

Forced degradation studies were performed in accordance with the ICH guidelines to evaluate the stability-indicating capability and specificity of the developed HPLC–PDA method. MPA microcrystalline samples were subjected to various stress conditions in order to generate potential degradation products and to assess chromatographic separation between the parent compound and its degradation products, as recommended in ICH Q1A(R2) and ICH Q2(R1).11,12 The applied stress conditions, including the type of stress, treatment medium, temperature, exposure time, and neutralisation procedure, are summarised in Table 2. Unless otherwise stated, all stressed samples were prepared by dissolving the stressed MPA microcrystals and adjusting the resulting solutions to a final concentration of 25 µg/mL prior to HPLC analysis. Each stress condition and time point was prepared independently prior to chromatographic analysis to ensure experimental reproducibility.

Table 2.

Forced Degradation Conditions Applied to MPA Microcrystals

Stress Condition	Medium/treatment	Temperature	Exposure Time	Neutralisation/Quenching
Control	—	—	—	—
Thermal (dry heat)	Hot air oven (solid state)	60 °C	12, 24 hours	Cooling to room temperature
Acidic	0.1 N HCl	Room Temperature	15, 30, 60 min	Neutralised with 0.1 N NaOH
Alkaline	0.1 N NaOH	Room Temperature	15, 30, 60 min	Neutralised with 0.1 N HCl
Acidic + thermal	0.1 N HCl	60 °C	15, 30, 60 min	Neutralised with 0.1 N NaOH followed by cooling to room temperature
Alkaline + thermal	0.1 N NaOH	60 °C	15, 30, 60 min	Neutralised with 0.1 N HCl followed by cooling to room temperature
Photolytic	Sunlight (natural UV radiation)	Room Temperature	15, 30, 60 min	Protected from light (closed container)

Thermal Degradation (Dry Heat)

Thermal degradation was carried out by exposing MPA microcrystals in the solid state to dry heat in a hot air oven at 60 °C for 12 and 24 h. After completion of the stress period, the samples were allowed to cool to room temperature, dissolved in methanol, and diluted as necessary to obtain a final concentration of 25 µg/mL prior to HPLC analysis.

Acidic Hydrolytic Degradation

Acidic degradation was performed by mixing 1.0 mL of MPA solution (50 µg/mL) with 0.5 mL of 0.1 N hydrochloric acid and maintaining the mixture at room temperature for 15, 30, and 60 min. The reaction was quenched by neutralisation with 0.5 mL of 0.1 N sodium hydroxide, resulting in a final MPA concentration of approximately 25 µg/mL before chromatographic analysis.

Alkaline Hydrolytic Degradation

Alkaline degradation was conducted by treating 1.0 mL of MPA solution (50 µg/mL) with 0.5 mL of 0.1 N sodium hydroxide at room temperature for 15, 30, and 60 min. Following the stress period, the reaction was neutralised with 0.5 mL of 0.1 N hydrochloric acid, yielding a final MPA concentration of approximately 25 µg/mL prior to HPLC analysis.

Combined Acidic–Thermal Degradation

For combined acidic–thermal stress, MPA solution (50 µg/mL) was treated with 0.1 N hydrochloric acid and heated at 60 °C for 15, 30, and 60 min. After stress exposure, the samples were neutralised with 0.1 N sodium hydroxide and allowed to cool to room temperature to terminate further degradation. The stressed solutions were then adjusted to a final concentration of 25 µg/mL prior to analysis.

Combined Alkaline–Thermal Degradation

Combined alkaline–thermal degradation was performed by exposing MPA solution (50 µg/mL) to 0.1 N sodium hydroxide at 60 °C for 15, 30, and 60 min. After completion of the stress period, the samples were neutralised with 0.1 N hydrochloric acid, cooled to room temperature, and diluted as required to obtain a final concentration of 25 µg/mL prior to HPLC analysis.

Photolytic Degradation

Photolytic degradation was assessed by exposing MPA microcrystals in the solid state to sunlight as a natural source of ultraviolet (UV) radiation at room temperature for 15, 30, and 60 min. Following exposure, the samples were immediately protected from further light exposure, dissolved in methanol, and diluted to achieve a final concentration of 25 µg/mL before chromatographic analysis.

Statistical Analysis

Experimental design and statistical analysis for RSM were performed using Design-Expert® software (Version 13, Stat-Ease Inc., Minneapolis, MN, USA). A three-factor, three-level Box–Behnken design was applied to evaluate the effects and interactions of chromatographic variables, including methanol proportion, acid modifier concentration, and flow rate. Model selection was based on sequential model sum-of-squares analysis, lack-of-fit testing, and goodness-of-fit statistics provided by Design-Expert® software. The most appropriate model for each response was selected considering statistical significance, model hierarchy, and predictive performance.

Analysis of variance (ANOVA) was used to determine the statistical significance of model terms and interaction effects. The adequacy of the fitted models was evaluated using the coefficient of determination (R²), adjusted R², predicted R², Adeq Precision values, and lack-of-fit testing. Numerical optimisation of chromatographic conditions was conducted using a desirability function approach. Statistical significance was considered at p < 0.05. For validation parameters, the reported %RSD values were calculated based on independently prepared replicates (n = 3).

Results and Discussion

Optimisation of Chromatographic Conditions Using Response Surface Methodology

Response Surface Methodology (RSM) was applied to systematically optimise the chromatographic conditions for the analysis of MPA using a Box–Behnken experimental design.16–18 Three critical chromatographic factors, namely the proportion of methanol in the mobile phase (A), the concentration of acid modifier (B), and the flow rate (C), were investigated with respect to their effects on retention time, peak tailing factor, and chromatographic peak area. A total of 15 experimental runs, including replicated centre points, were performed to evaluate both individual and interaction effects of the selected variables.19,20

Statistical analysis indicated that the quadratic model describing retention time was highly significant (p < 0.0001), with a high coefficient of determination (R² = 0.9962) and good agreement between the adjusted and predicted R² values (0.9893 and 0.9397, respectively), confirming the adequacy of the model to describe the experimental domain. The large Adeq Precision value (36.54) further demonstrated an adequate signal-to-noise ratio for navigation of the design space. The tailing factor model was statistically significant (p = 0.0181), indicating that the investigated chromatographic variables significantly influenced peak symmetry. In contrast, the peak area model was not statistically significant (p = 0.4396). Nevertheless, peak area was retained during numerical optimisation because it represents detector response and analytical sensitivity, which remain important considerations in chromatographic method development. Therefore, retention time and tailing factor were treated as the primary optimisation responses, whereas peak area was included as a supportive performance parameter.21,22 Although the lack-of-fit test for the retention time model was statistically significant (p = 0.0309), the model exhibited excellent predictive performance, as demonstrated by the high coefficient of determination (R² = 0.9962), close agreement between adjusted and predicted R² values, and a high Adeq Precision value. Therefore, the model was considered suitable for chromatographic optimisation within the investigated design space.

Response surface analysis revealed that the proportion of methanol and flow rate were the dominant factors affecting retention time and peak area. Increasing the methanol content in the mobile phase resulted in enhanced detector response and a pronounced reduction in retention time, reflecting reduced analyte–stationary phase interactions.23 Higher flow rates further shortened analysis time but, at extreme levels, tended to compromise chromatographic efficiency.22,24 The concentration of acid modifier showed a comparatively smaller effect on retention time and peak area but played a critical role in improving peak symmetry by suppressing secondary interactions between MPA and residual silanol groups on the stationary phase.23,24 The combined effects and interactions of the chromatographic variables on peak area, retention time, and tailing factor are illustrated in the response surface plots (Figures 1–3).

Figure 1.

Response surface plots and corresponding contour plots showing the effects of chromatographic variables on MPA peak area. (A) Effect of methanol proportion and flow rate; (B) effect of methanol proportion and acid modifier concentration; (C) effect of acid modifier concentration and flow rate; (D) Contour plot showing the combined effect of methanol proportion and flow rate on MPA peak area; (E) contour plot showing the combined effect of methanol proportion and acid modifier concentration on MPA peak area; (F) contour plot showing the combined effect of acid modifier concentration and flow rate on MPA peak area.

Figure 2.

Response surface plots and corresponding contour plots showing the effects of chromatographic variables on MPA retention time. (A) Effect of methanol proportion and flow rate; (B) effect of methanol proportion and acid modifier concentration; (C) effect of acid modifier concentration and flow rate; (D) Contour plot showing the combined effect of methanol proportion and flow rate on MPA retention time; (E) contour plot showing the combined effect of methanol proportion and acid modifier concentration on MPA retention time; (F) contour plot showing the combined effect of acid modifier concentration and flow rate on MPA retention time.

Figure 3.

Response surface plots and corresponding contour plots showing the effects of chromatographic variables on MPA tailing factor. (A) Effect of methanol proportion and flow rate; (B) effect of methanol proportion and acid modifier concentration; (C) effect of acid modifier concentration and flow rate; (D) Contour plot showing the combined effect of methanol proportion and flow rate on MPA tailing factor; (E) contour plot showing the combined effect of methanol proportion and acid modifier concentration on MPA tailing factor; (F) contour plot showing the combined effect of acid modifier concentration and flow rate on MPA tailing factor.

Simultaneous optimisation of all responses was performed using a desirability function approach, aiming to minimise retention time and tailing factor while maximising peak area.22,25 Numerical optimisation yielded an overall desirability value of 0.898, indicating a satisfactory compromise among the selected responses. The optimal chromatographic conditions consisted of a mobile phase containing 80.0% (v/v) methanol with 0.10% (v/v) acid modifier at a flow rate of approximately 1.0 mL/min, providing predicted values of 8.23 min for retention time, a tailing factor of 1.23, and a peak area of approximately 3.67 × 10⁶ AU. The reliability of the optimisation was further supported by prediction intervals and confidence intervals generated by the model, and the predicted optimal conditions were subsequently verified experimentally. Detailed optimisation outputs are provided in Supplementary Tables S8 and S9.

The predicted optimal conditions were experimentally verified, and the observed chromatographic responses were in good agreement with the model predictions, confirming the suitability of the RSM-assisted optimisation. Compared with previously reported HPLC methods for MPA developed using conventional trial-and-error or one-factor-at-a-time approaches,2,3,8 the present RSM-based strategy enabled systematic evaluation of factor interactions and resulted in a balanced chromatographic system with improved efficiency and robustness.21,22,25,26 The optimised conditions were subsequently applied for system suitability testing, method validation, and stability-indicating studies. The statistical performance of the developed models was further evaluated using ANOVA, coefficient of determination (R²), adjusted R², predicted R², adequate precision, and lack-of-fit analysis. Detailed model statistics, regression equations, and optimization outputs are provided in the Supplementary Tables S2–S9.

System Suitability

System suitability testing was performed using six replicate injections of an MPA standard solution (50 µg/mL). The chromatographic system exhibited good repeatability, with %RSD values of 0.349% for peak area and 0.724% for retention time. Peak symmetry was acceptable, as indicated by a mean USP tailing factor of 1.30. These results confirm that the HPLC system was suitable for quantitative analysis. Detailed system suitability data are provided in Table 3. These results meet the predefined acceptance criteria and confirm the adequacy of the chromatographic system for method validation.

Table 3.

System Suitability Results of the HPLC System for Medroxyprogesterone Acetate

Concentration (µg/mL)	Injection No.	Retention Time (min)	Peak Area	USP Tailing Factor (Tf)
50	1	8.029	2,816,259	1.3019
50	2	7.910	2,796,873	1.3108
50	3	7.987	2,802,655	1.2977
50	4	8.078	2,820,321	1.2987
50	5	8.032	2,798,678	1.3037
50	6	7.976	2,812,453	1.2962
Mean		8.002	2,807,873.17	1.3015
SD		0.058	9788.05	0.0053
%RSD		0.724	0.349	0.4093

Method Validation

Specificity

The specificity of the method was evaluated using overlay chromatograms of (A) the blank solution, (B) the MPA standard solution prepared in methanol, and (C) the microcrystalline MPA sample. As shown in Figure 4, no interfering peaks were observed in the blank chromatogram at the retention time of MPA (~7.9 min). A distinct and symmetrical MPA peak was obtained for the reference standard, while the microcrystalline MPA sample exhibited a peak at the same retention time without additional co-eluting signals. These results demonstrate that the method is specific for MPA and capable of clearly distinguishing the analyte from potential matrix interferences supporting the specificity of the method and its application for stability assessment.

Figure 4.

Overlay chromatograms of (A) blank solution, (B) MPA standard solution, and (C) microcrystalline MPA sample, demonstrating the specificity of the method.

Linearity and Range

The linearity of the proposed HPLC method was evaluated using eight calibration levels over the concentration range of 0.5–100 µg/mL. A strong linear relationship between concentration and peak area was observed, as illustrated in Figure 5.

Figure 5.

Calibration curve of MPA showing the linear relationship between concentration and peak area over the range of 0.5–100 µg/mL.

Linear regression analysis yielded a calibration equation of y = 55,556x + 30,343, with a correlation coefficient (r = 0.9999), demonstrating excellent linearity across the studied concentration range. The small positive intercept indicates minimal baseline contribution, while the excellent correlation coefficient (r = 0.9999) and the absence of apparent systematic deviation from linearity supported the suitability of the calibration model across the investigated concentration range. The analytical range was therefore established as 0.5–100 µg/mL, within which the method exhibited acceptable linearity, accuracy, and precision, in accordance with ICH Q2(R1) requirements.

Accuracy and Precision

The accuracy and precision of the HPLC method for MPA were evaluated at Low Quality Control (LQC, 40 µg/mL), Medium Quality Control (MQC, 50 µg/mL), and High Quality Control (HQC, 60 µg/mL) in accordance with ICH Q2(R1). Intraday precision was assessed using independently prepared samples analysed in triplicate (n = 3) per concentration level within a single analytical run, while interday precision was evaluated over three different days using newly prepared independent solutions.

As shown in Table 4, intraday %RSD values ranged from 0.32–1.56%, and accuracy ranged from 99.35% to 100.36%, indicating good repeatability. Interday precision showed %RSD values ≤0.19%, with accuracy between 99.58% and 100.19%, demonstrating minimal variability between analytical runs. Overall, the method met ICH Q2(R1) acceptance criteria and was considered accurate and precise for the quantitative analysis of MPA across the tested concentration range. The obtained precision values (%RSD ≤ 1.56%) and accuracy values (99.35–100.36%) complied with the acceptance criteria recommended by ICH Q2(R1), indicating satisfactory reproducibility and reliability of the developed method.

Table 4.

Intraday and Interday Precision and Accuracy of Medroxyprogesterone Acetate at Different Quality Control Levels

QC Level	Nominal Concentration (µg/mL)	Intraday Precision and Accuracy			Interday Precision and Accuracy
		Experimental Concentration (µg/mL, Mean ± SD)	%RSD	Accuracy (%)	Experimental Concentration (µg/mL, Mean ± SD)	%RSD	Accuracy (%)
LQC	40	40.0202 ± 0.129	0.32	100.05	40.0864 ± 0.062	0.15	100.19
		40.1429 ± 0.231	0.58	100.36	—	—	—
		40.0962 ± 0.274	0.68	100.24	—	—	—
MQC	50	49.9787 ± 0.192	0.38	99.96	49.9515 ± 0.093	0.19	99.61
		49.8482 ± 0.260	0.52	99.7	—	—	—
		50.0278 ± 0.305	0.61	100.06	—	—	—
HQC	60	59.6334 ± 0.927	1.56	99.39	59.6725 ± 0.086	0.14	99.58
		59.7712 ± 0.904	1.51	99.62	—	—	—
		59.6129 ± 0.585	0.98	99.35	—	—	—

LOD and LOQ

The LOD and LOQ were estimated in accordance with ICH Q2(R1) based on the standard deviation of the response (σ) and the slope (S) of the calibration curve. The calculated σ value was approximately 0.76 × 10³ area units, derived from the calibration regression response. Using the equations LOD = 3.3σ/S and LOQ = 10σ/S, the LOD and LOQ for MPA were determined to be 0.045 µg/mL and 0.137 µg/mL, respectively. These values demonstrate the adequate sensitivity of the developed HPLC–PDA method for the quantitative analysis of MPA within the validated concentration range.

The obtained LOD and LOQ values indicate that the developed method possesses adequate sensitivity for the determination of MPA in microcrystalline systems. Although direct comparison among published methods is complicated by differences in sample matrices, chromatographic conditions, and detection systems, the sensitivity achieved in the present study is comparable to that reported for previously published HPLC methods for MPA analysis. Furthermore, the present method offers the additional advantage of employing an RSM-assisted optimisation strategy and demonstrating stability-indicating capability through forced degradation studies, thereby supporting its suitability for routine quality control and stability assessment of MPA microcrystals.

Robustness

Robustness was evaluated by introducing small deliberate variations around the optimized chromatographic conditions, including changes in methanol proportion (79% and 81% v/v), flow rate (0.95 and 1.05 mL/min), and acetic acid concentration (0.09% and 0.11% v/v). As summarised in Table 5, these deliberate variations resulted in only minor changes in retention time and peak area. The observed responses remained within acceptable analytical limits, indicating that the chromatographic performance was not materially affected by the investigated changes.

Table 5.

Robustness Evaluation of the HPLC Method for Medroxyprogesterone Acetate Under Small Deliberate Variations of Chromatographic Conditions

Condition	Parameter Varied	Nominal Setting	Deliberate Change	Setting Used	RT (Min, Mean ± SD)	Peak Area (Mean ± SD)
1	Nominal	—	—	Methanol 80%; Acetic acid 0.10%; Flow 1.00	8.002 ± 0.058	2,807,873.17 ± 9788.05
2	Methanol (% v/v)	80	−1	79	7.983 ± 0.027	2,812,345.12 ±3878.06
3	Methanol (% v/v)	80	1	81	7.978 ± 0.044	2,807,873.17 ± 8738.07
4	Flow rate (mL/min)	1	−0.05	0.95	7.923 ± 0.061	2,799,873.44 ± 7688.08
5	Flow rate (mL/min)	1	0.05	1.05	8.016 ± 0.23	2,831,314.29 ± 9231.09
6	Acetic acid (% v/v)	0.1	−0.01	0.09	8.023 ± 0.017	2,812,765.81 ± 9605.10
7	Acetic acid (% v/v)	0.1	0.01	0.11	8.019 ± 0.024	2,765,845.21 ± 5688.11

The limited variability observed under the tested conditions indicates that the proposed HPLC–PDA method is sufficiently robust for routine analytical application. The robustness assessment was performed in accordance with ICH Q2(R1) by introducing small deliberate variations around the optimized chromatographic conditions. The results demonstrated stable chromatographic performance across the investigated operating range.

Forced Degradation Studies

Forced degradation studies were performed to assess the stability-indicating capability of the developed HPLC–PDA method for MPA microcrystals, as stress testing is widely applied to evaluate degradation pathways and to demonstrate the selectivity of stability-indicating analytical procedures.27,28 The samples were subjected to acidic, alkaline, thermal, photolytic, and combined stress conditions,29 and the observed degradation behaviour is summarised in Table 6.

Table 6.

Forced Degradation Behavior of Medroxyprogesterone Acetate Microcrystals

Stress Condition	Temperature	Time	Chromatographic Observation	Degradation Observed	Remarks
Control	—	—	Single sharp MPA peak, no additional peaks	No	Reference chromatogram
Acidic hydrolysis (0.1 N HCl)	RT	15 min	Single MPA peak, RT consistent	No	Stable
	RT	30 min	Single MPA peak, no new peaks	No	Stable
	RT	60 min	No change in peak profile	No	Stable
Alkaline hydrolysis (0.1 N NaOH)	RT	15 min	Minor early eluting peak, MPA peak dominant	No significant	Minimal degradation
	RT	30 min	Similar profile, MPA peak retained	No significant	Stable
	RT	60 min	MPA peak still dominant	No significant	Stable
Acidic + thermal	60 °C	15 min	Single MPA peak, no degradation peaks	No	Stable
	60 °C	30 min	MPA peak unchanged	No	Stable
	60 °C	60 min	No additional peaks observed	No	Stable
Alkaline + thermal (0.1 N NaOH)	60 °C	15 min	MPA peak reduced, new early peak appears	Yes	Degradation initiated
	60 °C	30 min	Increased intensity of degradation peak	Yes	Moderate degradation
	60 °C	60 min	Clear degradation profile, MPA further reduced	Yes	Significant degradation
Thermal (dry heat, solid)	60 °C	12 h	Single MPA peak	No	Thermally stable
	60 °C	24 h	No change in chromatogram	No	Thermally stable
Photolytic (sunlight)	RT	15 min	No additional peaks	No	Photostable
	RT	30 min	MPA peak intact	No	Photostable
	RT	60 min	No degradation observed	No	Photostable

MPA microcrystals remained stable under acidic hydrolysis at room temperature, thermal stress in the solid state, photolytic exposure, and combined acidic–thermal conditions, as indicated by the presence of a single MPA peak with no additional degradation peaks.30 Alkaline hydrolysis at room temperature resulted in only minimal changes in the chromatographic profile.

In contrast, clear degradation of MPA was observed when alkaline stress was combined with heating at 60 °C. Under these conditions, a progressive reduction in the MPA peak was accompanied by the appearance of additional peaks at earlier retention times, with the extent of degradation increasing with longer exposure times (15, 30, and 60 min) (Table 6). Representative chromatograms obtained under control conditions and after 15 and 60 min of alkaline–thermal treatment are presented in Figure 6, illustrating the progressive degradation of MPA under alkaline–thermal stress. (Figure 6). Additional representative chromatograms obtained under acidic–thermal, alkaline hydrolysis, thermal, and photolytic stress conditions are provided in the Supplementary Figure S1 to further demonstrate the chromatographic behavior of MPA under various stress environments and support the stability-indicating capability of the developed method. The resolution between the MPA peak and the nearest degradation-related peak was 1.18, indicating that the degradation peak remained chromatographically distinguishable from the parent compound peak under the evaluated stress conditions.

Figure 6.

Representative chromatograms of MPA under control conditions (A) and after alkaline–thermal stress (0.1 N NaOH, 60 °C) for 15 min (B) and 60 min (C). Progressive formation of degradation products was observed with increasing exposure time, accompanied by a reduction in the parent MPA peak.

Although a formal mass balance assessment was not performed, chromatographic profiles obtained under alkaline–thermal stress demonstrated a progressive reduction in the MPA peak area accompanied by an increase in degradation-related peaks. For example, the degradation-related peak area increased from 77,432 AU·s at 15 min to 95,600 AU·s at 30 min, while the corresponding MPA peak area decreased from 37,181 AU·s to 17,821 AU·s. Such chromatographic behavior is consistent with the conversion of MPA into degradation products under alkaline conditions and further supports the specificity and stability-assessment capability of the developed method.28,29,31 The observed degradation behaviour under alkaline–thermal stress is consistent with the susceptibility of ester-containing steroidal compounds to base-catalysed hydrolysis, which is accelerated at elevated temperatures.31,32 Similar degradation pathways for MPA under alkaline conditions have been reported in previous stability-indicating studies. For instance, Burana-Osot et al developed a stability-indicating HPLC method for MPA in bulk drug and injectable formulations using a conventional optimisation approach and demonstrated successful separation of MPA from its degradation products under alkaline stress conditions.2 Likewise, Batrawi et al reported a validated HPLC method for MPA in intravaginal sponges, showing that alkaline stress represented a critical condition for degradation, while acidic and photolytic stresses resulted in minimal degradation.3

More recently, Jenkins et al conducted comprehensive forced degradation studies on high-concentration MPA injectable suspensions using a combination of HPLC, mass spectrometry, and QSAR techniques, providing detailed mechanistic insights into degradation pathways.4 While these studies offered valuable information on MPA stability across different pharmaceutical matrices, optimisation of chromatographic conditions was primarily achieved using conventional trial-and-error strategies or focused on degradation characterisation rather than systematic multivariate optimisation.

In contrast to previous reports, the present study applied RSM to systematically investigate the interaction effects of critical chromatographic variables and to optimise multiple analytical responses simultaneously for MPA microcrystals. The comparison between alkaline hydrolysis at room temperature and alkaline–thermal stress at 60 °C clearly demonstrates the accelerating effect of temperature on MPA degradation. While exposure to 0.1 N NaOH at room temperature produced only minor chromatographic changes, the addition of thermal stress resulted in progressive degradation and formation of additional degradation peaks. These findings suggest that temperature acts synergistically with alkaline conditions to promote degradation of MPA microcrystals. This RSM-assisted approach enabled the identification of robust chromatographic conditions with a high desirability value and facilitated the detection of degradation-related peaks adjacent to the MPA peak under stressed conditions. Importantly, degradation-related peaks generated under alkaline–thermal stress remained distinguishable from the MPA peak, supporting the stability-indicating capability of the developed HPLC–PDA method, particularly suitable for the analysis of emerging solid-state systems such as microcrystalline formulations.33 The objective of the forced degradation study was to demonstrate the specificity and stability-indicating capability of the developed analytical method under various stress conditions rather than to establish degradation kinetics. Therefore, qualitative assessment of degradation behaviour and chromatographic separation of degradation products were considered appropriate for the intended purpose of method development and validation.

Conclusions

A robust HPLC–PDA method for the quantitative determination of MPA microcrystals was developed using a Response Surface Methodology-assisted optimisation approach, which enabled systematic evaluation of chromatographic parameter interactions and yielded balanced analytical performance.

The method was validated in accordance with the ICH Q2(R1) guideline and demonstrated chromatographic separation of MPA from degradation-related peaks under forced degradation conditions, supporting its stability-indicating capability and suitability for formulation development, stability studies, and routine quality control.

Declaration of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the authors used a generative artificial intelligence tool to assist with language editing and improving the clarity and organisation of the text. The authors carefully reviewed and edited the content generated by the AI tool and take full responsibility for the integrity and accuracy of the work.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Camelia Dwi Putri Masrijal: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Visualization, Writing – Original draft preparation.

Yahdiana Harahap: Conceptualization, Methodology, Validation, Supervision, Writing – Reviewing and Editing.

Delly Ramadon: Methodology, Formal analysis, Validation, Writing – Reviewing and Editing.

Surakit Nathisuwan: Formal analysis, Data interpretation, Writing – Reviewing and Editing.

Disclosure

The authors declare that there are no conflicts of interest.