Demonstrates a QbD-based analytical method validation for quantifying arformoterol tartrate in inhalation solutions, indicating enhanced robustness and sustainability.
The development and validation of a robust RP-HPLC method for the quantitative assay determination of arformoterol tartrate in inhalation solution were carried out using an Analytical Quality by Design (AQbD) approach. A Central Composite Design (CCD) was employed to systematically optimize critical method parameters and establish a design space ensuring consistent method performance. The optimized method demonstrated excellent specificity, linearity (R2 > 0.999), precision (%RSD < 2%), and accuracy (98–102% recovery) in accordance with ICH Q2(R2) guidelines. Forced degradation studies were conducted under acidic, alkaline, oxidative, thermal, photolytic, and neutral conditions to evaluate the stability-indicating capability of the method. The results demonstrated that the method is capable of quantifying arformoterol tartrate in the presence of potential degradation products. Peak purity assessment using PDA detection confirmed that the analyte peak was spectrally homogeneous, indicating the absence of coeluting impurities. The method also exhibited high environmental sustainability, as demonstrated by GAPI and BAGI scores exceeding 80, reflecting reduced solvent consumption and improved analytical efficiency. Compared to conventional approaches, the AQbD-based method provides enhanced robustness, reduced variability, and improved understanding of method performance. This approach is suitable for routine quality control and stability studies and demonstrates a transferable strategy for developing green, QbD-driven methods for inhalation therapies. Furthermore, the method aligns with the principles of sustainable pharmaceutical development, contributing to Sustainable Development Goal 3 (Good Health and Well-being) by ensuring reliable drug quality, and Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) by promoting innovative and efficient analytical methodologies.
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Alluri et al. (2026) studied this question.
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