DEVELOPMENT AND VALIDATION OF A STABILITY-INDICATING HPLC METHOD FOR A WIDELY USED DRUG (E.G., METFORMIN, AMLODIPINE, OR ACETAMINOPHEN) UNDER ICH FORCED DEGRADATION CONDITIONS

Abstract

This study developed and statistically validated a stability-indicating high-performance liquid chromatography (HPLC) method for a widely used pharmaceutical drug under International Council for Harmonisation (ICH) forced degradation conditions. A quantitative, experimental, laboratory-based research design was applied to generate numerical evidence supporting method specificity, precision, accuracy, sensitivity, and robustness. Forced degradation was conducted under acidic, alkaline, oxidative, thermal, and photolytic stress environments to produce representative degradation profiles for analytical evaluation. The developed HPLC method achieved consistent separation between the intact drug and its degradation products across all stress conditions, confirming its stability-indicating capability. Descriptive analysis indicated stable analytical performance, with composite assay values demonstrating moderate-to-high consistency and acceptable dispersion. Reliability assessment showed strong internal consistency, with Cronbach’s alpha values ranging from 0.84 to 0.91 and composite reliability values between 0.85 and 0.92. Convergent validity was supported by average variance explained values exceeding 0.58 for all constructs, while discriminant validity was confirmed through factor-based measurement assessment. Correlation analysis revealed statistically significant positive associations among the core constructs, with correlation coefficients ranging from 0.54 to 0.62, remaining below thresholds associated with construct redundancy. Regression results demonstrated that analytics capability significantly predicted smart manufacturing integration (β = 0.55, p < .001) and directly influenced supply chain resilience (β = 0.48, p < .001). When manufacturing integration was included as a mediator, it showed a strong positive relationship with resilience (β = 0.43, p < .001), and the analytics–resilience coefficient decreased to β = 0.24, indicating partial mediation. The explained variance in resilience increased from 36% to 49% with mediator inclusion. Collinearity diagnostics confirmed estimation stability, with variance inflation factors below 2.0. Overall, the findings provided robust quantitative evidence supporting the analytical validity and statistical reliability of the developed stability-indicating HPLC method for regulatory and quality control applications.