Strengthening Finerenone Assessment: A Central Composite Design Viewpoint for Liquid Chromatographic Method Development

ABSTRACT

Background: A RP-HPLC technique was created and proven for the measurement of Finerenone, a new medication currently being studied. The approach was improved to achieve excellent precision, accuracy, and sensitivity for use in pharmaceutical formulations. Materials and Methods: The separation by chromatography took place utilizing an isocratic elution method with a mobile phase of 0.01N dihydrogen of potassium orthophosphate and acetonitrile in a 65:35 (v/v) proportion. The velocity of the fluid was set at 1 mL/min, and the division happened via an Agilent C18 (250×4.6 mm, 5 µm) at 30ºC. Finerenone was detected at a wavelength of 230 nm, at a retention time of 2.306 min and the injection volume was 10 µL. Results: The developed method exhibited excellent linearity with an R² of 0.999 over the concentration range of 50% to 150%. Precision studies demonstrated good repeatability (RSD=0.4%) and day after (RSD=0.5%). The linearity range of 50% to 150% translates to a concentration range of 25 µg/mL to 75 µg/mL. The recovery of Finerenone from spiked samples was 99.47%, indicating high accuracy. The LOD and LOQ were determined to be 0.041 µg/mL and 0.123 µg/mL, respectively, suggesting high sensitivity of the method. When applied to a marketed formulation of Finerenone, the assay value obtained was 99.62%, confirming the expected concentration in the formulation. Conclusion: The RP-HPLC method developed in this study is precise, accurate, and highly sensitive, making it suitable for the quantification of Finerenone in pharmaceutical formulations. The method was successfully validated and can be applied in both research and quality control settings. Additionally, the incorporation of Quality by Design (QbD) and DOE principles throughout the method development process ensures its robustness and reliability.

KEYWORDS

• Central composite design
• Finerenone
• Perturbation
• Response surface plots
• Statistical analysis

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• DOI10.5530/ijper.20251429Copy

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